[0:20]Thank you. Thank you. Thank you. So during World War II, the first computer was invented. It cracked a German communication code and ensured a successful Normandy landing. The father behind this unprecedented machine, Alan Turing, wrote a paper, computing machinery and intelligence in 1950. And the paper opens with the words, I propose to consider the question, can machines think? Well, today, inspired by his thoughtful question, we'll try to answer the following. How can we create an intelligent computer and what will the future look like with intelligent machines? Well, in fact, AI has been growing exponentially in the past decade. It has already been touching our lives in ways that you might not notice. For example, every time you go on Google search, some kind of AI is being used to show you the best results. Every time you ask Siri a question, natural language processing and speech recognition is being used. So artificial intelligence will probably be one of the biggest scientific breakthroughs in the 21st century. It will give us the power to probe the universe and our humanity with a different approach. AI has the potential of forever changing our humanity. The backbone of artificial intelligence is machine learning and I think the term is pretty self-explanatory, we want to make machines learn based on its knowledge and make decisions. Machine learning can be understood in two major components. One is to use algorithms to find meaning in random and unordered data. And the second part is to use learning algorithms to find relationship between that knowledge and improve that learning process. So the overall goal for machine learning is actually quite simple, it's to improve the machine's performance on certain tasks. And that task can be predicting the stock market to complicated ones such as translating articles between languages. And the screenshot that you see right now is actually a depiction of Google Translate Neural Network. So speaking of translation, anyone here speaks a second or third language? Great, that's awesome. Well, I was born in China and I speak Chinese, and I also speak English plus a couple of programming languages if you want to count that. So when my family and I travel around the world, we often need something called Google Translate. And by examining Google Translate's artificial intelligence, we can actually gain a great understanding of how most AI works. Well, first of all, have you ever wondered how much data does Google have? Well, it turns out Google holds right around 10 to 15 Exabytes of data. Well, what does that even mean? Let me put that into perspective for you. If one personal computer is 500 GB, then Google's 15 Exabytes would be 30 million personal computers. And data turns out to be one of the fuels that powers Google Translate, a magical technology. So on the surface, Google Translate hasn't changed since 2007 when it first launched. But what you might notice is that the translator is getting faster and more accurate. So it turns out the learning process for Google Translate is inspired by our own. We as humans get better at doing things by practicing, just like what our math teachers and our musical teachers always tell us. It turns out Google Translate can get better at translating by reading more articles. So how do computer learn? We can actually come up with this flowchart that will give us a summarize, will give us a good picture of how artificial intelligence actually works. So it turns out we have to use some training input and put that into a learning algorithm, which will give us some knowledge. And that knowledge will be, um, what the computer knows about that specific subject. And you and me were the user, right? The user will give the computer some input and hopefully some output will come out. So in our case, Google's Google's 15 Exabyte of data will be the training input, and something you want to translate is going to be the user input, and the output is going to be something in a different language. So the most important part of this whole entire process is actually the learning algorithm. This is what powers computers to learn and be intelligent. So today we're going to focus on two parts, one is image processing, and the second part is neural networks. So let's begin by talking about image processing. We can't talk about computer vision without talking about human vision, right? And visual signal from our retina is relayed through our brain to our primary visual cortex in the back of our brain. Which is right here. And visual information is separated and processed in three different processing systems. One system mainly processes information about color, second one about shape, the third one about movement, location, and organization. So with all of that in mind, today we'll try to create an application that will be able to identify a Coca-Cola logo. So first of all, we have to understand that most pictures that we see on a computer screen are made of pixels, tiny, tiny things that represent color. Which is also why Steve Jobs named his company Pixar since every person in that world is made of pixels, which is great. So when the computer is trying to understand this image, it will first separate them into different features. Objects that we can easily see in this still image. And then each of these features will provide the computer some information about that image. And today we'll mainly focus on area, parameter and the skeleton and some details about these features. So now the computer has those things in memory, so when the user give the computer some input, it will be able to process the input and compare that with what is in memory, and then give you some output whether the image match with the template or not. So here's that technology in action. So I've created an application on this iPad that will be able to identify a Coca-Cola logo. And this application is actually powered by Open Computer Vision. And thanks to the great framework. So today we'll learn a Coca-Cola logo. So let's click on that. Great, we just learned this image. Wonderful. And as you can see, the image on top has little, um, green rectangles and squares around it. And those are regions the computer is processing. And in the image below, it's one of the biggest features in that image. And in a table as you can see, there are details the computer just remembered. So let's dismiss that and click start tracking. Oh, look at that. It's pretty sensitive. We successfully tell that the paper right in front of me has a Coca-Cola logo on it. Great. And also this is live, so, you know, I'm I'm not faking anything by the way. So wonderful. Thank you. So now let's recap. We can summarize everything that we did with this simple flowchart. We had some input data and we use some algorithm to find some meaning in that data. And in the future, we'll use neural networks to improve this whole entire process. And hopefully, learn more and more images. And the pixel in our case, for the input data, and the meaning are things like area, parameter, skeleton, those, you know, details the computer focused on. And hopefully, in the future, it will be able to classify any image we want. Remember in the very beginning we talked about there are two parts to learning algorithms, right? And the second part is neural networks. So let's talk about that a little bit. Our brain is made of gazillions and gazillions of neurons and those tiny things communicate with each other, process information and that's how we become intelligent. It took thousands and thousands of years of evolution. And it's such an amazing process. So scientists thought, what happened if we actually turn that and put that into a computer? So first of all, we have to understand different and similarities between artificial neuron and a biological one. So on your left, this is a biological neuron and it has cell bodies, axons, and terminal axons and dendrites and stuff like that. And those parts will take in information and process them and give you some output. Similarly, on our right, as you can see, we have a bunch of X's and from our algebra class, you might know that X are inputs in our case and f of X is a mathematical calculation and Y is an output. So this picture will represent the uh, basically the relationship between neurons since we have so many of them. Right? And then this, by altering the relationship between our neurons, which are called synapses, we will be able to learn and and gain a better understanding of things. And synapses are represented as lines on our right. So this is an animated version of what scientists believe our neurons would look like. So back in the old days, you know, in the 1970s, and before most of us were born, when scientists wanted to use something like image recognition or speech recognition, well, they had to do is they had to sit around a table and, you know, they had to put papers and pens and start doing math. They had to create lookup tables and this was a pain because it took so much manpower and it took a long time. So scientists thought what happened if we give the computer its own power to learn? That would be magical, because lookup tables would never exist if we can just make computers learn on their own. Instead, we would have computers own knowledge about a specific subject. And this is what this diagram represents, the computer's own knowledge about something. And this is really empowering, because scientists no longer have to create lookup tables for days and years. What they have to do is just write a simple program, train the computer, and then it can do things like image recognition and speech recognition in a matter of seconds. So with help from Google Cloud Platform, we're going to do another demonstration showing the power of combining image processing as well as neural networks.
[11:27]So once again, this is all live and we have a great audience here tonight. And we're going to take a picture, take a picture of my phone, let's say. And to see what the computer thinks. Oh, it's a mobile phone. It's a product, it's a gadget. That's wonderful. So what if we take a picture of the audience? It's a performance, yes, audience. And say hi to the camera. Great. Thank you.
[12:00]So all of the things that we just talked about are intangible, just like art, music, and language, and all of that. But technology like that plays such an important role in our daily lives. For example, in Google self-driving car project, they use image processing to be able to identify the difference between a police vehicle and a normal passenger car. And this is another picture from, um, Google's self-driving car project. They combine image processing and also laser and ultrasonic sensors to be able to form three-dimensional models of the car surrounding. So the car can navigate safely without lag. And this might surprise you. Back in the 90s, scientists actually implemented these technology on fisherman's boats. A well-trained computer can can identify the difference between a tuna and a cod. So next time when dining hall is serving you fish, you might appreciate the technical journey the little fish took to your plate. So what's next? Let's try to answer this question. What will the future look like with AI? Well, actually, let's jump back in history and talk about one of the biggest breakthroughs that we had with AI. And many of you might recall this historic event between Garry Kasparov and the IBM computer Deep Blue. The IBM computer became the first ever program to defeat a chess, world chess champion under tournament rules in a classic game. It was a very significant victory. It was a milestone. However, later analysis actually played down the intellectual value of chess as a game that can be simply defeated by brute force, which means that if you had enough calculation and enough computing power, chess can be defeated, which means that calculation does not equal to intelligence. And this is a very important understanding. However, Google took a different approach. They created AlphaGo, a program that can learn a game of Go as it goes. I mean, no pun intended there, but, um, Go is a program of far less rules but requires far more intuition. You cannot just calculate what are the possibilities of Go. So Google's AlphaGo was able to defeat the South Korean Go champion Lee Sedol in the 2016 game. And this was a breakthrough, another breakthrough, because the program used reinforcement learning as well as neural networks, which resembles our own decision-making process. So, um, AI will not only change our lives in small ways, like we talk about above. It will likely to bring us tremendous change. Change like we saw 200 years ago with the Industrial Revolution when humans first harnessed the power of coal and steam engines. Change like we saw in the 1990s when millions and millions of computers reached homes across the globe. AI will give us unprecedented amount of power, as well as the opportunity to change. Imagine, imagine 10 years from now when we're autonomously constructing a space station on Mars. Your car is driving you to work, while you are talking to a friend on the phone who works on Wall Street, and he doesn't have to worry about stock traders anymore because AI will ensure a fair and safe trading environment. Also, in hospitals across the globe, scientists are using AI to find mutations in human DNA databases and also cures for diseases. And these are just some of the possibilities, and the sky is no longer the limit. The power and the freedom that we have with artificial intelligence is empowering, but also humbling. We as humans are capable of creating machines that can learn and think just like us. In the long run, AI will not replace biological intelligence. Yet, it will enhance our lives, it will enhance our future. And I believe that most AI researchers out there will agree with me on that. So after all, you and I and all of us are on this journey together. All of us have the chance to witness and also decide how artificial intelligence will shape our future.
