One of the things that inspires me about working for Google is that when we solve a problem here, we can get that used by one million or even a billion people.
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.
There's nothing like necessity of needing to do something to cause you to come up with abstractions that help you break through the forms.
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.
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.
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.
Health care has a lot of interesting machine-learning problems - outpatient outcomes, or when you have x-ray images and you want to predict things.
AI is going to make software development much more productive, and India is especially well positioned in this space.
Thanks For Reading!
Elon Musk Launches New Starlink Device For Satellite Internet On The Go