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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.
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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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