The field of artificial intelligence has been moving extremely quickly in the last few years. Do you know how AI will affect your life in the future? It'S been around since the very earliest days of computing. It's a grand project to build machines that are intelligent and there's many ways of pursuing this. It has captivated computer scientists ever since the most promising approach, though, is the area of machine learning, rather than trying to embody machines with everything they need to know up front. Rather, we want to enable them to learn to learn how to learn so that they can learn from their observations of the world and to make inferences based on those observations.
The field of deep learning is a particular kind of machine learning, it's been seen in the last four or five years to be remarkably effective for a wide variety of problems. Although it's actually much older before we start, though, how do we learn why we learn from examples of things and from repeated practice and repeated practice are really key to machine learning as well, so in machine learning we're going to expose a system to examples Of the behavior we want it to have and those examples are going to teach it it's going to learn from those examples how to do something. So in this very simple diagram, I have a model where we're going to try to teach a computer to tell whether a photograph contains a cat or a dog and we're going to have examples where we know the right answer.
To give you a transcript of the words that were said in that audio stream, how cold is it outside they can take in an English sentence? Hello? How are you and spit out the corresponding French sentence? Bonjour comment: allez-vous, sorry for my French. They can take in the pixels of an image and give you more than just a category about it. They can actually write a sentence, a caption. If you will about that picture, a blue and yellow train travelling down the train tracks that perch hose a pretty high level of understanding of what's going on in that scene. One of the great things about deep learning is that all of those things that I showed you can be expressed using a relatively simple set of algorithms and can be expressed in a common software framework. So we can build a software framework that enables us to express all these different learning problems and then use it over and over for our research and for our products and so the system we built this called tensorflow, and we use it internally for everything that we Do for in these this area, ItĂ˘??s been really great to see different things that people have used it for so here's one example.
There was a Japanese cucumber farmer and it turns out in when you harvest your cucumbers. You have to sort them into all kinds of different categories for sale, small ones, medium ones, large ones, prickly ones, NOT prickly ones, straight ones, curved ones. This is pretty complicated, I'm pretty time consuming at harvest time, so the farmer was able to take a camera and using a computer vision model that he trained with tensorflow, actually have the vision model determine what category of cucumber I was looking at and then rigged it Up to some conveyor belts and some little switches that would push the cucumber into the right box, and so this eliminated many days of labor that the farmer and his wife would have to do at harvest time. That's just one tiny example of something we can do now, that would be harder before. In closing, AI is going to help us to be healthier, happier more productive and more creative.