The idea behind reinforcement learning is you don't necessarily know the actions you might take, so you explore the sequence of actions you should take by taking one that you think is a good idea and then observing how the world reacts. Like in a board game where you can react to how your opponent plays.
Health care - the ability of neural networks to ingest lots of data and make predictions is very well suited to this area, and potentially will have a huge societal impact.
As a society I think we are going to be much better off by having machines that can work in conjunction with humans to do things more efficiently and even better in some cases. That will 'enable humans to do things that they do better than machines.
I think one of the things about reinforcement learning is that it tends to require exploration. So using it in the context of physical systems is somewhat hard.
Some people are happy to work in a particular domain or some field of computer science for years, and years. I personally like to kind of move around every few years, just to learn about new areas.
Previously, we might use machine learning in a few sub-components of a system. Now we actually use machine learning to replace entire sets of systems, rather than trying to make a better machine learning model for each of the pieces.
It's nice to have short-term to medium-term things that we can apply and see real change in our products, but also have longer-term, five to 10 year goals that we're working toward.
Supervised learning works so well when you have the right data set, but ultimately unsupervised learning is going to be a really important component in building really intelligent systems - if you look at how humans learn, it's almost entirely unsupervised.
Computers don't usually have a sense of if you have a picture of something what is in that image. And if we can do a good job of understanding what is in an image, that can bring along a lot of new things you can do in applications.
As devices continue to shrink and voice recognition and other kinds of alternative user-interfaces become more practical, it is going to change how we interact with computing devices. They may fade into the background and just be around, allowing us to talk to them just as we would some other trusted companion.
I think there are sometimes issues with - no matter where you put a conference, there's always going to be constraints on that. For example, sometimes students studying in the U.S. have trouble leaving the U.S. to go to a conference. So if you hold it outside the U.S. in a particular place, that sometimes creates complications.
I do kind of think there's a bit of an overemphasis on - in the community - on sort of achieving ever-so-slightly better state-of-the-art results on particular problems, and a little underappreciation of completely different approaches to problems that maybe don't get state of the art because it's actually super hard and a pretty explored area.
I think robotics is a really hard problem - to make robots that operate in sort of arbitrary environments, like a big conference room with chairs and stuff.
Very simple techniques, when you have a lot of data, work incredibly well.
I think there are a lot of industries that are collecting a lot of data and have not yet considered the implications of machine learning but will ultimately use it.
If you only have 10 examples of something, it's going to be hard to make deep learning work. If you have 100,000 things you care about, records or whatever, that's the kind of scale where you should really start thinking about these kinds of techniques.