A Quote by Jeff Dean

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.
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.
A lot of the progress in machine learning - and this is an unpopular opinion in academia - is driven by an increase in both computing power and data. An analogy is to building a space rocket: You need a huge rocket engine, and you need a lot of fuel.
Ultimately, I hypothesize that technology will one day be able to recreate a realistic representation of us as a result of the plethora of content we're creating converging with other advances in machine learning, robotics and large-scale data mining.
There's a lot of work in machine learning systems that is not actually machine learning.
Machine learning is looking for patterns in data. If you start with racist data, you will end up with even more racist models. This is a real problem.
You need to use data science and machine learning to get the ground truth of what's happening inside of a company.
Scientific data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action. The step intermediate between the collection of data and the action is prediction.
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.
Definitely there's growing use of machine learning across Google products, both data-center-based services, but also much more of our stuff is running on device on the phone.
Everything is changing now that we are in the cloud in terms of sharing our data, understanding our data using new techniques like machine learning.
The USA Freedom Act does not propose that we abandon any and all efforts to analyze telephone data, what we're talking about here is a program that currently contemplates the collection of all data just as a routine matter and the aggregation of all that data in one database. That causes concerns for a lot of people... There's a lot of potential for abuse.
Our intelligence communities spend a lot of time and effort gathering a lot of strands and a lot of data [on Russian hacking]. There are times where they're very cautious and they say, "We think this is what happened, but we're not certain."
Apple knows a lot of data. Facebook knows a lot of data. Amazon knows a lot of data. Microsoft used to, and still does with some people, but in the newer world, Microsoft knows less and less about me. Xbox still knows a lot about people who play games. But those are the big five, I guess.
And you know, I've had great fun turning quite a lot of different industries on their head and making sure those industries will never be the same again, because Virgin went in and took them on.
Machine learning and artificial intelligence applications are proving to be especially useful in the ocean, where there is both so much data - big surfaces, deep depths - and not enough data - it is too expensive and not necessarily useful to collect samples of any kind from all over.
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