Sebastian Thrun Quotes

So the thing I realized rather gradually - I must say starting about 20 years ago now that we know about computers and things - there's a possibility of a more general basis for rules to describe nature.

The deep learning techniques, while relatively easy to learn, are quite foreign to traditional engineering modalities. It takes a different mindset and a relaxation of the presumption of control. The practitioners are like magi, sequestered from the rest of a typical engineering process.

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.

When you have a large amount of data that is labeled so a computer knows what it means, and you have a large amount of computing power, and you're trying to find patterns in that data, we've found that deep learning is unbeatable.

Take any old classification problem where you have a lot of data, and it's going to be solved by deep learning. There's going to be thousands of applications of deep learning.

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

I'm fascinated by artificial intelligence.

Interactive computers and software will, I think, provide a less costly method of doing some kinds of inquiry, in knowledge acquisition and even reasoning and interaction.

Graphics has lately made a great shift towards machine learning, which itself is about understanding data.

Not only have computers changed the way we think, they've also discovered what makes humans think - or think we're thinking. At least enough to predict and even influence it.

We have entered an era where the kinds of data and analyses that people run have gotten incredibly complicated. So you have researchers using sophisticated software programs that they probably don't understand but are generally accepted and everyone [in the field] uses.

There will always be plenty of things to compute in the detailed affairs of millions of people doing complicated things.

Artificial intelligence is always 20 years away.

Today, there are industries employing hundreds of thousands of engineers creating artificial intelligence.

If you have a lot of data and you want to create value from that data, one of the things you might consider is building up an AI team.

It seemed really amazing that you could write a few lines of code and have it learn to do interesting things.

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.

Machine learning allows us to build software solutions that exceed human understanding and shows us how AI can innervate every industry.

Deep learning allows you to create predictive models at a level of quality and sophistication that was previously out of reach. And so deep learning also enhances the product function of data science because it can generate new product opportunities.

Once computers can program, they basically take over technological progress because already, today, the majority of technological progress is run by software, by programming.

I think of 'data science' as a flag that was planted at the intersection of several different disciplines that have not always existed in the same place. Statistics, computer science, domain expertise, and what I usually call 'hacking,' though I don't mean the 'evil' kind of hacking.

You need to use data science and machine learning to get the ground truth of what's happening inside of a company.

I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.

Every part of your business will change based on what I consider predictive analytics of the future.

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.