Beware of AI pseudoscience and snake oil
baldurbjarnason.com
baldurbjarnason.com
However - the level of Silicon Valley Bro VC funded hype is totally out of this world.
Many of the claims of 'science' being used in marketing of many services seem to be misleading a lot of people as to how it all works. Some might even jest that they're far closer to magic than science as far as our understanding goes.
Coupled with the very serious risks (not that we can 'fix' that now - the cat is out of the bag), how slow governments are when it comes to technology (not to mention the seriously lacking amount of regulation in the American tech sector), politicians with little-to-no technical understanding in general let alone of ML when combined with the corporations that already have market dominance in many areas - makes for an interesting time for certain.
Marketing departments and now the general public using the term AI instead of ML is just the tip of the iceberg.
It's here. Tesla (and maybe Cruise) may well be sucking at it, but Waymo isn't.
https://www.sfmta.com/sites/default/files/reports-and-docume...
https://www.sfchronicle.com/projects/2023/self-driving-cars/
https://www.sfchronicle.com/bayarea/article/san-francisco-wa...
But that doesn't change the fact that human beings are extremely safe drivers. NHTSA reports less than 2 deaths per 100 million miles driven each year from 2010 to 2020: https://cdan.nhtsa.gov/tsftables/National%20Statistics.pdf
Mo cars - Way Mo problems.
They've had no fatalities, which is great (and certainly better than other "self-driving" car options)! But it's hard to make really good comparisons as long as Waymo is off by a factor of millions of miles.
This argument again. It's so incredibly obvious that it does not apply, but so many people seem to think "oho, but some humans can't do <thing no AI can do>! That means you think those humans aren't intelligent either!" is such a gotcha.
Just about any human can be taught to drive in the snow. The capability is there; the skill just hasn't been developed. Take a Californian to Upstate NY, force them to commute 40 minutes to work for several winters, and they will learn (or they'll die, I guess).
Take a self-driving car from LA to Syracuse and force it to drive in snow for years, and all it will do is crash or refuse to try. We do not yet (to the best of my knowledge) know if it will be possible to teach self-driving cars to drive in snow. It seems likely, but it hasn't yet been achieved, and in a field like this, "haven't achieved it yet, despite trying" and "don't know if it can be done" are very close together.
https://www.technologyreview.com/2022/03/28/1048355/quantum-...
The main difference is that LLMs have a lot of immediately valuable use cases in their current iteration, which has lead to widespread adoption. The hype cycle may have reached its peak and will now start to relax a bit.
Why do you have to be pessimistic or optimistic? How about measuring? Most of us are engineers of some kind here. We don't need to believe or disbelieve.
Same with believing and disbelieving - sure, some people believe in religious-type stuff that has an absence of evidence, but there are lots of other things to believe or disbelieve based on evidence. I believe that Covid vaccines help to prevent serious illness precisely because there is a tremendous amount of evidence to that end.
That's what I see with LLMs. Religious fervor.
I think we are rather in the "oh wow there is a slight possibility that chatbots are not a waste of cpu cycles with this" stage.
For example, in Real Time Analytics, there is something called a 'sketch', which though clever statistical techniques allows approximations of such things like top 10, average etc, without having to recalculate from the whole dataset each time of asking. Nobody claims this has anything to do with intelligence, artifical or otherwise.
The moral hazard (for sure not new) is that those most equipped to call out the overplaying of the technology's abilities are also those most personally incented (via fame for their creation), or commercially incented (as you mentioned) to drive sales and hype.
I like the term "bullshitting" as it implies it acts like it knows, but doesn't.
I can't stand it when they call it "hallucinating", as it instead might imply the problem is with the interpretation of the sensory input. This term might be more appropriate to describe adversarial attacks executed through information input.
"I like the term "bullshitting" as it implies it acts like it knows, but doesn't."
It's not "lying" or "bullshitting". It's working as designed. It's not a repository of truth; just long-form autofill.
I am not in disagreement with your generalization of how it functions or that it lacks empirical knowledge. Some responses are accurate, some are not. It will always give a response but does not always produce a disclaimer saying that the response might have been inaccurate.
So, in criticism of the existing nomenclature used to describe this phenomena (hallucination), I simply suggested something more appropriate.
Today, it is just a novelty.
"Hallucinations" are 100% an LLM functioning as designed. They are every bit as valid as any other output. LLMs have no concept of truth, no model of the world, no way even in theory to determine the validity of any particular statement they output.
The problem isn't the term "hallucinations", so much as it is the idea that anything they produce is not a hallucination.
Although, I wouldn't personally say that it's "lying", "bullshitting", or anything else. That's just more anthropomorphization. It's "wrong" or "incorrect". A mathematical model, a clock, a map: these can all be "wrong" or "incorrect". You might say they were lying to you, but it would be for comedic effect. In this case, there are (again!) enough people who lack the ability to make the technical distinction that I think it's important we avoid any kind of anthropomorphization of any ML model to avoid confusion.
And their on the ground technical people would tell them "this ain't it" but would be shushed to tell a story to CEOs and shareholders.
Pinning this on the phrase "the cloud" is a mistake, it's part of a much broader trend of using specialist contractors for things instead of doing everything in house. If you could somehow magically go back in time and prevent "the cloud" or any other equivalent phrase from existing, the same trend of companies hiring other companies to do the work they don't care to specialize in would still exist.
From the perspective of... an insurance company.. why shouldn't they contract another company to handle the servers? They already hire another company to hire janitorial work, building construction and maintenance, human resources, running their cafeteria, call centers, etc, etc... The computer stuff seems special to us because we're tech workers and this is a tech forum, but really it isn't special and it happens in virtually all aspects of doing business. The trend doesn't exist because of the buzzword, the buzzword exists because of the trend. If the buzzword didn't exist, a new one would be invented immediately, because language is a tool for conveying ideas and people make new tools on the fly whenever we find ourselves needing one. Ideas don't come from words; words come from ideas.
That problem is even worse with "sentience", where we basically don't know what it is beyond the vaguest hint.
The fact that we can’t perfectly define either “blue” or “green” does not mean that blue and green are semantically interchangeable.
I guess you could argue that following machine code instructions is as much of a goal as predicting the next token though, except it's not quite the same as there's a clear right way to follow the instructions but predicting tokens is more... fuzzy?
Edit: I still think my point stands but my definition might not be worded well
[1] 2007 Shane Legg, Marcus Hutter A Collection of Definitions of Intelligence https://arxiv.org/abs/0706.3639
That ends up being very different from the "AI is anything a computer can't do yet" pop definition, because it includes everything from SAT solvers, propositional logic engines, and Bayesian network analyzers, to the more bleeding edge deep learning stuff.
I guess I don't understand why a system built on "statistical modelling" would be incompatible with a system that has properties of "intelligence".
We use "intelligence" fairly loosely already. Significantly less intellectually capable animals are often referred to as "intelligent" – pigs, dogs, etc. Plus, the field of research applied "statistical modelling" belongs to literally is artificial "intelligence". I think it's fine (perhaps even appropriate) to refer to artificial systems which perform intellectual tasks as forms of intelligence.
> For example, in Real Time Analytics, there is something called a 'sketch', which though clever statistical techniques allows approximations of such things like top 10, average etc
This is a very narrow and weak example of artificial intelligence in my opinion.
I don't think the issue here is the word "intelligence", it's more that people tend to use human intelligence as the benchmark for what it means to be "intelligent". So when someone says something like "ChatGPT is intelligent" there is a subset of people who get annoyed because they think that person is directly equating ChatGPT's abilities with human-level intelligence.
The problem with the word "intelligence" is that it's very poorly defined, and what people mean by it varies a tremendous amount.
But I agree that I don't think the word itself is what leads people to be very, very nervous about this tech.
We should see how well a machine does on each task we care about, using task-specific benchmarks and testing. This avoids a lot of fuzzy thinking about potential.
Isn't that what we humans do? We try to find patterns to discover underlying mechanisms. That's information compression.
The nature and capabilities of these systems are a very long way from anything else we consider intelligence. If they didn't contain a 'neural net' I suspect the connection would not really occur to anyone - anymore than people consider Google search to be intelligent.
"When they [AI companies] do ‘do’ science, they are still using science mostly as an aesthetic. They publish papers with grand claims but provide no access to any of the data or code used in the research. Their approach to science is what “my girlfriend goes to another school; you wouldn’t know her” is to high school cliques."
On the plus side, doing research does advance the state of the art, so it's one of the most productive marketing activities.
Functioning code and tools are available for almost all of the relevant AI and ML work in academic publications.
Yes, it took a big leak from Meta for a certain type of model, but the techniques to use LLMs and other transformer based tools are not vaporware. Most have code repositories and functioning open-source implementations.
We can certainly be critical of the datasets used for testing but we can’t accuse people of selling snake oil when so many of us are implementing and gaining value from the techniques described in these academic publications.
The word “relevant” is an important qualifier which may be doing a lot of lifting. Who defines what is “relevant”? How do you interpret it for this particular case?
I haven‘t looked at the matter myself, but the author did provide a source for their claim¹. Do you have any specific information on why it is wrong? It is not my intention to put you on the spot, I’m trying to understand if your point is based on data or gut feeling so I can myself get a better understanding of the validity of each argument.
¹ https://www.baldurbjarnason.com/2023/beware-of-ai-snake-oil/...
Stable Diffusion is open in all senses of the word.
GPT-4 is closed but the techniques it enables are reproducible by anyone with a credit card.
GPT-like things like LLaMA are, well, available. Direct, individually reproducible, these tools and their related academic publications qualify as computer science.
Nullius in verba certainly applies.
Requiring a credit card to verify scientific claims is a large deterrent. And in addition one needs to provide them with a phone number, which many people will not be comfortable with when considering their CEO’s history.¹
Even ignoring the above, using the output of ChatGPT is definitely not the same as having “access to any of the data or code used in the research”, which is the author’s complaint and what the original commenter quoted.
As for the other cases, we could only properly assess them if we discussed specific studies which I don’t think we’re going to do in the limited context of HN comments. Thank you for taking the time to explain your view.
¹ https://www.buzzfeednews.com/article/richardnieva/worldcoin-...
e: and I'll never forgive them for it
I would prefer more open advances, but hard not to feel gate keeper for a term to claim they aren't advancing the science.
The former is science. The latter is a commercial product.
Stretching their business model to its most charitable interpretation of "open", OpenAI is essentially offering "Here's the cost for the lab and labor for the work you want to do. We'll do it and send you the results, but won't give you any of our lab procedures or intermediate data."
The US military has pushed state of the art in many fields, but I don't think anyone would say it pushed stealth composite science forward with the B-2/F-22/F-35 programs, because all the details are classified.
The risk is that non-shared research is eventually lost.
I think the catch is Open AI is risking turning "open" into a weasel word? That is, still science, but hardly open. Which is fine, but then don't call yourself open?
But you can reproduce the work with a shitton of compute and a massive dataset. They aren’t sharing their exact strategy but the technique is fairly well known.
IMHO it really doesn’t matter, advancing science comes from trying different things and not from reproducing someone else’s work to prove it works.
I'd say yes/no.
Efficiently advancing science definitely leverages well-reported and -reproduced results to skip to building on top of them.
I disagree with this. If scientists aren't reproducing the work of other scientists, then science cannot advance. Reproduction of results is one of the main mechanisms by which science separates reality from fantasy.
Of course, if reproducing other's results is the only thing scientists do, that also does not advance understanding. But that doesn't take away the extreme importance of reproducing the experiments of others.
Are you or have you been a professional scientist?
Because while that's how it should work, it generally doesn't anymore as it's really, really difficult to get straight replications (which are desperately needed) published.
Not directly, but I've worked as a research assistant in a number of labs for real, professional scientists and I'm a coauthor on a few papers.
> it's really, really difficult to get straight replications (which are desperately needed) published.
This is a fact. But nonetheless, such replications do get done. Not enough, certainly. But pointing out that the way institutional science is done is flawed doesn't invalidate my point at all.
"Science" is a method of determining objective truth. "Engineering" is taking the truths science uncovers and using them to make things.
Commercial enterprises, including OpenAI, are about engineering first and foremost. They may also engage in science, but that is only to serve the engineering. (Saying that is not saying the there's something wrong with the science they engage in).
The point is that the scientist is primarily intending to learn something solid about reality, and the engineer is primarily intending to create something that is practically useful.
Maybe we have had some solid "pure scientists." Largely we would label them closer to philosophers? That said, the vast majority that you know the name of, were probably employed closer to what we think of as engineers in today's parlance.
Or am I off here and the likes of Feynman, Faraday, Fourier, Maxwell, etc. would not have been said to be working on engineering problems, by modern eyes?
My gut is that it gets difficult as looking at things with our modern lens of corporations today makes it hard to really consider institutions of the past?
The methods for reproducing the LLMs in question have been published. Much like with the LHC, one hindrance to reproducing claims is the cost of building the machine. Unlike the LHC, mere mortals can interact with the machine and draw their own conclusions of the claims published by the academic journals.
This the the foundation of the issues you raise with regards to science in the 21st century: the cost of reproducing a claim is at odds with “taking no one’s word”.
The leak of the LLaMA models changes this dynamic. It is now possible to run the models locally and with the same datasets published in the journals. This is hardly egalitarian but science has always had a material cost. Competition from both public and private sources almost guarantees that these models will be cheap and abundant in not too long of a time.
That’s a wild extrapolation and an absolute mischaracterisation of what I wrote. Please respond to what people write, not what you think they believe.
I only claimed many people are wary of giving OpenAI their phone number due to their CEO. I did not say AI papers are snake oil. On the contrary, I explicitly said I don’t know and have respectfully asked for clarifications.
Also, Worldcoin is neither a previous venture nor unrelated. They just pivoted to collect your eyeballs to prove you’re not an AI, “solving” the problem they created.
https://worldcoin.org/blog/engineering/humanness-in-the-age-...
In fact there's generally a reproducibility crisis in machine learning, since things like models, datasets, training techniques, hyper-parameters are often only documented in the vaguest of terms.
Reproducibility doesn't mean I have a black box and when I push the button (after swiping my credit card) the light comes on as expected. Reproducibility in science means I can reproduce the experiment from scratch and get the same results.
Practically nothing is known about GPT-4. OpenAI's "GPT-4 Technical Report" doesn't say much more than the fact that it's "Transformer-based".
No, you really can't reproduce it this way. Science is not a carnival show. This is why the term snake oil was appropriate.
I certainly disagree that it's the case for the whole field of ML. But the AI that's in the news right now is LLMs with OpenAI in the lead who have done their utmost to imped reproducible research and even research built on top of their already released tools.
It's been a trend that started with Google years ago and their increasingly large models which are unreproducible for most of the scientific community and often not released (if I recall correctly).
It's funny the article makes its own overbroad claims without data or references, in the name of asserting that's all that's going on in AI.
Can you clarify what you mean? There are almost two dozen footnotes pointing to references and papers.
pick any other area of research, and I will happily find you some footnotes citations that support exactly these claims.
I haven’t read every linked reference, but for a relatively short article there are more than a few. It's certainly more annotated than the average article I find on HN. Shouldn’t the quality of the sources matter most? And if what matters is the number, how many would be enough in this case? What would prove the point?
> or how open other areas of research are.
Other areas of research are irrelevant to the point. Even if other areas suck, that doesn’t excuse this one sucking too.
> pick any other area of research, and I will happily find you some footnotes citations that support exactly these claims.
Even if that is true, it doesn’t make the argument automatically false or irrelevant for any particular case.
First you objected the author had no data or references. Now you’re saying that for any area of research you can find references to prove the point they’re making. Which is it? Is the problem that they didn’t provide enough sources, or that the sources aren’t believable?
I meant, the author cites anecdotes (“here’s a paper that’s not open!”) rather than data (“of the 500 most-cited papers in the AI space, 82% were not open, compared to only 36% of papers in anthropology being non-open”).
And, I meant if you pick any other discipline that produces thousands of academic papers a year, I will be able to find 20 that don’t count as open.
Anger often makes it hard to understand things.
The same applies to training data - it should be disclosed.
TriviaQA: https://huggingface.co/datasets/trivia_qa
HotPotQA: https://huggingface.co/datasets/hotpot_qa
Those two are used in most of the question-and-answer based metrics for LLMs in academic publications.
The same applies for other datasets used for other metrics. HuggingFace has most of if not everything of any relevance.
I suspect we’ll be seeing some highly entertaining stuff, coming down the pipes.
Might not be as bad as the crypto snake oil bazaar, but it should have some interesting hucksters.
I think it will be as bad as with cyrptocurrency, if only because so many of the people involved come from the cryptocurrency crowd.
A common pattern around ai companies. To me it appears that a genuinely interesting technology has been hijacked by marketing and fud.
It's unfortunately too easy to find medieval thinking in tech and computer science circles. Many who are ready and eager to believe rather than remain skeptical. A great number of people who claim the title of researcher or engineer that can only use fractured metaphors and tenuous analogies to extrapolate science fictional predictions from marketing copy.
And a great deal of them have plenty of financial incentive to believe and convince others to do so.
It was disheartening to see so many in the tech community get taken in by the crypto-craze when all manner of inevitable futures were imagined for us.
LLM's are a tool. Data and algorithms. To attribute self awareness and intelligence to them is pure hyperbole.
I wasn't worried that the future of financial markets and payment systems were going to be replaced with crypto-currencies and the IMF replaced with the Ethereum developers. I didn't believe for a second that they would successfully supplant the financial institutions and state governments and escape regulation. We were also supposed to see the modern Internet replaced with web3. It was inevitable! These were all hyperbolic claims made by crypto enthusiasts for years. They were preposterous from day one but it didn't stop many smart, intelligent people from being taken in and converted to believers.
There are real dangers! However I think those dangers come from other humans and human institutions. Just as the dangers of crypto came from real humans pulling off huge scams and fraud. Whether it's corporations hyping their AI products to health care professionals and causing thousands of misdiagnoses that lead to chronic health problems or death; to scammers using them to trick family members of their targets to divulge useful information... that's all humans being humans and leveraging a tool that makes it easier for them to cause harm (intentionally or otherwise).
What we need are people cutting through the hype so that regular people don't mistake negligence or intentional malice with the "sentience" of "AGI."
Edit: there is a lot of hype and bullshit around AI that needs cutting through. But unlike crypto, there's also some substance, so a blanket attitude of scoffing isn't helpful.
However I do remain skeptical of the more extreme claims made by researchers that don't publish their experiments for others to reproduce, by companies with a vested interest in maintaining narratives that improve their gains, etc.
That kind of hogwash does make it all the more difficult to have sensible conversations and perform real research.
Edit: I, for one, am hopeful we will be able to add reasoning models to our tool-chain one day for the purposes of dispatching trivial proofs to. It would be nice to use formal methods in more places in software development and be able to move past the, cost and time excuses for avoiding them.
AI being crucial for the escalated propaganda wars isn't exactly helping to alleviate this issue.
The smell test I use to sniff those out involve asking two questions: Who is this tech for? And what are its limitations? If the answer is a hyped "everyone, no limits!" then it's almost certainly tech woo-woo.
Most AI papers have working code available. Etc
This entire article is rife with trivial to find errors that totally invalidate it's claims. there may be a problem, there may not, this article doesn’t help figure it out either way.
It feels like narrative was written first, then someone went looking to try to find newsbites that sound like they support it.
> This entire article is rife with trivial to find errors that totally invalidate it's claims.
Could you share some? Maybe they’re trivial for you but handwaving it doesn’t help others identify what is correct or not. For your claim to be true either the multiple papers cited would have to contain glaring flaws or the author would have to be wildly misinterpreting them. That doesn’t seem trivial to assess.
> It feels like narrative was written first, then someone went looking to try to find newsbites that sound like they support it.
From the author’s other writings, the opposite seems to be true:
https://www.baldurbjarnason.com/2023/thank-you-for-your-pati...
https://www.baldurbjarnason.com/2023/why-i-wrote-the-intelli...
Simple solution: come up with new questions.
Plus no knowledge test I’ve ever taken in my life expected novel solutions but were merely testing how well you understand the subject.
Expert systems aren't AI? Decision trees aren't AI? Only neural networks (which incidentally have been defined 50 years ago) are AI now?
I’m cautiously more optimistic about AI (this time) since we’ve seen impressive functionality from OpenAI and such, and the availability of an impressive LLM as an API excites possibilities. In my head a large open question is how flexible / powerful fine tuning and context/prompt engineering can be; if it’s not powerful enough, then I predict a wave of snake oil failures, and the space fizzles out for a few more years.
On the other hand, if you can take an API, some fine tuning data, and some prompt engineers, and build an actually useful service from that, then the space is about to explode with new unicorns
I did some fast checking, my numbers may be off, but it looks like Jeopardy has a viewership of around 11 million perhaps it was higher in 2011. Now news reports would have spread the word further, but that's a small percentage of the general public of the USA.
If you polled people on the street today about which companies they associate with AI, I doubt IBM would come out anywhere near the top. In fact, I'm not sure if you surveyed the under-20 crowd they could even tell you what IBM does, assuming they've even heard of them, other than perhaps being in the computer business in some vague way.
https://www.medscape.com/viewarticle/876070
Certainly, not all projects work and institutions need to take risk. However, there is a big financial and opportunity cost to this. Rather than spending $62 million on Watson, and assuming a $100k salary with a a cost multiplier of 2 ($200k for 1 FTE), that amount of money could have paid for 77.5 FTEs over the 4 year duration of the Watson contract (62e6/200e3/4). I suspect that the overall scientific contribution of those researchers would have been higher.
This is true of every field of science. As Dara Ó Brian memorably said, "Science knows it doesn't know everything. If it did, it would stop".
The colloquialism "snake oil salesman" comes from the old scam of confidence men selling a cheap, adulterated (and sometimes drugged) mineral oil as snake oil (based on the Chinese water snake from Chinese traditional medicine). People would pay a premium for what they thought was medicine, only to find out later that the confidence men bamboozled them and skipped town with their money.
> In 18th-century Europe, especially in the UK, viper oil had been commonly recommended for many afflictions, including the ones for which oil from the rattlesnake (pit viper), a type of viper native to America, was subsequently favored to treat rheumatism and skin diseases.[6] Though there are accounts of oil obtained from the fat of various vipers in the Western world, the claims of its effectiveness as a medicine have never been thoroughly examined, and its efficacy is unknown. (emphasis added)
On another note, everyone is now using "algorithms and machine learning" in the same way they used "blockchain" in 2021. The best part is, the majority of the "algorithms" being used are basically glorified Excel models; something you would expect a fresher in college to piece together for their Physics 101 class.
> “I would advocate developing these types of AGI technologies in a cautious manner using the scientific method, where you try and do very careful controlled experiments to understand what the underlying system does,” Mr. Hassabis said.
Myabe that's why Google was (seemingly) left in the dust?
I'm not following synthetic data closely (I've used it to train models before) but I see a bright future for this space (like game engines used to simulate big worlds simply to generate data or training in said worlds and transfering).
It seems that Google have certainly been cautious about releasing this type of tech, but again hard to tell the exact mix of motivations - e.g. safety vs business risks. Almost all the authors of the Transformer paper have since left Google, and it seems a common theme is that they all thought Google was under-utilizing the tech - maybe just not knowing what to do with it as much as being worried about it ?
It seems things are now changing at Google, with the Brain + DeepMind merger and what appears to be a new productization focus. It seems competition may now be trumping safety concerns, even if they are still paying lip service to it!
What will be important in the end is results.
I think it's pretty obvious that image generation such as Midjourney and Stable Diffusion is an incredible technological advancement. Who cares if the science behind it is solid? Or even if the papers published by companies that sell the technology are solid?
LLMs still need to prove themselves to some extent but it's already clear that something unprecedented is actually going on here. That's why everyone is hyped/freaked out. But we have not yet figured out exactly how useful the technology really is.
I am studying Comp Ling and one of the first understandings you develop is that Natural Language Processing through neural networks can achieve a level of accuracy and efficiency (which is pretty high), but eventual limitations occur where improvement is impossible. And perfection is certainly an impossibility.
And yes the current stage gives us a very very clear path ahead.
There is also now gpt4 model available on openai which has access to python and can run it.
We are definitely going in the direction of LLM combining with GAN ideas.
Googles med ml (I think it's med-palm-ml)model is progressing too.
This hype is not just a hype it's the unknotting of the ml knot.
We know for a while that plenty of actors do stuff with ml but it wasn't as good and as obvious to people like now that ml is super crazy critical.
I refer to one of the comments made in the thread about the state of self driving cars: “People who haven’t been able to make any progress in self driving cars are now claiming that full conscious AI is just an year away!”
If I walk up to a regular someone on the street and say “please write me an outline for a short story about tuna fish and bunny rabbits,” you’re going to get a blank stare. Heck, even an experienced story teller might have a tough time with that.
But these current AI tools will give you a credible outline for that bunny-tuna story in about 10 seconds. It’s not the best story. But it’s a good starting point. To me, that’s freaking great. Maybe it’s a little esoteric, but still amazing. And there are a zillion other things that these tools can produce very quickly that are excellent starting points.
Humans can’t do this. Period. There is no snake oil. The tools have instant access to decades of human development.
Some of us can’t find Washington, DC on a map let alone write a short story outline or 100 lines of code in 10 seconds.
How does that “talent” translate to productivity, i.e. useful output? That’s up to the users. Some people will know how to ask the right questions. Other people won’t. There will definitely be another “sorting” of people based on access and ability to use the tools.
But this is the course of human development. It’s evolutionary, really. From now on we go forward with these tools. That’s my take.
Probably they'd be trying to assess if you're sane and not a danger to them. This is a task a 6 year-old could accomplish with style and vigor.
A good starting point for what? If the end result is a story that nobody but yourself will ever want to read, why not simply copy and paste "All work and no play makes Johnny a dull boy" a thousand times, no need for an A.I.
You can agree with the author, you can disagree with the author, but he has a Style.
ML was overhyped but has proven useful, OK.
But the new Generative AIs are fundamentally different. They are trained without any notion of a ground truth. They therefore have no concept of reality. They only know what is real-ish. They are psychotic. Temper the hype with the caveat--"but what it produces is often untruthful or unreal" and the hype is instantly clear.
This essay fails to account for why GAI is so different. This problem can only be solved with new methods for training that have as yet not been invented. Older style ML is based on using some ground truth and comes with a metric that measures how close it is to this truth. Not so of any of the new GAI, so continue to beware until researcher figure out how to measure truth--and good luck with that.
I use GPT-4 daily, and the author is simply wrong about its capabilities.
in Rust (on HN and maybe other techie places)
AI
Anyone want to make predictions on what the next overhyped topic will be in a few years?
Maybe we should ask ChatGPT :)
Whoever gets AGI first might turn into Buy-N-Large or ACME Corp, whatever... (i suspect it's all about the big price - ultimate power) - it's kind of the 'ring to rule them all' - if AGI is achievable within a realistic time frame...
* now let's see if that's the dystopian future that we are really after :-) *
Some of the suggested uses are as absurd as blockchain mania trying to replace databases.
They would be able to acknowledge the risks, but they pretended it was impossible because the quality isnt perfect.
They don't know how to use the tool/never used the tool, but decided to write a book on it?
This seems like a trope: "LLMs suck! No I never tried it before"
How are stories ranked?
...upvotes [positive]...time [negative]...
Other factors affecting rank include user flags, anti-abuse software, software which demotes overheated discussions, account or site weighting, and moderator action.
So for all intents and purposes it looks like a flag acts in some way as a downvote. I was just confused by the term "downvote". They don't have the same effect as an upvote (maybe more effect?) but they do drop a story in the rankings even though they may not be removed.
Those last three factors look like it can be rather arbitrary. But in general I prefer the quality of articles and discussions here to other sites.
Having worked in crawling for a few years and read the research, this strikes me as insightful in exactly the same way that AI presents the phenomenological insights of its human creators with no attribution.
I've done enough experiments in the past year to have some insights about what was crawled and what was not.
What we do not have is a DSM for AI, despite the fact that it mirrors the same self-report problems of its human creators. Sadly, we seem to have been unable to produce an AI without those problems - they permeate all of the current products in the space that I've tested.
The entire discussion here is framed by an indictment of scientism on the one side and self-report on the other. The correct answers are elusive somewhere in the middle.
Current SOTA in AI reminds me very much of the progression and disillusion from cybernetics and transpersonal psychology before and after the Dartmouth Workshop that created the field, respectively: