Personally, I’m much more interested in a universal ai learning Go than Chess…

From what Shane said (since he did not name a single ML approach), I assume that his forecast is (mostly) based on recent progress within the group of his collaborators. I look forward to seeing those results, when they get published.

In the future, we will see progress in: sequence prediction, integration with reinforcement learning, scaling to scenes with multiple objects. And new problems will be discovered on the way.

1) The simplest and least interesting is computer performance. Seeing the same again over the next 8 years probably isn’t necessary, but it will clearly make it more likely that an impressive proto-AGI will be developed.

2) Machine learning methods that I expect to be relevant to making a proto-AGI have made a lot of progress in the last 8 years, especially the last 3 years.

3) Neuroscience is making a lot of progress. Some important parts of the brain’s design are now fairly well understood, and I think we’re making reasonable progress on a number of other important elements of the brain’s design. This is providing us with many AGI design hints.

Could you elaborate on which developments in AI research make you think that a proto-AGI may appear so soon? Is it some new ideas (refs to published work or just names of researches would be helpful)? Or is it some old ideas + progress in CPU speed? Basically, what is the critical difference between today and 8 years ago?

Thanks!

What I think we will end up with is an integrated system that consists of multiple components that work together in a complementary way. I think this might be best explained by analogy. Image that you didn’t really know how to make a car. One approach might be to think as follows: Ok, so we need to be able to drive in straight lines so we need a driving in a straight line module, plus we need to be able to go around corners so we need a module that handles that case too, and then there are hills so we will need a subsystem designed for going up and down hills… and so on. This approach isn’t going to work for car building, and it won’t work for AGI either.

An alternative is how cars actually work: you need pistons, and a carburettor, and a fuel tank, and seats, and wheels, and gears and a clutch… and so on. It’s still a complex system with many parts that have functionally specific roles, but these roles are typically complementary and integral to the overall system. I think that this is how the brain is designed, for the most part, rather than having specific modules for, say, language.

One reason is that it’s kind of a lose-lose proposition. If my predictions are wrong then I have a cost now from people who think I’m crazy, plus more of the same in the future when I’m wrong. If my predictions are right, then I still have the same cost now, and in the future I doubt anybody will care much to give me much credit. Most likely the ardent skeptics will have somewhat forgotten just how skeptical they were. The main upside, I think, is that it might encourage people like yourself working on safety to consider whether they should devote more time to relatively near term practical approaches to AGI safety. The impression I get is that in the last 5 years this has happened to some extent. Hopefully my predictions have helped this along at least a tiny bit.

There is also a second reason why I’m most likely going to stop: I think the time has come now not for predicting and debating, but for doing. Predictions and arguments are useful food for thought, but in the end only two things actually matter: formal mathematical proofs (not merely the mathematically phrased arguments I keep on seeing), and thorougher empirical demonstrations. Once the assumptions in the proof or the range of testing conditions are properly understood, there isn’t much left to argue about.