Understanding Artificial Intelligence and Its Future | Neil Nie | TEDxDeerfield

[Music] [Applause] thank you thank you thank you so during World War two the first computer was invented they cracked the German communication cold and ensured a successful Normandy landing the father behind this unprecedented machine Alan Turing wrote the 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 a 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 is to improve the machines performance on certain tasks and that has 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 whoo speaking of translation anyone here speaks the second or third language right that's awesome well I was born in China and I speak Chinese and also speak English plus a couple of programming languages here on account that so when my family and I travel around the world we often need something called Google Translate and by examining Google Translate artificial intelligence we can actually gain a greater understanding of how most AI works well first of all have you ever wondered how much data is a Google have well it turns out Google hauls 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 gigabytes then Google's 15 exabyte would be 30 million personal computers and data turns out to be one of the fuels that powers Google Translate of magical technology so on the surface Google Translate hasn't changed since 2007 when it first launched but will you my 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 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 on a computer knows about that specific subject and you and me where the user right the user will give the computer some input and hopefully some alpha will come out so in our case Google Google's 15 X byte of data will be the training input and something you won't 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 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 virtual information is separated and processed in three different processing systems one system mainly processing 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 I mean of pixels tiny tiny things that represent color which is also why Steve Jobs names his company Pixar since every person that world is made of pixels which is great so the computer is trying to understand this image it will first separate them into different futures objects that we can easily see in the 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 skeleton and some details about these features so now the computer has those things in memory so when the user gives the computer some input it will be able to process an import 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 coca-cola logo and this application is actually powered by open computer vision and thanks to a 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 green rectangles and squares around it and those are regions that the computers are processing and in the image below is one of the biggest features in that image and in a table as you can see there are details that computer is remembered so let's dismiss that and click start tracking oh look at that that's pretty sensitive it successfully tailed that the paper right in front of me has a coca-cola logo on it great and also this is life so you know I'm not thinking anything by the way so wonderful thank you so now let's recap we can summarize everything 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 new networks to improve this whole entire process and hopefully learn more and more images and the pixel in our case or the input data and the meaning where things like area parameter skeleton those you know details the computer focused on and hopefully in the future we'll be able to classify any image we want remember in the very beginning we talked about there are two parts of learning algorithms right the second part is near networks so let's talk about that a little bit our brain is made of gazillions and good zillions 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 is such an amazing price so Times's thought what happened if we actually turn that and put that into a computer so first of all Russ will understand difference 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 axis and from our algebra class you might know that X our input in our case and f of X is a mathematical calculation and why it's an output so this picture will represent the basically the relationship between neurons since we have so many of them right and this by altering the relationship between our neurons which are called synapses we will be able to learn 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 do something like image recognition or speech recognition what they had to do is that'll sit around a table and you know they'd have to put papers and pens and start doing math they had to create lookup tables and this was a pain because they took so much manpower and it took a long time so scientists thought what happened if we give the computers its own power to learn that would be magical because lookup tables would never exist if we can just make computers learn on his own instead we'll have computers all knowledge about a sub Civic subject and this is what this diagram represents the computers 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 they 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 so once again this is all life 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 computer things oh it's a mobile phone it's a products at gadget that's wonderful so what if we take a picture of the audience it's a performance there's audience and say hi to the camera great thank you 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's 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 Google self-driving car project they combine image processing and also laser and ultrasonic sensors to be able to form three-dimensional models of the cars 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 how is serving you fish you might appreciate the technical journey the low fish took to be on your plate so what's next let's try to answer this question what will the future look like with AI well actually 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 Garrick House Burrell and the IBM computer blew the IBM computer became the first-ever program to defeat a chest a 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 chance 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 like to learn a game of go as it goes I mean no pun intended there um go is a program of far less rules but requires far more intuition you cannot just calculate what the possibilities of go so google's alphago was able to defeat the south korean go champion lee sedol in a 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 AI will not only change our lives in small ways like we talked about evolve 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 CO 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 well you are talking to a friend on the phone who works in Wall Street and he doesn't have to worry about stock tears 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 of 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 would 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 thank you you

Channel: TEDx Talks

Views: 502,909

Rating: 4.8411622 out of 5

Keywords: TEDxTalks, English, United States, Technology, Computers, Design, Intelligence, Learning

Id: SN2BZswEWUA

Length: 16min 51sec (1011 seconds)

Published: Mon Apr 10 2017