Quotes About Machine learning

Whatever you are studying right now, if you are not getting up to speed on deep learning, neural networks, etc., you lose. We are going through the process where software will automate software, automation will automate automation.

With the Google Brain project, we made the decision to build deep learning processes on top of Google's existing infrastructure.

India has a large base of tech talent, and I hope that a lot of AI machine learning education online will allow Indian software professionals to break into AI.

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.

Now, given the increasing importance of artificial intelligence, automation, machine learning, and other innovative technologies, we are evolving Accenture Digital to be even more relevant to our clients and drive even greater differentiation in the marketplace.

The brain sure as hell doesn't work by somebody programming in rules.

Computers have proved to be formidable chess players. In fact, they've beaten our top human chess champions.

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.

Machine learning is the most popular course for people from India. There is a window of time when India can embrace and capture a large fraction of the AI opportunity. But it will not remain open for ever.

One thing ImageNet changed in the field of AI is suddenly people realized the thankless work of making a dataset was at the core of AI research.

Artificial Intelligence (AI) : is the study of how to make computers do things which, at the moment, people do better.

If we're going to achieve compassion in the machines and also feel safe with the machines, to raise machines with human-like values, we need to make them human-like by simulating, or perhaps eventually imitating, human beings in high accuracy from top to bottom.

Artificial intelligence, machine learning... cell therapy, immunotherapy. There's just a constant stream of investment ideas we could pursue better in that fashion.

The unknown future rolls toward us. I face it, for the first time, with a sense of hope. Because if a machine, a Terminator, can learn the value of human life, maybe we can too.

We're training Google Street View to recognize street numbers.

If you want to solve very complex problems, you will have to end up letting machines work out a lot of the details for themselves, and in ways that we don't understand what they are doing.

As a Millennial and Gen Z expert at Accenture I had the opportunity to host and lead several workshops and panels with key clients at industry events. The topics ranged from virtual reality to blockchain; artificial intelligence to machine learning.

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.

AI does not keep me up at night. Almost no one is working on conscious machines. Deep learning algorithms, or Google search, or Facebook personalization, or Siri or self driving cars or Watson, those have the same relationship to conscious machines as a toaster does to a chess-playing computer.

C'est pour ça que je me bats. Je me bats pour ceux que j'aime, un sentiment auquel vous ne comprendrez jamais rien parce qu'une machine peut apprendre a penser, mais pas à aimer.

We think machine learning, as it relates to healthcare and life sciences, is extraordinary.

Computer vision and machine learning have really started to take off, but for most people, the whole idea of what is a computer seeing when it's looking at an image is relatively obscure.

Things that are so hard for people, like playing championship-level Go and poker, have turned out to be relatively easy for the machines. Yet at the same time, the things that are easiest for a person - like making sense of what they see in front of them, speaking in their mother tongue - the machines really struggle with.

Artificial intelligence is just a new tool, one that can be used for good and for bad purposes and one that comes with new dangers and downsides as well. We know already that although machine learning has huge potential, data sets with ingrained biases will produce biased results - garbage in, garbage out.