AI 혁명: 과거, 현재, 그리고 미래

인공지능이 우리 일상에 미치는 영향에 대해 알아보세요. 은행 업무부터 의료 서비스까지, 크래시 코스 인공지능을 통해 인공지능 혁명의 역할과 미래 가능성에 대해 알아보세요.

### 인공 지능의 진화
* 🤖  인공지능은 어디에나 존재하며 우리 삶에 큰 영향을 미치고 있으며, 그 미래에 대한 낙관론과 우려가 공존하고 있습니다.
* 🧠  AI는 기계가 단순히 인간처럼 생각하는 것이 아니라 데이터를 해석하고 학습하여 특정 목표를 달성하는 능력으로 정의됩니다.
* 💻  AI는 제한적인 정의에도 불구하고 가상 비서부터 의사 결정 과정에 이르기까지 우리 생활의 다양한 측면에서 중요한 역할을 담당하고 있습니다.
* 📅  AI 혁명은 1956년 '인공 지능'이라는 용어가 만들어지고 다트머스 회의에서 시작되었지만, 데이터와 컴퓨팅 성능의 부족으로 인해 초기에는 AI 기능에 대한 낙관론이 실현되지 못했습니다.
* 🔀  AI 혁명은 컴퓨팅 성능이 크게 향상되고 인터넷과 소셜 미디어를 통해 대량의 데이터를 사용할 수 있게 되면서 추진되었습니다.
* 🌐  인공지능은 우리 삶에 지속적으로 영향을 미치고 있으며, 지식을 통해 인공지능의 미래를 설계할 수 있기 때문에 인공지능을 이해하는 것은 매우 중요합니다.

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 of “learning from the data”
is 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 the “Dartmouth Summer
Research Project on Artificial Intelligence.” Most people call it the “Dartmouth Conference”
for 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
really “know” anything 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-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 power to make sense of it. And I get it. The idea that we’re generating a bunch of
data but don’t always know how, why, or if it’s being used by computer programs
can be kind of overwhelming. But through Crash Course AI, we want to learn
how artificial intelligence works because it’s impacting our lives in huge ways. And that impact will only continue to grow. With knowledge, we can make small decisions
that will help guide the AI revolution, instead of feeling like we’re riding a rollercoaster
we didn’t sign up for. We’re creating the future of artificial
intelligence together, every single day. Which I think is pretty cool. Next time, we’ll start to dive into technical
ideas like supervised, unsupervised, and reinforcement learning. And we’ll discuss what makes a Machine Learning
algorithm good. See you then! Thanks to PBS for sponsoring Crash Course AI!

If you want to help keep all Crash Course
free for everybody forever, you can join our community on Patreon. And if you want to learn more about how computers got so fast, check out our video on Moore’s Law.

What Is Artificial Intelligence? Crash Course AI #1

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인공지능은 은행 업무부터 의료 서비스, 스마트폰에 이르기까지 이미 우리 생활에 널리 퍼져 있습니다.

인공지능에 대해 긍정적인 변화의 가능성을 보는 시각과 감시와 일자리 감소에 대한 우려를 표명하는 시각이 대조적으로 존재합니다.

AI는 단순히 인간의 생각을 복제하는 것이 아니라 데이터를 해석하고 학습하여 특정 목표를 달성하는 능력으로 정의됩니다.

AI는 쇼핑부터 소셜 미디어, 심지어 대학 진학이나 취업 지원과 같은 인생의 주요 결정에 이르기까지 우리 삶의 다양한 측면에 깊숙이 통합되었습니다.

AI 혁명은 비교적 최근의 일이지만 산업혁명과 유사하게 우리의 생활과 업무 방식을 빠르게 변화시켰습니다.

'인공 지능'이라는 용어는 1956년 컴퓨터 과학자 존 매카시가 다트머스 여름 연구 프로젝트에서 만든 것으로, 이 프로젝트에서 많은 기본적인 AI 개념이 개발되었지만 AI 기능에 대한 지나치게 낙관적인 예측도 있었습니다.

복잡한 문제 해결을 위한 AI의 성능 요구 사항

인공지능은 처음에는 지식과 학습 능력이 부족하여 강력한 컴퓨팅 능력과 함께 학습할 방대한 양의 라벨링된 데이터가 필요했습니다.

AI 혁명은 컴퓨팅 성능과 속도가 크게 향상되면서 더 복잡한 데이터 처리가 가능해졌고, 이는 AI 발전에 필수적인 요소였습니다.

1956년 이후 컴퓨터의 트랜지스터 수는 2년마다 두 배씩 증가하여 AI가 진화하고 현실 세계의 문제를 해결하는 데 필요한 성능을 제공했습니다.

컴퓨터 처리 속도는 수년에 걸쳐 크게 향상되어 이제 iPhone X는 기존 iPhone 1000대 이상의 연산 능력을 갖추게 되었습니다.

최신 슈퍼컴퓨터는 초당 30경 회 이상의 연산을 수행할 수 있어 과거 기술보다 엄청난 발전을 보여줍니다.

AI 혁명은 지난 20년 동안 컴퓨터 성능의 향상과 인터넷 및 소셜 미디어 사용으로 인한 데이터의 폭발적인 증가가 결합되어 촉발되었습니다.

인공지능의 영향과 인공지능에 대한 이해의 중요성

AI 혁명은 풍부한 데이터와 강력한 컴퓨팅 기능의 결합에 의해 주도되고 있습니다.

크래시 코스 AI는 AI가 삶에 미치는 영향에 대해 교육하고 정보에 입각한 결정을 내릴 수 있도록 돕는 것을 목표로 합니다.

인공지능의 미래는 우리의 일상적인 선택에 의해 형성되고 있으므로 모든 사람이 인공지능의 작동 원리를 이해하는 것이 중요합니다.