It was a slippery slope, with those Neural Networks. They were able to do more and more things, previously unimagined to be possible for them. It was a big surprise for everyone, how good they were at chess, 3600 or so Elo points. Leela Chess Zero invented some theoretical breakthroughs, soon to be exploited by more algorithmic, non-NN chess engines like Stockfish, for its position evaluation function. Even back then, I was baffled by people expecting that this propagation will soon stop, due to some unexpected effect, which never came. Not in chess, nor anywhere else.
It was indeed a matter of “when”, not of “maybe not” anymore. Yes, those first mighty AI’s were quite fake, they have no real clue. Except that this mattered less and less and it was less and less true. In an increasing number of fields.
It was only a matter of time when the first translators from the gibberish weight tables learned by NN’s, to exact algorithms will emerge. Something which people have previously done, stealing ideas from Leela, implementing them with rigor into algorithmic schemes of Stockfish—AI learned as well. Only better, of course.
By then, the slope was very slippery, indeed. I still can’t comprehend, how this wasn’t clear to everyone, even back then, less than 10 years ago.
It was a slippery slope, with those Neural Networks. They were able to do more and more things, previously unimagined to be possible for them. It was a big surprise for everyone, how good they were at chess, 3600 or so Elo points. Leela Chess Zero invented some theoretical breakthroughs, soon to be exploited by more algorithmic, non-NN chess engines like Stockfish, for its position evaluation function. Even back then, I was baffled by people expecting that this propagation will soon stop, due to some unexpected effect, which never came. Not in chess, nor anywhere else.
It was indeed a matter of “when”, not of “maybe not” anymore. Yes, those first mighty AI’s were quite fake, they have no real clue. Except that this mattered less and less and it was less and less true. In an increasing number of fields.
It was only a matter of time when the first translators from the gibberish weight tables learned by NN’s, to exact algorithms will emerge. Something which people have previously done, stealing ideas from Leela, implementing them with rigor into algorithmic schemes of Stockfish—AI learned as well. Only better, of course.
By then, the slope was very slippery, indeed. I still can’t comprehend, how this wasn’t clear to everyone, even back then, less than 10 years ago.