Laurell K. Hamilton Quotes

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 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.

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.

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

I think people need to understand that deep learning is making a lot of things, behind-the-scenes, much better. Deep learning is already working in Google search, and in image search; it allows you to image search a term like "hug."

Machine learning is the science of getting computers to learn without being explicitly programmed.

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.

I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.

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

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.

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.

The paradigm shift of the ImageNet thinking is that while a lot of people are paying attention to models, let's pay attention to data. Data will redefine how we think about models.

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 already working in Google search and in image search; it allows you to image-search a term like 'hug.' It's used to getting you Smart Replies to your Gmail. It's in speech and vision. It will soon be used in machine translation, I believe.

Artificial intelligence is the science of making machines do things that would require intelligence if done by men.

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

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.

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

Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition.

There is huge demand for artificial intelligence technologies.

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.

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