In my case I fully grasp what such a future could be, but I don't think we are on the path to that, I believe people are too optimistic, i.e. they just believe instead of being truly skeptical.
From where I look at it, LLMs are flawed in many ways, and people who see progress as inevitable do not have a mental model of the foundation of those systems to be able to extrapolate. Also, people do not know any other forms of AI or have though hard about this stuff on their own.
The most problematic things are:
1) LLMs are probabilistic and a continuous function, forced by gradient descent. (Just having a "temperature" seems so crazy to me.) We need to merge symbolic and discrete forms of AI. Hallucinations are the elephant in the room. They should not be put under the rug. They should just not be there in the first place! If we try to cover them with a layer of varnish, the cost will be very large in the long run (it already is: step-by-step reasoning, mixture of experts, RAG, etc. are all varnish, in my opinion)
2) Even if generalization seems ok, I think it is still really far from where it should be, since humans need exponentially less data and generalize to concepts way more abstract than AI systems. This is related to HASA and ISA relations. Current AI systems do not have any of that. Hierarchy is supposed to be the depth of the network, but it is a guess at best.
3) We are just putting layer upon layer of complexity instead of simplifying. It is the victory of the complexifiers and it is motivated by the rush to win the race. However, I am not so sure that, even if the goal seems so close now, we are going to reach it. What are we gonna do? Keep adding another order of magnitude of compute on top of the last one to move forward? That's the bubble that I see. I think that that is not solving AI at all. And I'm almost sure that a much better way of doing AI is possible, but we have fallen into a bad attractor just because Ilya was very determined.
We need new models, way simpler, symbolic and continuous at the same time (i.e. symbolic that simulate continuous), non-gradient descent learning (just store stuff like a database), HAS-A hierarchies to attend to different levels of structure, IS-A taxonomies as a way to generalize deeply, etc, etc, etc.
Even if we make progress by brute forcing it with resources, there is so much work to simplify and find new ideas that I still don't understand why people are so optimistic.
Symbolic AI is dead. Either stop trying to dig out and reanimate its corpse, or move the goalposts like Gary Marcus did - and start saying "LLMs with a Python interpreter beat LLMs without, and Python is symbolic, so symbolic AI won, GG".
Hallucinations are incredibly fucking overrated as a problem. They are a consequence of the LLM in question not having a good enough internal model of its own knowledge, which is downstream from how they're trained. Plenty of things could be done to improve on that - and there is no fundamental limitation that would prevent LLMs from matching human hallucination rates - which are significantly above zero.
There is a lot of "transformer LLMs are flawed" going around, and a lot of alternative architectures being proposed, or even trained and demonstrated. But so far? There's nothing that would actually outperform transformer LLMs at their strengths. Most alternatives are sidegrades at best.
For how "naive" transformer LLMs seem, they sure set a high bar.
Saying "I know better" is quite easy. Backing that up is really hard.
> Hallucinations are incredibly fucking overrated as a problem. They are a consequence of the LLM in question not having a good enough internal model of its own knowledge, which is downstream from how they're trained. Plenty of things could be done to improve on that - and there is no fundamental limitation that would prevent LLMs from matching human hallucination rates - which are significantly above zero.
Why is there no fundamental limitation that would prevent LLMs from matching human hallucination rates? I'd like to hear more about how you arrived at that conclusion.
To avoid hallucinations, you, a human, need two things: you need to have an internal model of your own knowledge, and you need to act on it - if your meta-knowledge says "you are out of your depth", you either answer "I don't know" or look for better sources before formulating an answer.
This is not something that's impossible for an LLM to do. There is no fundamental issue there. It is, however, very easy for an LLM to fail at it.
Humans get their (imperfect, mind) meta-knowledge "for free" - they learn it as they learn the knowledge itself. LLM pre-training doesn't give them much of that, although it does give them some. Better training can give LLMs a better understanding of what the limits of their knowledge are.
The second part is acting on that meta-knowledge. You can encourage a human to act outside his knowledge - dismiss his "out of your depth" and provide his best answer anyway. The resulting answers would be plausible-sounding but often wrong - "hallucinations".
For an LLM, that's an unfortunate behavioral default. Many LLMs can recognize their own uncertainty sometimes, flawed as their meta-knowledge is - but not act on it. You can run "anti-hallucintion training" to make them more eager to act on it. Conversely, careless training for performance can encourage hallucinations instead (see: o3).
I read the page you linked, but I'm still not understanding why hallucination isn't an inevitability of LLMs. The explanation OpenAI gives doesn't feel like a complete answer to me.
OpenAI claims that hallucination isn't an inevitability because you can train a model to "abstain" rather than "guess" when giving an "answer". But what does that look like in practice?
My understanding is that an LLM's purpose is to predict the next token in a list of tokens. To prevent hallucination, does that mean it is assigning a certainty rating to the very next token it's predicting? How can a model know if its final answer will be correct if it doesn't know what the tokens that come after the current one are going to be?
Or is the idea to have the LLM generate its entire output, assign a certainty score to that, and then generate a new output saying "I don't know" if the certainty score isn't high enough?
The answer is that it does know. Not exactly, but the "general shape" of the answer is known to the LLM before the very first token of the answer is emitted!
"Next token prediction" is often overstated - "pick the next token" is the exposed tip of a very large computational process.
And LLMs are very sharp at squeezing the context for every single bit of information available in it. Much less so at using it in the ways you want them to.
There's enough information at "no token emitted yet" for an LLM to start steering the output towards "here's the answer" or "I don't know the answer" or "I need to look up more information to give the answer" immediately. And if it fails to steer it right away? An LLM optimized for hallucination avoidance could still go "fuck consistency drive" and take a sharp pivot towards "no, I'm wrong" mid-sentence if it had to. For example, if you took control and forced a wrong answer by tampering with the tokens directly, then handed the control back to the LLM.
By "shape" of the answer, what do you mean? I always visualized token prediction as a vector pointing off into some sort of cloud of related tokens, and if that's a fair way to visualize it, I could understand how you could say, before even emitting the first token of the answer, "we are pointing towards generally the correct place where the answer is found". But when a single token can make or break an answer, I still don't see how you can truly know whether the answer is correct until the very last token is reached. Because of this, I'm still not convinced hallucination can be stopped.
Symbolic AI isn't dead, we use it all the time, it's just not a good orchestrating layer for interacting with humans. LLMs are great as a human interface and orchestrator but they're definitely going to be calling out to symbolic models for expanded functionality. This pattern is obvious, we're already on the path with agentic tool use and toolformers.
This is what I mean by "move the goalposts like Gary Marcus did", yes.
If what you're claiming is that external, vaguely-symbolic tooling allows a non-symbolic AI to perform better on certain tasks, then I agree with that.
If you replace "a non-symbolic AI" with "a human", I agree with that too.
symbols and concepts are just collections of neurons that fire with the correct activation. its all about the bitter lesson, human beings cannot design ai, they can only find the most general equations, most general loss function, and push data in. and thats what we have, and thats why its a big deal. The LLM is just a manifestation of a much broader discovery, a generalized learning algorithm. it worked on language because of the information density, but with more compute, we may be able to push in more general sensory data...
Not sure this is a good counterpoint in defence of LLMs, but I'm reminded of how Unix people explain why (in their experience) data should be encoded, stored and transmitted as text instead of something more seemingly natural like binary. It's because text provides more ways to read and transform it, IN SPITE of its obvious inefficiency. LLMs are the ultimate Unix text transformation filter. They are extremely flexible out-of-the-box, and friendly towards experimentation.
> We are just putting layer upon layer of complexity instead of simplifying.
It really irks me that the direction every player seems to be going to is to layer LLMs on top of each other with the goal of saving money on inference while still making the users believe that they are returning high quality results.
Instead of discovering some radical new ways of improving the algorithms they are only marginally improving existing architectures and even that is debatable.
who cares about a bubble? we are on the cusp of intelligent machines. The implications will last for hundreds of years, maybe impact the trajectory of humanity
Bubble burst means current technical AI approach economic deadend, if the most resourced tech companies in the world can't afford to maintain AI improvment then it's probably not going to happen because public likely isn't going to let state spend $$$$ in lieu of services on sovereign Ai projects that will make them unemployed.
Except we aren’t. They aren’t thinking, and they can’t actually make decisions. They generate language. That feels magical at first but in the end it’s not going to solve human problems.
Explanation of reason and reasoning paths is a product of manipulating language.
Most ideas, even in the reasoning fields, are generated in non-linguistic processes.
Of course, some problems are solved by step-by-step linguistic (or math) A, then B, then C steps, etc., but even for those types of problems, when they get complex, the solution looks more like follow a bunch of paths to dead ends, think some more, go away, and then "Aha!" the idea of a solution pops into our head, then we back it up and make it explicit with the linguistic/logical 'chain of reasoning' to explain it to others. That solution did not come from manipulating language, but from some other cognitive processes we do not understand, but the explanation of it used language.
LLMs aren't even close to that type of processing.
It's nice that you say partially, that's bit different from every other HN comment that wondered this. Yeah, probably partially, as in, you have reason, you add in language, you get more reason.
I am so sure because of the self-evidence of my experience, the results of 2 millennia of analysis into the nature of cognition and experience, and consideration of the material composition of our organism (we obviously have lots of critical analog components, which are not selecting tokens, but instead connecting with flows from other continua).
Prediction is obviously involved in certain forms of cognition, but it obviously isn't all there is to the kinds of beings we are.
I am sure that if I am a fancy auto-complete, I'm way fancier than LLMs. A whole different category of fancy way above their league. Not just me, but any living human is.
Reminds me of crypto/Web-3.0 hype. Lots of bluster about changing economic systems, offering people freedom and wealth, only to mostly be scams, and coming with too serious inherent drawbacks/costs to solve many of the big problems it promises to solve.
In the end leaving the world changed, but not as meaningfully or positively as promised.
I’m watching Ken Burns Documentary on the Dust Bowl, and interesting that one of the causes of the dust bowl was a wheat hype cycle in western Oklahoma with all sorts of folks theorizing that because they were able to re-form the grassland to wheat land and grow wheat it would somehow cause more rains to come (to an area that is known for droughts) and it was thought the growth would go on forever. Turns out the grasses they replaced had roots like 6 feet deep that kept the soil in place and prevented events like the dust bowl during dry spells.
Basically the hype cycle is as American as Apple Pie.
the difference is the impact of crypto was always hypothetical, chatgpt can be used, explored, and if you are creative enough, levered in ways as the ultimate tool
You've done nothing but reuse the Sam Altman/Elon Musk playbook of making wild and extremely vague statements.
Maybe say something concrete? What's a positive real world impact of LLMs where they aren't hideously expensive and error prone to the point of near uselessness? Something that isn't just the equivalent of a crypto-bro saying that their system for semi-regulated speculation (totally not a rugpull!) will end the tyranny of the banks.
they speak in generalities because the models are profoundly general, a general learning system. below someone asked me to list the capabilities, its the wrong question to ask. its like asking what a baby can do
So to translate: You want concrete examples of capabilities for something billions are being spent on? What a Silly question! (hand waving about completely speculative future abilities "when they grow up")
The woo is laughable. A cryptobro could have pulled the same nonsense out of their ass about web 3.0
But you need to verify everything unless it’s self evident. The number of times CoPilot (Sonnett 4) still hallucinates Browser APIs is astonishing. Imaging trying to learn something that can’t be checked easily, like Egyptian archeology or something.
You have to verify everything from human developers too. They hallucinate APIs when they try to write code from memory. So we have:
- documentation
- design reviews
- type systems
- code review
- unit tests
- continuous integration
- integration testing
- Q&A process
- etc.
It turns out when include all these processes, teams of error-prone human developers can produce complex working software. Mostly -- sometimes there are bugs. Kind of a lot actually. But we get things done.
Is it not the same with AI? With the right processes you can get consistent results from inconsistent tools.
Taking the example of egyptian archeology, if you're reading the work of someone who is well regarded as an expert in the field, you can trust their word a lot more than you can trust the word of an AI, even if the AI is provided the text you're reading.
This is a pretty massive difference between the two, and your narrative is part of why AI is proving to be so harmful for education in general. Delusional dreamers and greedy CEOs talking about AI being able to do "PhD level work" have potentially ruined a significant chunk of the next generation into thinking they are genuinely learning from asking AI "a few questions" and taking the answers at face value instead of struggling through the material to build true understanding.
There needs to be a reasonable chance of correctness. At least the local toddlers around here don’t randomly provide a solution to a problem that would take me hours to find but only minutes to validate.
>I'll take a potential solution I can validate over no idea whatsoever of my own any day.
If you have to validate what the LLM says, I assume you'd do that by researching primary sources and works by other experts. At that point, the LLM did nothing except charge you for a few tokens before you went down the usual research path. I could see LLMs being good for providing an outline of what you'd need to research, which is definitely helpful but not in a singularity way.
> If you have to validate what the LLM says, I assume you'd do that by researching primary sources and works by other experts.
For research, yes, and the utility there is a bit more limited. They’re still great at digesting and contextualizing dozens or hundreds of sources in a few minutes which would take me hours.
But what I mean by “easily testable” is usually writing code. If I already have good failing tests, verification is indeed very very cheap. (Essentially boils down to checking if the LLM hacked around the test cases or even deleted some.)
> At that point, the LLM did nothing […]
I’d pay actual money for a junior dev or research assistant capable of reading, summarizing, and coming up with proofs of concept at any hour of the day without getting bored at the level of current LLMs, but I’ve got the feeling $20/month wouldn’t be appealing to most candidates.
All of the information available from an LLM (and probably more) is available in books or published on the internet. They can go to a library and a read a book. They can be fairly certain books written by subject matter experts aren’t just made up.
Sure, I just gave the Browser API example as evidence that the 'hallucination' problem is not gone.
OP said it's like "talking to a professor" and you can use it to learn college level stuff. This is where I disagree. I did not double check my professors or text books usually.
I just vibecoded a photo gallery 100% from scratch - Frontend, backend, infrastructure, hosting and domain, from 0 to launch, in a couple of hours the other night.
It would have taken me a whole day, easily, to do on my own.
I have seen AI improve the quality and velocity of my wife's policy analysis dramatically.
She doesn't like using Claude, but she accepts the necessity of doing so, and it reduces 3-month projects to 2-week projects. Claude is an excellent debating partner.
Crypto? Blockchain? No-one sceptical could ever see the point of either, unless and until their transaction costs were less than that of cash. That... has not happened, to put it mildly.
that was not the sentiment people had in 2017. they were almost certain every major visa card provider and payment gateway on the planet will scrap fiat cash and adopt crypto just like how they are all thinking about every major software company adopting AI. Dont forget hindsight bias
Except that nothing of crypto/web3 ever touched my day or day life - ‘blockchain’ is now shorthand for ‘scam’ - whereas LLM-generated content is now an everyday element of both personal and professional projects, and while we maybe already be seeing diminishing returns, even the current state of advancement has already changed how digital content is searched and created forever.
The hype is real, but there’s actual practical affordable understandable day-to-day use for the tech - unlike crypto, unlike blockchain, unlike web3.
I would really like to hear you explain how they revolutionized supermarkets.
I use them every day, and my shopping experience is served far better by going to a place that is smaller than one that has automated checkout machines. (Smaller means so much faster.)
Hell, if you go to Costco, the automated checkout line moves slower than the ones manned by experienced workers.
I think it's worth engaging with even if this guy's a troll (not saying he is) because it's not that freakish a view in the real world. What are the arguments to counter this kind of blind enthusiasm?
2. The category of computerized machines (of which self checkouts are one example) has absolutely revolutionized the world. Computerization is the defining technology of the last twenty years.
You know the quote "it is easier to imagine an end to the world than an end to capitalism"? Well, AI has allowed me to start imagining the end of capitalism. I'm not claiming we're necessarily very close, but I can clearly see the way from here to a post-scarcity society.
> I can clearly see the way from here to a post-scarcity society.
I would be interested to hear the way that you see. I don't have any problem seeing a huge number of roadblocks to post-scarcity that AI won't solve, but I am open to a different perspective.
Ok, so as a disclaimer: this obviously leans towards science-fiction, both because that informs my view of the world, and because I think that any prediction of the future must incorporate science fiction.
My own experience, using ChatGPT and Claude for both dev and other business productivity tasks, lends credence to the METR model of exponential improvement in task time-horizon [0]. There are obviously still significant open technical issues, particularly around memory/context management and around online learning, but extensive work is being done on these fronts, propelled amongst other things by the ARC-AGI challenge [1], and I don't see anything that is an actual roadblock to progress. If anything, from my perspective, it seems appears that there are significant low-hanging-fruit opportunities around plain-old software engineering and ergonomics for AI agents, more so than a need for fundamental breakthroughs in neural network architecture (although I believe that these too will come).
So then, with an increasing time horizon and improved task accuracy (much of it assured by improvements in QA mechanisms), we will see ourselves handing off more and more complex tasks to AI agents, until eventually we could have "the factory of the future ... [with] only two employees: a man and a dog", and at that stage I believe that there would be no imperative for humans to work (unless they choose to, or have a deeply ingrained Calvinist work ethic). And then, as you said, we're down to the non-technological roadblocks.
Obviously capitalists would fight to stay in control, and unlike some who expect a fully peaceful and organic transition, I do expect somewhat of a war here (whether kinetic or cold), but I do envision that when push comes to shove, those of us who believe in the free software movement and the foundational principles of democracy will be able to assert shared national/international (rather than corporate) control over the AIs and restructure society into a form where AI (and later robots) perform the work for the benefit of humans who would all share in the bounty. I am not an economist and don't have a clear prediction on the exact form this new society would take, but from my reading of the various pilot implementations of UBI [2], I think that we will see acceptance towards a society where people are essentially in retirement throughout their life. Just as currently, some retired people, choose to only stay home and watch TV, while others study, do art, travel the world, help raise and teach future generations or contribute to social causes close to their hearts, so we'll all be able to do what is in our hearts, without worrying about subsistence.
That's hypothetically possible if a government somehow forced corporations to redistribute their wealth. But a civil war is equally likely. Where society is destroyed after poor people with guns have their say.
The only reason this shit is getting pushed on us as hard as it is because of hyper-capitalist psychos like Thiel and Altman. You are buying into the capitalist hype by actually thinking these systems will be used for anything other than fattening the capitalist's wallets, anything else is a sci-fi fantasy
I had a computer that could talk and make decisions in 1982. It sounded pretty robotic and the decisions were 1982-level AI: Lots of if statements.
I'm not really trying to be snarky; I'm trying to point out to you that you're being really vague. And that when you actually get really, really concrete about what we have it ... starts to seem a little less magical than saying "computers that talk and think". Computers that are really quite good at sampling from a distribution of high-likelihood next language tokens based upon complex and long context window is still a pretty incredible thing, but it seems a little less likely to put us all out of a job in the next 10 years.
>I had a computer that could talk and make decisions in 1982.
And it became and industry that as completely and totally changed the world. The world was just so analog back then.
>starts to seem a little less magical than saying "computers that talk and think"
Computer thinking will never become magical. As soon as we figure something out it becomes "oh that is just X". It is human thinking that will become less magical over time.
Define "make decisions" such that an 'if' statement does not qualify but an llm does.
LLMs may be a stepping stone to AGI. It's impressive tech. But nobody's proven anything like that yet, and you're running on pure faith not facts here.
i mean, if given the choice between using a coding agent like the codex ui, or a CS intern, I would take the coding agent every time. to me its self evident whats going on
I get a new batch of CS interns for my team every year, and I use Claude Code every day. I think Claude is pretty amazing and it definitely provides value for me, but I would choose the intern every time. I am really skeptical of any claims of getting the kind of productivity and capability growth out of an LLM that would make it an adequate replacement for a human developer.
Well frankly your lack of concrete arguments makes it seem a lot like you don't actually understand what's going on here.
I'm enjoying the new LLM based tooling a lot, but nothing about it suggests that we're in any way near to AGI because it's very much a one trick pony so far.
When we see generative AI that updates its weights in real time (currently an intractible problem) as part of the feedback loop then things might get very interesting. Until then it's just another tool in the box. CS interns learn.
its a problem of imagination, "situational awareness". People are simply not aware of what we have discovered, their minds cannot see beyond a chatbox. thats not even to mention the smoothness of the loss functions the big ai labs are seeing, the smooth progression of the scaling laws. its all there, it progresses daily
The last thing we need is a bunch of random chatter, but that’s all these things can do. They can’t make decisions because they don’t actually relate to the real world. Humans (like you, apparently) may think a talking computer is a magic oracle but you’re fooling yourself.
I think you aren't understanding the meaning of the world bubble here. No one can deny the impact LLM can have but it still has limits.
And the term bubble is used here as an economic phenomenon. This is for the money that openai is planning on spending which they don't have. So much money is being l poured here, but most users won't pay the outrageous sums of money that will actually be needed for these LLM to run, the break even points looks so far off that you can't even think about actual profitability.
After the bubble bursts we will still have all the research done, the hardware left and smaller llms for people to use with on device stuff.
the real innovation is that neural networks are generalized learning machines. LLMs are neural networks on human language. The implications of world models + LLMs will take them farther
The neural net was invented in the 1940s, and LLMs were created in the 1960s. It's 2025 and we're still using 80yo architecture. Call me cynical, but I don't understand how we're going to avoid the physical limitations of GPUs and data to train AIs on. We've pretty much exhausted the latter, and the former is going to hit sooner rather than later. We'll be left at that point with an approach that hasn't changed much since WW2, and our only solution is going to hit a physical limit law.
Even in 2002, my CS profs were talking about how GAI was a long time off bc we had been trying for decades to innovate on neural nets and LLMs and nothing better had been created despite some of the smartest people on the planet trying.
they didnt have the compute or the data to make use of NNs. but theoretically NNs made sense even back then, and many people thought they could give rise to intelligent machines. they were probably right, and its a shame they didnt live to see whats happening right now
> they didnt have the compute or the data to make use of NNs
The compute and data are both limitations of NNs.
We've already gotten really close to the data limit (we aren't generating enough useful content as a species and the existing stuff has all been slurped up).
Standard laws of physics restrict the compute side, just like how we know we will hit it with CPUs. Eventually, you just cannot put things closer together that generate more heat because they interfere with each other because we hit the physical laws re miniaturization.
No, GAI will require new architectures no one has thought of in nearly a century.
We have evidence that general intelligence can be produced but a bunch of biological neurons in the brain and modern computers can process similar amounts of data to those so it's a matter of figuring how to wire it up as it were.
Despite being their namesake, biological neurons operate quite distinctly from neural nets. I believe we have yet to successfully model the nervous system of the nematodes, with a paltry 302 neurons.
dude who cares about data and compute limits. those can be solved with human ingenuity. the ambiguity of creating a generalized learning algorithm has been solved. a digital god has been summoned
I'm old enough to have heard this before, once or thrice.
It's always different this time.
More seriously: there are decent arguments that say that LLMs have an upper bound of usefulness and that we're not necessarily closer to transcending that with a different AI technology than we were 10 or 30 years ago.
The LLMs we have, even if they are approaching an upper bound, are a big deal. They're very interesting and have lots of applications. These applications might be net-negative or net-positive, it will probably vary by circumstance. But they might not become what you're extrapolating them into.
I love chatGPT5 and Claude but they aren't as big of a deal as going from no internet to having the internet.
That I think is the entire mistake of this bubble. We confused what we do have with some kind of science fiction fantasy and then have worked backwards from the science fiction fantasy as if it is inevitable.
If anything, the lack of use cases is what is most interesting with LLMs. Then again, "AI" can do anything. Probabilistic language models? Kind of limited.
I can't believe I still have to do my own laundry and dishes. Like that's some how way more powerful than the models of a megawatt powered data center and millions of dollars in 3nm silicon can conquer.
From a software development perspective, the more I think of it, the more I understand it's just another abstraction layer. Before that came high level languages and JVM of sorts, before that came the compiler, before that came the assembler.
Outside of the software world it's mostly a (much!) better Google.
Between now and a Star Trek world, there's so much to build that we can use any help we can get.
Maybe you are not grasping the convergence effect of the overall socio-political-economic trends that could actually label AI output as abhorrent plastic pollution or atleast not a high priority for public good.
It's blatantly obvious to see if you work with something you personally have a lot of expertise in. They're effectively advanced search engines. Useful sure.. but they're not anywhere close to "making decisions"
In what sense? It seems entirely possible to have a computer program that calls ChatGPT with questions on what stocks to buy, and for that computer program to then trade stock based on the results, entirely autonomously. No matter your opinion of the idea itself, why wouldn't that count as "making decisions"?
Not especially. The computer program does all the work. It's the one that hits ChatGPT for a list of trades, and then the computer program hits the brokerage's API to execute the trades. I made the decision to set the program up in the first place, and the trades are happening on an account that has my name on it, sure, but as I'm not nit-picking each individual trade that gets made and it runs autonomously without a human in the loop, it seems fair to claim that ChatGPT is making decisions on what to buy on my behalf, even though I do have veto authority over the program and ChatGPT, and can stop it at any time.
If they're that profound you should be able to come up with one example though, right?
Not that I think you're wrong, but come on - make the case!
I have the very unoriginal view that - yes, it's a (huge) bubble but also, just like the dot com bubble, the tevhnology is a big deal - but it's not obvious to see what will stand and fall in the aftermath.
Remember that Sun Microsystems, a very established pre-dot com business, rose to huge heights on the bubble and was then smashed by the fall when it popped. Who's the AI bubble's Sun and who's its Amazon? Place your bets...
Given the failure rate of people actually being able to use AI, and the continued inability to find any meaningful use-case, comments like this are starting to feel like cope. Call me in 10 years from your bitcoin operated phone and tell me all about the “revolution”.
This is true. The old original series (and later) Star Trek computers being able to interpret normal idiomatic humam speech and act upon it was, to those in the know, hilariously unrealistic until very suddenly just recently it wasn't. Pretty cool.
pretty much all of the classical ideas of what an ai could do, can be done with our existing capabilities. and yet, people continue to live as if the world has not changed
"AI" has been doing that since the 1950s though. The problem is that each time we define something and say "only an intelligent machine can X" we find out that X is woefully inadequate as an example of real intelligence. Like hilariously so. e.g. "play chess" - seemed perfectly reasonable at the time, but clearly 1980s budget chess computers are not "intelligent" in any very useful way regardless of how Sci Fi they were in the 40s.
Not OP, but I do think that because "humanlike abstract thinking and informal reasoning is completely unnatural to how computers work, and it's borderline impossible to make them do that" was by far the biggest AI roadblock, in my eyes.
And we've made it past. LLMs of today reason a lot like humans do.
They understand natural language, read subtext, grasp the implications. NLP used to be the dreaded "final boss" of AI research - and now, what remains of it is a pair of smoking boots.
What's more is that LLMs aren't just adept at language. They take their understanding of language and run with it. Commonsense reasoning, coding, math, cocktail recipes - LLMs are way better than they have any right to be at a range of tasks so diverse it makes you head spin.
You can't witness this, grasp what you see, and remain confident that "AGI isn't possible".
