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Ha, wow! Was not aware of that. I guess that makes sense w/r/t the funding.
You've written a lot. I think it's best to focus. (As much as I'm tempted to talk about concepts.)
What I understand to be your main point is (my words because you did not state it in concrete terms):
Which is a fair point! The only counterargument to that is on the specifics: why is it improving and what do we expect future improvements to look like? Almost all of the improvement thus far is based on throwing more compute at the problem - so if we're going to see improvements of the same kind, we should see them based on more compute. However, improvements in models are logarithmic - steps up in capacity tend to require 10x compute (by appearances you're pretty educated about AI, so I suspect that is not news to you). So although improvements in efficiency can effectively allow for somewhat more compute, like with Deepseek, we should expect that throwing more compute at the problem will get prohibitively expensive. I believe this has already happened. So while under hypothetical conditions of infinite compute we could have an LLM that infinitely approximates an AGI, similar to the implausible premise of Searle's Chinese Room (a book that allows one to construct a correct response to any input), we are unlikely to see that in practice.
So, how are we to get to AGI, in my opinion? By improving AI on completely different parameters from what currently exists - a revolution in thought about how AI should function. And tests like Claude Plays Pokemon are a fun way of showing us where the gaps in our thinking are.
For my own point of view:
That's not the argument. The argument is: this AI is struggling in a VERY non-human way with what we would consider a pretty trivial task. This reveals that its operational parameters are not like those of a human, and that we should figure out where else it is going to perform at sub-human levels. The fact that we're seeing this at the same time as it performs at SUPERhuman levels in other tasks shows that this is not AGI, or even in the direction of AGI, but rather is tool AI. (I assume you think humans are, at the very least, general intelligence - right?)
I don't think you've addressed this point, except here:
Why should I care? AGI is supposed to be GENERAL. This is the stuff that's supposed to be taking people's jobs in a few years! And yet it gets lost in Cerulean City? As a tech demo, this is very cool - it's remarkable that someone was able to pipe these pieces together, and the knowledge base idea is very cool and is a plausible direction to take new LLMs into. A hypothetical Claude 3.8 that is explicitly trained to make knowledge base manipulation a central feature of the model could potentially perform miles better on some of these tasks. But all you've told me is that I should expect AI to struggle with these tasks. In which case: doesn't it sound like we agree? We both agree that there was no reason to expect Claude to succeed with Pokemon at the level of an eight-year-old. So, from the perspective of an uncommitted third party, given that an AI skeptic and an AI optimist have both agreed that an LLM can't play Pokemon like an eight-year-old... well, it feels pretty clear to me.
Obviously, if this becomes a big selling point for the next generation of LLMs, then we'll see them all benchmarked on Pokemon Red speedruns and you can I-told-you-so about AI being able to beat Pokemon. I don't doubt the ability of motivated corporations to "teach to the test" - it's what we've been seeing with "reasoning" AIs. It's just one of the problems with setting up real tests of ability for some of these AIs, because they get so much data that it's all but impossible to ensure you have a pure test like what the IQ test aspires to.
Compute can be spent in many different ways. We're moving from a paradigm of scaling up the size of a model, in terms of parameter count, in favor of scaling run-time compute (time spent thinking) and reinforcement learning.
Scaling worked well, but was known to have diminishing returns, and limited by availability of high quality data to train on. It turns out that raw text dumps of the internet will only get you so far, and then curation matters.
RL, however, side-steps the issue entirely, models like OAI o1, o3 and DeepSeek's R1 were further trained on synthetic data. You take a normal LLM (or a base model), get it to attempt to solve a well defined problem where you can grade an answer. In the event they succeed at the task, you save that particular conversation/reasoning trace, and then use it to further train the model. This generates a data flywheel, which by all rights sounds like it shouldn't work (and many people didn't expect it to, thinking that the exhaustion of human generated data would stymie progress), but it turned out to work exceedingly well.
That's why models are doing particularly well at tasks like maths or coding, because you can rigorously vet the answers. This is harder to do with tasks like being a good writer, or poetry, because human evaluators often don't even agree with each other, let alone have a ground truth to refer to.
As dozens of benchmarks have shown us, doing better than chance on a metric is half the battle. The time taken to get there dwarfs the rapidity with which models then conquer and saturate the benchmark. If we have an LLM doing poorly on Pokémon (but far better than previous models, GPT-3.5 would have flunked it, GPT-2 would flop around like a magikarp), then it's not going to be much longer before it does it in its sleep.
There isn't much of a market for AI playing Pokémon. There is immense demand for them to be good at coding and maths. We've seen stunning progress in that regard, as you acknowledge. You attempt to back-chain your argument, saying that they're said to be good at maths but look, they're shit at Pokémon, which apparently invalidates the former. It really doesn't.
That's just Moravec's Paradox. Intelligence can be spiky.
Tell me, if you saw a human genius in the field of physics struggle with tying his laces or riding a bike, does that make his genius in his field invalid?
Claude is excellent in maths and coding, a good conversationalist and writer, if you described a human being with those properties, I'd be impressed, and it being bad at Pokémon doesnt invalidate the former.
If you're concerned about job losses, employers will be looking at coding skill before laying off programmers, not at their ability to play games.
I had never given it any thought before the demonstration. But plenty of people have speculated that LLMs would never be any good at video games, and now that they're not good but not terrible, it's only a matter of time before they're great. And that time can be very short.
We've got the first AI agents out there. This was something impossible even a few years back, and they're only getting better.
Very interesting aside! However, it doesn't address the question of diminishing returns.
I've used AI for coding, which you mention further down as a crowning triumph. It is... not particularly good. It struggles at anything past a very general form of the problem. It was very useful for copy-pasting similar pieces of code! Not very useful for building new features. It had a distinct habit of waiting until the interesting or important part of the problem and leaving a comment saying "Implement a function to do X!" Hmm, very interesting, if I tried that I'd get fired. So no, I think this is a valid argument. AI can be taught to the test, and indeed appears to have been, but the actual world involves far more de novo work than the test includes. That's why school-trained pre-professionals tend to need a pretty hefty ramp-up to start being really useful - they've only been working on tests so far. Pokemon is interesting precisely because it has not been trained for. You should expect more, not less, untrained situations for AI to do anything meaningful in the job market - and you should weight untrained situations in your analysis several orders of magnitude higher than trained situations.
Do you use AI to augment your work? Is it going to take your job? On what kind of a timescale? Do you think you'd be able to substitute yourself for an unmonitored AI without issue on any tasks? If not, what errors do you think it would make, and why? Honestly interested in your answers here, if nothing else. I would greatly respect you for putting your money where your mouth is on this one and bringing receipts.
Hmm... you think getting stuck in what appears to be a permanent loop is not terrible? Is this the behavior you'd accept from anyone working for you?
The thing that keeps puzzling me about your comments is that you seem to simultaneously view ANY capacity in a task as an impressive accomplishment at the same time as you assert that AI has overwhelming general ability. Those two don't go hand in hand, except maybe by this little quote. Any capacity seems to be, for you, an indisputable sign of unlimited future capacity - as though the only question to be answered is total disability versus infinite ability. There's no clear reason that this has to be the limit of the answer space. Line go up... forever? Like with bitcoin? There's also the rather bizarre fixation on LLMs - even though something like, say, an octopus is very obviously not an LLM and still has meaningful if primitive intelligence. The sheer gnostic power of your position is hard to argue against, and unfortunately I don't find it very convincing based on my own experience. It takes rather a lot on faith.
Diminishing returns != No returns or negative returns.
The important question is whether the gains/$ invested are positive.
GPT 4.5 is extremely expensive, for the very limited increase in benchmark performance it represents.
And how expensive is it, that people are throwing a fit? Barely more expensive than the original GPT-4. That was absolutely worth paying the money for, when compared to GPT 3.5. GPT 4.5 has the disadvantage of peer competition.
That being said, the price of GPT-4 tokens and that of equivalent models dropped an OOM in price. DeepSeek R1/V3 and Gemini Flash 2.0 spank the OG GPT-4 with paddles and are practically free.
We've known that scaling laws are log-linear for a while now, at least since the Chinchilla days. Now that pure scaling of model size is getting super expensive, we've managed to discover a brand new opportunity to start scaling something else entirely, in the form of RL. Since we're starting off at the bottom of the curve, we've got several orders of magnitude of growth to spare there.
GPT 4.5 is not, however, a bust. The very capable and inexpensive reasoning models benefit immensely from having a strong and capable base model to RL further. You can then distill down, drastically cutting model size and inference costs, while keeping almost all the performance. It may or may not have been the progenitor of o3, which is very good.
There are probably a thousand people on my Twitter feed, some of them rather famous, who disagree. Of course, I concede that there are people who think they're slop. And it also depends on which model you're trying to use for coding. There was a period where GPT-4 was updated and became ridiculously lazy. That was fixed pronto. Claude 3.7 Sonnet is apparently overeager, if left unchecked, it'll turn a request for a basic app into a full SAAS business.
If you have had issues with a model being lazy, you can always ask for it to output complete and working code! Prompting has become less and less important, but it's not dead yet.
I'm a doctor. Yes, I use LLMs on the regular. Yes, I expect them to put me out of business eventually, probably in 3-7 years for a 50% CI, 2-10 for a 70%.
A current LLM would do an excellent job at medical diagnostics and formulating treatment plans. It could probably handle patient interviews, for less complex cases where voice or text suffice. You could also use video if you had to.
The main reasons they couldn't replace me today are regulatory and implementation concerns. Governments mandate people with medical credentials in the loop, because that was a sensible thing to do for most of recent history. Hospitals aren't set up for LLMs.
I'm a psychiatry resident, which is uniquely safe and also uniquely at risk in some regards. It'll get the radiologists first, surgeons last. I'll be somewhere in the middle.
I am capable of verifying the information that SOTA LLMs provide in terms of medical advice. Almost all of it is good. Clinical medicine, outside of procedural specialties, hinges far more on factual knowledge, including that of guidelines, over having to figure things out on the fly in entirely novel situations.
Hallucinations aren't a solved problem, so if I had to replace myself with an LLM, I would probably set up a sort of democracy, with multiple models arguing to build consensus, a best of N scheme for multiple instances of a single model, with multiple rounds of grounding through search. I expect this to work very well, and if you do need to keep a human around for physical tasks or procedures, they don't have to be a highly paid doctor.
In other words, I'd be happy to go to Dr. LLM for my medical care, presuming very cheap measures are taken to stop it hallucinating.
Given that we're testing Claude at a task it was neither designed nor trained to do, it's very much not terrible. For important tasks, it'll be trained to do them. An employer seeking to replace employees will, if they have any sense, test models for obvious flaws. For all practical use cases, LLMs don't really mode collapse these days, and in this particular case, it's more of an artifact of Claude's limited context window than an insurmountable difficulty.
Like I said in this thread, it can take decades for AI models to progress from as bad as random chance to better than random chance. It takes far less time to go from there to human or superhuman performance. We are nowhere near the physical limits, and as I've said before, diminishing returns in absolute terms do not mean diminishing returns in value.
Forever? Not likely in a constrained universe. Unfortunately, the point on that line that equals human performance, or even superhuman performance, is not uniquely privileged.
All we have to do is get past that, and in many aspects, we're there. Terence Tao is on record saying that o1 is equivalent to a competent grad student in research mathematics. Once again, that's Tao, considered one of the world's best mathematicians, for his high standard of "competent".
I'm not aware of companies spending hundreds of billions of dollars on scaling up Octopus Intelligence. LLMs are by far the most intelligent entities on this planet other than humans, and they're only getting better. I know which one I'd worry about, even if it is entirely possible that LLMs as we understand them today prove to be a dead end, and what really kicks things off is another discovery on pat with the original Transformer architecture.
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From what you mention, I think the problem is likely that the "knowledge base" isn't actually working nearly as well as a human's memory, so Claude is effectively amnesic; "when experience is not retained, as among savages, infancy is perpetual. Those who cannot remember the past are condemned to repeat it". This is known to be fixable (if probably requiring at least fine-tuning).
So yes, human agency involves a bunch of different capabilities and you need all of them at some level to be able to function (I don't think a human anterograde amnesiac would do very well at this task either, though one that had already learned how to take notes effectively might muddle through). This particular AI skimped on one of them, because it's not as useful to the task it was designed for, and that's crippling its capabilities at this task to levels well below its underlying reasoning ability.
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