[ArtTimeInvestor]

That's what I meant.

That's how it usually goes with technological progress.

In any field.

Progress is minimal for a few years and then suddenly jumps up very suddenly.

So to predict what's coming, you can't just extrapolate the progress of recent years. You have to account for it being exponential with a very uneven distribution of sudden jumps.

[zehaeva]

I'm not sure one can, from today that is, really understand how huge of a leap was made by AI at this time.

Even going back to the closest analogue, chess, there were good chess engines for a long time prior to Kasparov loosing in 97 to deep blue. Even before Kasparov lost Chess engines were pretty good, just look at the game in 96 when Kasparov won. A grand master would still need to put some thought into how he played.

In Go however even the best engines couldn't hold a candle to a professional player, let alone someone who was the equivalent of a chess grand master. Hell, even as a lowly amateur player I was able to trounce some of the most powerful AIs at the time. Looking at some of the Pro vs AI games back in the early 2010s it's almost painful how bad they were.

It's hard to communicate just how huge of a leap this was, and just how shocking to the whole Go community. It would be like a child one day being unable to speak and the literal next day reciting Shakespeare.

[xcv123]

AlphaGo took many AI researchers by surprise. An even bigger surprise came next year, with AlphaZero:

"AlphaZero was a reinforcement learning system that was able to master three different perfect information games - chess, shogi (Japanese chess), and Go - at superhuman levels by just learning from self-play, without using any human expert games or domain knowledge crafted by programmers.

Its predecessor AlphaGo, which defeated the world champion Go player in 2016, was revolutionary but relied on human expert games and domain-specific rules coded by the DeepMind researchers.

AlphaZero started from random play and used a general-purpose reinforcement learning algorithm to iteratively improve its gameplay through self-play, ending up with superior performance compared to the best human players and previous game-specific AI systems.

Many experts were stunned that a general algorithm could rediscover from scratch the millennia-old principles and strategies for these highly complex games, often discovering novel and counterintuitive moves along the way."

[pvg]

In any field.

That would be pretty strange. For a trivial counterexample, you can look up the history of integrated circuits from invention to today.