Wowzers, it’s happening imminently. Great to know that we can expect agents that learn from experience very very soon!

When they’re here I’ll make an upvote farming bot that learns from experience how not to get caught and unleash it on HM.

After that I’ll make an agent that runs a SaaS company that learns from experience how to make money and I’ll finally be able to chill out and play video games.

That last thing I’ll actually do myself, I won’t use an agent, although the experience revolution stared with games. Ironic!

But I’ll make an agent that learns from experience what kind of games I like and how to make them. This way I’ll have an endless supply.

It's ironic that machine intelligence is advancing during an era when human intelligence is declining.

Richard Sutton: "Ultimately (this) will become a chapter in the book 'Designing an Intelligence' edited by George Konidaris and published by MIT Press."

Is it me or this is yet another PR stunt masked as a serious article with LaTeX and all the fancy things? The graph doesn’t even make sense.

I’m burning out from all this hypester type of thing, it’s really really tiring.

Related: https://www.deepmind.com/publications/reward-is-enough. Not sure if I buy the reward is enough hypothesis though. Imo an AI with a fixed reward function doesn't seem like agi to me

I want to clarify whether the "learn from experience" is still done through RL offline and not autonomously and continuously?

I think the core idea from the paper is that while we have already hit the ceiling of normal kind of data; there's a new kind of data from agents acting in the real world and users (or some one else?) providing rewards based on some ground truth.

Somehow I misinterpreted from this paper that this kind of learning would be autonomous and continuous.

Honestly? Well, we entering an era (beside possible global war, famine, ... etc) where knowledge application will be more and more automated, while knowledge creation will be human.

Meaning we need less strong arms and more strong brains. Not something that new anyway, the "information age" already makes clear intelligent people could do pretty anything they want to do, while less intelligent are constrained in what they can actually do even if they want.

Experience means essentially automation in the chapter terms, something we have already "solved" could be automated by some machine. To solve new things we need humans. That's is.

Small potatoes new knowledge, meaning knowledge emerging merely crossing per-existing knowledge like from a literature review paper could be a machine game, it's not really creation of new knowledge in the end.

BUT the real point is another: who own the model? LLMs state a clear thing, we need open knowledge just to train them, copyright can't be sustained anymore. But once a model is created who own it? Because the current model is dramatically dangerous since training is expensive and not much exiting, so while it could be a community procedure in practice is a giant-led process, and the giant own the result while harvesting anything from anyone. The effect implied by such evolution are much more startling then the mere automation risk in Lisanne Bainbridge terms https://ckrybus.com/static/papers/Bainbridge_1983_Automatica... or short/mid term job losses.

I suspect this text was generated by an LLM.

...yeah? It'll be great if machines could learn and adapt on-the-fly instead of just compressing the scrapes for 1 epoch over the course of few months into a 1TB download. Making machines learn and adapt on-line is what AI was always about.

I hate that "scientific papers" do not have date of publication.

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Any such article or book must be read with https://ai-2027.com/ in the back of the mind. Exponential processes are… exponentially… dependent on starting conditions, but if takeoff really happens this decade, we’ll be at the destination before Winds of Winter.