Joshua Lederberg Quotes

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

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.

I'm fascinated by artificial intelligence.

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

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.

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.

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.

A new, a vast, and a powerful language is developed for the future use of analysis, in which to wield its truths so that these may become of more speedy and accurate practical application for the purposes of mankind than the means hitherto in our possession have rendered possible.

I came in as an engineer and worked on artificial intelligence at Google. I worked on related sites and matching advertising to queries with some of our earliest ads.

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.

Everybody right now, they look at the current technology, and they think, 'OK, that's what artificial neural nets are.' And they don't realize how arbitrary it is. We just made it up! And there's no reason why we shouldn't make up something else.

Now that neural nets work, industry and government have started calling neural nets AI. And the people in AI who spent all their life mocking neural nets and saying they'd never do anything are now happy to call them AI and try and get some of the money.

If you step back a little and say we want to do A.I., then you will realize that A.I. needs knowledge, reasoning, and explanation.

A lot of what people are calling 'artificial intelligence' is really data analytics - in other words, business as usual. If the hype leaves you asking 'What is A.I., really?,' don't worry, you're not alone.

I'd like to make a fundamental impact on one of the most exciting, intelligent questions of all time. Can we use software and hardware to build intelligence into a machine? Can that machine help us solve cancer? Can that machine help us solve climate change?

Artificial Intelligence (AI) is the science of how to get machines to do the things they do in the movies.

I think that the artificial-intelligence people are making a lot of noise recently, claiming that artificial intelligence is making huge progress and we're going to be outstripped by the machines.

You may not realize it, but artificial intelligence is all around us.

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.

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.

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.

I think what's really amazing is that given the scale of the web and getting the compute power we have today, we're starting to see things that appear intelligent but actually aren't semantically intelligent.

We wanted to solve robot problems and needed some vision, action, reasoning, planning, and so forth. We even used some structural learning, such as was being explored by Patrick Winston.

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

My mission at Google is to develop natural language understanding with a team and in collaboration with other researchers at Google.