A Quote by Jeff Dean

There's a lot of work in machine learning systems that is not actually machine learning. — © Jeff Dean
There's a lot of work in machine learning systems that is not actually 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.
We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.
Nature is a self-made machine, more perfectly automated than any automated machine. To create something in the image of nature is to create a machine, and it was by learning the inner working of nature that man became a builder of machines.
I think you have to find how the machine can work for you. That's what I mean by "attaching yourself to the machine," 'cause the machine is going to be there, and you can rage against the machine, which is cool, but there's ways that you can benefit off the machine if you're savvy enough and you're sharp enough, smart enough. We all got to live and eat.
Deep learning is a subfield of machine learning, which is a vibrant research area in artificial intelligence, or AI.
We need to be vigilant about how we design and train these machine-learning systems, or we will see ingrained forms of bias built into the artificial intelligence of the future.
Sexism, racism, and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many 'intelligent' systems that shape how we are categorized and advertised to.
If all individuals were conditioned to machine efficiency in the performance of their duties there would have to be at least one person outside the machine to give the necessary orders; if the machine absorbed or eliminated all those outside the machine, the machine will slow down and stop forever.
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.
We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.
Who is all-powerful in the world? Who is most dreadful in the world? The machine. Who is most fair, most wealthy, and all-wise? The machine. What is the earth? A machine. What is the sky? A machine. What is man? A machine. A machine.
There's a lot of potential for machine learning all around the world. We're seeing it in academia, at other companies, in government.
The nice thing about the violin repertoire is that it's small enough that you can plan on learning everything at some point - whereas the piano repertoire is so enormous it wouldn't be possible unless you're a learning machine.
And initially, a lot of companies avoid trying to make a really radical new kind of title for a new system, because that would involve learning a new machine and learning how to make the new title at the same time.
My approach is to start from the straightforward principle that our body is a machine. A very complicated machine, but none the less a machine, and it can be subjected to maintenance and repair in the same way as a simple machine, like a car.
You can imagine all the things that people want in machine learning and artificial intelligence area that we're working on, that we'll continue to work on in the future.
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