Quotes About Machine learning

Artificial intelligence is one of 50 things that Watson does. There is also machine learning, text-to-speech, speech-to-text, and different analytical engines - they're like little Lego bricks. You can put intelligence in any product or any process you have.

This process—called "machine learning"—was the core method by which AIs taught themselves.

Cyber hygiene, patching vulnerabilities, security by design, threat hunting and machine learning based artificial intelligence are mandatory prerequisites for cyber defense against the next generation threat landscape.

Signature-based malware detection is dead. Machine learning based Artificial Intelligence is the most potent defense the next gen adversary and the mutating hash.

I propose to consider the question, "Can machines think?"

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.

Robots have already surpassed human beings in calculation and memory, but I have no doubt that the time will come when they will surpass in wisdom as well.

Turing's report left these questions hanging, but he did suggest a forerunner of what would eventually become the Turing Test: if you played chess against a learning machine, would you know if it was a human being or a computer?

To pass the Turing Test the computer would have to do more than imitate. It would have to be a learning entity.

Can a machine, educated through a system of reward and punishment, be said to be able to think? Are children, when they cry or laugh, revealing some spark of soul that distinguishes them from machines, or simply following "rules of behavior" with which we as spectators empathize because we are familiar with them? Or to put it another way, does asking whether computers think require us to ask, as well, whether humans compute?

Il y a l'intelligence artificielle, il y a aussi la connerie naturelle.

Some claim that computers will, by 2050, achieve human capabilities. Of course, in some respects they already have.

AI in the form of machine learning, and especially deep learning, is letting us benefit from data we used to exclude as too vast, messy, and trivial.

Our newly capacious machines can get closer to understanding it than we can, and they, as machines, don't really understand anything at all.

We need to build EQ in our AI systems because, otherwise, they're not going to be as effective as they were designed to be.

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.

In fact, AI might make centralized systems far more efficient than diffused systems, because machine learning works better the more information it can analyze. If you disregard all privacy concerns and concentrate all the information relating to a billion people in one database, you can train much better algorithms than if you respect individual privacy and have in your database only partial information on a million people.

In a recent experiment a computer algorithm correctly diagnosed 90 per cent of lung cancer cases presented to it, while human doctors had a success rate of only 50 per cent.

The algorithms are watching you right now. They are watching where you go, what you buy, whom you meet. Soon they will monitor all your steps, all your breaths, all your heartbeats. They are relying on Big Data and machine learning to get to know you better and better. And once these algorithms know you better than you know yourself, they can control and manipulate you, and you won't be able to do much about it.

A learning machine is any device whose actions are influenced by past experience.

We do know that we can set certain algorithms for machines to do certain things - now that may be a simple task. A factory robot that moves one object from here to there. That's a very simple top-down solution. But when we start creating machines that learn for themselves, that is a whole new area that we've never been in before.

Computers add convenience to our everyday lives, but we are limited in what we can do with technology others have imagined. The ability for humans to teach machines entirely new things - coding - is nothing short of a superpower.

When you apply computer science and machine learning to areas that haven't had any innovation in 50 years, you can make rapid advances that seem really incredible.

Machine learning is the science of getting computers to learn without being explicitly programmed.