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  • Hey there! I'm Jabril.

  • John Green Bot: And I am John Green Bot

  • and welcome to Crash Course Artificial Intelligence.

  • Now, I want to make sure we're starting on the same page.

  • Artificial intelligence is everywhere.

  • It's helping banks make loan decisions, and helping doctors diagnose patients, it's

  • on our cell phones, autocompleting texts, it's the algorithm recommending YouTube

  • videos to watch after this one!

  • AI already has a pretty huge impact on all of our lives.

  • So people, understandably, have some polarized feelings about it.

  • Some of us imagine that AI will change the world in positive ways, it could end car accidents

  • because we have self-driving cars, or it could give the elderly great, personalized care.

  • Others worry that AI will lead to constant surveillance by a Big Brother government.

  • Some say that automation will take all our jobs.

  • Or the robots might try and kill us all.

  • No, we're not worried about you John Green Bot.

  • But when we interact with AI that's currently available like Siri...

  • Hey Siri.

  • Is AI going to kill us all?”

  • Siri: “I don't understand 'Is AI going to kill us all.'”

  • it's clear that those are still distant futures.

  • Now to understand where artificial intelligence might be headed, and our role in the AI revolution,

  • we have to understand how we got to where we are today.

  • [INTRO]

  • If you know about artificial intelligence mostly from movies or books, AI probably seems

  • like this vague label for any machine that can think like a human.

  • Fiction writers like to imagine a more generalized AI, one that can answer any question we might

  • have, and do anything a human can do.

  • But that's a pretty rigid way to think about AI and it's not super realistic.

  • Sorry John Green-bot, you can't do all that yet.

  • A machine is said to have artificial intelligence if it can interpret data, potentially learn

  • from the data, and use that knowledge to adapt and achieve specific goals.

  • Now, the idea oflearning from the datais kind of a new approach.

  • But we'll get into that more in episode 4.

  • So let's say we load up a new program in John Green-bot.

  • This program looks at a bunch of photos, some of me and some of not of me, and then learns

  • from those data.

  • Then, we can show him a new photo, like this selfie of me here in the studio filming this

  • Crash Course video, and we'll see if he can recognize that the photo is me.

  • John Green Bot: You are Jabril.

  • If he can correctly classify that new photo, we could say that John Green-bot has some

  • artificial intelligence!

  • Of course, that's a very specific input of photos, and a very specific task of classifying

  • a photo that's either me or not me.

  • With just that program John Green-bot can't recognize or name anyone who /isn't/ me

  • John Green Bot: You are not Jabril.

  • He can't navigate to places.

  • Or hold a meaningful conversation.

  • No.

  • I just don't get it.

  • Why would anyone choose a bagel when you have a perfectly good donut right here?

  • John Green Bot: You are Jabril

  • Thanks John Green Bot.

  • He can't do most things that humans do, which is pretty standard for AI these days.

  • But even with this much more limited definition of artificial intelligence, AI still plays

  • a huge role in our everyday lives.

  • There are some more obvious uses of AI, like Alexa or Roomba, which is kind of like the

  • AI from science fiction I guess.

  • But there are a ton of less obvious examples!

  • When we buy something in a big store or online, we have one type of AI deciding which and

  • how many items to stock.

  • And as we scroll through Instagram, a different type of AI picks ads to show us.

  • AI helps determine how expensive our car insurance is, or whether we get approved for a loan.

  • And AI even affects big life decisions.

  • Like when you submit your college (or job) application AI might be screening it before

  • a human even sees it.

  • The way AI and automation is changing everything, from commerce to jobs, is sort of like the

  • Industrial Revolution in the 18th century.

  • This change is global, some people are excited about it, and others are afraid of it.

  • But either way, we all have the responsibility to understand AI and figure out what role

  • AI will play in our lives.

  • The AI revolution itself isn't even that old.

  • The term artificial intelligence didn't even exist a century ago.

  • It was coined in 1956 by a computer scientist named John McCarthy.

  • He used it to name theDartmouth Summer Research Project on Artificial Intelligence.”

  • Most people call it theDartmouth Conferencefor short.

  • Now, this was way more than a weekend where you listen to a few talks, and maybe go to

  • a networking dinner.

  • Back in the day, academics just got together to think for a while.

  • The Dartmouth Conference lasted eight weeks and got a bunch of computer scientists, cognitive

  • psychologists, and mathematicians to join forces.

  • Many of the concepts that we'll talk about in Crash Course AI, like artificial neural

  • networks, were dreamed up and developed during this conference and in the few years that

  • followed.

  • But because these excited academics were really optimistic about artificial intelligence,

  • they may have oversold it a bit.

  • For example, Marvin Minsky was a talented cognitive scientist who was part of the Dartmouth

  • Conference.

  • But he also had some ridiculously wrong predictions about technology, and specifically AI.

  • In 1970, he claimed that in "three to eight years we will have a machine with the general

  • intelligence of an average human being."

  • And, uh, sorry Marvin.

  • We're not even close to that now.

  • Scientists at the Dartmouth Conference seriously underestimated how much data and computing

  • power an AI would need to solve complex, real world problems.

  • See, an artificial intelligence doesn't reallyknowanything when it's first

  • created, kind of like a human baby.

  • Babies use their senses to perceive the world and their bodies to interact with it, and

  • they learn from the consequences of their actions.

  • My baby niece might put a strawberry in her mouth and decide that it's tasty.

  • And then she might put play-doh in her mouth and decide that it's gross.

  • Babies experience millions of these data-gathering events as they learn to speak, walk, think,

  • and not eat play-doh.

  • Now, most kinds of artificial intelligence don't have things like senses, a body, or

  • a brain that can automatically judge a lot of different things like a human baby does.

  • Modern AI systems are just programs in machines.

  • So we need to give AI a lot of data.

  • Plus, we have to label the data with whatever information the AI is trying to learn, like

  • whether food tastes good to humans.

  • And then, the AI needs a powerful enough computer to make sense of all the data.

  • All of this just wasn't available in 1956.

  • Back then, an AI could maybe tell the difference between a triangle and a circle, but it definitely

  • couldn't recognize my face in a photo like John Green-bot did earlier!

  • So until about 2010 or so, the field was basically frozen in what's called the AI Winter.

  • Still there were a lot of changes in the last half a century that led us to the AI Revolution.

  • As a friend once said: “History reminds us that revolutions are not so much events

  • as they are processes.”

  • The AI Revolution didn't begin with a single event, idea, or invention.

  • We got to where we are today because of lots of small decisions, and two big developments

  • in computing.

  • The first development was a huge increase in computing power and how fast computers

  • could process data.

  • To see just how huge, let's go to the Thought Bubble.

  • During the Dartmouth Conference in 1956, the most advanced computer was the IBM 7090.

  • It filled a whole room, stored data on basically giant cassette tapes, and took instructions

  • using paper punch cards.

  • Every second, the IBM 7090 could do about 200,000 operations.

  • But if you tried to do that it would take you 55 and a half hours!

  • Assuming you did one operation per second, and took no breaks.

  • That's right.

  • Not. Even. For. Snacks.

  • At the time, that was enough computing power to help with the U.S. Air Force's Ballistic

  • Missile Warning System.

  • But AI needs to do a lot more computations with a lot more data.

  • The speed of a computer is linked to the number of transistors it has to do operations.

  • Every two years or so since 1956, engineers have doubled the number of transistors that

  • can fit in the same amount of space.

  • So computers have gotten much faster.

  • When the first iPhone was released in 2007, it could do about 400 million operations per second.

  • But ten years later, Apple says the iPhone X's processor can

  • do about 600 billion operations per second.

  • That's like having the computing power of over a thousand original iPhones in your pocket.

  • (For all the nerds out there, listen you're right, it's not quite that simple - we're

  • just talking about FLOPS here)

  • And a modern supercomputer, which does computational functions like the IBM 7090 did, can do over

  • 30 quadrillion operations per second.

  • To put it another way, a program that would take a modern supercomputer one second to

  • compute, would have taken the IBM 7090 4,753 years.

  • Thanks Thought Bubble!

  • So computers started to have enough computing power to mimic certain brain functions with

  • artificial intelligence around 2005, and that's when the AI winter started to show signs of thawing.

  • But it doesn't really matter if you have a powerful computer unless you also have

  • a lot of data for it to munch on.

  • The second development that kicked off the AI revolution is something that you're using

  • right now: the Internet and social media.

  • In the past 20 years, our world has become much more interconnected.

  • Whether you livestream from your phone, or just use a credit card, we're all participating

  • in the modern world.

  • Every time we upload a photo, click a link, tweet a hashtag, tweet without a hashtag,

  • like a YouTube video, tag a friend on Facebook, argue on Reddit, post on TikTok [R.I.P.

  • Vine], support a Kickstarter campaign, buy snacks on Amazon, call an Uber from a party,

  • and basically ANYTHING, that generates data.

  • Even when we do something that /seems/ like it's offline, like applying for a loan to

  • buy a new car or using a passport at the airport those datasets end up in a bigger system.

  • The AI revolution is happening now, because we have this wealth of data and the computing