Tom Chatfield Quotes

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.

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

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

I think the way we're doing computer vision is just wrong.

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.

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

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.

Computer vision and machine learning have really started to take off, but for most people, the whole idea of what is a computer seeing when it's looking at an image is relatively obscure.

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

By developing deep learning solutions that are faster, easier, and less expensive to use, Nervana is democratizing deep learning and fueling advances in medical diagnostics, image and speech recognition, genomics, agriculture, finance, and eventually across all industries.

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.

Want to train a machine translation system? Train it on a gazillion pairs of sentences of parallel corpora, and that creates a lot of breakthrough results. Increasingly, I'm seeing results on small data where you want to try to take in results even if you have 1,000 images.

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.

Artificial intelligence... I've been following that since I was in high school.

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

When I was a graduate student in computer science in the early 2000s, computers were barely able to detect sharp edges in photographs, let alone recognize something as loosely defined as a human face.

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.

Understanding vision and building visual systems is really understanding intelligence.

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.

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

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

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