This TED Talk features computer scientist Joseph Redman, recorded live at TED 2017.

Ten years ago, computer version researchers thought that getting a computer to tell the difference between a cat and a dog would be almost impossible.

Even with a significant advance in the state of artificial intelligence,

now we can do it at a level greater than 99% accuracy.

This is called image classification, given an image, put a label to that image, and computers

know thousands of other categories as well.

I'm a graduate student at the University of Washington, and I work on a project called

Darknet, which is a neural network framework for training and testing computer vision models.

So let's just see what Darknet thinks of this image that we have.

When we run our classifier on this image, we see we don't just get a prediction of dog or cat.

We actually get specific breed predictions.

That's the level of granularity we have now.

And it's correct. My dog is in fact a malamune.

So we've made amazing strides in image classification.

But what happens when we run our classifier in an image

that looks like this? Well, we see that the classifier comes back with a pretty similar

prediction. And it's correct, there is a malamute in the image, but just given this label, we don't

actually know that much about what's going on in the image.

We need something more powerful.

I work on a problem called object detection, where we look at an image and try to find all

of the objects, put bounding boxes around them, and say what those objects are.

So here's what happens when we run a detector on this image.

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