Here we have the best Jeff Dean Quotes. Find the perfect quotation from our collection.
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.
In order to reason, you need a network to be able to bring in knowledge from several different areas, such as math, science, and philosophy, to reach reasonable conclusions on what it’s been tasked with.
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.
Microsoft is in a lot of the same businesses that Google is in.
Computers can see, and understand what people say via speech recognition.
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.
A lot of human learning comes from unsupervised learning where you’re just sort of observing the world around you and understanding how things behave.
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.
There’s a lot of potential for machine learning all around the world. We’re seeing it in academia, at other companies, in government.
You need to find someone that you’re gonna pair-program with who‘s compatible with your way of thinking, so that the two of you together are a complementary force.
Very simple techniques, when you have a lot of data, work incredibly well.
In a lot of these areas, from machine translation to search quality, you’re always trying to balance what you can do computationally with each query.
We want to build systems that can generalize to a new task. Being able to do things with much less data and with much less computation is going to be interesting and important.
There’s a lot of work in machine learning systems that is not actually machine learning.
The healthcare space is a very complicated one for a variety of reasons: It’s much more regulated than some other kinds of industries, for good reason.
Deep neural networks are responsible for some of the greatest advances in modern computer science.