| The articles wasn't saying that AlphaGo Zero was a marginal improvement. On the contrary, I think it was saying that the type of improvement AlphaGo offered is an outlier because it approached the task in a fundamentally different way (especially with AlphaGo Zero where it didn't use training data). I think the primary argument is that much of the industry has generally not been doing that sort of research for ground-breaking moon-shots. Instead most AI researchers focus on optimizing for metrics which lead to small and safer short-term improvements in a particular niche application as opposed to pursuing large and riskier long-term ones. I've seen this same line of criticism before of the way in which AI systems have been generally designed for decades and to me it rings true. Things like the Turing award arguably lead researchers astray. Essentially, the crux of the argument is that most people focus on climbing trees to achieve success when the real goal is reaching the moon or they build better springs when the real goal is achieving flight. [1] Both show some short-term improvements on certain heuristics, but they are obviously the wrong approach if you want serious breakthroughs. I think it's a valid criticism that most of us are still focusing too much on the wrong metrics to make many serious advances. If you look at AI, there haven't really been that many major breakthroughs. We just haven't been all that creative and most advances are ultimately tweaks on existing technology or involve throwing more data through deeper neural nets. Back-propagation (popularized by Geoffrey Hinton) for deep-learning with neural nets was a big deal and an important idea. Since then, there hasn't been much that's earth-shattering. Generative adversarial networks are arguably a big idea. LSTM as well. The work by Naftali Tishby's group on understanding what's going on with information in neural nets is a significant development. Hinton's capsule networks also seem like they may be a big idea. A few people have recently started publishing some work on building AI that builds better AI. However for the most part, it seems like the vast, vast majority people in AI aren't aiming to do any fundamentally ground-breaking things. They mostly look around at the existing body of research and slightly tweak the tools that seem most suitable for the problem domain they are working on. (This isn't entirely surprising for a number of reasons involving the incentives in the industry, but it is something worth discussing.) Personally, I don't think that it's necessarily a bad thing that we've been sluggish with AI advances. You don't necessarily want to hand power tools to children. I think our society is unfortunately full of unwise and unkind people with already more power than they should have. Our social institutions for distributing power wisely and responding to abuse of power are far less mature than our technology and I suspect the risks of abusing advanced technology dwarf the enormous benefits they can bring. [1] https://www.eecs.harvard.edu/shieber/Biblio/Papers/loebner-r... |
Basically, we incentivize researchers to work on the most near-term solvable problems, rather than the most difficult problems where we understand how to check for a solution -- let alone to work on developing the solution-properties we can check, to get past wholesale conceptual confusions.