The Bitter Lesson (2019)

So I know Moore's law is "dead" (dead as in Cobol or dead as in Elvis?) and progress is definitely slower than it has been historically however we have only began to really start leveraging parallelization at scale from a software perspective, so I think we have some runway in that direction, and of course the looming elephant on the horizon, Quantum computing.

Sure it is in it's infancy but assuming that the research continues to prove that quantum computing is viable I expect it to be an even bigger deal than the move from vacuum tubes to transistors. At that point we'll be dealing with an entirely different world in computing.

>> In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search.

"Massive, deep search" that started from a book of opening moves and the combined expert knowledge of several chess Grandmasters. And that was an instance of the minimax algorithm with alpha-beta cutoff, i.e. a search algorithm specifically designed for two-player, deterministic games like chess. And with a hand-crafted evaluation function, whose parameters were filled-in by self-play. But still, an evaluation function; because the minimax algorithm requires one and blind search alone did not, could not, come up with minimax, or with the concept of an evaluation function in a million years. Essentially, human expertise about what matters in the game was baked-in to Deep Blue's design from the very beginning and permeated every aspect of its design.

Of course, ultimately, search was what allowed Deep Blue to beat Kasparov (3½–2½; Kasparov won two games and drew another). That, in the sense that the alpha-beta minimax algorithm itself is a search algorithm and it goes without saying that a longer, deeper, better search will inevitably eventually outperform whatever a human player is doing, which clearly is not search.

But, rather than an irrelevant "bitter" lesson about how big machines can perfom more computations than a human, a really useful lesson -and one that we haven't yet learned, as a field- is why humans can do so well without search. It is clear to anyone who has played any board game that humans can't search ahead more than a scant few ply, even for the simplest games. And yet, it took 30 years (counting from the Dartmouth workshop) for a computer chess player to beat an expert human player. And almost 60 to beat one in Go.

No, no. The biggest question in the field is not one that is answered by "a deeper search". The biggest question is "how can we do that without a search"?

Also see Rodney Brook's "better lesson" [2] addressing the other successes of big search discussed in the article.

_____________

[1] https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)#Des...

[2] https://rodneybrooks.com/a-better-lesson/

building a model for and with domain knowledge == premature optimization? In the end a win on kaggle or a published paper seems to depend on tweaking hyperparameters based on even more pointed DK: data set knowledge...

I wonder what would be required to build a model that explores the search space of compilable programs in say python that sorts in correct order. Applying this idea of using ML techniques to finding better "thinking" blocks for silicon seems promising.

When it comes to games, exploitation (of tendencies, weaknesses), misdirection, subterfuge and yomi play a far bigger role in winning than actual skill. Humans are much better than computers at all of those. Perhaps a dubious honour, but an advantage nonetheless. We're only really in trouble when the machine learns to reliably replicate the same tactics.