Andrew Ng Quotes

Men and machines are good at different things. People form plans and make decisions in complicated situations. We are less good at making sense of enormous amounts of data. Computers are exactly the opposite: they excel at efficient data processing but struggle to make basic judgments that would be simple for any human.

Note that I am not proposing that AI research be ignored or less funded.

The thing that's going to make artificial intelligence so powerful is its ability to learn, and the way AI learns is to look at human culture.

Watson augments human decision-making because it isn't governed by human boundaries. It draws together all this information and forms hypotheses, millions of them, and then tests them with all the data it can find. It learns over time what data is reliable, and that's part of its learning process.

Even companies like Baidu and Google, which have amazing AI teams, cannot do all the work needed to get us to an AI-powered society. I thought the best way to get us there would be creating courses to welcome more people to deep learning.

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

Formula creation is magnitudes harder for computer algorithms than actually executing within a formula. But once the genre is created and the formula is known, then the computer can do the repetitive task of executing within the genre.

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.

Facebook is at the forefront. It's the company that can fundamentally change the way information is being exchanged and processed. It can be the basis for artificial intelligence to develop over time.

Knowledge created a new culture of business derived from the information gathering and analysis capabilities of first the mainframe and then the PC.

The purpose of computing is insight, not 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.

When people speak of creating superhumanly intelligent beings, they are usually imagining an AI project.

When human judgment and big data intersect there are some funny things that happen.

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

Systems are going to get a lot more sophisticated.

As a society, we are underinvesting resources in research on the societal implications of AI technologies.

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

This is a magical time for computer programming. We are moving towards the artifical intelligence holy grail of programming the computer to simulate the human brain.

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

Artifical Intellegence is the most important of the coming technologies. Whoever can crack that will have a company worth 10 Microsofts.

Deep-learning will transform every single industry. Healthcare and transportation will be transformed by deep-learning. I want to live in an AI-powered society. When anyone goes to see a doctor, I want AI to help that doctor provide higher quality and lower cost medical service. I want every five-year-old to have a personalised tutor.

As the founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and now overall lead of Baidu's AI team of some 1,200 people, I've been privileged to nurture many of the world's leading AI groups and have built many AI products that are used by hundreds of millions of people.

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.

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.