Quotes About Machine learning

When we automated away the elevator operator function, who knew that all the descendants of those operators would become social media marketers, machine learning engineers, and all these other jobs that we didn't even have a language to describe back then.

One of the things we did at PayPal was collaborative filtering and machine learning: looking at patterns of human behavior. We used it there to predict when people would try to cheat the system to get money. But you can predict pretty much any behavior with a certain amount of accuracy.

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.

Climate change makes machine learning that much more valuable, too: So much of the data available to scientists is not necessarily accurate anymore, as animals move their habitats, temperatures rise and currents shift. As species move, managing populations becomes even more critical.

There is a real danger that computers will develop intelligence and take over. We urgently need to develop direct connections to the brain so that computers can add to human intelligence rather than be in opposition.

Perhaps someone, somewhere already has a primitively aware machine, with its brain equipped with the essential learning software.

As inspired by Alan Turing, intelligence equals behavior.

Google or other search engines are examples of AI, and relatively simple AI, but they're still AI. That plus an awful lot of hardware to make it work fast enough.

There's a lot of potential for machine learning all around the world. We're seeing it in academia, at other companies, in government.

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.

Deep learning is a subfield of machine learning, which is a vibrant research area in artificial intelligence, or AI.

Computers are like humans - they do everything except think.

Computers aren't intelligent, they only think they are.

There's a new set of transformative technologies such as machine learning, AI, and virtual reality that will spawn another set of big tech franchises. But in terms of cultural impact, perhaps we are at peak Valley.

Conifer. It's a dynamically typed Lisp without parenthesis tuned for machine learning.

There's a lot that machine learning can't do that humans can do very, very well.

The Truly SAGE System, or Toward a Man-Machine System for Thinking

Computers lack common sense. We can train machine learning algorithms to spot patterns and detect signals, but to date we haven't been able to give them the ability to reason from context.

Machine learning tends to be more focused on developing efficient algorithms that scale to large data in order to optimize the predictive model. Statistics generally pays more attention to the probabilistic theory and underlying structure of the model.