Marie von Ebner-Eschenbach Quotes

The strange flavour of AI work is that people try to put together long sets of rules in strict formalisms which tell inflexible machines how to be flexible.

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.

As commentators like the American psychologist Gary Marcus have noted, it's extremely difficult to teach a computer to recognise cats. And that's not for want of trying.

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

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.

The revolution in deep nets has been very profound. It definitely surprised me, even though I was sitting right there.

Most of the value of deep learning today is in narrow domains where you can get a lot of data. Here's one example of something it cannot do: have a meaningful conversation.

Having been trained as a computer scientist in the '90s, everybody knew that AI didn't work. People tried it. They tried neural nets, and none of it worked.

With the Google Brain project, we made the decision to build deep learning processes on top of Google's existing infrastructure.

We now think of internal representation as great big vectors, and we do not think of logic as the paradigm for how to get things to work. We just think you can have these great big neural nets that learn, and so, instead of programming, you are just going to get them to learn everything.

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

Pretend I asked, now answer the question.

I'm fascinated by artificial intelligence.

I want to build the next CNN with 'VICE' - it's within my grasp.

There is huge demand for artificial intelligence technologies.

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.

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.

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

I am suggesting that we recognize that in network and interface research there is something as profound (and potential wild) as Artificial Intelligence.

I'm working on artificial intelligence. Actually, natural language understanding, which is to get computers to understand the meaning of documents.

Is artificial intelligence less than our intelligence?

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.

The revolution in AI [artifical intelligence] has been profound. It surprised me, even though I was sitting right there.