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Bonus Material

In this section, you will find some bonus materials that will enrich your learning experience in all things digital. These videos are not part of the overall Google Digital Garage certification, so think of them as an opportunity to explore more exciting subjects.

Solving problems small, big and prickly

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As a kid, I was really inspired by the explorers, I grew up in Seattle, and Lewis and Clark were kind of heroes locally. I wanted to be an explorer when I grew up. As an electrical engineer, I would always look for new things that we can do that just--wherever possible. Machine learning and research is an exploration, it feels like an intellectual exploration.

We've definitely seen a big uptick in the last five years in what machines are able to do, compared to, say, the previous decade or two.

With the advent of a lot more data and a lot more computing power, we really can think bigger and sort of change the game about what sort of models we can envision.

The real world is actually very messy, hard, logical rules are not the way to solve real world problems. So machine learning is all about learning from examples.

Rather than writing 500,000 lines of code, we instead have the machine learn from observations about the world. We look through a bunch of these examples in the machine-learning algorithm, maybe millions, maybe billions, maybe even trillions, to identify the patterns and generalize from there.

In the task of image recognition, we've been able to train models to take the pixels of an image and from those pixels, learn high level features.

It starts to learn that, if you see a cake and you see a kid, it's maybe a birthday party. If you see a cake and lots of kids, it's very likely a birthday party. That's essentially teaching the machine to do the perceptions that we humans are so natural and so good at, you realize just how amazing humans are, just how amazing your four-year-old is, who can recognize faces. Machine learning has really been the beginning of a big revolution in the field of speech recognition.

To teach speech recognition, try to interact with a noisy room, we used real world sounds and we mix it into the examples that we already have, “Is it cold outside, is it cold outside?” [repetitive voice]

Now, no matter what the noise in the environment, our speech recognition systems can understand what you're saying. They can separate out one speaker from another.

With machine learning, we have now an algorithm that learns how to simulate a human linguist. A lot of the language that we see today, it's very informal, “Blah blah blah blah blah, blah blah, and they say "Okay."

Interspersed with emojis and stickers. Now, with Google, we're getting to the point where you can have a much more natural conversation.

The assistant product that we're building at Google uses the best of our machine learning techniques, image understanding, natural language understanding. That's a promising direction for developing systems that can really navigate the mess of the real world. We wanted to make this an open source project, so that everyone outside of Google could use the same system we're using inside Google.

There are lots of people who have made very, very creative uses of it without knowing a single bit of machine learning. So they have the ideas. They don't need to do the heavy lifting that we've already done.

I saw a cool example where somebody used to have a cat going around their house all the time, so they trained the model to identify whenever the cat was there and it would turn the sprinklers on to scare the cat away.

This elderly couple in Japan who ran a cucumber farm and one of the big tasks is to sort cucumbers into, like, prickly ones, less prickly ones, straight ones, curved ones. It's actually a complicated task. So the wife would spend many hours a day sorting cucumbers, so the son picked up a computer vision model and was able to build a system to categorize the cucumbers and sort them automatically. All the time wasted sorting cucumbers is just gonna be used in much better ways.

387 million people with diabetes are at risk for diabetic retinopathy. It causes blindness, the way that you can find signs of diabetic retinopathy is by taking pictures of the back of the eye, but there's just simply not enough doctors and it takes hours for an interpretation. So we trained an algorithm that can read the images right then and there. The algorithm can help the doctors get more people screened for the disease.

The more you see machine learning and the kinds of things it can do, the more you see opportunity for it to improve people's lives. You can use machine learning to save power at significant scales, even track the spread of diseases and epidemics.

We can use a computer vision model for everyone who's visually impaired. We could make a speech recognizer for everyone on the planet and drastically improve the experience of millions and billions
of people.

I don't see any area of science or even of human endeavor that learned systems can't help with. If you'd asked me a few years ago if a computer would be able to do this any time soon, I would have said,
"I don't really think so."

It's very, very empowering to imagine what's going to come. We're thinking thoughts and doing things that, you know, no man has ever done, and sort of setting forth and setting foot in really new intellectual territory here.

The promise of AI and machine learning is that we can actually produce solutions to previously unsolved problems that will really help people.
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From helping farmers in Japan sort cucumbers to assisting doctors in India diagnose eye disease, machine learning is changing the way people use code to solve problems and improve lives. In this video, we'll explore how machine learning is useful in solving a wide array of problems across industries, fields, and applications.

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