What will the state of AI be in 10 years?

I can offer a perspective as someone whose graduate studies in machine learning were during the "AI Winter", the time period after the first hype wave of NN and before the second wave we're currently in today.

The math progress has not been substantial, frankly. The very same ideas were there before. Sure, there are some new ideas, but they too are the result of the real fundamental change, which was the scale-up in compute and in memory.

So where I see a good analogy is were computing was in the age of the mainframe and these huge computers that Bill Gates and his chums were fiddling with in assembly, compared with personal computing.

ML is going to be everywhere, the way databases are everywhere. To an extent, it'll be an extension of SQL, in the sense that if SQL is about "counting stuff on data" then ML is a more sophisticated take on that idea. Where you'll have data, you'll have an engine that extracts stuff from it the ML way. I am guessing transfer learning will play a substantial part in this, but I'm less certain on that statement.

Just like with personal computers, our expectations of what you can "just do" will adjust. But all of that will not stem from progress in ML, just that current technology becomes more widespread. As far as the cutting edge of ML, judging by the sort of papers I come across, I think we're currently at the stage of doing more-of-the-same only bigger.

Again, I find history a really useful teacher in that sense. Take a trip to a science museum to see that people have always first taken any technology they've had to its utmost boundary, alongside the diminishing gains, before something new came along and the curve restarts. We're already in diminishing-gains territory, but it's hard to say how long that kind of momentum lasts.

Whoever leads in artificial intelligence in 2030 will rule the world until 2100

https://www.brookings.edu/blog/future-development/2020/01/17...

I personally think that there is still enormous room for improvement in applying even modern cutting edge techniques to real world usecases and industry.

Also, I think interpretability and the introduction of better understood priors will lead to extremely large, sparse networks being used across multiple modalities in applications and software.

I think we’ll have started integrating the three laws of robotics into all ai systems by that point. I know the Asimov books show lots of ways that can go wrong but it still makes me feel safer.

The only roadblock is I can’t think of any way to build in the three laws in such a way that they can’t easily be removed.

- AutoML will perform most of the model development [ This will occur within the next 5 years or even less].

- Custom chips will perform the inference and probably the training. For example, Tesla managed to create a chip, which is 10x cheaper than a GPU for training.

- There will be some breakthrough that will combine logic and stat. This would eventually lead to AGI.