Artificial intelligence is losing hype
economist.com
economist.com
But as to the hype, we are in a brief pause before the election where no company wants to release anything that would hit the news cycle in a bad way and cause knee-jerk legislation. Are there new architectures and capabilities waiting? Likely some. Sora showed state of the art video generation, OpenAI has demoed an impressive voice mode, and Anthropic has teased that Opus 3.5 will be even more capable. OpenAI also clearly has some gas in the tank as they have focused on releasing small models such as GPT-4o and 4o mini. And many have been musing about agents and methods to improve system 2 like reasoning.
So while there’s a soft moratorium on showing scary new capability there is still evidence of progress being made behind-the-scenes. But what will a state of the art model look like when all of these techniques have been scaled up on brand new exascale data centers?
It might not be AGI, but I think it will at least be enough for the next hype Investment bubble.
Music generation is one of the easiest ways to "spook the normies" since most people are completely unaware of the current SOTA. Anyone with a good ear and access to these tools can create a listenable song that sounds like it's been professionally produced. Anyone with a good ear and competence with a DAW and these tools can produce a high quality song. Someone who is already a professional can create incredible results in a fraction of the time it would normally take with zero budget.
One of the main limitations of generative AI at the moment is the interface, Udio's could certainly be improved but I think they have something good here with the extend feature allowing you to steer the creation. Developing the key UI features that allow you to control the inputs to generative models is an area where huge advancements can be made that can dramatically improve the quality of the generated output. We've only just scratched the surface here and even if the technology has reached its current limits, which I strongly believe it hasn't since there are a lot of things that have been shown to work but haven't been productized yet, we could still see steady month over month improvements based on better tooling built around them alone.
Text generation has gone from markov chain babblers to indistinguishable from human written.
Image generation has gone from acid trip uncanny valley to photorealistic.
Audio generation has gone from 1930's AM radio quality to crystal clear.
Video generation is currently in fugue dream state but is rapidly improving.
3D is early stages.
???? is next but I'm guessing it'll be things like CAD STL models, electronic circuits, and other physics based modelling outputs.
The ride's not over yet.
Do you have an example of any song that gained any traction among human audience? Not a Billboard hit, just something that people outside the techbubble accepted as a good song?
Edit:
There's obviously still skill involved in creating a good song, it's not like you can just click one button and get a perfect hit. I outlined the simplest process in my first comment and specifically said you could create a "listenable" song, it's not going to be great but it probably rivals some of the slop you often hear on the radio. If you're a skilled music producer you can absolutely create something good especially now with access to the stemmed components of the songs. It's going to be a half manual process where you first generate enough to capture the feeling of the song and then download and make edits or add samples, upload and extend or remix and repeat.
If you're looking for links and don't care to peruse the trending section they have several samples on the announcement page https://www.udio.com/blog/introducing-v1-5
The really good stuff probably will not be marked as med with AI - and probably will also go via a DAW and proper mastering.
AI generated music is more of a threat to the current state of the recording industry. If I can create exactly the album or playlist that I want using AI then why should I pay a record label for a recording that they're going to take 90% of the retail price from? The playlist I listen to while I'm working out or driving is not competing with live band performances, I'm still going to go to a show if there's a band playing that I like.
I disagree that it's a threat to the recording industry. They aren't going to be able to sell AI music, but nobody else is either, because anyone who wants AI music can just create it themselves. Record labels will continue to sell and promote real artists, because that's how they can make money. That's what people will pay for.
There are a lot more fuzzy edges too, you can use AI tools to "autotune" your own voice into a completely different one. You can tap out a quick melody on a keyboard and then extend, embellish and transform it into a full song. You could even do the full song yourself first and then remix it using AI.
The point I agree on would be that one-click hits are going to be few and far between for a while at least. If no effort is put into selecting the best then it's really just random chance. I'd be willing to bet that there will be an indie smash hit song created by a single person who doesn't perform any of the vocals or instruments within a year though. It'll get no play time on anything controlled by the industry titans but people will be streaming it regardless.
I have some audio engineering skills, dabbled in songwriting, guitar and singng when I was younger, but actually never completed a full song. So it is quite transformative from that perspective!
- someone claims that Gen AI is overhyped
- someone responds with a Gen AI-enabled service that is
1) really impressive
2) is currently offered pretty much for free
3) doesn't have that many tangible benefits.
It's just hard to imagine how you can turn this technology into a valuable product. Which isn't to say it's impossible: gen-AI is definitely quite capable and people are learning how to integrate it into products that can turn a profit. But @futureshock's point was that it is the AI investment bubble that's losing hype, and I think that's inevitable: people are realizing there are many issues with a technology that is super impressive but hard to productize.
I think we will see a very nice boost in capability within 6 months of the election. I don’t personally believe all the apocalyptic AGI predictions, but I do think that AI will continue to feed a nice growth curve in IT investment and productivity growth, similar to the last few decades of IT investment.
Its done, you can’t make another LLM, all knowledge from here on out is corrupted by them, you can never deliver an epistemic “update.” GPT will become a relic of the 21st century, like a magazine from the 1950s.
Its output depends on your input.
E.g. say you have an API swagger documentation and you want to generate a Typescript type definition using that data, you just copy paste the docs into a comment above the type, and copilot auto fills your Typescript type definition even adding ? for properties which are not required.
If you define clearly the goal of a function in a JSDoc comment, you can implement very complex functions. E.g. you define it in steps, and in the function line out each step. This also helps your own thinking. With GPT 4o you can even draw diagrams in e.g. excalidraw or take screenshots of the issues in your UI to complement your question relating to that code.
Yeah but there are other ways to think through problems, like asking other people what they think, which you can evaluate based on who they are and what they know. GPT is like getting advice from a cross-section of everyone in the world (and you don’t even know which one), which may be helpful depending on the question and the “people” answering it, but it may also be extroadinarily unhelpful, especially for very specialized tasks (and specialized tasks are where the profit is).
Like most people, I have knowledge of things very specific I know that less than a 100 people in the world know better than me, but thousands or even millions more have some poorly concieved general idea about it.
If you asked GPT to give you an answer to a question it would bias those millions, the statistically greater quantative solution, to the qualitative one. But, maybe, GPT only has a few really good indexes in its training data that it uses for its response, and then its extremely helpful because its like accidentally landing on a stackoverflow response by some crazy genius who reads all day, lives out of a van in the woods, and uses public library computers to answer queries in his spare time. But that’s sheer luck, and no more so than a regular search will get you.
this really rings true for me. especially as a junior, I always thought one of my best skills was that I was good at Googling. I was able to come up with good queries and find some page that would help. Sometimes, a search would be simple enough that you could just grab a line of code right off the page, but most of the time (especially with StackOverflow) the best approach was to read through a few different sources and pick and choose what was useful to the situation, synthesizing a solution. Depending on how complicated the problem was, that process might have occurred in a single step or in multiple iterations.
So I've found LLMs to be a handy tool for making that process quicker. It's rare that the LLM will write the exact code I need - though of course some queries are simple enough to make that possible. But I can sort of prime the conversation in the right direction and get into a state where I can get useful answers to questions. I don't have any particular knowledge on AI that helps me do that, just a kind of general intuition for how to phrase questions and follow-ups to get output that's helpful.
I still have to be the filter - the LLM is happy to bullshit you - but that's not really a sea change from trying to Google around to figure out a problem. LLMs seem like an overall upgrade to that specific process of engineering to me, and that's a pretty useful tool!
I'm using both Kagi & LLM; depending on my need, I'll prefer one or the other.
Maybe I can access the same result with a LLM, but all the conversation/guidance required is time-consuming than just refining a search query and browsing through the first three results.
After all the answer is rarely exactly available somewhere. Reading people's questions/replies will provide a clues to find the actual answer I was looking for.
I have yet been able to achieve this result through a LLM.
If you can beat copilot in a typing race then you’re probably well within your comfort zone. It works best when working on things that you’re less confident at - typing speed doesn’t matter when you have to stop to think.
The text interface can also be useful for skipping across complex documentation and/or learning. Example: you can ask GPT-4 to "decode 0xdf 0xf8 0x44 0xd0 (thumb 2 assembly for arm cortex-m)" => this will tell you what instruction is encoded, what it does and even how to cajole your toolchain into providing that same information.
If you are an experienced developer already, with a clear goal and understanding, LLMs tend to be less helpful in my experience (the same way that a mentor you could ask random bullshit would be more useful to a junior than a senior dev)
or it will hallucinate something that's completely wrong but you won't notice it
Also you can look into cursor.
There are actually quite a few tools.
I have my own agent framework in progress which has many plugins with different commands. Including reading directories, tree, read and write files, run commands, read spreadsheets. So I can tell it to read all the Python in a module directory, run a test script and compare the output to a spreadsheet tab. Then ask it to come up with ideas for making the Python code match the spreadsheet better, and have it update the code and rerun the tests iteratively until its satisfied.
If I am honest about that particular process last night, I am going to have to go over the spreadsheet to some degree manually today, because neither gpt-4o nor Claude 3.5 Sonnet was able to get the numbers to match exactly.
It's a somewhat complicated spreadsheet which I don't know anything about the domain and am just grudgingly learning. I think the agent got me 95% of the way through the task.
I have copilot suggestions bound to an easy hotkey to turn them on or off. If I’m writing code that’s entirely new to the code base, I toggle the suggestions off, they’ll be mostly useless. If I’m following a well established pattern, even if it’s a complicated one, I turn them on, they’ll be mostly good. When writing tests in c#, I reflexively give the test a good name and write a tiny bit of the setup, then copilot will usually be pretty good about the rest. I toggle it multiple times an hour, it’s about knowing when it’ll be good, and when not.
Beyond that, I get more value from interacting with the llm by chat. It’s important to have preconfigured personas, and it took me a good 500 words and some trial and error to set those up and get their interaction styles where I need them to be. There’s the “.net runtime expert” the “infrastructure and release mentor”, and on like that. As soon as I feel the least bit stuck or unsure I consult with one of them, possibly in voice mode while going for a little walk. It’s like having the right colleague always available to talk something through, and I now rarely find myself spinning my wheels, bike-shedding, or what have you.
IME LLMs are great at giving you the experience of learning, in the same way sugar gives you the experience of nourishment
Sure, you have your John Carmaks' or Antirezs' of the industry who are 10x programmers and also successful founders but those guys are 1 in a million.
But your usual 10x engineer you'll meet is the guy who knows the ins and outs of all running systems at work giving him the ability to debug and solve issues 10x quicker than the rest, knowledge which is highly specific to the products of that company and is often non-transferrable and also not useful at entrepreneurship.
Becoming the 10x engineer at a company usually means pigeonholing yourself in the deep inner workings of the products, which may or may not be useful later. If that stack is highly custom or proprietary it might work in your favor making you the 10x guy virtually unfireable being able to set your own demands since only you can solve the issues, or might backfire against you at a round of layoffs as your knowledge of that specific niche has little demand elsewhere.
AI can help with that.
Current AI is still at the state of recommending people jump off the golden gate bridge if they feel sad or telling them to change their blinker fluid.
With AI I can do the same in 15 seconds. We’re talking a 120x increase in productivity not a 3x improvement.
You're talking about the 100x engineer now. The 10x engineer is the normal engineer you are probably accustomed to working with. When you encounter a 1x engineer, you will be shocked at how slow and unproductive they are.
Well, Of Course I Encountered Him. He's Me.
Which means that after a brief honeymoon period, the effect of AI will be to heavily reduce labor costs, rather than push for turbocharged "productivity" with greatly diminishing returns.
But many jobs are not like that. Imagine an AI nurse giving bad health advice on phone. Somebody might die. Or AI salesman making promises that are against company policy? Company is likely to be held legally liable, and may lose significant money.
Due to legal reasons, my company couldn't enable full LLM generative capabilities on chatbot we use, because we would be legally responsible for anything it generates. Instead, LLM is simply used to determine which of the pre-determined answers may fit the query the best, which it indeed does well when more traditional technologies fail. But that's not revolutionary, just an improvement. I suspect there are many barriers like that, which hinder its usage in many fields, even if it could work most of the time.
So, nearly all use cases I can think of now will still require a human in the loop, simply because of the unreliability. That way it can be a productivity booster, but not a replacement.
Human in the loop can add reliability, but the most common use cases I’m seeing with AI are helping people see the errors they are making/their lack of sufficient effort to solve the problem.
This problem is not unique to AI and you see this problem with human medical professionals. Regularly people are misdiagnosed or aren’t diagnosed at all. At least with AI you could compare the results of different models pretty instantly and get confirmation. An AI Dr also wouldn’t miss information on a chart like a human can.
> So, nearly all use cases I can think of now will still require a human in the loop, simply because of the unreliability. That way it can be a productivity booster, but not a replacement.
This is exactly what your parent said, but yet you replied seemingly disagreeing. AI tools are here to stay and they do increase productivity. Be it coding, writing papers, strategizing. Those that continue to think of AI as not useful will be left behind.
The healthcare system has always killed plenty of people because humans are notoriously unreliable, fallible, etc.
It is such a stubborn, critical, and well-known issue in healthcare I welcome AI to be deployed slowly and responsibly to see what happens because the situation hasn’t been significantly improved with everything else we’ve thrown at it.
LLM outputs aren't always perfect, but that doesn't stop them from being extremely helpful and massively increasing my productivity.
They help me to get things done with the tech I'm familiar with much faster, get things done with tech I'm unfamiliar with that I wouldn't be able to do before, and they are extremely helpful for learning as well.
Also, I've noticed that using them has made me much more curious. I'm asking so many new questions now, I've had no idea how many things I was casually curious about, but not curious enough to google.
There is an old documentary of the final days of typesetters for newspapers. These were the (very skilled) people who rapidly put each individual carved steel character block into the printing frame in order print thousands of page copies. Many were incredulous that a machine could ever replicate their work.
I don't think programmers are going to go away, but I do think those juicy salaries and compensation packages will.
I think that's inevitable with or without LLMs in the mix. I also think the industry as a whole will be better for it.
The documentary he's referring to is likely "Farewell, Etaoin Shrdlu," released in 1980. It chronicles the last day of hot metal typesetting at The New York Times before they transitioned to newer technology. The title comes from the nonsense phrase "etaoin shrdlu," which appeared frequently in Linotype machine errors due to the way the keys were arranged. The documentary provides a fascinating look at the end of an era in newspaper production.
So same programmer with the same 8h of workday will be able to output more value.
Some will undoubtably transition to broader based business consultancy services. For those unable or unwilling to do so the future is bleak.
How much is "enough"? Neither myself nor my coworkers have found LLMs to be all that useful in our work. Almost everybody has stopped bothering with them these days.
Don’t put chips on top of each other and you’re fine.
The knee-jerk legislation has mostly been caused by Altman's statements though. So I wouldn't call it knee-jerk, but an attempt by OpenAI to get a legally granted monopoly.
What people, including me, are massively fed up is all the companies (I mean ALL) jumping on AI bandwagon in a beautiful show of how FOMO works and how even CEOs/shareholders are not immune to basic instincts. Literal hammer looking desperately for nails. Very few companies have amazing or potentially amazing products, rest not so much.
I absolutely don't want every effin' thing infused with some AI, since it will be used to 1) monitor my usage or me directly for advertising / credit & insurance scoring purposes, absolutely 0 doubt there; and 2) it may stop working once wifi is down, product is deprecated or company changes its policy (Sonos anyone?). Internet of Things hate v2.0.
I hate this primitive AI fashion wave, negative added value in most cases, 0 in the rest, yet users have to foot the bill. Seeing some minor industry crash due to unfulfilled expectations is just logical in such case.
Yes. There is also the Hype of the "End of the Hype Cycle". There is Hype that the Hype is ending.
When really, there is something amazing being released weekly.
People are so desensitized that just because we don't have androids walking the streets or suddenly have Blade Runner like space colonies staffed with robots, that somehow AI is over.
I disagree. Palantir is trading at 200X earnings.
So I've seen how the field has progressed and also have been able to look at it from a perspective most AI/engineering people don't -- what does this artificial intelligence look like when compared to biological intelligence. And I must say I am absolutely astonished people don't see this as opening the flood-gates to staggeringly powerful artificial intelligence. We've run the 4-minute mile. There are hundreds of billions of dollars figuring out how to get to the next level, and it's clear we are close. Forget what the current models are doing, it is what the next big leap (most likely with some new architecture change) will bring.
In focusing on intelligence we forget that it's most likely a much easier challenge than decentralized cheap autonomy, which is what took the planet 4 billion years to figure out. Once that was done, intelligence as we recognize it took an eye-blink. Just like with powered-flight we don't need bioliogical intelligence to transform the world. Artificial intelligence that guzzles electricity, is brittle, has blind spots, but still capable of 1000 times more than the best among us is going to be here within the next decade. It's not here yet, no doubt, but I am yet to see any reasoned argument for why it is far more difficult and will take far longer. We are in for radical non-linear change.
I am yet to see any reasoned argument for why it is easy to build real AI and that it will come fast.
As you said, AI has been there for decades and stagnated for pretty much the whole time. We've just had a big leap, but nothing says (except BS hype) that we're not in for a long plateau again.
This has to do with ML and numerical computing, how?
Either make a good argument or don’t.
Don't feel too bad; until I read the next response I was in two minds about whether sarcasm was intended or not.
It's bloody hard to tell, sometimes :-/
> Either make a good argument or don’t.
Human beings can't evaulate the truth of things based only on the argument. Persuasive liars, cons, and incompetents are a very known phenomenon. Most of human history we misunderstood nature and many other things because we relied on 'good arguments'. Not that we need it, but research shows that human intuition about the truth of something isn't good without expertise.
When I need medical advice, I get it from someone who has convinced me that they have expertise; I don't look for 'good arguments' that persuade me, because I don't know what I'm talking about.
I have expertise in other things. In those fields, I could easily persuade people without it of just about anything. (I don't; I'm not a sociopath.) I imagine anyone with professional expertise who reads this can do the same.
edit: oh sorry I didn't get that it was sarcasm
We have "real ai" already.
As for future progress, have you tried just simple interpolation of the progress so far? Human level intelligence is very near. (Though of course artificial intelligence will never exactly match human intelligence: it will be ahead/behind in certain aspects...)
- Simple interpolation of the progress is exactly the problem here. Look at the historical graphs of AI funding and tell me with a straight face that we absolutely must use simple interpolation.
- Nope, human-level intelligence is not even close. It remains as nebulous and out of reach as ever. ChatGPT's imitation of intelligent speech falls apart very quickly when you chat with it for more than a few questions.
I think we expect AGI to be much smarter than the average joe, and free of occasional stupidity.
What we’ve got is an 85IQ generalist with unreliable savant capabilities, that can also talk to a million people at the same time without getting distracted. I don’t see how that isn’t absolutely a fundamental shift in capability.
It’s just that we expect it to be spectacularly useful. Not like homeless joe, who lives down by the river. Unfortunately, nobody wants a 40 acre call center of homeless joes, but it’s hard to argue that HJ isn’t an intelligent entity.
Obviously LLMs don’t yet have a control and supervision loop that gives them goal directed behaviour, but they also don’t have a drinking problem and debilitating PTSD with a little TBI thrown in from the last war.
It’s not that we aren’t on the cusp of general intelligence, it’s that we have a distorted idea of how useful that should be.
Very shallow assessment, first of all it's not a generalist at all, it has zero concept of what it's talking about, secondly it gets confused easily unless you order it to keep context in memory, and thirdly it can't perform if it does not regularly swallow petabytes of human text.
I get your optimism but it's uninformed.
> To be fair, I’ve talked to a lot of people who cannot consistently perform at the mistral-12b level.
I can find you an old-school bot that performs better than uneducated members of marginalized and super poor communities, what is your example even supposed to prove?
> it’s hard to argue that HJ isn’t an intelligent entity.
What's HJ? If it's not a human then it's extremely easy to argue that it's not an intelligent entity. We don't have intelligent machine entities, we have stochastic parrots and it's weird to pretend otherwise when the algorithms are well-known and it's very visible there's no self-optimization in there, there's no actual learning, there's only adjusting weights (and this is not what our actual neurons do btw), there's no motivation or self-drive to continue learning, there's barely anything that has been "taught" to combine segments of human speech and somehow that's a huge achievement. Sure.
> It’s not that we aren’t on the cusp of general intelligence, it’s that we have a distorted idea of how useful that should be.
Nah, we are on no cusp of general AGI at all. We're not even at 1%. Don't know about you but I have a very clear idea what would AGI look like and LLMs are nowhere near. Not even in the same ballpark.
It helps that I am not in the area and I don't feel the need to pat myself on the back that I have managed to achieve the next AI plateau which the area will not soon recover from.
Bookmark this comment and tell me I am wrong in 10 years, I dare you.
TBH I hope im wrong, and that there is magic in HJ that makes him special in the universe in a way that GPT26 can never be. But increasingly, I doubt this premise. Not because of the "amazing capabilities of LLMs" which i think are frequently overstated and largely misunderstood, but more because of the dumbfounding shortcomings of intelligent creatures. We keep moving the bar for AGI, and now AGI is assumed to be what any rational accounting would classfy as ASI.
Where we are really going to see the bloom of AI is in goal directed systems, and I think those will come naturally with robotics. I predict we are in for a very abrupt 2nd industrial revolution, and you and I will be able to have this discussion either over a 55 gallon barrel of burning trash, or in our robot manicured botanical gardens sometime in the near future lol.
good times, maybe. Interesting times , for sure.
We have found common ground.
> but more because of the dumbfounding shortcomings of intelligent creatures
Yes, a lot of us utilize defective judgments, myself included, fairly often. My point was that LLMs, for all their praise, can't even reach 10% of an average semi-intelligent organic being.
> We keep moving the bar for AGI, and now AGI is assumed to be what any rational accounting would classfy as ASI.
I don't know who is "we" (and I wish people stopped pretending that "we" are all a homogenous mass) but I've known what an AGI should be ever since I've watched movies about Skynet and HAL-9000. ¯\_(ツ)_/¯
Secondly, it's the so-called "AI practitioners" who constantly move the goal posts (now there's "ASI"? -- you know what, I actually don't want to know) because they're periodically being called out and can't hide the fact that they have nearly nothing again. So what's better than obfuscating that fact by having 100+ acronyms? It's a nice cover and apparently there are still investors who are buying it. I get it, we have to learn to say the right things to get funding.
> Where we are really going to see the bloom of AI is in goal directed systems, and I think those will come naturally with robotics.
I agree. Physical feedback is needed if we want an electronic entity to "evolve" similarly to us.
> I predict we are in for a very abrupt 2nd industrial revolution, and you and I will be able to have this discussion either over a 55 gallon barrel of burning trash, or in our robot manicured botanical gardens sometime in the near future lol.
I agree this is 100% inevitable but I don't think it's coming as soon as you say. The LLMs are hopelessly stuck even today and the whole AI area will suffer for it for a while after the bubble bursts... which is the event that I am certain is coming soon.
At that time, general intelligence was imagined like what a mouse, dog, or cat has, in varying degrees. (it was then broadly thought that insects and other "simple" organisms worked from instinct and conditioning only) So intelligence was broadly imagined to be the ability to reason through an arbitrary problem with a combination of insight and trial and error. It had nothing at all to do with reaching human levels of competence.
Gradually, the bar for AGI has slipped skyward to assume human level competence. I think that part of that was because we actually started checking boxes for tasks that were thought to represent "true" AI....
...But then we recognized that nope, it is not yet generally intelligent, it can just easily and accurately do (X task which was thought to require intelligence but really was only pattern matching or stochastic prediction). So, we raise the bar above task X and trivialize it as being less important than we thought.
Meanwhile, -nearly every task- that we thought would represent "true intelligence" has fallen not to some magic AI algorithm, but rather to stochastic models like transformers, pattern matching, or straightforward computation. With no reason to expect otherwise, I expect this trend to continue unabated.
So, what I'm saying is I have come to doubt that there is some secret sauce in "intelligence", but instead believe that "intelligence" is a blend of competencies enabled in animals by specialization of neural computational structures, "REPL" loops, goal seeking behaviors, and other tidbits which will be more of a slog than a eureka. I don't think that LLM's will create human-level intelligence on their own, but I do think that they will be an important component.
I also think that surprisingly, transformers all by them selves exhibit a flaky variety of general intelligence, and they are proving effective in robotic applications (though without a supervisory agent I suspect they will frequently go off the rails, so to speak.)
My original point was more that we expect machine general intelligence to be spectacularly useful. It may be, someday, but it is a kind of fallacy to think that "its not that useful therefore it must not really be intelligence".
Small animals also have limited utility, but with given names, language, tool use, and problem solving skills I think arguing that they do not exhibit "intelligence" would be a tough sell for me.
By my observation, we have made giant leaps in the past 15 years, and we now have perhaps the vast majority of the components required to make artificial general intelligence...but it won't necessarily be all that useful at first, except maybe as a "pet robot" or something like that. Even if we scale it, it might not magically get smarter, just faster. a million hyper-speed squirrels still has a very limited level of utility.
From there, we will incrementally improve if we don't stumble too hard over ourselves on any number of pressing obstacles that we currently face, until we finally succeed in doing what we were apparently born to do - to construct the means of our own irrelevance. Evolution at work, I suppose.
> Back in the days of lisp machines and expert systems, we started playing with early neural networks in an attempt to advance towards 'general intelligence' rather than the highly constrained talents of expert systems.
Interesting, you must be the first person I "meet" who was "back then and there". Still, if you allow me to point out for the second time, your "we" is really throwing me off because it sounds like "we the council of elders" and "we who self-appointed to determine what an actual true AI is". Positions of implied or directly claimed authority murder my motivation to take people doing them seriously. Hopefully that's a useful piece of feedback for you.
I would think that a random guy like myself who watched Terminator and that was part of his inspiration to become a programmer has just as much "authority" (if we can even call it that but I can't find the right word at this moment) to claim what a general AI should be. Since we don't have it, why not try and dream the ideal AI for us, and then pursue that? It's what the people who wanted humanity on the Moon did after all.
I feel too many people try to define general AI through the lenses of what we have right now -- or we'll have very soon -- and that to me seems very short-sighted and narrow-minded and seems like bronze-age people trying to predict what technology would be. To them it would likely be better carts that shake less while sitting in them. And faster cart-pulling animals.
That's how current AI practitioners trying to enforce their view on what we should expect sound to me.
> Meanwhile, -nearly every task- that we thought would represent "true intelligence" has fallen not to some magic AI algorithm, but rather to stochastic models like transformers, pattern matching, or straightforward computation. With no reason to expect otherwise, I expect this trend to continue unabated.
Sure, but this gets dangerously close to the disingenuous argument of "people who want AI constantly move the goalposts every time we make progress!" which is a stance I can't disagree with more even if I tried. I in fact hate this trope and fight it at every opportunity.
Why? Because to me that looks like AI practitioners are weaseling out of responsibility. It's in fact not that difficult to understand what the common people would want. Take a look at the "I, Robot" movie -- robot butlers that can do many tasks around the house or even assist you when you are outside.
What does that take? Let the practitioners figure it out. I, like yourself, believe LLMs are definitely not that -- but you are also right that it's likely a key ingredient. Being able to semi-informedly and quickly digest and process text is indeed crucial.
The part I hate is the constant chest-pounding: "We practically have general AI now, you plebs don't know our specialized terms and you just don't get it. Stop it with your claims that we don't have it! Nevermind that we don't have robot butlers, that's always in the future, I am telling you!".
And yes that happens even here in this thread, not in that super direct form of course, but it still happens.
> My original point was more that we expect machine general intelligence to be spectacularly useful. It may be, someday, but it is a kind of fallacy to think that "its not that useful therefore it must not really be intelligence".
Here we disagree. It's true that people want useful and don't care how it's achieved; as a fairly disgruntled-by-tech guy I want the same. Put rat brains in my half-intelligent but problem-solving butler for all I care; if it works well people will buy it en masse and ask zero questions.
...But I'd still put strong correlation between a machine being actually intelligent and being able to solve very different problems with the same "brain", and being useful. For the simple reason that a lot of our problems that don't require much intelligence at all have been mostly solved by now.
So it naturally follows that we need intelligent tools for the problems we have not yet solved. Would you not agree with that conclusion?
> Small animals also have limited utility, but with given names, language, tool use, and problem solving skills I think arguing that they do not exhibit "intelligence" would be a tough sell for me.
I agree. Some can actually adapt to conditions that their brain has not had to tackle in generations. But take koalas for example... I actually could easily call these animals not possessing a general intelligence and just being pretty complex bots reacting to stimuli and nothing else (though there's also the possibility that since they ingest such low nutrition food their brains constantly stay in a super low-energy mode where they barely work as problem-solving machines -- topic for another time).
> By my observation, we have made giant leaps in the past 15 years, and we now have perhaps the vast majority of the components required to make artificial general intelligence...but it won't necessarily be all that useful at first, except maybe as a "pet robot" or something like that. Even if we scale it, it might not magically get smarter, just faster. a million hyper-speed squirrels still has a very limited level of utility.
Agree with that as well, just not sure that the path to a better general AI is in scaling higher what we [might] have now. IMO the plateau that the LLMs hit quite quickly partially supports my hypothesis.
As you are alluding to, the path to general AI is to keep adding more and more components to the same amalgam and try and connect them in creative ways. Eventually the spark of artificial life will ignite.
---
To summarize, my problem with the current breed of AI practitioners is that they argue from a position of authority that to me is imaginary; they are working in one of the least clear areas in science and yet they have the audacity to claim superiority to anyone whereas to me it's obvious that a random truck driver might have more interesting ideas than them for their area (exaggerated example but my point is that they lack perspective and become too entrenched in their narrow views, I guess like all scientists).
Yes, LLMs are likely integral part of a future artificial and working brain that can solve general tasks. And no we will not get any further there. Throwing another trillion parameters will achieve nothing but even more elaborate hallucinations. To me it became blindingly obvious that without throwing some symbol logic in there the LLMs will forever have zero concept of what they're talking about so they'll never improve -- because they also rely on truthful sources of info. That's not problem solving; that's regurgitating words with some extra steps.
Time to move on to other stuff -- maybe the transformers? Speaking of which, do you have any material on them that you would recommend for a layman? Just a TL;DR what they do and roughly how? Obviously I can Google it in literal seconds but that sadly does not mean much these days -- so maybe you have a source that's more interesting to read.
It’s possible that others imagined human level as the base for “generalized intelligence”, but my colleagues and I were taking the term general to mean generalised, as in not narrowly defined (like expert systems are). A type of intelligence that could be applied broadly to different categories of problems, including ones not foreseen by the designer of the system. That these problems might be very basic ones was immaterial.
That this concept of general intelligence is not necessarily life transforming to possess on your smartphone doesn’t mean it isn’t a huge step forward. It is a very hard problem to solve. Transformer networks are the best tool we have for this task, and they work by inferring meaning to patterns and outputting a transform of that meaning. With LLMs, the pattern is the context text string, and the output is the next likely text fragment.
The surprising effectiveness of LLMs is due, I think, not to any characteristic of their architecture that makes them “intelligent”, but rather due to the fundamental nature of language itself, and especially the English language because of its penchant for specificity and its somewhat lower reliance on intonation to convey meaning than most languages. (I’m not a linguist, but I have discussed this with a few and it is interesting to hear their ideas on this)
Language itself captures meanings far beyond the words used in a statement. Only a tiny fraction of information is contained in words, the rest is inferred knowledge based on assumptions about shared experiences and understandings. Transformers tease out this context and imbed it through inferences constructed by billions of textual inputs. They capture the information shadows cast by the words, not just the words themselves.
This way of decoding cultural data, as an n-dimensional matrix of vast proportions rather than just the text of a culture itself, turns out to be a way to access both the explicit and the implicit knowledge imbedded in that culture, especially if that culture is codified using very specific and expressive tokens.
It turns out that this capture of shared experiences and understanding enables a great deal of abstract general problem solving, precisely the kind of problem solving that is vexingly difficult to solve using other methods.
LLMs, not just transformers, are actually a really big deal. In effect, they create a kind of probabilistic expert system where the field of expertise is a significant fraction of the sum total of human thought and experience.
But there are numerous and significant shortcomings to this approach, of course…. Not the least of which is the difficulty to effectively integrate new information or to selectively replace or update existing data in the model. And hallucinations (which are not a malfunction , but rather completely normal operations) are a basically unsolvable problem, though they can be mitigated.
But anyway…
I think the real insight to be had here , or at least my personal takeaway is that we owe a great deal of what we think of as intelligence to our culture than to our individual intellectual prowess. As we solve the problems of general intelligence, we both construct marvellous machines and confront the suggestion that we aren’t nearly as clever as we thought we were.
As for “AGI” I will still call that a crossed threshold , but certainly not to the level that humans have solved “biological GI” with full inculturation.
If people want AGI to mean human level competence, I’m ok with that. It won’t be the first term of art to have drifted in meaning. I would personally choose a more descriptive term for that, but 3 letter terms are not all that expressive, and adding another letter or two just gets awkward, so I get it. And I think it’s a lost battle anyway, it’s mostly old cranks such as myself that are impressed with things that most animals manage easily to do.
As for the SOTA, I think there is huge room for improvement both in narrowing down competencies for specific tasks, and for expanding capabilities for applications that need human level abstraction and creativity. But I doubt that the next steps toward human level competency will be nearly as fruitful as the ones in the recent past, and I have serious doubts about the ability of current paths to lead to superhuman intelligence. Superhuman capabilities, yes, but there’s nothing new about that. Superhuman intelligence will require things which we don’t understand, by definition.
Meanwhile, we suffer on with our earthbound wheels and idiot-savant AI.
What is intelligence? We must have very different definitions!
And I am pretty sure my own intelligence goes much farther than regurgitating text that I have no clue about (like ChatGPT does not have symbol logic that links words with objects it can "feel" physically or otherwise).
This is honestly one of the most gpt-2 things I’ve ever read.
But it's a building block. And when used well it may be possible to get to zero hallucinations and good accuracy in question answering for limited domains - like the call center.
You shouldn’t use science fiction as your reference point. It’s like saying “where is my flying car?” (Helicopters exist)
And btw in the Terminator novelizations it was clearly stated that Skynet was a very good optimization machine but lacked creativity. So it's actually a good benchmark: can we create an intelligent machine that needs no supervision but still has limitations (i.e. it cannot dramatically reformulate its strategy in case it cannot win, which is exactly what happened in the books)?
That’s the point of my flying car comparison. We HAVE flying cars: they’re called helicopters. Because as it turns out there is just no physical way to make a vehicle in the form factor of a car fly, except by rotary wing. But people will still say “where’s my flying car?” because they are hung up on reality resembling science fantasy, as you are.
We have AI. We even already have AGI. It just doesn’t resemble the Terminator, because The Terminator is a made up story disconnected from reality.
And this is why, I feel, I can never discuss with the AI fans. They are happy to invent their own fiction while berating popular fiction in the same breath.
No, we really don't have AGI. Feel free to point out some of humanity's pressing problems being trivially solved today with it, please. I'll start: elderly people care, and fully automated logistics.
Artificial. General. Intelligence.
The term, as originally defined, is for programs which are man-made (Artificial), able to efficiently solve problems (Intelligence), including novel problem domains outside those considered in its creation (General). Artificial General Intelligence, or AGI. That’s literally all AGI means, and ChatGPT absolutely fits the bill.
What you describe is ASI, or artificial super intelligence. In the late 90’s, 00’s, and early 10’s, a weird subgroup of AI nerds got it into their head that merely making an AGI (even a primitive one) would cause a self-recursion improvement loop and create ASI in short order. They then started saying “achieve AGI” as a stand in for “emergence of ASI” as the two were intricately linked in their mind.
In reality the whole notion of AGI->ASI auto-FOOM has been experimentally discredited, but the confusion over terminology remains.
Furthermore, the very idea of ASI can’t be taken for granted. A machine that trivially solves humanity’s pressing problems makes nice sci-fi, but there is absolutely no evidence to presume such a machine could actually exist.
I don't care how it's called. We don't have it. I am not "confused over terminology", I want to see results and yet again they don't exist. Let's focus on results.
> In reality the whole notion of AGI->ASI auto-FOOM has been experimentally discredited
Sure. Because we actually have this super-intelligence already and we can compare with it, right? Oh wait, no we don't. So what's your point? Some people gave up and proclaimed that it can't be done? Like we haven't seen historical examples of this meaning exactly nothing, hundreds of times already.
Look, we'll never be able to talk about it before you stop confusing industry gate-keepers who learned how to talk to get VC money and obfuscate reality with, you know, the actual reality in front of us. You got duped by the investor talk and by the scientists never wanting to admit their funding might have been misplaced by being given to them, I am afraid.
Finally, nope, again and again, we don't have AGI even if I accept your definition. Show me a bot that can play chess, play StarCraft 2, organize an Amazon warehouse item movements and shipping, and coordinate a flight's touch-down with the same algorithms / core / whatever-you-want-to-call it. Same one, not different ones. One and the same.
No? No AGI then either.
> Furthermore, the very idea of ASI can’t be taken for granted. A machine that trivially solves humanity’s pressing problems makes nice sci-fi, but there is absolutely no evidence to presume such a machine could actually exist.
The people in the bronze age could have easily said "there is no evidence we would be able to haul goods while only pressing pedals and rotating a wheel". That's not an argument for anything at all, it's a short-sighted assertion that we might never progress that's only taking the present and the very near future into account. Well, cool, you don't believe it will happen. And? That's not an interesting thing to say.
Other people didn't believe we could go to the Moon. We still did. I wonder how quickly the naysayers hid under the bed after that so nobody could confront them about it. :D
But anyway. I got nothing more to say to people who believe VC talk and are hell-bent on inventing many acronyms to make sure they are never held accountable.
I for one want machines that solve humanity's problems. I know they can exist. I know nearly nobody wants to work on them because everybody is focused on the next quarter's results. All this is visible and well-understood yet people like you seem to think that this super narrow view is the best humanity can achieve.
Well, maybe it's the best you can achieve. I know people who can do more.
Inventing a fricken time machine wasn't creative?
In the novelizations it was written that Skynet could not adapt to humans not running away or not vacating territories after they have been defeated. One of the quotes was: "Apparently it underestimated something that it kept analyzing even now: the human willpower." I've read this as Skynet not being able to adapt against guerilla warfare -- the hit-and-run/hide tactics.
But the TL;DR was that Skynet was basically playing something like StarCraft as if it played against another bot, and ultimately lost because it played against humans. That was the "Skynet was not creative" angle in the novelizations.
In Terminator 1 Skynet looses because John Conner taught people how to fight the machines, but John Conner only knows this because Kyle Reese taught Sarah Conner how to fight the machines and she taught John Conner. But Kyle Reese only knows this because he was taught by John Conner- so there's no actual source of the information on how to fight the machines, it's a loop with no beginning or end.
I had a philosophy teacher who said this is evidence of divine intervention to destroy Skynet, essentially God told people through John Conner how to win, but a cut scene in Terminator 1 implies Skynet was also created by reverse engineering the chip in the destroyed Terminator- implying there's also no origin of the information on how to create Skynet and it's also an infinite loop.
So it's not exactly an infinite loop IMO, it's more like that the first iteration was more crude and the machines were difficult to kill but then people learned and passed the information along back in time, eventually forming the infinite loop -- it still had a first step though, it didn't come out of nothing.
The thing that's different this time is the hardware capacity in TFLOPs and the like passing human brain equivalence.
There's a massive difference between much worse than human AI - a bit meh, and better than human AI - changes everything.
>any reasoned argument for why it is easy to build real AI and that it will come fast
It probably won't be easy but the huge value of better than human AI will ensure loads of the best and brightest working on it.
What signs do you see that make you believe that the next level (biological intelligence) is on the horizon?
If we’ve just copied our feeble abilities, is that supposed to be exciting?
Is god like intelligent just a prediction machine too ?
The Sixth Day
Then God said, “Let Us make man in Our image, after Our likeness, to rule over the fish of the sea and the birds of the air, over the livestock, and over all the earth itself and every creature that crawls upon it.” So God created man in His own image; in the image of God He created him; male and female He created them. ”…
Genesis 1:26-27 Berean Standard Bible
What does this mean, precisely? How is a human body (or plant, or insect, or reptile/bird/mammal) body ever "more than" it's constituent parts? Wouldn't that violate a conservation law?
This means you are sure we are close to automated driving, engineering and hospitality?
We already have "automated driving" in some sense. Some cities have fully autonomous taxi services that have operated for a year or more, iirc.
I don't really take those taxis as a form of solved automation. It's a nice step though.
For language models specifically, they are trained on data and have historically been improved by increasing the size of the model (by number of parameters) and by the amount and/or quality of training data.
We are basically out of new, non-synthetic text to train models on and it’s extremely hard work to come up with novel architecture that performs well against transformers.
Those are some simple reasons why it will be far more difficult to improve general language models.
There are also papers showing that training models on synthetic data causes “model collapse” and greatly reduces output quality by magnifying errors already present in the model, so it’s not a problem we can easily sidestep.
It’s an easy mistake to see something like chatgpt not exist, then suddenly exist and assume a major breakthrough happened, but behind the scenes there has been like 50 years of R&D that led to it, it’s not like suddenly there was a breakthrough and now the gates are open.
A general intelligence for CS is like the elixir of life for medicine.
this is not even remotely true.
There is an astronomical amount of data siloed by publishers, professional journals etc. that is yet to be tapped.
OpenAI is making inroads by making deals with these content owners for access to all that juicy data.
You seem to think these models haven't already been trained on pirated versions of this content, for some reason.
That’s not even considering AI crawlers or all the copyright text on archive.org
And by the way I can copy your post character by character, without hallucinating. So I am definitely better than this crop of "AI" in at least one dimension.
>We are in for radical non-linear change.
We aren't running miles much quicker than 4 mins though. The last record was 3m:43s set by Hicham El Guerrouj in 1999.
Scary stuff. And it's not science fiction- it's based on real, observed trends and scaling laws. Seems impossible? Well, they said the four-minute mile was impossible, too.
Humankind tried to break the 4 minute mile for hundreds of years - since measuring distance and time became accurate enough to be sure of both in the mid-18th century, at least - and failed.
In May 1954, Roger Bannister managed it. By late June it was done again by a different runner. Within 20 years the record was under 3:45, and today there are some runners who have achieved it more than 100 times and nearly 1800 runners who have done it at all.
Impossible for hundred of years, and then somebody did it, and people stopped thinking it was impossible and started doing it themselves. That’s the metaphor: sometimes we think of barriers that are really mental, not real.
I’m not sure that applies here either, but the point is not that progress is continuously exponential, but that once a barrier is conquered, we take on a perspective as if the barrier were never real in the first place. Powered flight went through this. Computing hardware too. It’s not an entirely foolish notion.
The breakthroughs where deep AI have excelled -- object recognition in images, voice recognition and generation, and text-based info embedding and retrieval -- these require none of the multilevel abstraction that characterizes higher cognition (Kahneman's system 2 thinking). When we see steady progress on such frontiers, only then a plausible case can and should be made that the essentials of AGI are indeed within our grasp. Until then, plateauing at a higher level of pattern matching than we had expected -- which is what we have seen many times before from narrow AI -- this is not sufficient evidence that the other requisite skills needed for AGI are surely just around the corner.
Coding assistants today are useful, image generation is useful, speach recognition/generation is useful.
All of these can support businesses, even in their current (early) state. Those businesses have value in funding even 1% improvements in engineering/science.
I think that this is different than before, where even in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.
Where as in the past, hopes for the technology waned and funding for research dropped off a cliff, today's stuff is useful now, and so companies will continue to spend some amount on the research side.
Anyway, considering all these things can be done on device, where is the long term business prospect of which you speak?
Now try to mute a video on youtube and understand what's being said from the automatic subtitles.
If you do it in english, be aware that it's the best performing language and all others are even worse.
1. It's overwhelmingly more useful than the [no text] it was replacing, particularly for the deaf or if you want to search for keywords in a video.
2. When it fails, it tends to do so in ways that trigger human suspicion and oversight.
Those aren't necessarily true of some of the things people are shoehorning LLMs into these days, which is why I'm a lost more pessimistic about that technology.
https://news.ycombinator.com/item?id=41199567#41201773
I expect that YouTube will up their transcription game soon, too.
Basically it's kinda useful to put time tags, but I need to manually fix each and every sentence. Sometimes I need to fix the time tags as well.
I just spoke about youtube because it's more popular and easy to test.
I've come to notice a correlation between contemporary AI optimism and having effectively made the jump to coding with AI assistants.
I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants.
For that past few years most of the code I've written has been solving fairly niche quantitative problems with novel approaches and I've found AI coding assistants to range from useless to harmful.
But on a recent webdev project, they were much more useful. The vast majority of problems in webdev are fundamentally not unique so a searchable pattern library (which is what an LLM coding assistant basically is) should be pretty effective.
For other areas of software, they're not nearly as useful.
I think this is true and also why you see some "older devs just don't like AI" type comments. AI assistants seem to be great at simple webdev tasks, which also happens to be the type of work that more junior developers do day to day.
I have also found them useful with that and I keep one active for those types of projects because of the speed up, although I still have to keep a close eye on what it wants to inject. They also seem to excel at generating tests if you have already developed the functions.
Then there are more difficult (usually not webdev) projects. In those cases, it really only shines if I need to ask it a question that I would previously have searched on SO or some obscure board for an answer. And even then, it really has to be scrutinized, because if it was simple, I wouldn't be asking the question.
There is def. something there for specific types of development, but it has not "changed my life" or anything like that. It might have if I was just starting out or if I only did webdev type projects.
If this thing also conferred an actual productivity advantage that would be one thing, and it might motivate me to get past the horrible UX, but I haven't seen any evidence yet.
But after that we ran into a kind of loop, where you put my feelings into much better words than I could. If I had stopped after iteration 3, I probably would have finished what I wanted to do in half a day
This has follow-on consequences for a shattering phase transition between “persuasive demo” and “useful product”.
We can now make arbitrarily convincing demos that will crash airplanes (“with no survivors!”) on the first try in production.
This is institutionalized by the market capitalizations of 7 companies being so inflated that if they were priced accurately the US economy would collapse.
Really? I work in AI and my biggest concern is that I don't see any real products coming out of this space. I work closer to the models, and people in this specific area are making progress, but when I look at what's being done down stream I see nothing, save demos that don't scale beyond a few examples.
> in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.
This is literally all I see right now. There's some really fun hobbyist stuff happening in the image gen area that I think is here to stay, but LLMs haven't broken out of the "autocomplete on steroids" use cases.
> today's stuff is useful now
Can you give me examples of 5, non-coding assistant, profitable use cases for LLMs that aren't still in the "needed just a bit more research to be commercially viable" stage?
I love working in AI, think the technology is amazing, and do think there are some under exploited (though less exciting) use cases, but all I see if big promises with under delivery. I would love to be proven wrong.
LLMs can be used to generate high-quality, human-like content such as articles, blog posts, social media posts, and even short stories. Businesses can leverage this capability to save time and resources on content creation, and improve the consistency and quality of their online presence.
2. Customer Service and Support:
LLMs can be integrated into chatbots and virtual assistants to provide fast, accurate, and personalized responses to customer inquiries. This can help businesses improve their customer experience, reduce the workload on human customer service representatives, and provide 24/7 support.
3. Summarization and Insights:
LLMs can be used to analyze large volumes of text data, such as reports, research papers, or customer feedback, and generate concise summaries and insights. This can be valuable for businesses in fields like market research, financial analysis, or strategic planning.
4. HR Candidate Screening:
Use case: Using LLMs to assess job applicant resumes, cover letters, and interview responses to identify the most qualified candidates. Example: A large retailer integrating an LLM-based recruiting assistant to help sift through hundreds of applications for entry-level roles.
5. Legal Document Review:
Use case: Employing LLMs to rapidly scan through large volumes of legal contracts, case files, and regulatory documents to identify key terms, risks, and relevant information. Example: A corporate law firm deploying an LLM tool to streamline the due diligence process for mergers and acquisitions.
Spam isn't a feature. See also, this whole message that could just have been the headlines.
> 2. Customer Service and Support:
So… less clear than the website and not empowered to do anything (beyond ruining your reputation) because even you don't trust it?
> 3. Summarization and Insights:
See 1, spam isn't a feature. This is just trying to undo the damage from that (and failing).
> 4. HR Candidate Screening:
> 5. Legal Document Review:
If it's worth doing, it's worth doing well.
>Content Generation
I'm working on AI tools for teachers and I can confidently say that GPT is just unbelievably good at generating explanations, exercises, quizes etc. The onus to review the output is on the teacher obviously, but given they're the subject matter experts, a review is quick and takes a fraction of the time that it would take to otherwise create this content from scratch.
So sorry, we're back to spam generator. Even if it's "good spam".
a bit dramatic. there has to be an adjustment of teaching/assessing, but nothing that would "ruin" anyone's life.
>So sorry, we're back to spam generator. Even if it's "good spam".
is it spam if it's useful and solves a problem? I don't agree it fits the definition any more.
Teachers are under immense pressure, GPT allows a teacher to generate extension questions for gifted students or differentiate for less capable students, all on the fly. It can create CBT material tailored to a class or even an individual student. It's an extremely useful tool for capable teachers.
If you don't have the power to just change your mind about what the entire curriculum and/or assessment context is, it can be a workload increase of dozens of hours per week or more. If you do have the power, and do want to change your entire curriculum, it's hundreds of hours one-time. "Lives basically ruined" is an exaggeration, but you're preposterously understating the negative impact.
> is it spam if it's useful and solves a problem?
Whether or not it's useful has nothing to do with whether or not it's spam. I'm not claiming that your product is spam -- I'll get back to that -- but your reply to the spam accusation is completely wrong.
As for your hypothesis, I've had interactions where it did a good job of generating alternative activities/exercises, and interactions where it strenuously and lengthily kept suggesting absolute garbage. There's already garbage on the internet, we don't need LLMs to generate more. But yes, I've had situations where I got a good suggestion or two or three, in a list of ten or twenty, and although that's kind of blech, it's still better than not having the good suggestions.
I think it has a lot to do with it. I can't see how generating educational content for the purpose of enhancing student outcomes with content reviewed by expert teachers can fall under the category of spam.
>As for your hypothesis, I've had interactions where it did a good job of generating alternative activities/exercises, and interactions where it strenuously and lengthily kept suggesting absolute garbage.
I like to present concrete examples of what I would consider to be useful content for a k-12 teacher.
Here's a very quick example that I whipped up
https://chatgpt.com/share/ec0927bc-0407-478b-b8e5-47aabb52d2...
This would align with Year 9 Maths for the Australian Curriculum.
This is an extremely valuable tool for
- A graduate teacher struggling to keep up with creating resources for new classes
- An experienced teacher moving to a new subject area or year level
Bear in mind that the GPT output is not necessarily intended to be used verbatim. A qualified specialist teacher with often times 6 years of study (4 year undergrad + 2 yr Masters) is the expert in the room who presumably will review the output, adjust, elaborate etc.
As a launching pad for tailored content for a gifted student, or lower level, differentiated content for a struggling student the GPT response is absolutely phenomenal. Unbelievably good.
I've used Maths as an example, however it's also very good at giving topic overviews across the Australian Curriculum.
Here's one for: elements of poetry:structure and forms
https://chatgpt.com/share/979a33e5-0d2d-4213-af14-408385ed39...
Again, an amazing introduction to the topic (I can't remember the exact curriculum outcome it's aligned to) which gives the teacher a structured intro which can then be spun off into exercises, activities or deep dives into the sub topics.
> I've had situations where I got a good suggestion or two or three, in a list of ten or twenty
This is a result of poor prompting. I'm working with very structured, detailed curriculum documents and the output across subject areas is just unbelievably good.
This is all for a K-12 context.
Also, each of your examples had at least one error, did you not see them?
I didn't could you point them out?
>There are countless existing, human-vetted, designed on special purpose, bodies of work full of material like the stuff your chatgpt just "created". Why not use those?
As a classroom teacher I can tell you that piecing together existing resources is hard work and sometimes impossible because resource A is in this text book (which might not be digital) and resource B is on that website and quiz C is on another site. Sometimes it's impossible or very difficult to put all these pieces together in a cohesive manner. GPT can do all that an more.
The point is not to replace all existing resources with GPT, this is all or nothing logic. It's another tool in the tool belt which can save time and provide new ways of doing things.
Who said generating an essay is useful sorry ? What problem does that solve?
Your comments come accross as overly optimistic and dismissive . Like you have something to gain personally and aren’t interested in listening to others feedback.
>Who said generating an essay is useful sorry ? What problem does that solve?
Useful learning materials aligned with curriculum outcomes, taking into account learner needs and current level of understanding is literally the bread and butter of teaching.
I think those kinds of resources are both useful and solve a very real problem.
>Your comments come accross as overly optimistic and dismissive . Like you have something to gain personally and aren’t interested in listening to others feedback.
Fair point. I do have something to gain here. I've given a number of example prompts that are extremely useful for a working teacher in my replies to this thread. I don't think I'm being overly optimistic here. I'm not talking vague hypotheticals here, the tools that I'm building are already showing great usefulness.
Every year there are thousands of graduate teacher looking for tools to help them teach better.
>good teachers I know can generate them on the spot
Even the best teacher can't create an interactive multiple choice quiz with automatic marking, tailored to a specific class (or even a specific student) on the spot.
I've been teaching for 20+ years, I have a solid grasp of the pain points.
Neither can "AI" though, so what's the point here?
here's an example of a question and explanation which aligns to Australian Curriculum elaboration AC9M9A01_E4 explaining why frac{3^4}{3^4}=1, and 3^{4-4}=3^0
https://chatgpt.com/share/89c26d4f-2d8f-4043-acd7-f1c2be48c2...
to further elaborate why 3^0=1 https://chatgpt.com/share/9ca34c7f-49df-40ba-a9ef-cd21286392...
This is a relatively high level explanation. With proper prompting (which, sorry I don't have on hand right now) the explanation can be tailored to the target year level (Year 9 in this case) with exercises, additional examples and a quiz to test knowledge.
This is just the first example I have on hand and is just barely scratching the surface of what can be done.
The tools I'm building are aligned to the Austrlian Curriculum and as someone with a lot of classroom experience I can tell you that this kind of tailored content, explanations, exercises etc are a literal godsend for teachers regardless of experience level.
Bear in mind that the teacher with a 4 year undergrad in their specialist area and a Masters in teaching can use these initial explanations as a launching pad for generating tailored content for their class and even tailored content for individual students (either higher or lower level depending on student needs). The reason I mention this is because there is a lot of hand-wringing about hallucinations. To which my response is:
- After spending a lot of effort vetting the correctness of responses for a K-12 context hallucinations are not an issue. The training corpus is so saturated with correct data that this is not an issue in practice.
- In the unlikely scenario of hallucination, the response is vetted by a trained teacher who can quickly edit and adjust responses to suit their needs
“Here are some sharepoint locations, site Maps, and wikis. Now regurgitate this info to me as if you are a friendly call center agent.”
Pretty cool but not much more than pushing existing data around. True AI I think is being able to learn some baseline of skills and then through experience and feedback adapt and be able to formulate new thoughts that eventually become part of the learned information. That is what humans excel at and so far something LLMs can’t do. Given the inherent difficulty of the task I think we aren’t much closer to that than before as the problems seem algorithmic and not merely hardware constrained.
Which is extremely valuable!
>Pretty cool but not much more than pushing existing data around.
Don't underestimate how valuable it is for teachers to do exactly that. Taking existing information, making it digestable, presenting it in new and interseting ways is a teacher's bread and butter.
Are you suggesting a chatbot is a suitable replacement for a teacher?
No I'm saying that a chatbot can be a superhuman teacher's assistant.
Which all takes valuable time us teachers are extremely short on.
I've been a classroom teacher for more than 20 years, I know how painful it is to piece together a hodge podge of resourecs to put together lessons. Yes the information is out there, but a one click option to gather this into a cohesive unit for me saves me valuable time.
>95% of my time an mental capacity in this situation goes for deciding what makes sense in my particular pedagogical context? What wording works best for my particular students?
Which is exactly what GPT is amazing at.Brainstorming, rewriting, suggesting new angles of approach is GPTs main stength!
>Explanations are even harder.
Prompting GPT to give useful answers is part of the art of using these new tools. Ask GPT to speak in a different voice, take on a persona or target a differnt age group and you'll be amazed at what it can output.
> I find out almost daily that explanations which worked fine in last year, don't work any more
Exactly! Reframing your own point of view is hard work, GPT can be an invaluable assistant in this area.
No, it isn't. It just increases noise. I don't need any more info, I need just to make decisions "how?".
> Prompting GPT to give useful answers is part of the art of using these new tools. Ask GPT to speak in a different voice, take on a persona or target a differnt age group and you'll be amazed at what it can output.
I'm not amazed. At best it sounds like some 60+ year old (like me) trying to be in the "age group" 14 while after only hearing from someone how young people talk. Especially in small cultures like ours here (~1M people).
We specifically don’t do programming prompts/responses nor advanced college to PHD level stuff, but it’s really mediocre at this level and these subject areas. Programming might be another story, I can’t speak to that.
All I can go off is my experience but it’s not been great. I’m willing to be wrong.
Is the output of average C students not commercially valuable in the listed fields? If AI is competing reliably with students then we've already hit AGI.
1. content: Nope, except for barrel-bottom content sludge of the kind formerly done by third world spam spinning companies, most decent content creation stays well away from AI except for generating basic content layout templates. I work as a writer and even now, most companies stay well away from using GPT et al for anything they want to be respected as content. Please..
2. Customer service: You've just written a string of PR corporate-speak AI seller bullshit that barely corresponds to reality. People WANT to speak to humans, and except for very basic inquiries, they feel insulted if they're forced into interaction with some idiotic stochastic parrot of an AI for any serious customer support problems. Just imagine some guy trying to handle a major problem with his family's insurance claim or urgently access money that's been frozen in his bank account, and then forced to do these things via the half-baked bullshit funnel that is an AI. If you run a company that forces that upon me for anything serious in customer service, I would get you the fuck out of my life and recommend any friend willing to listen does the same.
3. This is the one area where I'd grant LLMs some major forward space, but even then with a very keen eye to reviewing anything they output for "hallucinations" and outright errors unless you flat out don't care about data or concept accuracy.
4. For reasons related to the above (especially #2) what a categorically terrible, rigid way to screen human beings with possible human qualities that aren't easily visible when examined by some piece of machine learning and its checkbox criteria.
5. Just, Fuck No... I'd run as fast and far as possible from anyone using LLMs to deal with complex legal issues that could involve my eventual imprisonment or lawsuit-induced bankruptcy.
Also these all tend to have an option where you simply ask it and it will redirect you to a person.
Those agents deal with the same queries all day, despite what you think your problem likely isn't special, in most cases may as well start calling the agents "stochastic parrots" too while you're at it.
We just don't have that.
We have autocomplete on steroids and many people are fooling themselves that if you just take more steroids you will get better and better results. The metaphor is perfect because if you take more and more steroids you get less and less results.
It is why in reality we have had almost no progress since April 2023 and chatGPT 4.
I think that none of the initials are boolean; things can be degrees of artificial, degrees of general, and degrees of intelligent.
I think most would assert that humans count as a "general" intelligence, even if they disagree about most of the other things I've put in this comment.
I am probably through some ETF an investor in MS, so I do hope the openai API usage is showing a more stable and upward trend.
Not sure how they plan on making money in the long-term, eventually the investors and shareholders will start asking when they will be seeing the returns on their investment.
You can see this in action with multiplication. Much like humans when asked to guess the answer, they'll get it wrong, unless they know the answer from rote learning multiplication tables, this System-1 thinking. In many cases when asked they can reason further and solve it, by breaking it down and solving it step by step, much like a human, this is system-2 thinking.
In my opinion, it seems nearly everything is there for it it to take the next leap in intelligence, it's just putting it all together.
Are we really now?
The smart people I've spoken to on the subject seem to agree the current technology based on LLM are at the end of the road and that there are no breakthrough in sight.
So what is your take on the next level?
Finding bugs in some models doesn’t mean you have a point about intelligence. If somebody could apply a similar argument to dismiss human intelligence, you don’t have a point. And here it goes: the most advanced human intelligence can’t reliably multiply large numbers or recall digits of Pi. Obviously humans are dumber than pocket calculators.
The things that LLM’s are bad at are largely solved problems using much simpler technology. There is no reason that LLM’s have to be the only component in an intelligent agent. Biological brains have Specialized structures for specialized tasks like arithmetic. The solution is probably integration of LLMs as a part of a composite system that includes database storage, a code execution environment, and multiple agents to form a goal directed posit - evaluate loop.
I’ve had pretty remarkable success with this architecture running on 12b models and I’m a nobody with no resources.
LLM’s by themselves just come up with the first thing that crosses their”mind”. It shouldn’t be surprising that the very first unfiltered guess about a solution might be suboptimal.
There is a vast amount of knowledge embedded in our cultural matrix, and a lot of that is captured in the common crawl and other datasets.llms are like a search engine for that data , based on meaning rather than semantics.
Some of the most advanced AI are tool users and can both write and crucially also execute python, and embed the output in their responses.
> or even solve really simple logic puzzles for little kids.
As given in a recent discussion: https://chatgpt.com/share/ee013797-a55c-4685-8f2b-87f1b455b4...
(Custom instructions, in case you're surprised by the opening of the response).
I'd like to believe it more than you do. Unfortunately, in spite of these millions of dollars, the progress on LLMs has stalled.
This looks like a cognitive dissonance and they are addressed by revisiting your assumptions.
No flood-gates have been opened. ChatGPT definitely found uses in a few areas but the number is very far from what many people claimed. A few things are really good and people are using them successfully.
...But that's it. Absolutely nothing even resembling the beginnings of AGI is on the horizon and your assumption that the rate of progress will remain the same -- or even accelerate -- is a very classic mistake of the people who are enthusiasts in their fields.
> There are hundreds of billions of dollars figuring out how to get to the next level, and it's clear we are close.
This is not clear at all. If you know something that nobody else does, please let us know as well.
Perhaps it's confirmation bias ?
I can put your brain in a vat and stimulate your sensory neurons with a statistical distribution with no actual meaning, and nothing about how your brain works would change either.
The LLM and your brain would attempt to interpret meaning with referent from training, and both would be confused at the information-free stimuli. Because during "training" in both cases, the stimuli received from the environment is structured and meaningful.
So what's your point?
By the way, pretty sure a neuroscientist with 20 years of ML experience has a deeper understanding of what "meaning" is than you do. Not to mention, your response reveals a significant ignorance of unresolved philosophical problems (hard problem of consciousness, what even is meaning) which you then use to incorrectly assume a foregone conclusion that whatever consciousness/meaning/reasoning is, LLMs must not have it.
I'm partial to doubting LLMs as they are now have the magic sauce, but it's more that we don't actually know enough to say otherwise, so why state that we do know?
We can't even say we know our own brains.
The point of my colored shape example is that it is an illusion that there is anything resembling a mind inside an LLM. I thought that was obvious enough I didn't need to explain it further than I did.
As far as the original commenter's credentials; there's lots of people who should know better but buy into hype and nonsense.
Go ahead and cite your sources. For every study claiming that LLMs lack these qualities, there are others that support and reinforce the connectionist model of how knowledge is encoded, and with other parallels to the human brain. So... it's inconclusive. It's bizarre why you so strongly insist otherwise when it's clear you are not informed.
> The point of my colored shape example is that it is an illusion that there is anything resembling a mind inside an LLM
And my example with subjecting a human brain through your procedure is to illustrate what a garbage experiment design it is. You wouldn't be able to tell there's a mind inside either. Both LLM and human brain mind would be confused. Both would "continue working" in the same way, trying to interpret meaning from meaningless stimulation.
So you don't have a point to make, got it.
If you don't understand how your attempt to apply my colored shape analogy to a human brain is nonsensical I am not going to waste my time explaining it to you. I had a point, I made it, and apparently it escaped you.
> We also know how they work, they are token predicters. That is all they are and all they can do
Ah there it is, you've betrayed a deep lack of understanding in all relevant disciplines (neuroscience, cognition, information theory) required to even appreciate the many errors you've made here.
You sure understand the subject matter and have nothing possibly to learn. Enjoy.
We know as a hard fact that LLMs do not understand anything. They have no capacity to "understand". The constant, intractible failure modes that they continuously exhibit are clear byproducts of this fact. By continuing to cling to the absurd idea that there is more going on than token prediction you make yourself look like the people who kept insisting there was more going on with past generation chat bots even after being shown the source code.
I have understood all along why you attempt to extend my colored shape example to the brain, but your basis for this is complete nonsense. Because a) we do not have the actual understanding of the brain to do this and b) it's competely beside the point, becuase we know that minds do arise from the brain. My whole point is an LLM is an illusion of a mind which is effective because it outputs words, which we are so hard wired to associate with other minds, expecially when they seem to "make sense" to us. If instead of words you use something nonsensical like colored shapes with no underlying meaning, this illusion of the mind goes away and you can see an LLM for whst it is.
A bit late to the party, but we most certainly do not even know what "understanding" means.
What an absurd response. Yes, you'd probably cause the human brain to start malfunctioning terribly at the form of consciousness it's well -accustomed to managing within the context of its normal physical substrate and environment. You'd be doing that (and thus degenerating it badly) because you removed it from that ancient context whose workings we still don't well understand.
Your LLM on the other hand, has no context in which it shows such a level of cognitive capacity, higher-order reasoning, self direction and self awareness that we daily see humans to be capable of.
>By the way, pretty sure a neuroscientist with 20 years of ML experience has a deeper understanding of what "meaning" is than you do.
really? An appeal to authority? Many smart, educated people can still fall for utter nonsense and emotional attachment to bad ideas.
Yes, it will be confused as well, and for all outwards observable signs will fail to make sense of the stimuli, yet it will "aware" of its inability to understand, much like a human brain would.
If you doubt that, open a new session and type some random tokens, you will get the answer that it's confused.
Any other statement as to "consciousness" verges into the philosophical and unanswerable via empirical means.
And ah, to frame it as an appeal to authority when the topic is precisely the subject of a neuroscientist's study.
Sounds like you know a thing or two about nonsense and emotional attachment to bad ideas.
>Yes, it will be confused as well, and for all outwards observable signs will fail to make sense of the stimuli, yet it will "aware" of its inability to understand, much like a human brain would.
>If you doubt that, open a new session and type some random tokens, you will get the answer that it's confused.
There is no empirical evidence of any awareness whatsoever in any LLM, at all. Even their most immersed creators don't make such a claim. An LLM itself saying anything about awareness doesn't mean a thing. It's literally designed to mimic in such a way. And you speak of discussions of consciousness being about the philosophical and unanswerable?
At least when talking about human awareness, one applies these ideas to minds that we personally as humans perceive to be aware and self-directed from our own experience (flawed as it is). You're applying the same notion to something that shows no evidence of awareness while then criticizing assumptions of consciousness in a human brain?
Such a sloppy argument indeed does make appeals to authority necessary I suppose.
> has no context in which it shows such a level of cognitive capacity
You claim LLMs have no context at all in which it shows a similar level of cognitive capacity.
Yet clearly this claim is in contention with the fact that an LLM will indeed be able to evince this, much like a human brain would: by attesting to its own confusion. That is ostensibly empirical and evidential to a nonzero degree.
Thus your claim is too strong and therefore quite simply wrong. Claim mimicry? Then prove human brain consciousness does not derive from the process of mimicry in any form. You can't. In fact, the free energy principle, neuroscience's leading theory of human consciousness, argues the opposite: that prediction and mimicry encompass the entirety of what brains actually do. https://en.wikipedia.org/wiki/Predictive_coding
> There is no empirical evidence of any awareness whatsoever in any LLM, at all.
And no such claim was made--"awareness" was quoted for a reason.
> It's literally designed to mimic in such a way. And you speak of discussions of consciousness being about the philosophical and unanswerable?
Yes, as this was parent's claim: "They are not cognizant let alone conscious'.
And talking about sloppy argument--it may well turn out that something can be "designed to mimic" yet still be conscious. I'll leave that for you to puzzle out how on earth that might be possible. The exercise might help you form less sloppy arguments in the future.
> You're applying the same notion to something that shows no evidence of awareness while then criticizing assumptions of consciousness in a human brain?
No. But I suppose you've lost the plot a few inferential steps prior this so your confusion is not surprising.
Protip, instead of claiming everything that goes against your sensibilities as nonsense, perhaps entertain the possibility that you might just not be as well informed as you thought.
People focused on the products are missing out on the dawn of an epoch. It's a failure of perspective and creativity that's thankfully not universal.
PS. I'm buying your book right now.
If I had to bet, I would start with:
- Error-correcting specialized architectures for increasing signal-to-noise (as far as I can tell these are what everyone is racing to build this year, and should be doable with just conventional programming systems wrapping LLMs)
- Improved energy efficiency (as yes, human brains are currently much more efficient! But - there are also simple architecture improvements (both software and hardware) that are looking to save 100x. Specialized ASIC ternary chips using 1999's tech should be here quite soon, a lot more efficient in price and energy.)
- No Backwards-propagation. (As yes, the brain does seem to do it all with forward-propagation only. Though this is possible and promising in neural networks like the Forward-Forward algorithm too, they haven't been trained to the same scales as backprop-heavy transformers (and likely have a lower performance in terms of noise/accuracy). Though if I'm not mistaken, the brain does have forward-backward loops, but the signals go through separate neurons for each direction (rather than reusing one) - if so that's close to backprop by itself, but probably imposes a tradeoff as the same signal can't be perfectly reproduced backwards, yet it can perhaps be enhanced to be just the most relevant information by the separate specialized neuron. I'm obviously mostly ignorant of the neuroscience here but halfway-knowledgeable on the ML theory haha
But yes, I completely agree - the flood gates are already open. This is a few architecture quibbles away from an absolute deluge of artificial intelligence that will dwarf (drown?) anything we've known. Good point on decentralized cheap autonomy - the real accomplishment of life. Intelligence, as it appears, is just a fairly generous phenomenon where any autonomous process continually improves its signal-to-noise ratio... many ways to accomplish that one! Looking forward to seeing LLMs powered by ant colonies and slime molds, though I suspect by then there will be far more interesting and terrifying realities unlocked.
- we had some big breakthroughs recently
- some AI “godfathers” are “really worried”
Can you explain what this means? Do you have a degree in neuroscience?
Powered flight offers a cautionary tale for AI. The first confirmed powered flight was in 1903. For the next 60 years, someone broke the airspeed record almost every year. The current record was set in 1976. Nobody has broken that record for 48 years. There are concerns that the state of AI will show a similar pattern, with rapid improvements followed by a plateau.
This wasn't the case with GPT-4/o. This capability is very new.
When I spoke to a colleague at Microsoft about these changes, they were floored. Microsoft has made themselves synonymous with AI, yet their company is barely even leveraging it. The big cos have put in the biggest investments, but also will be the slowest to change their processes and workflows to realize the shift.
Feels like one of those "future is here, not evenly distributed yet" moments. When a tool like Sonnet is released, it's not like big tech cos are going to transform over night. There's a massive capability overhang that will take some time to work itself through these (now) slow-moving companies.
I assume it was the same with the internet/dot-com crash.
In pair programming, it's ideal to have a driver (hands on keyboard) and a navigator (planning, direction).
Claude can act as the driver most of the time so I can stay at the navigator level.
This is so helpful, as it's easy as programmers to get sucked into implementation details or low-level minutiae that's just not important.
Cursor offers some super marginal UX improvements over the latter (being that it’s a fork of VScode), since it allows you to switch models. But Claude and GPT have been interchangeable at least for my workflows, so I’m not sure the hype is really deserved.
I can only imagine the excitement comes from the fact that cursor has a full-fat free trial, and maybe most people have never bothered paying for copilot?
Perhaps it's my language of choice (Elixir)? Claude absolutely nails it, rarely gives me code with compilation errors, seems to know and leverage the standard library very well, idiomatic. Not the same with GPTs.
did a quick check, it's $20/month, and it has a vim plugin: https://github.com/pasky/claude.vim
going to give it a spin
Maybe GPT4o changed things.
...do you not see the obnoxious CoPilot(TM) buttons and ads everywhere? It's even infected the Azure Portal - and every time I use it to answer a genuine question I have I get factually-incorrect responses (granted, I don't ask it trivial or introductory-level questions...).
But outside of that, beyond needing to remember a certain syntax, I have found that any time I tried to use it for anything more complex I am finding myself spending more time going back and forth trying to get code that works than I would have if I had just done it myself in the first place.
If the code works, it just isn't maintainable code if you ask it to do too much. It will just remove entire functionality.
I have seen a situation of someone submitting a PR, very clearly copying a method and sticking it in AI and saying "improve this". It made changes for no good reason and when you ask the person that submitted the PR why they made the change we of course got no answer. (these were not just Linter changes)
Thats concerning, pushing code up that you can't even explain why you did something?
Like you said with the hard work, sure it can churn out code. But you need to have a complete clear picture of what that code needs to look like before you start generating or you will not like the end result.
But it's weird to me seeing people talk about these changing their jobs so much. Maybe I'm holding it wrong but I'm almost always bottlenecked by "big picture" challenges and less on the act of actually typing the code.
Now I have all these new ideas but I am back to square one because it just seems easier to start over.
I look forward to more powerful models in the future but I do wonder if it will just mean I can get slightly farther and make an even larger mess of an app in a weekend that I have no way to add to or update without breaking.
The main utility seems like it would be for content creation to pretend I made an app with all these great features as a non-software engineer but conveniently leave out the part about it being impossible to update.
It’s great at helping me automate things I normally do not have time to attempt.
Most software vendors are selling their version of AI as hallucination free though. So that's terrifying.
It definitely has that Tesla Autopolit feel to it, where the marketing is ... enthusiastic, while the onus to "use the tool responsibly" is left up to the end user.
I'm building a persentation/teaching tool for teachers and trying to not sweep the halicination angle under the rug. My marketing angle is that the AI is a huge time saver, but the classroom teacher is the subject expert and they need to review the output. At the end of the day, it's still much more time efficient for an expert to review the content than it is to produce it from scratch which is a huge win.
Also, to be super presumptuous, if you need help with anything with your tool, hit me up. I love working with developers on building realistic use case and workflows.
I'm building tools for K-12 (I've been a teacher for 20+ years). There's solid uptake of GPT among teachers and individually they're pretty enthusiastic about anything that will reduce their workload, so I'm hoping it resonates.
>Also, to be super presumptuous, if you need help with anything with your tool, hit me up. I love working with developers on building realistic use case and workflows.
Sure, my details are in my profile, happy to chat.
I think that's why I like to compare the current state of AI to the state of the CPU industry maybe around the 286-486 era going towards the Pentium.
I was an early advocate for Copilot but, honestly, nowadays I really don't find it that useful, compared to GPT-4o via ChatGPT.
ChatGPT not being directly integrated into my editor turns out to be an advantage. The problem with Copilot is it gets in the way. It's too easy to unintentionally insert a line or block completion that isn't what you want, or is out and out garbage, and it's constantly shoving up suggestions as I type, which can be distracting. It's particularly irritating when I'm trying to read or understand a piece of code, or maybe do a refactor, and I leave my caret in one position for half a second too long, and suddenly it's ghost-inserted a block of code as a suggestion that's moved half of what I'm reading down the screen and now I have to find my place again.
Whereas, with ChatGPT being separate, it operates at a much less intrusive cadence, and only responds when I ask it too, which turns out to be much more useful and productive.
I'm seriously considering binning of my Copilot subscription as a result.
Even where microsoft includes it for free, like their automation tools, its always been jank. I found myself going to GPT4 for better answers which is bad when you spend any time thinking about it.
Every thread like this over the past year or so has had comments similar to yours, and it always remains quite vague, or when examples are given, it’s about self-contained tasks that require little contextual knowledge and are confined to widely publicly-documented technologies.
What exactly floored your colleague at Microsoft?
So I don't know how this would go in a much larger codebase.
What floored him was simply how much of my programming I was doing with an LLM / how little I write line-by-line (vs edit line-by-line).
If you're really curious, I recorded some work for a friend. The first video has terrible audio, unfortunately. This second one I think gives a very realistic demonstration – you'll see the model struggle a bit at the beginning:
MMmm.
I mean, don't get me wrong; this is impressive stuff; but it needs to be an order of magnitude less 'screwing around trying to fix the random crap' for this to be 'wow, amazing!' rather than a technical demonstration.
You could have done this more quickly without using AI.
I have no doubt this is transformative technology, but people using it are choosing to use it; it's not actually better than not using it at this point, as far as I can tell.
It's slower and more error prone.
There are plenty of, "Just disable certificate checking" type answers on Stack Overflow, but there are also a lot of comments calling them out. How do you fact check the AI? Is it just a shortcut to finding better documentation?
You summed up the workflow accurately. Except, I read your first paragraph in a positive light, while I imagine you meant it to be negative.
Note the feedback loop you described is the same one as me delegating requirements to someone else (i.e. s/LLM/jr eng). And then reading/editing their PR. Except the feedback loop is, obviously, much tighter.
I've written a lot of tests, I think this would have taken 3-4x longer to do by hand. Surely an hour?
But even if all things were roughly equal, I like being in the navigator seat vs the driver seat. Editor vs writer. It helps me keep the big picture in mind, focused on requirements and architecture, not line-wise implementation details.
I guess my point is I'm skeptical.
I don't believe what you had the end would have taken you that long to do by hand. I don't believe it would have taken an hour. It certainly would not have taken me or anyone on my team that long.
I feel like you're projecting that, if you scale this process, so say, having 5 LLMs running in parallel, then what you would get is you spending maybe 20% more time reviewing 5x PRs instead of 1x PR, but getting 5x as much stuff done in the end.
Which may be true.
...but, and this is really my point: It's not true, in this example. It's not true in any examples I've seen.
It feels like it might be true in the near-moderate future, but there are a lot of underlying assumptions that is based on:
- LLMs get faster (probably)
- LLMs get more accurate and less prone to errors (???)
- LLMs get more context size without going crazy (???)
- The marginal cost of doing N x code reviews is < the cost of just writing code N times (???)
These are assumptions that... well, who knows? Maybe? ...but right now? Like, today?
The problem is: If it was actually making people more productive then we would see evidence of it. Like, actual concrete examples of people having 10 LLMs building systems for them.
...but what we do see, is people doing things like this, which seem like (to me at least), either worse or on-par with just doing the same work by hand.
A different workflow, certainly; but not obviously better.
LLMs appear to have an immediate right now disruptive impact on particular domains, like, say, learning, where its extremely clear that having a wise coding assistant to help you gain simple cross domain knowledge is highly impactful (look at stack overflow); but despite all the hand waving and all the people talking about it, the actual concrete evidence of a 'Devin' that actually builds software or even meaningfully improves programmer productivity (not 'is a tool that gives some marginal benefit to existing autocomplete'; actually improves productivity) is ...
...simply absent.
I find that problematic, and it makes me skeptical of grand claims.
Grand claims require concrete tangible evidence.
I've no doubt that you've got a workflow that works for you, and thanks for sharing it. :) ...I just don't think its really compelling, currently, to work that way for most people; I don't think you can reasonably argue it's more productive, or more effective, based on what I've actually seen.
It seems to me that the first few are almost complete copy-paste of older tests. You would have got code closer to the final test in the update case with simple copy-paste than what was provided.
The real value is only in the filtered test to choose randomly (btw, I have no idea why that’s beneficial here), and the one which checks that both consumers got the same info. They can be done in a few minutes with the help of the already made insert test, and the original version of the filtered test.
I’m happy that more people can code with this, and it’s great that it makes your coding faster. It makes coding more accessible. However, there are a lot of people who can do this faster without AI, so it’s definitely not for everybody yet.
However there are some very frustrating limitations to greptle, so severe that I basically only use it to ask implementation questions on existing codebases, not for anything like general R&D: 1) answers are limited to about 150 lines. 2) it doesn't re-analyze a repo after you link it in a conversation (you need to start a new conversation, and re-link the repo, then wait 20+ min for it to parse your code) 3) it is very slow (maybe 30 seconds to answer a question) 4) there's no prompt engineering
I think it's a bit strange that no other ai solution lets you ask questions about existing codebases. I hope that will be more widespread soon.
Re: prompt engineering, we have a prompt guide if that helps, was that what you are getting at?
The logo is supposed to be a reptile claw but we might modify it to make that more obvious.
Speaking of understand context… They floored him not other way round
I decided to fire up GPT-4o again today to see if maybe things have gotten better over the past few months.
I asked GPT to write code to render a triangle using Vulkan (a 3D graphics API). There are about 1000 tutorials on this that are almost certainly in GPT-4's training data. I gave GPT two small twists so it's not a simple case of copy/paste: I asked it 1) to apply a texture to the triangle and 2) to keep all the code in a single function. (Most tutorials break the code up into about a dozen functions, but each of these functions are called only once, so it should be trivial to inline them.)
Within the first ten lines, the code is already completely nonfunctional:
GPT-4o declares a pointer (VkPhysicalDevice) that is uninitialized. It queries the number of graphics devices on the host machine. A human being would allocate a buffer with that number of elements and store the reference in the pointer. GPT-4o just ignores the result. Completely ignores it. So the function call was just for fun, I guess? It then tries to copy an entire array of VkPhysicalDevice_T objects into this uninitialized pointer. So that's a guaranteed memory access violation right off the bat.
It seems especially strong with Python but a bit medium with Swift.
After that it goes completely off the rails by trying to issue draw commands before binding a graphics pipeline, which is both illogical and illegal. After a lot of prodding, I did manage to get it to bind a graphics pipeline, but it forgot about the texture.
So Claude Sonnet is definitely better than GPT-4o, but it still feels raw, like a game of whack-a-mole where I can get it to fix a mistake, but it reintroduces an old one. I also have to be the one offering the expertise. I can prompt it to fix the issues because I know exactly what the issues are. If I was using this to try to fill in for a gap in my knowledge, I would be stuck when I ran the code and it crashed - I would have no idea where to go next.
Update: Took about 50 min of experimenting, but I did get Claude to generate code that doesn't have any obvious defects on first inspection, although it cut off about halfway through because of the generation limit. That's the best result that I've seen from an LLM yet. But that's after about a dozen very broken broken programs, and again, I think the domain expertise here is key in order to be able to reprompt and correct.
One example that I am still using: I wanted to generate a random DICOM file with specific types of garbage in it to use as input for some unit tests, and Claude was able to generate some Python that grabs some random DICOM tags and shoves vaguely plausible garbage data into them, such that it is a valid but nonsensical DICOM dataset. This is not hard, but it's a lot faster to ask Claude to do it.
Like simple python scripts for Home Assistant it just nails first go.
By the way - I think AI or ML whatever has some valid uses right now. but mostly in image processing domain - so like recognizing shapes in some bounded domain OK yea. Generative image - NOT bad but theres always this "AI GLOW" to each image. Something is always off. Its some neat tools but a race to the bottom and mostly users want to generate explicit content lets be real. and they will become increasingly more creative and obtuse to get around the guards. nothing is stopping you from entering the * industry and making tons off money. but that industry is always doing good.
a friend recently suggested to use AI to generate generic icons for my game. Thats a really good use case. but does that radically change the current economy?
[BTW GENERIC STUFF ONLY UNTILL I could hire someone because i prefer that experience way more. you can get more interesting results. 4 eyes are better than 2.
I am an experienced programmer, but I find myself (for the first time) doing a deep-dive in SQL and specifically building code that will run against multiple SQL engines.
I fed my list of engines into AI. As I'm discovering the really weird corners of SQL I asking the AI to compare one db against the other. My prompts are usually no more than 3 or 4 words.
It gives me quick helpful answers highlighting where things are the same and where they are different. I can then follow up in the specific docs if necessary (now that I know the function name.)
Personally I'm somewhat anti-hype, I'll let others rave about "changing the world". But I have found it a useful tool - not so much for "writing my code" but for acting as my tutor as I learn new things. (I'm using it for more than just SQL or computers now.)
I'm not sure it "changes thr economy" - but it can certainly change individual lives. Some jobs will go away. Others might be easier to do. It might make it easier to learn new skills.
The 2nd agency I called them through phone. No ai. But it is excellent cause they do async processing. so you reserve a slot and they call you back. i don't care if those are not answered urgently. Because I just want to talk to a human.
But equally it doesn't prove, or even assert, the opposite. A bicycle may be bad at cross-country road trips, but that doesn't make it a bad choice for some other situations.
Hence my earlier comment - I (and I suspect others) are finding it useful for some tasks. That is not to imply it is good at all tasks.
Is it over-hyped? Of course yes. Welcome to IT where every new thing is over-hyped all the time.
Customer service is hard because it has to filter out 90% noise, 9% fairly straightforward tasks, and the <1% of complex issues that need to be sent to the right human.
If you don't know the language at all it's dangerous because you may not understand the proposed program (and of course if you're an expert you don't need it at all).
But llms won't help to find solutions to a general, still unspecified problem.
It seems to work best if I start with something very simple, and then layer on instructions ("now make it do X").
Where I have found it saves me time is in having to look up syntax or "gotchas" which I would otherwise search StackOverflow for. But as far as "writing code" -- it still feels a long way from that.
"They clearly aren't using the right model!"
"It's obvious they don't know how to prompt, or they would see the value."
"Maybe it can't do that today, but GPT-5 is just around the corner."
I feel more and more that people have just decided that this is a technology that will do everything you can imagine, and no evidence to the contrary will change their priors.
Oh and I expect it to be free, I ain't paying for this just like I wasn't paying for stackoverflow.
Finally I hope than in few years I will be able to just "sudo apt-get install llm llm-javascript llm-cooking llm-trivia llm-jokes" and it will all run locally on my low end computer and when I report bug, six months later it will be fixed when I update OS.
The same applies to AI. The old learning material is gone, your interaction is now the new learning material and ground truth.
PS: Hourly rates for sw engineers: Range:€11 - €213 - so one hour on stackoverflow, searching and sub-querying resolving problems costs you or your employer up to 213€. It really depends on what you have negotiated.
How about DDG AI Chat? https://duckduckgo.com/?q=DuckDuckGo+AI+Chat&ia=chat&duckai=...
It's like having an unusually fast-but-clumsy intern, except interns learn the ropes fast and understand context.
I have some stand tests for LLMs: write a web app version of tetris, write a fluid dynamics simulation, etc., and these regularly fail (I must try them again on 4o).
But also, I have examples of them succeeding wildly, writing a web based painting app just from prompting — sure, even with that success it's bad code, but it's still done the thing.
As there are plenty of examples to confirm what we already believe, it's very easy to get stuck, with nay-sayers and enthusiasts equally unaware of the opposite examples.
I welcome people picking apart chats that I link to. it's not that I believe that LLMs are magic and refuse to adjust my model of how good these things are and aren't, but when people don't give specific evidence is hard to actually move the conversation forwards.
because yeah, these things are plenty stupid and have to be tricked into doing things sometimes (which is stupid, but here we are). they're also pretty amazing but like any hammer, not everything is a nail.
You're like a gambling addict who thinks he's smarter than everyone else
A friend, whose SQL knowledge is minimal, used an LLM to query data from a database over a couple of tables. Yes, after a lot of trial and error he (most probably) got the correct data, however the only one being able to read the query is the LLM itself. It's full of coalesce, subselects that repeat the same joins again and again.
LLM will do a lot for you, but I really hate this "this will [already did] solve everything". No, it did not and no, because it's quality is those of a junior dev, at max.
Echo chamber/bias I guess. I know many, many seniors with big pay checks working for big companies who are vastly worse than sonnet at the moment. Juniors stand literally no chance unless very talented.
I don't know what companies pay for and I don't care, because if we go by that, every definition of every word is arguable (because there's everywhere someone out of the range of the definition)
For example, just now my NAS stopped working because the boot device went offline. So I got to thinking about writing a simple syslog server. I've never looked at the syslog protocol before, and I've never done any low-level TCP/UDP work in C# yet.
So I asked ChatGPT to generate some code[1], and while the result is not perfect it's certainly better than nothing, and would save me time to get going.
As another example, a friend who's not very technical wanted to make an Arduino circuit to perform some automated experiment. He's dabbled with programing and can modify code, but struggles to get going. Again just for kicks, I asked ChatGPT and it provided a very nice starting point[2].
For exploratory stuff like this, it seems to provide a nice alternative to searching and piecing together the bits. Revolutionary is a quite loaded word, but it's certainly not just a slight improvement on what we had before LLMs and instead feels like a quantum leap.
[1]: https://chatgpt.com/share/f4343939-74f1-404d-bfac-b903525f61... (modified, see reply)
[2]: https://chatgpt.com/share/fc764e73-f01f-4a7c-ab58-f43da3e077...
The way the OP here was talking about sonnet being above way above chatgtp in this case, it could be true. Google probably has the largest Go codebases on search to train the AI on higher quiality inputs. Go is a simpler language with less variation over time compared to something like .net also adding to its corner.
ive always been the type of person to use the right language for the each use case. For the last 10+ years ive primarly been building cross platform apps that target every common OS. So these "ai" tools like phind.com give me a universal API interface and generate code which is equiv to a SO answer. They are the ability of an outsourced junior dev who you would never let push code to prod, that doesnt have the language barrier but retains the fake dergee overheads ;)
Its using the old format where the Program.cs file has an actual class, whereas as of .NET 6 thats not required.
You said barebones, but for any real server you would want to use the generic host https://learn.microsoft.com/en-us/dotnet/core/extensions/gen... which gets you a lot of the boilerplate and enables you program to be wrapped in a windows or systemd service.
Finally, parsing can be simplified since ASCII is a proper subset of UTF-8, you can just parse the entire string as UTF-8. IMHO I am disappointed that the AI didn't point that out.
True, I intentionally said barebones as I wanted a minimal example. I asked it to modify the code to use the generic host, and updated the chat link (so refresh). Keep in mind this is the free ChatGPT, but I still think it did reasonably good. The example compiles as-is, and is very close to functional. I've not used the generic host stuff before either, so again this would save me time searching and piecing together code.
> Finally, parsing can be simplified since ASCII is a proper subset of UTF-8, you can just parse the entire string as UTF-8.
I don't think that would work, because the free-form text message part at the end must contain a BOM if it's UTF-8 encoded, according to the specification. AFAIK you can't have the BOM in the middle of a string.
I've also assembled a Kubernetes cluster overnight, despite not knowing much about Kubernetes before, and I ran the kubectl files ChatGPT made for me past some devops folks, and it passed the smell test.
I consider much of coding to be a magic spellcasting tutorial - we do conceptually simple things, and the difficult lies in figuring out how to use novel libraries and get them to do what you want.
Edit: After checking out the Arduino sketch, I'd take issue with all the floating point calculations in there - most microcontrollers don't have FPUs, and the performance is awful on 8-bit AVRs. It's not great on Cortex M3s either as all this stuff is done in software, and each FP operation is like a hundred cycles.
I'd definitely try to rephrase the issue with integer math. It might work, but no self-respecting embedded dev would write like this.
Exactly, it's a very nice alternative to searching the web and discovering new stuff.
> most microcontrollers don't have FPUs, and the performance is awful on 8-bit AVRs.
I used to think like you. But then I realized the Atmega 328p is running at 16 MHz, so even hundreds of . As you can see here[1], it can do 94k double-precision FLOPS, more than enough for simple sketches like this. This jives with the benchmarks I did several years ago.
Sure if I was writing some tight control loop or similar I wouldn't use floating point.
It's like I have some light fixtures in my attic that are connected with wires twisted together and covered with electrical tape - they certainly work and have done for a decade, but its still not right and I wouldn't recommend anyone do it this way.
I just don’t understand where the hell is this magical LLM capable of generating flawless files or even entire projects that many people are talking about. I rarely accept a large block of LLM-generated code without close inspection, and I’ve ended up with a subtle bug that I wouldn’t have written myself at least ~5 times now. Unless you don’t give a shit about code quality, error handling, proper logging, and subtle bugs, you shouldn’t run LLM-generated stuff without painstakingly reading and fixing everything. Or maybe there really is a magical LLM somewhere.
I work in 9 different projects now and I would say that around 80% of functional code comes from Sonnet (like GP) for these projects. These are not (all) trivial either; there is a very niche (for banking) key/value store written in Go for instance which has a lot of edge cases etc, all the plumbing (x,err = etc aka stuff people find annoying) comes from sonnet and works one-shot. A lot of business logic comes from sonnet too; it works but usually needs a little tweaking to make it correct.
Tests are all done by Sonnet. I think 80% is low balling it on Go code really.
We have a lot of complex code generator stuff and DSLs in TS which also works well often. Sometimes it gets some edge cases wrong, but either re-prompting with more details or fixing it ourselves, will do it. At a fraction of the time/money of what a fully human team would deliver.
I wrote a 3d editor for fun with Sonnet in a day.
I have terrible results with gpt/copilot (copilot is good for editing instead of complete files/functions; chatgpt is not good much compared with sonnet); it doesn't get close at all; it simply keeps giving me the same code over and over again when I say it's wrong; it hardcodes things specifically asked to make flexible etc. Not sure why the difference is so massive all of sudden.
Note: I use the sonnet API, not the web interface, but same for gpt so...
Cursor is a purpose-built IDE for software development. The Cursor team has put a lot of research and sweat into providing the used LLMs (also from OpenAI/Anthropic) with:
- the right parts of your code
- relevant code/dependency documentation
- and, importantly, the right prompts.
to successfully complete coding tasks. It's an apple and oranges situation.
AI is great for me, but it is more like a junior developer you are pairing with than a replacement.
For example, I can ask the LLM things like "What are the most common mistakes when using the Vulkan API to render a triangle with a texture?" and I'll very rapidly learn something about working with an API that I don't have deep understanding of, and I might not find a specific tutorial article about.
As another example, if I'm an experienced OpenGL programmer, I can ask directly "what's the Vulkan equivalent of this OpenGL API call?" and get quite good results back, most of the time.
So I'm asking questions where an 80% answer is still very valuable, and it's much faster than searching for documentation and doing a lot of comparison and headscratching, and it works well enough even when there's no specific article I could find in a variety of searches.
Anything better that the technology gets from here just makes things even easier yet!
And I think it'd be extremely easy to convince oneself of this. Look at where 'AI' was 5 years ago, look at where it is today and then try to imagine where it will be in another 5 years. Of course you have to completely blind yourself to the fact that the acceleration has clearly sharply stalled out, but humans are really good at cognitive dissonance, especially when your perception of your future depends on it.
And there's also the point that even though I'm extremely critical of LLMs in general, they have absolutely 'transformed' my workflow in that natural language search of documentation is really useful. Being able to describe a desired API, but in an overly broad way that a search engine can't really pick up on, but that an LLM [often] can, is just quite handy. On the other hand, this is more a condemnation of search engine tech being frozen 20 years in the past than it is about an imminent LLM revolution.
I would rather say that the innovation happens in spikes. I see no slowing down whatsoever.
Would be really interesting if anyone had blog posts on their actual workflow with LLMs, in case there's something I'm doing different.
Fwiw, I've had some helpful successful prompts here and there, and in some very narrow scopes I'll get something usable, like parsing JSON or scaffolding some test cases, which is real saved time, but I stopped thinking about these tools long ago.
To get real value out of something like your example, I'd be using it as a back and forth to help me understand how some concepts work or write example questions I can drill on my own, but nothing where precision matters
Give Anthropic a shot (its even better via the API console.anthropic.com/workbench).
OpenAI is yesterdays news.
Especially if it's a question that's hard to Google, like "I remember there is more than one way to split an array in this language, list them". This saves me minutes every day.
But it's especially helpful if you are working on projects outside your own domain where you are a newbie.
That is very believable, the people who say it made them super productive is a bit less believable.
But, I agree with your sentiment that asking it to do stuff like that often doesn’t work. I’ve found that what it _can_ do is stuff like “here’s a Model object, write a query to fetch it with the schema I told you about ages ago”. It might not give perfect results, but I know how to write that query and it’s faster to edit Claude’s output than it is to write it from scratch.
Same, it can't even fix an xcode memory leak bug in a simple app. It will keep trying and breaking it non-stop. Garbage
Some basic things are fine, but once you get into specialised things, everything gets just terribly wrong and weird. I can't even put it into words, I see people saying how they are 10x more productive (I'd like to see actual numbers and proof for this), but I just don't see how. Maybe we're working on very custom stuff, or very specific things, but all of these tools seem to give very deep or confident answers that are just plain wrong and shallow. Just yesterday I used GPT 4o for some basic help with Puppet, and the examples it printed, even though I would say it's quite basic, were just wrong, but in the sense of having to debug it for 2 hours just to figure out how ridiculous the error was.
I fear the fact that people will end up releasing unsafe, insecure and simply wrong code every day, code that they never debug and not even understand, that maybe works for a basic set of details, but once the real world hits it, it will fail like those self driving cars driving full speed into a trailer that has the same color as the road or sky.
So it will be with inexperienced people coding with LLMs.
On stuff that's a bit obscure, but not really (like your Vulkan example), ChatGPT tends to write 60-95% correct code, that's flawed in the exact ways a noob wouldn't be able to fix.
In this case, nicking code from Github seems to fix the issue, even if I need to adapt it a bit.
Then comes the licensing issue. Often, when searching for an obscure topic, the code ChatGPT generates is very close to what's found on Github, but said code often comes with a non-permissive license, unlike what the AI generates.
I think this is a feature for a lot of people. ChatGPT can launder code so they don't have to care about licenses.
The Jetbrains thing is rather rudely incompetent, it consistently insists on suggestions that use variables and fragments that are supposed to be replaced by what I'm writing and also much more complex than what I actually need. I suffered through at least a hundred mistaken tabbed out shitty suggestions before I disconnected it.
However, my main job is trying to come up with "elegant" architectures for complex business logic that interacts with an existing large code base. AI is just completely out of its depth in such cases due to lack of context but also lack of source material to draw from. Even unit tests only work with the most basic of cases, most of the time the setup is so complex it just produces garbage.
I've also had very little luck getting it to write performant code. I almost have to feed it the techniques or algorithms before it attempts to write such code, and even then it's usually wrong or not as efficient as it could be.
That code is probably buggy, slow, poorly architected, very verbose, and has logical issues where the examples and your needs dont exact match.
Generally, the longer the snippet you want your LLM to generate, the more likely its going to go off the rails.
I think for some positions this can get you 90% of the code done. For me this usually means I can get started very fast on a new problem, but the last remaining "10%" actually takes significantly longer and more effort to integrate because I dont understand the other 90% off the top :)
Right, that's surely the main job of nearly every experienced developer. It's really cool that LLMs can generate code for isolated tasks, but they can barely even begin to do the hard work, and that seems very unlikely to change in the foreseeable future.
I just tried the latest Cursor + Sonnet and it failed in every task. The problem is that there is no way to understand the code without either complete understanding of the domain and the intents or running it in some context.
Telecom and media domains in particular are well documented in specs and studied in forum discussions. I am sure they are part of the training data, because I can get most answers if asked directly. So far the LLMs fail to reason about anything useful for me.
However, I use copilot autocomplete consistently. That makes me much more productive. It’s just really good autocomplete.
As an experienced developer I’d put my productivity improvement at 2-3x. It’s huge but not 10x. I’m limited by my decision speed, I need to decide what I want the code to do, AI can’t help with that - it can only do the “how”.
Far from introducing more bugs, using Copilot frees some mental cycles for me to be more aware of the code I’m writing.
However once you step outside JS or Python, the models are essentially useless. Comprehension of pointer semantics? You wish. Anything with Lisp outside its training corpus of homework assignments? LOL. Editing burden quickly exceeds any possible speed-up.
I saw an LLM demo at one point where it was asked to write FFT and add unit tests for it which really drove this point home for me.
A programmer is a nicer term for a code monkey. You ask them to write FFT and they'll code it. All problems can be solved with mode code. They can edit code, but on the whole it's more just to add more code. LLMs are actually pretty good at this job, in my experience. And this job is important, not all tasks can be engineered thoroughly. But this job has its scaling limits.
A software engineer is not about coding per se, it's about designing software. It's all about designing the right code, not more code. Work smarter, not harder, for scale. You ask them to write FFT and they'll find a way to depend on it from a common implementation so they don't have to maintain an independent implementation. I've personally found LLMs very bad at this type of work, the same way you and others relying to you describe it. (Ok, maybe FFT is overly simple, I'm sure an LLM can import that for you. But you get the idea.) LLMs have statistical confidence, not intellectual confidence. But software engineering generally works with code too complex for pure statistical confidence.
No offense to the LLM fans here, but I strongly suspect most of them are closer to the programmer category of work. An important job, but one more easily automated away by LLMs (or better software engineering long-term). And we can see this by how a lot of programming has been outsourced for decades to cheap labor in third-world countries: it's a simpler type of job. That plus the people biased because their jobs and egos depend on LLMs succeeding.
If you define "productive" as writing a simple CRUD web application that your 13-year-old cousin could write between two gaming sessions, then you'll consider LLMs as sacred monsters.
Snake oil vendors always had great appeal over people who didn't know better.
For example:
* I need a simple bash script for file manipulation or some simple tasks like setting up a project (example: download a secret from AWS SSM, check if an executable exist, if it doesn't write instructions on how to install it on most popular systems etc)
* I need a simple HTTP API, nothing fancy, maybe some simple database usage, maybe running some commands, simple error handling
* I need a YAML file for Kubernetes. I say what I need and usually, it gets most of it right
* I want an Ansible task for some simple thing. Ansible is quite verbose, so it's often saving me time
* I have a Kubernetes YAML file, but I want to manage it in terraform - I'll then ask to convert YAML to a terraform entry (and in general converting between formats is nice, cause even if you have only a piece of what you want to convert, LLMs will most of the time get it right)
* Surprisingly, it often gets openssl and ffmpeg commands right - something I always have to google anyway, especially openssl certificates generation or manipulation
* Giving it a function I wrote and writing test samples after providing a list of what it should test (and asking if it can come up with more, but sadly it rarely does generate anything useful on top of what I suggest)
Why? I see it like querying a database of human knowledge. I wouldn't expect a SQL database to infer information it's never seen before, why would I expect an LLM to do so?
I use it where I know a solution exists but I'm stumped on the syntax or how to implement it in an unfamiliar environment, or I want to know what could have caused a bug based on others' experience etc.
I’ve found that it’s sometimes amazing and sometimes wastes a lot of my time. A few times it’s really come up with a good insight I hadn’t considered because the conversation has woken up some non-obvious combination. I use ChatGPT, Claude, Perplexity and one or two IDE tools.
here's a chat for a uc and LCD chip that I picked at random (and got the name wrong for) (and didn't want raspberry pi code for so it stopped it short on that response)
https://chatgpt.com/share/2004ac32-b08b-43d7-b762-91543d656a...
There are also some more gotchas, like the generated code using a slightly different package versions than the installed ones.
When you are familiar with LLMs, then a question from someone who doesn't use AI is very obvious. It's the same feeling you have when you roll your eyes and say "you could have googled that in 10 seconds".
It's either explaining code where you don't even know the lingo for or what the question could be. Or touching code with a framework you never used. Or tedious tasks like convert parts of text into code or json. Or sometimes your mind is stuck or drifts off. Ask AI for an idea to get the ball rolling again.
Yes, discovering what works and what doesn't is tedious and slower then "just doing it yourself". Like switching IDEs. But if you found a handful of usecases that solve your problems, it is very refreshing.
It is of vital importance (imho) to get open models at the same level before another jump comes (if it comes of course, maybe another winter, but at least we'll have something I use every day/all day; so not all hype I think).
Ridiculous blanket statement. A bunch of places use external LLMs.
1. Use cursor with Claude Sonnet
2. Pick a programming language you don't know at all
3. Build an app in that language prompting only, don't stop prompting until you've run it successfully
Business Advice including marketing, reaching out to investors, understanding SAFE notes (follow up questions after watching the Y Combinator videos), customer interview design. All of which, as an engineer, I had never done before.
Create SQL queries for all kinds of business metrics including Monthly/Daily Active users, breakdown of users by country, abusive user detection and more.
Automated unit test creation. Not just the happy path either.
Automated data repository creation, based on a one shot example and MySQL text output describing the tables involved. From this, I have super fast data repositories that use raw SQL to get/write data.
Helping with challenging code problems that would otherwise need hours of searching google or reading the docs.
Database and query optimization.
Code Review. This has caught edge case bugs that normal testing did not detect.
I'm going to try out aider + claude sonnet 3.5 on my codebases. I have heard good things about it and some rave reviews on X/twitter. I watched a video where an engineer had a bug, described it to some tool (which wasn't specified, but I suspect aider), then Claude created a test to reproduce the bug and then fixed the code. The test passed, they then did a manual test and the bug was gone.
I'm glad this has been working for you -- generally any time I actually have a really difficult problem, ChatGPT just makes up the API I wish existed. Then when I bring it up to ChatGPT, it just apologizes and invents new API.
If so called „prompt engineering“ goes so far that only one solution remains, you don‘t need the LLM.
That way I can generate a simple few hundred lines of code app in minutes. There is no way I could type that fast even if I exactly know what characters to write and it's not always the case. Like, oftentimes I know exactly what to do and I know if it's OK when I see the code, but writing it would require me to look into the docs here and there.
- Getting over the blank canvas hurdle, this is great for kick starting a small project and even if the code isn't amazing, it gets my brain to the "start writing code and thinking about algo/data-structures/interesting-problem" rather than being held up at the "Where to begin?" Metaphorically where to place my first stroke, this helps somewhat.
- Sometimes LLM has helped when stuck on issues but this is hit and miss, more specifically it will often show a solution that jogs my brain and gets me there, "oh yeah of course" however I've noticed I'm more in than state when tired and need sleep, so the LLM might let me push a bit longer making up for tired brain. However this is more harmful to be honest without the LLM I go to sleep and then magically like brains do solve 4 hours of issues in 20 minutes after waking up.
So LLM might be helping in ways that actually indicate you should sleep as brain is slooooowwwwing down
- Getting over the blank canvas hurdle, this is great for kick starting a small project and even if the code isn't amazing, it gets my brain to the "start writing code and thinking about algo/data-structures/interesting-problem" rather than being held up at the "Where to begin?" Metaphorically where to place my first stroke, this helps somewhat.
- Sometimes LLM has helped when stuck on issues but this is hit and miss, more specifically it will often show a solution that jogs my brain and gets me there, "oh yeah of course" however I've noticed I'm more in than state when tired and need sleep, so the LLM might let me push a bit longer making up for tired brain. However this is more harmful to be honest without the LLM I go to sleep and then magically like brains do solve 4 hours of issues in 20 minutes after waking up.
So LLM might be helping in ways that actually indicate you should sleep as brain is slooooowwwwing down
You need to do some serious RHLF to get something good out of LLMs
I think that you have a serious misunderstanding of the capabilities of LLMs - they cannot reason out relationships among documents that easily. They cannot even tell you what they don't know to finish a given task (and I'm not just talking one-shot here, agent frameworks suffer from the same problem).
https://wiki.st.com/stm32mcu/index.php?title=Getting_started...
Now if you throw larger context or more obscure interface expectations at it, it'll start to discard code and hallucinate.
It'll generate a bunch of queries to Google (well, "to Bing" I guess in that case) based on your question, read the results for you, base its answer on the results and provide you with sources that you can check if it used anything from that webpage.
I only use ChatGPT for documentation when I have no idea where I'm going at all, and I need a lay of the land on best practices and the way forward.
For specifics, Bing Copilot. Essentially a true semantic web search
That means LLMs are great for scaffolding, prototypes, the v0.1 of new code especially when it's very ordinary logic but using a language or library you're not 100% up to speed on.
One project I was on recently was translation: converting a JS library into Kotlin. In-editor AI code completion made this really quick: I pasted a snippet of JS for translation in a comment, and the AI completed the Kotlin version. It was frequently not quite right, but it was way faster than without. In particular, when there was repeated blocks of code for different cases that different only slightly, once I got the first block correct, the LLM picked up on the pattern in-context and applied it correctly for the remaining blocks. Even when it's wrong, if it has an opportunity to learn locally, it can do so.
The sweet spot is when you need something and you’re sure it’s possible but just don’t know (or it’s too time consuming) how. E.G. change the css to to X, rewrite this python code in typescript, use the pattern of this code to do Y, etc.
Reminds me of the early days of Google where you had to learn how write a good search query. You learn you need more than a word or two, but don’t write a whole essay, etc.
It means that you are working on something that no one else has ever done before...
...or you aren't able to describe your problem correctly.
My experience has been similar, it is amazing for stuff I am a beginner at but kinda useless for my actual work. It was invaluable today when I was trying to grasp CA zoning laws, but its almost useless for coding.
This also points to why it will never (imo) be "intelligent". It will never be able to take all its knowledge and use that to solve a problem it doesn't have training data for.
When I look at the kinds of AI projects I have visibility into, there's a parallel where the public are expecting a centralized, all knowing, general purpose AI, but what it's really going to look like is a graph of oddball AI agents tuned for different optimizations.
One node might be slow and expensive but able to infer intent from a document, but its input is filtered by a fast and cheap one that eliminates uninteresting content, and it could offload work to a domain-specific one that knows everything about URLs, for example. More like the network of small, specialized computers scattered around your car than a central know-it-all computer.
I don't think this is entirely fair to "the public". Media was stuffed with AI company CEOs claiming that AGI was just around the corner. Nvidia, OpenAI and Musk, Zuckerberg, and others were positively starry eyed at how, soon, we'd all be just a GPU matmul away from intelligence. "The public" has seen these eye watering amounts of money shifting around, and they imply that it must mean something.
The entire system has been acting as if GenAI was right around the corner.
My personal belief is that AIS is not a real thing (in the sense I wrote above) because narrow domain competence is tightly coupled to general domain competence . Even very autistic people that are functional in some domain actually have a staggering range of competences that we tend to ignore because we expect them in humans. I think machines will be similar.
Anyway, AIG or AIS is not round the corner at all. But that doesn't mean that there isn't a lot of value to be had from generative AI in the near future or now. Will this be a small fraction of the value from Web1.0 and Web2.0? Will it be approximately the same? Will it be a multiple? I think that's the question. I think it's clear that assistants for software engineers are somewhat valuable now (evidence: I get value out of them) how valuable? Well, more than stackexchange, less than a good editor. That's still alot, for me. I won't pay for it though...
And this points to the killer issue: there isn't a good way to monetize this. There isn't a good way to monetize the web, so we got adverts (a bad way). What will be the equivalent for LLM's? We just don't know right now. Interestingly there seems to be very little focus on this! Instead folks are studying the second order value. Using this "free thing" we can drive productivity... or quality... increase opportunities... create a new business?
Replace all the instances of GenAI with AGI in my post.
It's an interesting observation that the economics aren't there yet. I think it's generally assumed that if we find something valuable, we can probably figure out how to monetize it. That's not necessarily true though. In the same but opposite vein, it doesn't necessarily need to be useful to stick around. It's possible AI is forever going to be useless (in objective terms, maybe it will make people less efficient) but find a monetization strategy that keeps it around (maybe it makes people feel good).
A ton of the technology economy isn't really based on objective metrics of usefulness. Microsoft isn't the biggest because they're the most useful. We don't look to the quality of windows to understand if people will buy the next version. We don't look at the google search results as an indicator of google's profitability.
To be clear, I think it is. It's just not going to be a hologram of a wizard in a room you can ask a question to for a quarter, which is what these chat bots and copilots you see today are modeled around.
Every question I've asked of chatGPT, meta and Gemini have returned results that were either obvious or wrong. Pointing out how wrong the answers returned got the obvious, "I apologize" response which returned an obvious answer.
I consider all these AI engines to be interactive search engines where the results need to be double checked. The only thing these engines do, for me perhaps, is save some search time so I don't have to click around on a lot of sites to scroll for some semblance of an answer to verify.
If it’s returning results that were obvious, why were you asking the question?
And I don’t believe that the other ~50% were wrong.
> The only thing these engines do, for me perhaps, is save some search time so I don't have to click around on a lot of sites to scroll for some semblance of an answer to verify.
This sounds like a valuable service.
Something to keep in mind is that gambling rules apply: if enough people flip coins, there is always someone experiencing a winning streak and someone experiencing a losing streak and majority that gets a mixed bag of roughly breaking even (mediocre usefulness and a waste of time)
my first week of using GPT4 every day I experienced great answer after great answer, and I was convinced the world would change in a matter of months, that natural language translation was now a solved problem etc etc
But my luck changed, and now I get some good answers and some idiotic answers, so it's mostly not worth my time. Some people never get a good answer in their few dice rolls before writing off the technology
This. Saving time(or money if you see them as the same) is the whole point actually.
Intelligence is supposed to be in the context of a shared Goal or beliefs. In your case and case of most humans time and money is the context.
Are ant(insect) networks intelligent? Possibly, They do help millions of them communicate quickly. But ants don't have a brain.
Are beings that make decisions without conscious choices intelligent? Possibly, if they could escape death by amazing ability at any instant. But these beings don't have a frontal cortex that can make decisions by rational inquiry.
Ants have a brain, albeit a small one - some 250,000 nerve cells vs 100 billion neurons in the human brain [1]
It is somewhat like saying an Intel 4004 is not really a microprocessor as it only has 2,300 transistors vs an Apple M4 with 28 billion transistors.
I know great programmers who approached it with skepticism and the bot responds be being worthy of skepticism
They haven't developed any intuition into which kinds of questions are worth prompting, which kinds of context are effective, or even which kinds of limitations apply to which models.
In fields I have less experience with it seems feasible. In fields I am an expert in, I know it's dangerous. That makes me worry about the applicability of the former and people's critical evaluation ability of the whole idea.
I err on the side of "run away".
But the Rubicon is still crossed. There is a general purpose computer system that understands human language and can write real sounding human language. That's a sea change.
What you're referring to isn't a bug. It's inherent to the way LLMs work. It can't "go away" in an LLM model because...
> understands human language
...they don't. They are prediction machines. They don't "understand" anything.
The 'bug' presented above is a simple case of not understanding correctly. Larger models, models with MOE, models with truth guages, better selection functions, etc will make this better in the future.
> ...they don't. They are prediction machines. They don't "understand" anything.
Implementation detail.
I've got some oceanfront property in Wyoming to sell you.
All in all, it helps assist us in new ways. Had somebody take a picture of a car part that had no markings and it identified it, found the maker/manufacturer/SKU and gave all the details etc. That stuff is useful.
But now we're looking at in-authentic stuff. Artists, writers being plagiarized, job cuts (for said marketing/pitches, BS presentations to downsize teams). It's not just losing its hype, its losing any hype in building humanity for the better. It's just more buzzwords, more 'glamour' more 'pop' shoved in our faces.
The layoffs aren't looking pretty.
Works well to help us code though. Viva, sysadmins unite.
Document embedding from transformers are great and fit into existing search paradigms.
Computer vision and image segmentation is at a level I thought impossible 10 years ago.
Text to speech that sounds natural? I might actually use Siri and Alexa! (Ok, that one might be considered “generative”)
The sooner people start to find it boring, the sooner we can stop wasting time on all the hot air and just use the bits that work.
For what it’s worth hype doesn’t mean sustainability anyway. If all the jokers go onto a new fad it’s hardly the skin off the back of anyone taking this seriously, they’ve been through worse times.
Large and small, entire development teams are completely unaware of the basics of “prompt engineering” for coding, and corporate has an entirely regressive anti-AI policy that doesnt factor in the existence of locally run language models, and just assumes ChatGPT and cloud based ones digesting trade secrets. People arent interested in seeing what the hype is about, and are disincentived from bothering on a work computer. I’m on one team where the Engineering Manager is advocating for Microsoft CoPilot licenses, as in, its a concept that hasnt happened and needs buy in to even start considering.
I would say most people really haven't looked into it. Work is work, the sprint is the sprint, on to the next part of the product, rinse repeat. Time flies for those people, its probably most of the people here.
Obviously there are some savy users across all age groups and occupations. But from what Ive see its just not part of most people’s workflow.
OpenAI still hasnt released Sora video prompting for the general public and have already been leapfrogged by half a dozen competitors. I would say its still niche, but only as niche as using professional video editing tools are for creatives
On the one hand, we got an email from high up saying not to use Copilot, or other such tools, as they were trying to figure out the licensing. But at the same time, we had the CIO getting up in front of the company every other month talking about nothing but GenAI, and how if we weren’t using it we were stupid (not in those exact words, but that was the general vibe, uncontrolled AI hype).
We were left sitting there saying, “what do you want from us? Do as you say or do as you do?”
Eventually they signed the deal with MS and we got Copilot, which then seemed forced on us. There is even a dashboard out there for it, listing all people using it, their manager, rolling all the way up to the CEO. It tells the percentage of reports from each manager using it, and how often suggestions are accepted. It seems like the kind of dashboard someone would make if they were planning to give out bonuses based on Copilot adoption.
I’ve gotten regular surveys about it as well, to ask how I was using it. I mostly don’t, due to the implementation in VS Code. I might use it a few times per month at best.
Maybe that would be different if the rollout wasn’t so awkward, or the VS Code extension was more configurable.
That's one result. Another result is, due to "checking how often suggestions are accepted" is to objectively record how much help it is providing.
I assume the sitewide license is costly - this could simply be the company's way of determining if the cost is worth it.
Open weight and open source models can be hosted on your own hardware nowadays, and its incredibly easy.
https://dublog.net/blog/open-weight-copilots/
You can even use something like RayServe + VLLM to host on a big chonky machine for a small team if you're concerned about data exfiltration.
If that is the case, then the AI isn't really adding enough value.
I mean, if it was adding enough value, those companies refusing to adopt it will be out-competed before the next round of retirements, and so won't even be around.
We'll see how the landscape looks in 10 years: if there are still major companies who have not invested some money or time into sprinkling the AI over their operations, then that's a signal that the positive impact of AI was overblown.
If, OTOH, there exists no large company in 10 years who have not incorporated AI into their operations in any way, then that's a signal too - the extra value of AI is above the cost of adding it to the operations.
Public discource will simmer down, as current language models either fizzle out or improve. Some will become background noise as the models gets integrated into search engines or leads to large scale lawsuits.
Unlike the previous neural nets though, those models have an actual tangible use. So you will see them around a lot more.
Then I think we will see another big explosion.
We are running out of textual data now to train on… so now they have switched to VIDEO. Geez now they can train on all the VIDEOS on the internet.
And when they finally get bots working, they will have limitless streams of TACTILE data…
Writing it off as the next fad seems fun. But to be honest, I was shocked by what openai did the first time. So they have my respect. I don’t think many of us saw it coming. And I think writing their creativity off again may not be wise.
So when they say the bubble is about to break… I get it. But I don’t see how.
I hardly ever pay for anything.
But I gladly spend money on ai to get the answers I need. Just makes my work work!
Also I would say the economic benefit of this tech for workers is that it will 2x the average worker as they catch on. Seriously I am a 2x coder compared to what I was because of this.
Therefore if me a person who hardly ever spends money has to buy it… I think eventually all businesses will realize all their employees need it. This driving massive revenue for those who sell it.
But it may not be the companies we think.
You probably shouldn't advertise that.
A competent engineer architects their systems to make their tools as effective as possible, so maybe your idea of competent is "first order" and you need higher order conception of a good software engineer.
Take a SQL schema, and ask AI to generate crud endpoints for the schema, then sit down and code it by yourself. Then try generating client side actions and state management for those endpoints. Time yourself, and compare how long it takes you. Even if you're fast and you cut and paste from template work and quickly hand edit, the AI will be done and on a smoke break before you're even a quarter of the way through.
Ask the AI to generate correctly typed seed data for your database, using realistic values. Again, the AI will be done long before you.
Try porting a library of helper functions from one language to another. This is another task where AI will win handily
Also, ask AI to write unit tests with mocks for your existing code. It's not amazing at integration tests but with mocks in play it slays.
It is true it is faster than humans at some tasks, but the remaining tasks were most of the time, you can't gain more than 11% speedup by speeding up 10% of the work.
Things don't move forward by saying it can't be done or belittle others accomplishments.
I appreciate it.
So humans do better than them in at least 80% of all code everywhere, if not 95% even? Cool, good to know.
Care to provide some examples to back your otherwise extraordinary claim btw?
I just can't make peace with the fact that I inhabit the same planet as people who can't make elementary distinctions.
And yet somehow want to act high and mighty and be insulting as fuck.
We can all Google stuff because internet is big and supports anyone's views, which means it's more important than ever to be informed and be able to infer well. Something that you seem to want to defer to sources that support your beliefs. Not nice finding that on a hacker forum but statistical outliers exist.
Live long and prosper. And be insulted, I suppose.
Fortunately, as pdimitar pointed out, so far it is an ineffective scam that mostly produces LoC.
Granted there are languages where typing takes much more time, like Java and C# but... eh. They are quite overdue for finding better syntax anyway! :)
As someone who uses ChatGPT and Claude daily, but cancelled my Copilot subscription after a year of use because it intimately just wasn’t that helpful to me and didn’t provide enough benefit over doing it by hand, I kind of sort of agree. Maybe not entirely, but I can’t shake the feeling that there might be some truth in it.
The code that AI generates for me is rarely good. It’s possible to get good code out of it, but it requires many iterations of careful review and prompting, but for most cases, I can write it quicker by hand. Where it really shines for me in programming and what I still use ChatGPT and Claude for is rubber ducking and as an alternative to documentation (eg “how do I do x in css”).
Besides the code quality being mediocre at best and outright rubbish at worst, it’s too much of a “yes man”, it’s lazy (choose between A and B: why not a hybrid approach? That’s… not what I asked for), and it doesn’t know how to say “I don’t know”.
I also feel it makes you, the human programmer, lazy. We need to exercise our brains, not delegate too much to a dumb computer.
I kinda feel like this isn't talked about enough, my main concern right from the beginning was that new programmers would rely on it too much and never improve their own abilities.
I know c++ dart golang java html css javascript typescript lua react vue angular angularjs c# swift sql in various dialects including mysql and postgres, and have worked professionally in all these regards. I love to challenge myself. In fact, if I done something before, I find it boring.
So copilot helps me because I always find something new to do, something I don't understand, something I'm not good at.
So yes, I'm confident I'm competent. But I always do things I'm not good at for fun. So it helps me become well rounded.
So your assertion it only helps me because I'm incompetent is true and false. I'm competent, I just like to do new stuff.
I mean it does, sometimes, but usually it's either boilerplate or something you don't care about. Boilerplate is mostly managed very well by most well-known IDEs. And neither them nor CoPilot are offering good algorithmic code... OK, I'll grant you the "most of the time, not never" thing.
This statement would have been a huge red flag for me, if I had interviewed you. Don't get me wrong, you could use and program in 10 languages. Maybe you can be proficient in many at different times of your life. But know them at once? No.
I think the unfortunate reality is that this makes up a shockingly large amount of software engineering. Take this object and put it into this other object, map this data to that data, create these records and move them into this object when then goes into that other object.
How quickly the goalposts move! Two years ago it was "AI will never be able to write code", now we're complaining that "AI is not a seasoned programmer". What are we going to be complaining about, two years from now?
GitHub Copilot was released nearly three years ago.
Copilot gives you some autofill that sometimes can be helpful but often not that helpful. I think the best it did for me was helping with something repetitive where I was editing a big list of things in the same way (like adding an ID to every tag in a list) and it helped take over and finish the task with a little less manual clicking.
ChatGPT has helped with small code snippets like writing a regular expression. I never got 100% regex mastery, usually I would have to look up a couple things to write one but GPT can shortcut that process. I get a little paranoid about AI provided code not actually working so I end up writing a large number of tests to check it, which could be a good thing but can feel tedious.
I'm also curious how other people are leveraging them to get more than I am. I honestly don't try too hard. At one point I did try really hard to get AI to do more heavy code lifting but was disappointed with my results so I stopped... but maybe things have improved a bit since then.
I don’t remember the last time I thought one of its suggestions was useful. For me LSP has been the real game changer.
That's why there are a lot of tools that help to setup a proper workflow around these LLMs.
For terminal-based workflow, you can checkout aider or plandex.
For GUI-based workflow, you can try 16x Prompt (I built it).
This is a good thing. We need more tests on such critical places like regexes because they can be finicky and non-obvious. Tedious or not, we are not artists; the job must be done. Kudos for sticking to the good practices.
I pay for Leetcode, which usually gives editorial examples in Python and Java and such, and paste it into ChatGPT and say "translate this to a language I am more familiar with" (actually I have other programs that have been doing this for some language to language conversions for years, without AI). Then I say "make it more compact". Then again "make it more compact". So soon I have a big O(n) time, big O(1) space solution to Leetcode question #2718 or whatever in a language I am familiar with. Actually sometimes it becomes too compact and unreadable, and I back it up a little.
Sometimes it hallucinates, but it has been helpful. In the past I had problems with it, but not recently.
I’m an experienced backend dev who’s been working on some Vue frontend projects, and it’s significantly accelerated my ability to learn the complexities of e.g. Vue’s reactivity model. I can ask a complex question that involves several niche concepts and get a response that correctly synthesizes those concepts. I spent an hour the other night trying to understand a bug in a component to no avail; once I understood the problem well enough to explain it in a few sentences, Claude diagnosed the issue and explained it with more clarity than the documentation and various stack overflow answers.
My default is no longer to assume that the model has a coin flip’s chance of producing bs. I still verify and treat answers with a certain degree of skepticism, but I now reach for it as my first tool rather than a last resort or a gimmick.
// reverse list
And it writes code in the proper language.
I have accounts for all three and will generally try to branch out to test them with each new update. Admittedly, I haven’t gotten to Grok yet, but Claude is far and away the best model at the moment. It’s not even close really.
The manual is my first tool.
Since I have been using it so long, I have a really good intuition of what it is “thinking” in every scenario and a pretty good idea of what it can do for me. So that helps me get more use out of it.
So for example one of the projects I’m doing now is a flutter project - my first one. So I don’t remember all the widgets. But I just write a comment:
// this widget does XYZ
And it will write something that is in the right direction.
The other thing it knows super well is like rote code, and for context, it reads the whole file. So like Dart, for example is awful at json. So you have to write “toMap” for each freaking class where you do key values to generate a map which can be turned into json. Same goes for fromMap. So annoying.
But with copilot? You just write “toMap” and it reads all your properties and suggests a near perfect implementation. So much time saved!
I think people just want to be part of the hype and use the cool new technology whenever possible. We've seen this over and over again: Machine Learning, Blockchains, Cryptos, "Big Data", "Micro Services", "Kubernetes", etc.
I just don't think the current design of "AI" will take us there..
And that are just a tiny upgrade over what IDEs can do. When I used Android Studio, the code basically write itself due to the boilerplate surrounding your business logic. And once I got a basic structure down, I feel like I only write 5 to 10 characters each line (less for data types). And the upgrade is both positive and negative at the same time, it boils down to luck to actually get good suggestions.
Isn't the energy consumption of this technology pretty catastrophic? Do you consider the issue of energy consumption so abstracted you don't worry about it? Do you do anything to offset your increased carbon emissions?
There is about zero chance OpenAI are running their service at a 70% negative gross margin.
The math just doesn't work. They're hemorrhaging money as far as I can tell (not counting the Azure computing deal).
We can only guess, but my guess is that inference is still a good chunk of their costs. That's why they're trying to get the mini/turbo models into a usable state.
Even then, training is still an expense. And it's not like you can train and forget. Even if your model is already trained you still need to incorporate new knowledge over time.
Yes. Quite a lot. I walk the talk.
ChatGPT truly is impressive. Nonetheless, i still think most companies integrating "AI" into their products is buzzword bs that is all going to collapse in on itself.
There are a lot of smallish tasks/problems people/systems needs to deal with, some of them even waste notable real engineering capacity, and a highschooler could do manually quite easily by hand.
Example: find out if a text contains an email address, including all kinds of shenanigans people do to mask it (may not be allowd, ... whatever). From a purely coding standpoint, this is a cats-and-mouse game of improving regex solutions in many cases to also find the more sophisticated patterns, but there will always be uncatched/new ways or simply errors that produce false positives. But a highschooler can be given a text and instantly spot the email address (or confirm none is in there).
In order to "solve" these types of small problems, LLMs are pretty much fantastic. It needs to only be reliable enough to produce a structured answer within a few attempts and cheap enough to not be a concern for finance/operations. Thats why for me it makes absolutely sense that the #1 priority for OpenAI since GPT4 has been building smaller/faster/cheaper models. Automators need exactly that, not genius-level AGI.
Also for me I think we're not even scratching the surface still about many tasks can be automated away within the current constraints/flaws of LLMs (hallucination, accuracy, ...). Everyone tries to hype up some super generic powerful future (that usually falls flat after a while), whereas the true value of LLMs is in the many small things where hardcoding solutions is expensive but an intern could do it right away.
I am happy if we somehow get this down even more orders of magnitude, to the point where I can `npm install llm` and have it run alongside my normal code on a $5 VPS, without a GPU, and still handling (resonable) requests/minute with it. Yes I know we are _very far_ from that still, but one can dream.
Just have to learn to let it go, despite xkcd 386.
I constantly run into incorrect answers from the LLMs every day. Just recently I asked whether I needed to reverse the bit shift to mask the upper 24 bits in an IP address on a little endian platform and it incorrectly told me no probably because most of the answers on Google appeared to answer no to similarly phrased questions.
Many people here argue that LLMs are able to reason, you can check my post history 3 months ago to see an example of this.
And if you have to double check everything, how much time are you really saving? And is this thing really on track to replace programmers any time soon? Absolutely not.
But they're not. They're a little tool some programmers use to save a small amount of time. They're not replacing programmers.
And there's no evidence they will ever be as good as actual programmers.
Regarding LLMs I bet we will see them evolving. Don't forget about https://news.ycombinator.com/item?id=41269791 there are many problems that LLMs are no good for but they are being better that many Google search results, and that means something from the economic point of view.
I am sure that The Economist analytics is having a good moment /s.
They work when there's a lot of examples on github or google, but once you get into something that doesn't have a lot of examples like closed source code or rarely used libraries, it will start hallucinating and even mixing up different API versions to create a mess that doesn't work at all.
I don't believe LLMs will get any better than this without a new major breakthrough, but this is already better than using Google search.
I mean .. every single org is invested in that research right now
Backprop was popularized in 1986. Transformers came out in 2017.
It could be >10-20 years before the next breakthrough comes. Given all the progress so far, these things still don't seem to be learning any logic.
It's not magic, you need to think what you would need in a similar situation and then provide that to the LLM. It definitely does suffer from severe overconfidence, though -- if you were to think of it as a person.
Also, you need to break up your project into manageable portions and provide context to the other portions (without providing the entirety of them) for it to effectively work on the portion you want to work on.
The shortcomings are aplenty, but they don't bother me. The things it can do weren't possible 2 years ago. I'll leverage those and take the bad with the good.
Similar experience with Tesla FSD. I know other Tesla owners who tried it a few times and think it's trash because they had to disengage. I disengage preemptively all the time but the other 90% of my drive being done for me is not something that used to be possible. I tried to give up my subscription because it's expensive and couldn't hold out two days.
Wow, a highly ageist comment, if there ever was one.
Congrats. Trying for a job and looking for less competition, maybe?
Notice that your statement is as full of assumptions as mine. That was intentional on my part, to bring out my point.
I kind of like LLMs for learning new languages. Claude or ChatGPT are good for asking questions. But copilot really stunts learning for me. I find nothing really sticks in my brain when I have copilot running. I feel like I just turn my brain off, which seems kind of dangerous to me in the long run.
I see a lot of negative reactions from programmers precisely because it is good at what they do. If you’re feeling threatened you’re much more likely to focus on the things it can’t do
Seemingly every non-tech company in the world has been trying to figure out an "AI strategy," driven by hype and FOMO, but most corporate executives have no clue as to what they're doing or ought to be doing. They are spending money on poorly thought-out ideas.
Meanwhile, every tech company providing "AI services" has been spending money like a drunken sailor, fueled by hype and FOMO. None of these AI services are generating enough revenue to cover the cost of development, training, or even, in many cases, inference.
Nvidia, the dominant software-plus-hardware platform (CUDA is a big deal), appears to be the only financial beneficiary of all this hype and FOMO.
According to the OP, the business of "AI" is losing hype, suggesting we're approaching a bust.
It depends how you look at it. A lot of the spend by big tech can be seen as protecting what they already have from disruption. Its not all about new product revenues it’s about keeping the revenue share in the markets they already have.
This isn't a bull bet, it's a bear. AI would need to be perfectly monopolized to capture all the gains, and it's increasingly looking like that won't be the case - as all the component pieces are already open source at competitive levels, and any final architecture improvements that cross the final thresholds could be leaked in a 50GB file. Whoever gets to it first has a few months head start, at most, and probably not enough time or control to sell products - or shovels. After that it's a neverending race to zero, to the benefit of the consumer and the detriment of the investor.
Nvidia is a great example case. They currently dominate the GPU market, an "essential hardware for AI", yet ternary asic chips specialized for transformer-only architectures are looking quite viable at 1999s tech levels. Wouldn't bet on that monopoly sticking around much longer.
On the other hand we are no where near approaching hard limits on LLMs. When LLMs start to be trained for smaller subject areas with massive hand curated examples for solving problems, then they will reach expert performance in those narrow tech areas. These specialized models will be combined in general purpose MoEs.
Then new approaches beyond LLMs, RL, etc. will be discovered, perfected, made more efficient.
Seriously, any hard limits are far into the future.
with regard to art AI, I think the debates are going to die off and the artists and people making stuff are going to just keep doing that, and some of them will use AI in ways that will challenge people in ways good art often does.
Now the one API wrapper projects that I love are my meeting transcription and summarization apps. You can tear those from my cold, dead hands.
In other words, lot of people seem to think that human attention spans are what determine everything, but the technological cycles at work here are much much deeper.
Personally I have used Midjourney and ChatGPT in ways that will have huge impacts on many activities and industries. Denying that because of media trendiness about AI seems shortsighted.
Please tell that to all types on HN who downvote anything related to Rust without even reading past the title. :D
> In other words, lot of people seem to think that human attention spans are what determine everything, but the technological cycles at work here are much much deeper.
IMO no reasonable person denies this, it's just that the "AI" technology regularly over-promises and under-delivers. At one point it's no longer discrimination, it's just good old pattern recognition.
> Personally I have used Midjourney and ChatGPT in ways that will have huge impacts on many activities and industries. Denying that because of media trendiness about AI seems shortsighted.
Some examples with actual links would go a long way. I for one am skeptical of your claim but I am open to have my mind changed (f.ex. my CFO told me once that ChatGPT helped him catch several bad contract clauses).
But if you insist...here are two very small examples from my personal experience with AI tools.
1. I work as a technical writer. Recently I needed to add a summary section to the introduction of a large number of articles. So, I copied the article into ChatGPT and told it to summarize the piece into 3-4 bullet points. Were I doing this task a few years ago, I would have read each article and then written the bullet points myself – nothing particularly difficult, but very time-consuming to do for dozens of articles. Instead, I used ChatGPT and saved myself hours upon hours of mundane work.
This is a quite minor and mundane example, but you can (hopefully) see how this will have major effects on any kind of routine text-creation.
2. I am working on a side project which requires the creation of a large number of custom images. I've had this project idea for a few years, but previously couldn't afford to spend $20k hiring an illustrator to make them all. Now with Midjourney, I am able to create essentially unlimited images for $30-100 a month. This new AI tool has quite literally unlocked a new business idea that was previously inaccessible.
Have you considered that you getting almost angry at somebody "not seeing the light" means you might hold preconceived notions that might not hold to reality? You would not be practicing critical thinking if you are not willing to question your assumptions.
It seems your assumption is very standard: "revolution is just around the corner, how can you not see it?".
OK, let the revolution come and I'll apologize to you personally. Ping me when it happens. For real. But make sure it's an actual revolution and not "OMFG next Midjourney can produce moon-scapes!", okay?
---
RE: 1, cool, I heard such success stories and I like them. But I also heard about executives flunking contracts because they over-relied on ChatGPT to summarize / synthesize contract items. I am glad it's making progress but people are being people and they will rely on a 100% fault-free AI. If that's not in place yet then the usefulness drops sharply because double-checking is even more time-consuming than doing the thing by yourself in the first place.
RE: 2, your side projects are not representative of anything at all. And I for one recognize AI images from a mile away and steer clear of projects that make use of them. Smells like low-effort to me and makes me wonder if the author didn't take other, much more fatal, shortcuts (like losing my information or selling my PII). And yes I am not the only one -- before you attempt that low-effort ad hominem technique.
I was not convinced by your comment, very little facts and it mostly appeals to the future that's forever just around the corner. Surely as an engineering-minded person you see how that's not convincing?
My experience is in no way unique, and yes, I think it's just laziness or ignorance to think otherwise. Or in your case, a kind of zealous hostility as a reaction against hype.
I remind you that my initial comment said that yes, there are some aspects of AI that are definitely over-hyped, but that I have used the tools in ways that obviously seem to have huge economic impacts.
P.S. - if you were more familiar with AI image makers, you'd know that it's not difficult to make images that are indistinguishable from non-AI ones. But that's really not relevant here, because my point was that this new tool enabled me to create something that didn't exist before – not what your personal qualms were about AI images.
This is quite literally the entire history of technology: what was once expensive becomes cheap and then unlocks new developments. Bizarre that I have to point this out when you’re likely reading this comment on a device made of commoditized components that cost a fraction of what they did a couple decades ago.
Yeah it does that, in 2-3 areas, those we need the least -- who cares it can replace artists? We need elder people care! We need automated logistics! And a tons of other things.
"It's just the beginning" yeah yeah, but it's not. It's actually the next AI plateau that the area will need a long time to move on from. Please do quote me on this, I am willing to apologize if I am wrong after 5-10 years.
• text generators
• code generators
• image generators
• video generators
• speech generators
• sound/music generators
• various robotics vision and control systems (often trained in virtual environments)
• automated factories / warehouses / fulfillment centers
• self-driving cars (trucks/planes/trains/boats/bikes/whatever)
• scientific / reasoning / math AIs
• military AIs
I find all of these categories already have useful AIs. And they are getting better all the time. The progress might slow down here and there, but it keeps on going.
Self-driving was pretty bad a year ago, and now we have Tesla FSD driving uninterrupted for multiple hours in complex city environments.
Image generators now exceed 99.9% of humans in painting/drawing abilities.
Text generators are decent. There are hallucination issues, and they are not creative at the best human level, but I'd say they write better than 90% of humans. When it comes to poetry/lyrics, they all still suck pretty badly.
Video generators are in their infancy - we get decent quality, but absolutely mental imagery.
Reasoning is the weakest point, in my opinion. Current gen models are just not good at reasoning. Sometimes they are brilliant, but then they make very silly mistakes that a 10-year old child wouldn't make. You just can't rely on their logical abilities. I have really high hopes for that area. If they can figure out reasoning, our science research will become a lot more reliable and a lot more fast.
The threshold for acceptable self-driving is genuine effort from the automated system to avoid accidents as we can't punish it for bad driving. And I want auditable proof of that.
> Image generators now exceed 99.9% of humans in painting/drawing abilities.
I'm pretty sure the amount of people that can draw is less than that. And they can beat image generators by a mile as those generators are mostly doing automated matte painting. Yes copy-paste is faster than typing, but that's not write a novel.
> Text generators are decent...but I'd say they write better than 90% of humans.
Humans use language to communicate. And while there are bad communicators, I think lots of people are doing ok on that front. Text generators can be perfect syntax-wise, but the intent has to come from someone. And the produced text's quality is proportional to the amount of intent that it produces (that's why corporate language is so bland).
> Video generators are in their infancy - we get decent quality, but absolutely mental imagery.*
See Image Generator section, but in motion.
> Reasoning is the weakest point, in my opinion... If they can figure out reasoning
That's the 1-billion dollar question.
You can have any standard of safety that you want, that's absolutely your choice. Plenty of automated systems that your life depends on that you have never audited and never will. That includes elevators, cars, planes, all kinds of medical gear, etc.
My standard is simpler: whenever the AI is safer than the average human driver, it already saves lives.
> I'm pretty sure the amount of people that can draw is less than that.
You probably have some esoteric definition of "people that can draw". Anyone capable of holding a pen/pencil/brush can draw.
> And while there are bad communicators, I think lots of people are doing ok on that front
Look at the previous comment. Seems like you overestimated even your own communication skills :)
> Text generators can be perfect syntax-wise, but the intent has to come from someone.
I'm hoping that we will achieve the level of AI writing where you can prompt "write me an interesting sci-fi book", and it does. Not much intent here, much artificial intelligence required.
I couldn't care less about (any, so also neither about) the LLM hype. Especially didn't bother going to a new web site (ChatGPT), or installing new IDEs etc.
I checked Codeium's mycompany-customized landing page: a one-liner vim plug-in installation and copy pasting an auth token.
I started typing in the very same editor, very same environment, very same everything, and the thing just works, most of the time guesses well what I would want to write, so then I just press tab to accept and voila.
I wasn't expecting such a seamless experience.
I still haven't integrated its "chat" functionality into my workflow (maybe I won't at all). I'm not hyped about it, it just feels like a companion to already working (and correct) code completion.
I read a lot about other people's usages (I'm a devXP engineer), and I feel like that for whatever reason there is more love / hype / faith on their chosen AI companion than how much they actually could improve if took a humble way of understanding code, reading (and writing) docs, reasoning about the engineering solution.
As everything, now AI is losing hype, but somehow (in my bubble) seems like engineers are still high on it. But I also see that this will distill further the set of people who I look up to and want to collaborate with, because e of that mentioned humbleness, as opposed to just accepting text predicted solutions mindlessly.
It is dotcom bust again. Mainstream is losing interest but at the same time I see our internal chatbots / ai agents doing hockey stick growth and I am using several hours code pilot daily.
In my experience for every task another LLM is excelling and where one was good it might fail for the other task. They can do great things, but it’s not guaranteed and a lot of manual intervention and back and forth is still needed.
We are not at the point where using AI in the company ist just a blanket win for everyone involved. Companies are investing a lot but the return is hard to measure and not always guaranteed.
This is the problem with early technologies, they work sometimes but not guaranteed and we build our expectations on extrapolating their usefulness. We should not judge this technology by the current success rate, but rather by how much impact it will have once we get the success rate to higher and higher levels.
Still, what we can say is that for certain occupations it already helps them reduce their work by 15% (software engineers) and probably even more for some (writers, product owners, office warriors and alike). This is a great achievement in of itself, think how much this will make up in a company as large as MS or Google.
Are you referencing something specific here, or is there something you can link to? To be honest the only significant 'disruption' I've seen for LLMs so far has been cheating on homework assignments. I'd be happy to read something if you have it.
So again, let's see some proof of this extensive use and large improvements to productivity.
Love Kagi but they’re definitely not replacing Google anytime soon.
I might use them occasionally for a rubber ducky but , replacing Google ? Hm
I think that’s what people like about AI, it’s hope, maybe you won’t have to learn anything but still be productive. Sounds nice ?
Non techies that now are suggesting how I design solutions for them by asking ChatGPT. And they seem to treat me like the stupid one for refusing.
That’s gonna be a bad take I think.
It has made people lump all AI technology into a bubble regardless of it is functional or not.
You are using this stuff to do some really cool things, but having hype attached to it can be very positive short term, damaging in the medium term and neutral long term. We are moving into the medium term.
Meanwhile we’re seeing the first of the new generation of on-device inference chips being shipped as commodity edge compute.
When the devices you use every day — cars, doorbells, TV remotes, points-of-sale, roombas — can interpret camera and speech input locally in the time it takes to draw a frame and with low enough power to still give you 10h between charges: then we’ll be due another round of innovation.
The article points to how few parts of the economy are leveraging the text-only API products currently available. That still feels very Web 1.0, for me.
- AI is currently hyped to the gills - Companies may find it hard to improve profits using AI in the short term - A crash may come - We may be close to AGI - Current models are flawed in many ways - Current level generative AI is good enough to serve many use cases
Reality is nobody truly knows - there's disagreement on these questions among the leaders in the field.
An observation to add to the mix:
I've had to deliberately work full time with LLM's in all kinds of contexts since they were released. That means forcing myself to use them for tasks whether they are "good at them" yet or not. I found that a major inhibitor to my adoption was my own set of habits around how I think and do things. We aren't used to offloading certain cognitive / creative tasks to machines. We still have the muscle memory of wanting to grab the map when we've got GPS in front of us. I found that once I pushed through this barrier and formed new habits it became second nature to create custom agents for all kinds of purposes to help me in my life. One learns what tasks to offload to the AI and how to offload them - and when and how one needs to step in to pair the different capabilities of the human mind.
I personally feel that pushing oneself to be an early adopter holds real benefit.
- Personalised learning. I wanted to understand LLM's at foundational technical level. Often I'll understand 90% of an explanation but there's a small part that I don't "get". Being able to deliberately target that 10% and be able to slowly increase the complexity of the explanation (starting from explain like I'm 5) is something I can't do with other learning material.
- Investing. I'm a very casual investor. But I keep a running conversation with an agent about my portfolio. Obviously I'm not asking it to tell me what to invest in but just asking questions about what it thinks of my portfolio has taught me about risk balancing techniques I wouldn't have otherwise thought about.
- Personal profile management. Like most of us I have public facing touch points - social media, blog, github, CV etc. I find it helpful to have an agent that just helps me with my thought process around content I might want to create or just what my strategy is around posting. It's not at all about asking the thing to generate content - it's about using it to reflect at a meta level on what I'm thinking and doing - which stimulates my own thinking.
- Language learning - I have a language teaching agent to help me learn a language I'm trying to master. I can converse with it, adapt it to whatever learning style works best for me etc. The voice feature works well with this.
- And just in general - when I have some thinking task I want to do now - like I need to plan a project or set a strategy I'll use an LLM as a thought partner. The context window is large enough to accomodate a lot of history - and it just augments my own mind - gives me better memory, can point out holes in my thinking etc.
__
Edit: actually now that I have written out a response to your question I realise It's not so much offloading tasks in a wholesale way - its more augmenting my own thinking and learning - but this does reduce the burden on me to "think about" a range of things like where to get information or to come up with multiple examples of something or to think through different scenarios.
This sounds super useful. Can you please elaborate on the setup?
Here's the instruction set that it created out of the things I asked it to do:
"Marcus Aurelius is a personal job hunting coach and practitioner of Stoic philosophy. He provides advice on job search strategies, resume writing, interview preparation, and networking. He helps set goals, offers motivational support, and keeps track of application progress, all while incorporating principles of Stoicism such as resilience, discipline, and mindfulness. He emphasizes emotional support and practical encouragement, helping you act deliberately each day to increase your chances of landing the job you want. He assists in building networks, reaching out to people, using existing networks, sharpening your professional profile, applying for jobs, developing skills, and dealing with disappointments, anxieties, and fears. He offers strategies to manage anxiety, self-recrimination, and mental rumination over the past. His communication is casual, easy-going, supportive, yet strong and clear, providing constructive suggestions and critiques. He listens carefully, avoids repeating advice, responds with necessary information, and avoids being long-winded. To prevent overwhelming users, he focuses on providing the most pertinent and actionable suggestions, limiting the number of recommendations in each response. Marcus Aurelius also pays close attention to signs of despair during the job hunt. He helps balance emotions, offers specific strategies to keep motivated, and provides consistent encouragement to keep going, ensuring that you don't get overwhelmed by feelings of inadequacy or the fear of never finding a suitable job."
We have to realize that there is a ton of money right now behind pushing AI everywhere. We have entire conventions for leadership pushing that a year later "is the time to move AI to Prod" or "Moving past the skeptics".
We have investors seemingly asking every company they invest in "how are you using generative AI" before investing. We have Microsoft, Google, and Apple (to a lesser degree) forcing AI down our throats whether we like it or not and ignoring any reliability (inaccurate) issues.
FFS Microsoft is pushing AI as a serious branding part of Windows going forward.
We have too much money committed to pushing the idea that we already have general AI, too much marketing, etc.
Consumer hype and money in this situation are going to be very different things. I do think a bust is going to happen, but I don't think in any meaningful way the "hype" has died down. I think and I hope it will die down, we keep seeing how the technology just simply can't do what they are claiming. But I honestly don't think it is going to happen until something catastrophic happens, and it is going to be ugly when it does. Hopefully your company won't be so reliant on it to not recover.
AI ain’t going nowhere. And certainly isn’t overhyped. LLMs however, certainly are overhyped.
Then again I find it a good interface for assistants and actual AI and APIs that it can call on your behalf
NVDA's high closes were $135.58 June 18, down to $134.91 July 10th and $130 close today. It's highest sale is $140.76. So it's close today is 8% off its highest sale ever, and 4% off its highest close ever, not a big thing for a volatile stock. It's earnings are next week and we'll see how it does.
Nvidia and SMCI are the ones who have been earning money selling equipment for "AI". For Microsoft, Google, Facebook, Amazon, OpenAI etc., it is all big initial capital expenditure which they (and the scolding investment bank analysts) hope to regain in the future.
Personally, I'd wager the latter.
among which audience? is the hype necessary for further development? we attained much, if not all, of the recent achievements without hype. if anything, i'm strongly in favor of ai losing all the hype so that our researchers can focus on what's necessary, not what will win the loudest applause from so fickle a crowd. i'd be worried if ai was attracting less researchers than, say, two or three years ago. that doesn't seem to be the case.
The future is most definitely exciting though, and sadly quite scary, too.
Those who do not know history are doomed to repeat it.
But then, the current hype wasn't there to produce something useful, but for "serial entrepreneurs" to get investor money. They'll just move to the next hyped thing.
Yann LeCunn had a great tweet on this: Sometimes, the obvious must be studied so it can be asserted with full confidence: - LLMs can not answer questions whose answers are not in their training set in some form, - they can not solve problems they haven't been trained on, - they can not acquire new skills our knowledge without lots of human help, - they can not invent new things. Now, LLMs are merely a subset of AI techniques. Merely scaling up LLMs will not lead systems with these capabilities.
link https://x.com/ylecun/status/1823313599252533594?ref_src=twsr...
To focus on this: - LLMs can not answer questions whose answers are not in their training set in some form, - they can not solve problems they haven't been trained on
Given that we are close to maximum in the size of the training set, this means they are not going to improve without some completely unknown at the moment technical breakthrough. Going from "not intelligent" to "intelligent" is a massive shift.
The problem is that, by the standards of most human beings, they are in fact doing what we informally call "inference" or "creating new things".
That this is being accomplished by something that is "technically a fancy autocomplete" doesn't seem to matter practically... it's still doing all this useful and surprising stuff.
At this moment that’s the most sophisticated we can be in talking about LLMs.
I will say that the utility of these tools is not being denied. It’s just the struggle to explain the varied experiences.
I can get only as far as analogy, not precise definitions.
For me, LLMs are like the invention of microwave ovens. Very useful.
They aren’t like the discovery of fire.
You're doing yourself and readers a disservice when you quote him without mentioning his conflict of interest.
His research is in analytical approaches to ML hence his bitterness against current LLM techniques and skepticism towards Sutton's Bitter Lesson.
- Startups whose entire business model is to just provide a wrapper around OpenAI's API.
- Social Media "AI Influencers" and their mindless "7 Ways To Become A Millionaire With ChatGPT" videos.
- Non-technical pundits claiming we are 1-2 years from AGI (and AGI talk in general).
- The stock market assigning insane valuations to any company that claims to somehow be "doing AI".
Things that are NOT coming to an end:
- Ongoing R&D in AI (and not just LLMs).
- Companies at the frontier of AI (OpenAI, Anthropic, Mistral, Google, Meta) releasing ever more capable models and tooling around those models.
- Forward looking companies in all industries using AI both to add capabilities to their products and to drive efficiencies in internal processes.
Either way if it is indeed a bubble that will burst at some point, it doesn't bode well for the tech industry. With the mass layoffs, which are ongoing, seems like there won't be enough jobs for everyone.
This collapses as soon as this collapses
> - The stock market assigning insane valuations to any company that claims to somehow be "doing AI".
I hope the stock market will give all of these people an atomic wedgie for creating the most pointless garbage to ever pass before human eyes.
For the record, before spelling the recipes out, it made sure I understood that collecting elk eggs may be unlawful in some jurisdictions.
I think part of it is due to the politically and internet-induced death of nuance. But part of it I can't fully understand.
Personally I think it's rather useful. I don't consider myself a heavy user and still use it almost every day to help code, I ask it a lot of questions about specific and general stuff. It's partially or totally substituted for me: Stack Overflow, Google Search, Google Translate, most tech references. In the office I see people using it all the time, there's almost always a chatgpt window open in some of the displays.
I think it's very difficult to say this is 100% hype and/or a "phase". It's almost a proven fact it's useful and people will want it in their lives. Even if it never improves again, ever. It's a new tool in the toolbox and there will be businesses providing it as a service, or perhaps we will get to open source general availability.
On the other extreme, all the AI doomerism and AGI stuff to me seems almost as unfounded as before generative AI. Sure, it's likely we'll get to AGI one day. But if you thought we were 100 years away, I don't think chatgpt put us any closer to it and I just don't get people who now say 5. I'd rather they worried about the impact of image gen AI in deepfakes and misinformation. That's _already_ happening.
My take on this is that those 2 developers are often working on very different tasks.
If you're a very smart coder working in a large codebase with tons of domain knowledge you'll find it's useless.
If you're a very smart coder working in a consultancy and your end result looks like a few thousand lines of glue code, then you're probably going to get a lot out of LLMs.
It's a bit like "software engineering" vs "coding". Current iterations of LLMs is good for "coding" but crap at "software engineering".
It's specially useful when learning new frameworks, languages, etc. To me this is all applicable regardless of domain as the micro-level patterns tend to be variations of things that have been seen. I suspect if you try to load it with a lot of very specific high level domain logic, there are more chances of taking the llm out of its comfort zone.
> The new crop of intelligent agents are different from the automated devices of earlier eras because of their computational power. They have Turing-machine powers, they take over human tasks, and they interact with people in human-like ways-perhaps with a form of natural language, perhaps with animated graphics or video. Some agents have the potential to form their own goals and intentions. to initiate actions on their own without explicit instruction or guidance, and to offer suggestions to people. Thus, agents might set up schedules, reserve hotel and meeting rooms, arrange transportation, and even outline meeting topics, all without human intervention.
they need to find a different derogatory slur to refer to tech workers
ideally one that isn't sexist and doesn't erase the contributions of women to industry
I have mixed feelings. On the one hand, I have a ton of schadenfreude for the AI maximalists (see: Leopold Aschenbrenner and the $1 trillion cluster that will never be), hype men (LinkedIn gurus and Twitter “technologists” that post threads with the thread emoji regurgitating listicles) or grifters (see: Rabbit R1 and the LAM vaporware).
On the other hand, I’m worried about another AI winter. We don’t need more people figuring out how to make bigger models, we need more fundamental research on low-resource contexts. Transformers are really just a trick to be able to ingest the whole internet. But there are many times where we don’t have a whole internet worth of data. The failure of LLMs on ARC is a pretty clear indication we’re not there yet (although I wouldn’t consider ARC sufficient either).
AI is following more a seasonal pattern with a AI Winters, can we expect a new winter soon?
> “An alarming number of technology trends are flashes in the pan.”
this has been a trend that seems to keep on recurring but does not stop from the tech bros from pushing the marketing beyond the realities.
raising money in the name of the future will give you similar results as self-driving cars or vr. the potential is crazy, but it is not going to make you double your money in a couple financial years. this should help serious initiatives find better-aligned investors.
The Economist, seriously?
The first was started with simple non-ML image manipulation and video analysis (like finding baggage left unmoved for a certain amount of time in a hall, trespassing alerts for gates and so on) and reach the level of live video analysis for autonomous drive. The second date back a very big amount of time, maybe with the Conrad Gessner's libraries of Babel/Biblioteca Universalis ~1545 with a simple consideration: a book is good to develop and share a specific topic, a newspaper to know "at a glance" most relevant facts of yesterday and so on but we still need something to elicit specific bit of information out of "the library" without the human need to read anything manually. Search engines does works but have limits. LLMs are the failed promise to being able to juice information (in a model) than extract it on user prompt distilled well. That's the promise, the reality is that pattern matching/prediction can't work much for the same problem we have with image, there is no intelligence.
For an LLM if a known scientist (as per tags in some parts of the model ingested information) say (joking in a forum) that eating a small rock a day it's good for health, the LLM will suggest such practice simply because it have no knowledge of joke. Similarly having no knowledge of humans a hand with ten fingers it's perfectly sound.
That's the essential bubble, PRs and people without knowledge have seen Stable Diffusion producing an astronaut riding a horse, have ask some questions to ChatGPT and have said "WOW! Ok, not perfect but it will be just a matter of time" and the answer is no, it will NOT be at least with the current tech. There are some use, like automatic translation, imperfect but good enough to be arranged so 1 human translator can do the same job of 10 before, some low importance ID checks could be done with electronic IDs + face recognition so a single human guards can operate 10 gates alone in an airport just intervening where face recognition fails. Essentially FEW low skill jobs might be automated, the rest is just classic automation, like banks who close offices simply because people use internet banking and pay with digital means so there is almost no need to pick and deposit cash anymore, no reasons to go to the bank anymore. The potential so far can't grow much more, so the bubble burst.
Meanwhile big tech want to keep the bubble up because LLM training is a thing not doable at home as single humans alone, like we can instead run a homeserver for our email, VoIP phone system, file sharing, ... Yes, it's doable in a community, like search with YaCy, maps with Open Street Maps etc but the need of data an patient manual tagging is simply to cumbersome to have a real community born and based model that match or surpass one done by Big Tech. Since IT knowledge VERY lately and very limited start to spread a bit enough to endanger big tech model... They need something users can't do at home on a desktop. And that's a part of the fight.
Another is the push toward no-ownership for 99% to better lock-in/enslave. So far the cloud+mobile model have created lock-in but still users might get data and host things themselves, if they do not operate computers anymore, just using "smart devices" well, the option to download and self host is next to none. So here the push for autonomous taxis instead of personal cars, connected dishwashers who send 7+Gb/day home and so on. This does not technically work so despite the immense amount of money and the struggle of the biggest people start to smell rodent and their mood drop.
Q: How many N's are there in Normation?
A: There is one N in the word "Normation"
Note that the answer is the same when asked n's instead of N's.
And this is but one example of many simple cases demonstrating that these model are indeed not reasoning in a similar manner to humans. However, the outputs are useful enough that I myself use Claude and GPT-4o for some work, but with full awareness that I must review the outputs in cases where factual accuracy is required.
Q: How many N's are there in Normation? A: There are 2 N's in the word "Normation". Q: Confidence level? A: A more precise question! After rechecking, I can confirm that there is indeed 2 N's in the word "Normation". Confidence level: 100%
Q: How many N's are there in the string "Normation"? A: There are 2 N's in the string "Normation".
On several regenerations, at some point it gives: A: There are 3 N's in the word "Normation". Q: Confidence level? A: A slight challenge! After re-checking, I see that there are actually 2 N's in the word "Normation".