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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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