What an LLM cannot do today is almost irrelevant in the tide of change upon the industry. The fact is, with improvements, it doesn't mean an LLM cannot do it tomorrow.
LLMs are not like this. The fundamental way they operate, the core of their design is faulty. They don't understand rules or knowledge. They can't, despite marketing, really reason. They can't learn with each interaction. They don't understand what they write.
All they do is spit out the most likely text to follow some other text based on probability. For casual discussion about well-written topics, that's more than good enough. But for unique problems in a non-English language, it struggles. It always will. It doesn't matter how big you make the model.
They're great for writing boilerplate that has been written a million times with different variations - which can save programmers a LOT of time. The moment you hand them anything more complex it's asking for disaster.
Modern coding AI models are not just probability crunching transformers. They haven't been just that for some time. In current coding models the transformer bit is just one part of what is really an expert system. The complete package includes things like highly curated training data, specialized tokenizers, pre and post training regimens, guardrails, optimized system prompts etc, all tuned to coding. Put it all together and you get one shot performance on generating the type of code that was unthinkable even a year ago.
The point is that the entire expert system is getting better at a rapid pace and the probability bit is just one part of it. The complexity frontier for code generation keeps moving and there's still a lot of low hanging fruit to be had in pushing it forward.
> They're great for writing boilerplate that has been written a million times with different variations
That's >90% of all code in the wild. Probably more. We have three quarters of a century of code in our history so there is very little that's original anymore. Maybe original to the human coder fresh out of school, but the models have all this history to draw upon. So if the models produce the boilerplate reliably then human toil in writing if/then statements is at an end. Kind of like - barring the occasional mad genious [0] - the vast majority of coders don't write assembly to create a website anymore.
[0] https://asm32.info/index.cgi?page=content/0_MiniMagAsm/index...
It seems you were not aware you ended up describing probabilistic coding transformers. Each and every single one of those details are nothing more than strategies to apply constraints to the probability distributions used by the probability crunching transformers. I mean, read what you wrote: what do you think that "curated training data" means?
> Put it all together and you get one shot performance on generating the type of code that was unthinkable even a year ago.
This bit here says absolutely nothing.
And even with all that, they still produce garbage way too often. If we continue the "car" analogy, the car would crash randomly sometimes when you leave the driveway, and sometimes it would just drive into the house. So you add all kinds of fancy bumpers to the car and guard rails to the roads, and the car still runs off the road way too often.
Every time someone says "LLMs are good at boilerplate" my immediate response is "Why haven't you abstracted away the boilerplate?"
After a while, it just make sense to redesign the boilerplate and build some abstraction instead. Duplicated logic and data is hard to change and fix. The frustration is a clear signal to take a step back and take an holistic view of the system.
This is lipstick on a pig. All those methods are impressive, but ultimately workarounds for an idea that is fundamentally unsuitable for programming.
>That's >90% of all code in the wild. Probably more.
Maybe, but not 90% of time spent on programming. Boilerplate is easy. It's the 20%/80% rule in action.
I don't deny these tools can be useful and save time - but they can't be left to their own devices. They need to be tightly controlled and given narrow scopes, with heavy oversight by an SME who knows what the code is supposed to be doing. "Design W module with X interface designed to do Y in Z way", keeping it as small as possible and reviewing it to hell and back. And keeping it accountable by making tests yourself. Never let it test itself, it simply cannot be trusted to do so.
LLMs are incredibly good at writing something that looks reasonable, but is complete nonsense. That's horrible from a code maintenance perspective.
Not to disagree, but "non-english" isn't exactly relevant. For unique problems, LLMs can still manage to output hallucinations that end up being right or useful. For example, LLMs can predict what an API looks like and how it works even if they do not have the API in context if the API was designed following standard design principles and best practices. LLMs can also build up context while you interact with them, which means that iteratively prompting them that X works while Y doesn't will help them build the necessary and sufficient context to output accurate responses.
This is the first word that came to mind when reading the comment above yours. Like:
>They can't, despite marketing, really reason
They aren't, despite marketing, really hallucinations.
Now I understand why these companies don't want to market using terms like "extrapolated bullshit", but I don't understand how there is any technological solution to it without starting from a fresh base.
They are hallucinations. You might not be aware of what that concept means in terms of LLMs but just because you are oblivious to the definition of a concept that does not mean it doesn't exist.
You can learn about the concept by spending a couple of minutes reading this article on Wikipedia.
https://en.wikipedia.org/wiki/Hallucination_(artificial_inte...
> Now I understand why these companies don't want to market using terms like "extrapolated bullshit", (...)
That's literally in the definition. Please do yourself a favour and get acquainted with the topic before posting comments.
GP is perfectly aware of this, and disagrees that the metaphor used to apply the term is apt.
Just because you use a word to describe a phenomenon doesn't actually make the phenomenon similar to others that were previously described with that word, in all the ways that everyone will find salient.
When AIs generate code that makes a call to a non-existent function, it's not because they are temporarily mistakenly perceiving (i.e., "hallucinating") that function to be mentioned in the documentation. It's because the name they've chosen for the function fits their model for what a function that performs the necessary task might be called.
And even that is accepting that they model the task itself (as opposed to words and phrases that describe the task) and that they somehow have the capability to reason about that task, which has somehow arisen from a pure language model (whereas humans can, from infancy, actually observe reality, and contemplate the effect of their actions upon the real world around them). Knowing that e.g. the word "oven" often follows the word "hot" is not, in fact, tantamount to understanding heat.
In short, they don't perceive, at all. So how can they be mistaken in their perception?
>(also called bullshitting,[1][2] confabulation,[3] or delusion)[4]
Here's the first linked source:
https://www.psypost.org/scholars-ai-isnt-hallucinating-its-b...
Irrelevant. Wikipedia does not create concepts. Again, if you take a few minutes to learn about the topic you will eventually understand the concept was coined a couple of decades ago, and has a specific meaning.
Either you opt to learn, or you don't. Your choice.
> Here's the first linked source:
Irrelevant. Your argument is as pointless and silly as claiming rubber duck debugging doesn't exist because no rubber duck is involved.
I will follow one of the linked sources to the paper 'ChatGPT is bullshit'
>Hicks, M.T., Humphries, J. and Slater, J. (2024). ChatGPT is bullshit. Ethics and information technology, 26(2). doi:https://doi.org/10.1007/s10676-024-09775-5.
Hicks et al. note:
>calling their mistakes ‘hallucinations’ isn’t harmless: it lends itself to the confusion that the machines are in some way misperceiving but are nonetheless trying to convey something that they believe or have perceived.
What an enlightening input. I will now follow another source, 'Why ChatGPT and Bing Chat are so good at making things up'
>Edwards, B. (2023). Why ChatGPT and Bing Chat are so good at making things up. [online] Ars Technica. Available at: https://arstechnica.com/information-technology/2023/04/why-a....
Edwards notes:
>In academic literature, AI researchers often call these mistakes "hallucinations." But that label has grown controversial as the topic becomes mainstream because some people feel it anthropomorphizes AI models (suggesting they have human-like features) or gives them agency (suggesting they can make their own choices) in situations where that should not be implied. The creators of commercial LLMs may also use hallucinations as an excuse to blame the AI model for faulty outputs instead of taking responsibility for the outputs themselves.
>Still, generative AI is so new that we need metaphors borrowed from existing ideas to explain these highly technical concepts to the broader public. In this vein, we feel the term "confabulation," although similarly imperfect, is a better metaphor than "hallucination." In human psychology, a "confabulation" occurs when someone's memory has a gap and the brain convincingly fills in the rest without intending to deceive others. ChatGPT does not work like the human brain, but the term "confabulation" arguably serves as a better metaphor because there's a creative gap-filling principle at work
It links to a tweet from someone called 'Yann LeCun':
>Future AI systems that are factual (do not hallucinate)[...] will have a very different architecture from the current crop of Auto-Regressive LLMs.
That was an interesting diversion, but let's go back to learning more. How about 'AI Hallucinations: A Misnomer Worth Clarifying'?
>Maleki, N., Padmanabhan, B. and Dutta, K. (2024). AI Hallucinations: A Misnomer Worth Clarifying. 2024 IEEE Conference on Artificial Intelligence (CAI). doi:https://doi.org/10.1109/cai59869.2024.00033.
Maleki et al. say:
>As large language models continue to advance in Artificial Intelligence (AI), text generation systems have been shown to suffer from a problematic phenomenon often termed as "hallucination." However, with AI’s increasing presence across various domains, including medicine, concerns have arisen regarding the use of the term itself. [...] Our results highlight a lack of consistency in how the term is used, but also help identify several alternative terms in the literature.
Wow, how interesting! I'm glad I opted to learn that!
My fun was spoiled though. I tried following a link to the 1995 paper, but it was SUPER BORING because it didn't say 'hallucinations' anywhere! What a waste of effort, after I had to go to those weird websites just to be able to access it!
I'm glad I got the opportunity to learn about Hallucinations (Artificial Intelligence) and how they are meaningfully different from bullshit, and how they can be avoided in the future. Thank you!
how so? programs might use english words but are decidedly not english.
I pointed out the fact that the concept of a language doesn't exist in token predictors. They are trained with a corpus, and LLMs generate outputs that reflect how the input is mapped in accordance to how the were trains with said corpus. Natural language makes the problem harder, but not being English is only relevant in terms of what corpus was used to train them.
> Prove to me that human thought is not predicting the most probable next token.
Explain the concept of color to a completely blind person. If their brain does nothing but process tokens this should be easy.
> How can you tell a human actually understands?
What a strange question coming from a human. I would say if you are a human with a consciousness you are able to answer this for yourself, and if you aren't no answer will help.
Oh, I dunno. The whole "mappers vs packers" and "wordcels vs shape rotators" dichotomies point at an underlying truth, which is that humans don't always actually understand what they're talking about, even when they're saying all the "right" words. This is one reason why tech interviewing is so difficult: it's partly a task of figuring out if someone understands, or has just learned the right phrases and superficial exercises.
By this thought experiment you can make any computational process into "predict the most probable next token" - at an extreme runtime cost. But if you do so, you arguably empty the concept "token predictor" of most of its meaning. So you would need to more accurately specify what you mean by a token predictor so that the answer isn't trivially true (for every kind of thought that's computation-like).
Religious fervor in one's own opinion on the state of the world seems to be the zeitgeist.
We can make arguments for informed guesses but there are simply still too many unknowns to be certain either way. People who claim to be certain are just being presumptuous.
that's the thing, I'm not certain that "computers" can replicate human level intelligence. for one that statement would have to include a rigorous definition of what a computer is and what is excluded.
no, I just don't buy the idea that human level intelligence is only achievable in human born meatbags. at this point the only evidence has been "look, birds flap their wings and man doesn't have wings, therefore man will never fly".
If this was about man flying we would be making an airplane instead of talking about how the next breakthrough will make us all into angels. LLMs are clever inventions they're just not independently clever.
It's basically the silicon valley playbook to offer a service for dirt cheap (completely unprofitable) and then once they secure the market they make skyrocket the price.
A mid range model is what most people will be able to use.
Said like a true software person. I'm to understand that computer people are looking at LLMs from the wrong end of the telescope; and that from a neuroscience perspective, there's a growing consensus among neuroscientists that the brain is fundamentally a token predictor, and that it works on exactly the same principles as LLMs. The only difference between a brain and an LLM maybe the size of its memory, and what kind and quality of data it's trained on.
Hahahahahaha.
Oh god, you're serious.
Sure, let's just completely ignore all the other types of processing that the brain does. Sensory input processing, emotional regulation, social behavior, spatial reasoning, long and short term planning, the complex communication and feedback between every part of the body - even down to the gut microbiome.
The brain (human or otherwise) is incredibly complex and we've barely scraped the surface of how it works. It's not just nuerons (which are themselves complex), it's interactions between thousands of types of cells performing multiple functions each. It will likely be hundreds of years before we get a full grasp on how it truly works - if we ever do at all.
This is trivially proven false, because LLMs have far larger memory than your average human brain and are trained on far more data. Yet they do not come even close to approximating human cognition.
I feel like we're underestimating how much data we as humans are exposed to. There's a reason AI struggles to generate an image of a full glass of wine. It has no concept of what wine is. It probably knows way more theory about it than any human, but it's missing the physical.
In order to train AIs the way we train ourselves, we'll need to give it more senses, and I'm no data scientist but that's presumably an inordinate amount of data. Training AI to feel, smell, see in 3D, etc is probably going to cost exponentially more than what the AI companies make now or ever will. But that is the only way to make AI understand rather than know.
We often like to state how much more capacity for knowledge AI has than the average human, but in reality we are just underestimating ourselves as humans.
Can you cite at least one recognized, credible neuroscientist who makes this claim?
Tokens are a highly specific transformer exclusive concept. The human brain doesn't run a byte pair encoding (BPE) tokenizer [0] in their head. anything as tokens. It uses asynchronous time varying spiking analog signals. Humans are the inventors of human languages and are not bound to any static token encoding scheme, so this view of what humans do as "token prediction" requires either a gross misrepresentation of what a token is or what humans do.
If I had to argue that humans are similar to anything in machine learning research specifically, I would have to argue that they extremely loosely follow the following principles:
* reinforcement learning with the non-brain parts defining the reward function (primarily hormones and pain receptors)
* an extremely complicated non-linear kalman filter that not only estimates the current state of the human body, but also "estimates" the parameters of a sensor fusing model
* there is a necessary projection of the sensor fused result that then serves as available data/input to the reinforcement learning part of the brain
Now here are two big reasons why the model I describe is a better fit:
The first reason is that I am extremely loose and vague. By playing word games I have weaseled myself out of any specific technology and am on the level of concepts.
The second reason is that the kalman filter concept here is general enough that it also includes predictor models, but the predictor model here is not the output that drives human action, because that would logically require the dataset to already contain human actions, which is what you did, you assume that all learning is imitation learning.
In my model, any internal predictor model that is part of the kalman filter is used to collect data, not drive human action. Actions like eating or drinking are instead driven by the state of the human body, e.g. hunger is controlled through leptin and insulin and others. All forms of work, no matter how much of a detour it represents, ultimately has the goal of feeding yourself or your family (=reproduction).
[0] A BPE tokenizer is a piece of human written software that was given a dataset to generate an efficient encoding scheme and the idea itself is completely independent of machine learning and neural networks. The fundamental idea behind BPE is that you generate a static compression dictionary and never change it.
As much as I may agree with your subsequent claims, this is not how users are expected to engage with each other on HN.
We can reasonably speak about certain fundamental limitations of LLMs without those being claims about what AI may ever do.
I would agree they fundamentally lack models of the current task and that it is not very likely that continually growing the context will solve that problem, since it hasn't already. That doesn't mean there won't someday be an AI that has a model much as we humans do. But I'm fairly confident it won't be an LLM. It may have an LLM as a component but the AI component won't be primarily an LLM. It'll be something else.
Neural networks are necessary but not sufficient. LLMs are necessary but not sufficient.
I have no doubt that there are multiple (perhaps thousands? more?) of LLM-like subsystems in our brains. They appear to be a necessary part of creating useful intelligence. My pet theory is that LLMs are used for associative memory purposes. They help generate new ideas and make predictions. They extract information buried in other memory. Clearly there is another system on top that tests, refines, and organizes the output. And probably does many more things we haven't even thought to name yet.
Alternatively, the goalposts keep being moved.
1. People are trying to sell a product that is not ready and thus are overhyping it
2. The tech is in its early days and may evolve into something useful via refinement and not necessarily by some radical paradigm shift
In order for (2) to happen it helps if the field is well motivated and funded (1)
The premise that an AI needs to do Y "as we do" to be good at X because humans use Y to be good at X needs closer examination. This presumption seems to be omnipresent in these conversations and I find it so strange. Alpha Zero doesn't model chess "the way we do".
> The premise that an AI needs to do Y "as we do" to be good at X because humans use Y to be good at X needs closer examination.
I don't see it being used as a premise. It see it as speculation that is trying to understand why this type of AI underperforms at certain types of tasks. Y may not be necessary to do X well, but if a system is doing X poorly and the difference between that system and another system seems to be Y, it's worth exploring if adding Y would improve the performance.
The sooner people stop worrying about a label for what you feel fits LLMs best, the sooner they can find the things they (LLMs) absolutely excel at and improve their (the user's) workflows.
Stop fighting the future. Its not replacing right now. Later? Maybe. But right now the developers and users fully embracing it are experiencing productivity boosts unseen previously.
Language is what people use it as.
This is the kind of thing that I disagree with. Over the last 75 years we’ve seen enormous productivity gains.
You think that LLMs are a bigger productivity boost than moving from physically rewiring computers to using punch cards, from running programs as batch processes with printed output to getting immediate output, from programming in assembly to higher level languages, or even just moving from enterprise Java to Rails?
Skepticism isn't the same thing as fighting the future.
I will call something AGI when it can reliably solve novel problems it hasn't been pre-trained on. That's my goal post and I haven't moved it.
EDIT - I see now. sorry.
For all intents and purposes of the public. AI == LLM. End of story. Doesn't matter what developers say.
This is interesting, because it's so clearly wrong. The developers are also the people who develop the LLMs, so obviously what they say is actually the factual matter of the situation. It absolutely does matter what they say.
But the public perception is that AI == LLM, agreed. Until it changes and the next development comes along, when suddenly public perception will change and LLMs will be old news, obviously not AI, and the new shiny will be AI. So not End of Story.
People are morons. Individuals are smart, intelligent, funny, interesting, etc. But in groups we're moronic.
Almost always, yes, because I know what I'm doing and I have a brain that can think. I actually think before I do anything, which leads to good results. Don't assume everyone is a junior.
>Didn't think so.
You don't know me at all.
If you always use your first output then you are not a senior engineer, either your problem space is THAT simple that you can fit all your context in your head at the same time first try, or quite frankly you just bodge things together in non-optimal way.
It always takes some tries at a problem to grasp edge cases and to easier visualize the problem space.
Sure sometimes I do stuff I am not confident about to learn but then I don't say "here I solved the problem for you" without building confidence around the solution first.
Every competent senior engineer should be like this, if you aren't then you aren't competent. If you are confident in a solution then it should almost always work, else you are over confident and thus not competent. LLM are confident in solutions that are shit.
Unfortunately, discourse has followed an epistemic trajectory influenced by Hollywood and science fiction, making clear communication on the subject nearly impossible without substantial misunderstanding.
I have the complete opposite feeling. The layman understanding of the term "AI" is AGI, a term that only needs to exist because researchers and businessmen hype their latest creations as AI.
The goalposts for AI don't move but the definition isn't precise but we know it when we see it.
AI, to the layman, is Skynet/Terminator, Asimov's robots, Data, etc.
The goalposts moving that you're seeing is when something the tech bubble calls AI escapes the tech bubble and everyone else looks at it and says, no, that's not AI.
The problem is that everything that comes out of the research efforts toward AI, the tech industry calls AI despite it not achieving that goal by the common understanding of the term. LLMs were/are a hopeful AI candidate but, as of today, they aren't but that doesn't stop OpenAI from trying to raise money using the term.
If you want some semantic rigour use more specific terms like AGI, human equivalent AGI, super human AGI, exponentially self improving AGI, etc. Even those labels lack rigour, but at least they are less ambiguous.
LLMs are pretty clearly AI and AGI under commonly understood, lay definitions. LLMs are not human level AGI and perhaps will never be by themselves.
That's certainly not clear. For starters, I don't think there is a lay definition of AGI which is largely my point.
The only reason people are willing to call LLMs AI is because that's how they are being sold and the shine isn't yet off the rose.
How many people call Siri AI? It used to be but people have had time to feel around the edges where it fails to meet their expectations of AI.
You can tell what people think of AI by the kind of click bait surrounding LLMs. I read an article not too long ago with the headline about an LLM lying to try and not be turned off. Turns out it was intentionally prompted to do that but the point is that that kind of self preservation is what people expect of AI. Implicitly, they expect that AI has a "self".
ChatGPT doesn't have a self.
Engaging in semantic battles to try to change the meanings of those terms is just going to create more confusion, not less. Instead why not use more specific and descriptive labels to be clear about what you are saying.
Self-Aware AGI, Human Level AGI, Super-Human ANI, are all much more useful than trying to force general label to be used a specific way.
You're doing that. I've never seen someone state, as fact, that LLMs are AGI before now. Go ask someone on the street what Super-Human ANI means.
Then you probably haven't been paying attention.
https://deepmind.google/research/publications/66938/
> I've never seen someone state, as fact, that LLMs are AGI before now.
Many LLMs are AI that weren't designed / trained to solve a narrow problem scope. They can complete a wide range of tasks with varying levels of proficiency. That makes them artificial general intelligence or AGI.
You are confused because lots of people use "AGI" as a shorthand to talk about "human level" AGI that isn't limited to a narrow problem scope.
It's not wrong to use the term this way, but it is ambiguous and vague.
Even the term "human level" is poorly defined and if I wanted to use the term "Human level AGI" for any kind of discussion of what qualifies, I'd need to specify how I was defining that.
It's actually very funny to me that you are stating these definitions so authoritatively despite the terms not having any sort if rigor attached to either their definition or usage.
Huh? My entire point was that AI and AGI are loose, vague terms and if you want to be clear about what you are talkng about, you should use more specific terms.
This is not a fault of the users. These labels are pushed primarily by "AI" companies in order to hype their products to be far more capable than they are, which in turn increases their financial valuation. Starting with "AI" itself, "superintelligence", "reasoning", "chain of thought", "mixture of experts", and a bunch of other labels that anthropomorphize and aggrandize their products. This is a grifting tactic old as time itself.
From Sam Altman[1]:
> We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence
Apologists will say "they're just words that best describe these products", repeat Dijkstra's "submarines don't swim" quote, but all of this is missing the point. These words are used deliberately because of their association to human concepts, when in reality the way the products work is not even close to what those words mean. In fact, the fuzzier the word's definition ("intelligence", "reasoning", "thought"), the more valuable it is, since it makes the product sound mysterious and magical, and makes it easier to shake off critics. This is an absolutely insidious marketing tactic.
The sooner companies start promoting their products honestly, the sooner their products will actually benefit humanity. Until then, we'll keep drowning in disinformation, and reaping the consequences of an unregulated marketplace of grifters.
LLMs may get better, but it will not be what people are clamoring them to be.
maybe they should have; a lot of the engineering techniques and methodologies that produced the assembly line and the mass produced vehicle also lead the way into space exploration.
It can also learn new things using trial and error with mcp tools. Once it has figured out some problem, you can ask it to summarize the insights for later use.
What would define as an AI mental model?
To me as a layman, this feels like a clear explanation of how these tools break down, why they start going in circles when you reach a certain complexity, why they make a mess of unusual requirements, and why they have such an incredible nuanced grasp of complex ideas that are widely publicized, while being unable to draw basic conclusions about specific constraints in your project.
Text and words are the concepts we use to transfer knowledge in schools, across generations, etc. we describe concepts in words, so other people can learn these concepts.
Without words and text we would be like animals unable to express and think about concepts
* are many times the size of the occupants, greatly constricting throughput.
* are many times heavier than humans, requiring vastly more energy to move.
* travel at speeds and weights that are danger to humans, thus requiring strictly segregated spaces.
* are only used less than 5% of the day, requiring places to store them when unused.
* require extremely wide turning radiuses when traveling at speed (there’s a viral photo showing the entire historical city of Florence fit inside a single US cloverleaf interchange)
Not only have none of these flaws been fixed, many of them have gotten worse with advancing technology because they’re baked into the nature of cars.
Anyone at the invention of automobiles with sufficient foresight could have seen the intersecting incentives that cars would wreak, same as how many of the future impacts of LLMs are foreseeable today, independent of technical progress.
Yeah, but where's the money to be made in not selling people stuff?
https://imgur.com/few-shareholders-had-good-value-least-jpsP...
Dismissing a concern with “LLMs/AI can’t do it today but they will probably be able to do it tomorrow” isn’t all that useful or helpful when “tomorrow” in this context could just as easily be “two months from now” or “50 years from now”.
"Every critique of AI assumes to some degree that contemporary implementations will not, or cannot, be improved upon.
Lemma: any statement about AI which uses the word "never" to preclude some feature from future realization is false.
Lemma: contemporary implementations have almost always already been improved upon, but are unevenly distributed."
And with fusion, we already have a working prototype (the Sun). And if we could just scale our tech up enough, maybe we’d have usable fusion.
(Sometimes that sort of criticism is spot on. If someone says they've got a brilliant new design for a perpetual motion machine, go ahead and tell them it'll never work. But in the general case it's overconfident.)
That is too reductive and simply not true. Contemporary critiques of AI include that they waste precious resources (such as water and energy) and accelerate bad environmental and societal outcomes (such as climate change, the spread of misinformation, loss of expertise), among others. Critiques go far beyond “hur dur, LLM can’t code good”, and those problems are both serious and urgent. Keep sweeping critiques under the rug because “they’ll be solved in the next five years” (eternally away) and it may be too late. Critiques have to take into account the now and the very real repercussions already happening.
But I'm really worried that the benefits are very localized, and that the externalized costs are vast, and the damage and potential damage isn't being addressed. I think that they could be one of the greatest ever drivers of inequality as a privileged few profit at the expense of the many.
Any debates seem neglect this as they veer off into AGI Skynet fantasy land damage rather than grounded real world damage. This seems to be deliberate distraction.
A crucial ingredient might be missing.
I mean, there was and then there wasn't. All of those things are shrinking fast because we handed over control to people who care more about profits than customers because we got too comfy and too cheap, and now right to repair is screwed.
Honestly, I see llm-driven development as a threat to open source and right to repair, among the litany of other things