Quotes of All Topics . Occasions . Authors
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.
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 that is one of the main goals of pushing forward in machine learning: having computers provide the wisdom that a human companion would be able to provide in offering advice, looking up more information when necessary and those kinds of things.
Vision I think is going to be an important input. Like, if you're using Google Glass, it's going to be able to look around and read all the text on signs and do background lookups on additional information and serve that. That will be pretty exciting.
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.
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.
I like working in small teams where people on the team have very different skills than what I have and that banter back and forth, and the ability to build something collectively that none of you could do individually is actually a really useful and valuable thing.
One thing I think is true is that is you have someone who's really good in one or a few areas they can pick up something new pretty quickly and that's kind of a hallmark of someone you really want to hire because they can be very useful in a whole bunch of different areas.
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.
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.