I really feel like yall spend too much time focusing on the how, and not the results. Was the analysis of chess moves useful? Great then it’s a good tool. Even better you found the limitation of the tool. That would make you a better user of the tool.

I approached the topic asking “ can the tool do real world work?” Or “can I use the tool to benefit myself?” Or even “ am I enjoying learning how to use the tool”

A piece of software that cost $20 a month and can help me create a game I can play and share with my friends in under 10 days. Pretty cool if you ask me. I learning how to make a video game felt good, the result is real, I’m going to make another one.

And to be fair they know exactly how the game works… they trained my ais and programmed my game. I don’t know how my game works.

There's some semantics here.

Large neural networks have been shown to build parallel models of the world that they learn through their training data. A chess model trained only on chess move notation does have an interior board and state that it tracks, despite never being told about a board, the number of pieces, how they move, etc.

LLMs do something similar with language, mapping tokens to ideas in ways that can produce meaningful and grammatically correct output without needing to be formally taught any of this.

Our brains also do things like this - we observe X and then try to intuit Y, the truth of the world and reason for X.

Where this breaks down is humans can then take our guess for Y and propose Z, and try to find it out in the world. If we can't, then Y is probably wrong, and our new understanding of the world is Y* that is consistent both with X and the absence of Z.

I cannot stress enough how far we are from machines that can do this. They can learn to approximate Y through training, but they have no means to extend that to propose an unobserved Z, no means to update their internal state given the results of that observation (without fully retraining the model on new data - which makes it a new model), and no way to question X given Z - they can talk at great length about data biases but can literally never excise bad data from their training weights. There have been experiments with training models on time-boxed datasets (e.g. stuff up to AD 1880) and asking it about modern concepts - predictably hilarious results.

All the current 'AI' stuff is smoke and mirrors around this very hard problem. The machines can talk, but they can't think (Z) and can't learn (Y*).

If you feed it every chess game, it can tell you which spaces the knight is likely to move to, and which it very unlikely to move to, but it does not understand how the knight moves.

Well, it knows how the knight moves, but it might forget that's it's an important rule after it starts to lose context. That's also how LLMs start to lose track of what's actually on the board.

There are definitely differences between LLMs and Neural Networks, but at the end of the day, they are both just gigabytes of weights. And really, both types are similar to what the human brain possesses. People tend to forget that the human brain isn't just some monolithic thing. There's a lot of difference specialized pieces. If you ask Magnus Carlsen how he calculated the best move on the chess board in a blitz game, he literally cannot tell you! It just appears, because that part of the brain is trained and hardwired to calculate good moves, based on the current board state.

A LLM uses logic and reasoning to arrive at a solution. That's not really compatible with intense pre-trained calculation. You can't talk yourself into a chess move at the speed the game requires. You can quickly think about possibilities, but you have to already have pre-trained calculations to get at least 90-95% of the way there, to pick from a few different lines.

On the other hand, Neural Networks beat the shit out of the greatest grandmasters in chess. Hands down. On average, they are at least +500 ELO to the best GMs. And everybody in the chess world knows this, which is why everybody uses these tools outside of 1v1 human to human games. (Obviously, cheating is taken very seriously, and has made plenty of headlines over the years.) Every GM analyzes their own games using those engines, to pick apart mistakes.