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Danh ngôn của Jeff Dean (Sứ mệnh: 6)

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.
I think true artificial general intelligence would be a system that is able to perform human-level reasoning, understanding, and accomplishing of complicated tasks.
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.
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.
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.
Reinforcement learning is the idea of being able to assign credit or blame to all the actions you took along the way while you were getting that reward signal.
Traditionally computers have not been that good at interacting with people in ways that people feel natural interacting with.
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.
It would be great to have every engineer have at least some amount of knowledge of machine learning.
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 pretty clear that machine learning is going to a big part of science and engineering.
AI can help solve some of the most difficult social and environmental challenges in areas like healthcare, disaster prediction, environmental conservation, agriculture, or cultural preservation.
Computers can see, and understand what people say via speech recognition.