Mike Krieger Quotes

One thing that humans still do better than computers is recognize images.

Understanding vision and building visual systems is really understanding intelligence.

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

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.

My system uses the speed of components in cameras and cell phones to get four inches of depth through the brain.

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.

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.

The research on vision machines was mainly conducted at the Stanford Research Institute in the US. So, we can say that the events that took place in the Kosovo War were a total confirmation of the thesis of The Vision Machine.

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

There is huge demand for artificial intelligence technologies.

Yes, iD is a machine vision and sensor browser for the physical world. That's what we have been working on with Coca-Cola, Verizon, Bank of America and Disney to launch content when an image is recognised.

In the past, Google has used teams of humans to 'read' its street address images - in essence, to render images into actionable data. But using neural network technology, the company has trained computers to extract that data automatically - and with a level of accuracy that meets or beats human operators.

A lot of the future of search is going to be about pictures instead of keywords. Computer vision technology is going to be a big deal.

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.

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.

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.

Artificial intelligence is one of 50 things that Watson does. There is also machine learning, text-to-speech, speech-to-text, and different analytical engines - they're like little Lego bricks. You can put intelligence in any product or any process you have.

We really believe that long-term, the way AI will drive is similar to the way humans drive - we don't break the problem down into objects and vision and localization and planning. But how long it will take us to get there is questionable.

Traditionally, images have functioned as representations of something in the world, but we are quickly approaching the point where vast majority of images are produced for other machines, and no human being will ever see them.

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

My Ph.D. is in computer vision and machine learning. I developed software that can read your emotions from your face as part of my doctorate work.

I think the automation of vision is a much bigger deal than the invention of perspective.

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

Pretend I asked, now answer the question.

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