Yes, this is essentially how AlphaGo and AlphaZero algorithms work to train superhuman Go/chess/shogi agents. It’s an elegant algorithm that is analogous to how humans learn games.
Well except AlphaZero played 44 million chess games in that time (and actually played with a 44 core computer). So I'd like to point out that the human is still just a few orders of magnitude more efficient.
Yes, we all know that biological systems are more efficient than machines through billions of years of evolution and natural selection but the overall process is largely the same (interacting with an environment, learning from results, improving underlying architecture, etc); efficiencies will come with more time and improvements.
Well if we make AI that learns at the rates humans do, it'll fundamentally undermine and destroy the relevance of all existing AI. It sounds to me like you're saying "we basically are there it's just a matter of degree" and I'm saying "No it's orders of magnitude off and probably won't be using LLMs at all and maybe a very fundamentally different type of neural net technology that hasn't been invented yet."
If you gave a human a book or two on chess they would not become a decent player (they would be closer to 500-600 than 1100 ELO) and they would only get better after playing hundreds or thousands of games (often making illegal moves and moves that violate the rules of chess as they learn).
Your assumptions/intuition about generic human intelligence feels quite incorrect, considering LLMs currently play better than a brand new human player would (presumably without any attempt to fine tune them specific on chess, such as playing thousands of games).
the discussion isn’t really about whether language models can become strong chess players though, the point is they seem to struggle to consistently make valid moves. Most humans don’t need to read two books to pick that up, just a couple lines of basic instructions
That has not been my experience with new players, they regularly make invalid or incorrect moves even after detailed instructions especially in novel situations.
Maybe it depends on the person? My six year old isn’t great at strategy but they can pretty consistently make valid moves. Sometimes they ask for confirmation on a move which is also not a trait I see in language models (at least unprompted)
That's quite untrue. I taught my (adult) brother the moves, the only illegal move he ever tried against me (over his 6 first games) was a castle with a rook that already moved twice. Within a few hundred games (less than 500 for sure, he played 3 minutes blitz but always took at least 10 minutes analyzing his games) he was rated 1100 on lichess (which is like 1050 on chess.com and unranked in the real world).
So your brother tried to make illegal moves while learning the game and it took your brother hundreds of games to get to be a decent player? I don't see how this contradicts anything I said...
The _only_ illegal move a human might make as a beginner is a failed en passant or a bad castle. And yes, a few hundred games is all it takes to be better than any publicly available LLM at the moment.
Yes, but my point is that humans can’t even do the thing that the above comments are claiming humans can do (read a book or two and be decent at chess), and then they complain that LLMs can’t do the same thing (that humans can’t do either).
We seem to be moving goalposts to the point that humans don’t even live up to the expectations of the AI critics. The only way you get better at chess is by playing a lot of games and learning from mistakes, that goes for humans or AI agents, not simply by reading about chess.
> The only way you get better at chess is by playing a lot of games and learning from mistakes
How can you play without being aware of the rules and how can you learn from your mistakes without knowing they are mistakes? That’s what I said about reading a book of two. It is to kickstart the process. Then mastery is gained over time through practice.
This kickstarting then gradual refinement is how most people learn. And the foundational knowledge stays. Even a basic player knows to not do illegal moves.
Reading can kickstart the process, but you can also make random moves guided by some sort of system (such as a computer GUI) or learn by watching other players play. The overall point is that you learn through observation and lots of trial and error (whether you are a human or a computer). And beginners in chess often make illegal moves even after learning the rules, it's fairly common.
It feels like you're trying to say that humans never make illegal moves while learning chess, which doesn't match with my experience. I'm trying to understand your overall point.
> The overall point is that you learn through observation and lots of trial and error
That’s the most inefficient way and people usually avoid doing that. Instead they find someone that knows how to do the thing and ask him to be a teacher. Or use a proxy like a book or videos.
> It feels like you're trying to say that humans never make illegal moves while learning chess, which doesn't match with my experience. I'm trying to understand your overall point
There’s learning the basic stuff (which is done after a few games) and there’s mastery. The thread started with the observation that even with all that knowledge (through content ingested in training), LLMs still makes illegal moves. Humans can be erratic, but they can constrain themselves to the rules for the task at hand after learning them.
The only way to master anything is lots of trial and error. Coaches and teachers can help guide you towards more focused trial and error paths but the student still has to do the lessons and put in the work of learning, and learning only truly happens through doing.
Humans are not perfect and make mistakes in learning even when they have memorized the rules. A simple example is new players will often move a piece, exposing their king to check, and a more experienced player must point out to them that they have made an illegal move (because a new player often has not encoded that pattern for looking for exposed checks because they're more focused on how the pieces move, not what that piece exposes.)
We're just going to have to agree to disagree here.
Do you have any solid evidence to back up anything you’re asserting here?
In my understanding of history (pre-enlightenment), people didn’t gather around ideas that were “correct more often” but gathered around ideas and personalities that reinforced their own ignorance, superstitions and biases and were openly hostile to “correct” ideas and truth, especially if those ideas upset tradition or dogma.
I also don’t understand how you’re getting to “violence was not a default until populations grew” since, perhaps, “default” violence was only properly recorded after populations grew and education/writing became more widespread. It’s very likely everyday violence in small groups was simply accepted as “the way things are”and simply ignored, as it often is even to this day.
Yes I do. In anthropology the surviving “undiscovered” indigenous tribes of the 20th century showed a diverse range of social configurations - matriarchies, patriarchies, everything!
Systemic violence came with the agricultural revolution, food and population stores, the ages of Bronze and Iron. Life was too precious in small bands for intense violence, even against rivals.
[Edit: 100 men armed with wooden clubs is an extremely bloody rugby match. Things generally did not start getting very-very bad until metal weapons were combined with boats or horses.]
This still feels more like an opinion than facts/proof. I was hoping for actual cited papers or studies, it feels like you just reiterated what you said above but adding "In anthropology..."
How does punishing someone make crashes better? I would rather have fewer crashes because of a system that can't be punished than more crashes with punishment.
Being able to punish someone doesn't change the fact that a crash happened and it doesn't change the fact that someone died. There is a difference between punishment and justice, there is rarely ever perfect justice in life.
The best we can do is mitigate harms as much as possible, and it seems self-driving might be the best mitigation of traffic accidents compared to human drivers.
Based on how humans grappled with these exact same questions (and still do) concerning other animals much closer to humans on the sentient gradient, it’s fair to assume we will have similar misunderstandings in regards to artificial intelligence (or artificial life, if you will) because of human hubris and ego blinding us to deeper truths.
It’s important to question our basic assumptions in the face of entirely new circumstances and new areas of exploration, such as the potential for emergent artificial consciousness.
You might have been called unhinged for caring about animal rights during the era of Descartes when public displays of animal vivisections were considered perfectly fine because animals have no “soul”, but today we would find such displays brutal and horrifying.
We don’t know what we don’t know, so it is important that someone is asking the hard or uncomfortable questions at the edge of our understanding to grope past our own biases even if it seems to be pointless to you right now.
We might just discover something wonderful, that our assumptions were wrong, paving the way to greater enlightenment.
Even with just teleoperation, the first robotics companies are poised to have some of the most efficient in-field technician operations, as long as there are at least two robots on site.
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