Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen.
One of the reasons people keep making the same "mistake" is because moores law isn't actually a law, and just an observation that was true from the 1960s to 2010s, so rightly assumed moores law wouldn't go on forever so relying on ever increasing availability of compute wasn't the best strategy.
Moores law has been dead for years now and the growth in compute is gradually slowing further. Yes, compute will continue to increase, but the pace is slowing and there is potential for it to stagnate.
On the other hand, Moore's law only applies to silicon. We already know a number of other viable substrates, and some are already being put into production as we speak. A substrate with different physical properties has the potential of making silicon look like vacuum tubes going forward. On top of that, architecture is evolving as well. Huawei's tau law being a great example. Turns out the scales we're dealing with are so small that the time it takes a signal to travel from one end of the chip to another becomes significant. So, if you fold the chip you can dramatically improve its speed by connecting the opposite ends together. And these things are obviously mutually reinforcing. So, there's little reason to think that the current plateau we're hitting is permanent.
No, I agree, there is huge potential to get back on the curve or even blow right through it, but currently and over roughly the last decade, moores law has been dead. At some point in future compute will grow massively. Humans have only been around for 10,000 years, and silicon tech isn't even a blim in that history. Give it another 10, 100, 1000 years, we will be able to brute force anything we currently think is out of scope. But in the short term, the here and now, it's worth exploring more elegant solutions that don't require compute beyond our current availability
Yeah, I'm not arguing against efficiency on the software side. The way I read the article isn't really that it advocates for brute force, but rather that we should be focusing on general solutions because they scale. The work on efficiency should focus on how to make search and statistical inference more efficient. Incidentally, we see a lot of that work happening in China right now precisely because they don't have as much compute to work with.
When a simpler, search-based approach with special hardware and software proved vastly more effective, these human-knowledge-based chess researchers were not good losers. They said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess. These researchers wanted methods based on human input to win and were disappointed when they did not.
careful; lemmings HATE ai and and pro-ai stance is going to get you blacklisted. lol
Don't over-focus on chess. The Lesson is that if you take two identical machine-learning labs which currently have a working model today, and ask them to come back and show you how much they improved in a year or so, the lab which forward-ports the model to newer hardware will tend to outperform the lab which tries to tweak the model to fit the problem domain more precisely. Sheer amounts of compute dominate any sort of domain-specific (here, chess-specific) knowledge. Further, there's a suggested mechanism, Moore's Law, which is tapering off now but used to be exponential; how could a polynomial amount of effort ever compete with exponentially-better new hardware?
lmao