What Is Artificial Intelligence? Crash Course AI #1


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 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 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.

100 comments

Thank you so much CC for taking on this series! When I finish my degree and get a job, I’m giving right back to those who inspired me the most: CC, of course! ❤️❤️❤️

This series is really cool but I wish you'd speak faster, the energy feels kind of slow. Not hating, I enjoy the content alot

Loading a program using a cassette tape sure brings back sweet memories of my Apple II, coding in hexadecimal, and paying extra to get it to a full 16K RAM. I got so much grief over that 16K. The time shared university's Xerox Sigma 7 main frame only had 16K. "No microcomputer needs 16K!" Lord I loved those days! Of course, it was the late stone age of computing. It sure beat the 4K monster that was my first computer. (Well not mine, but the one I worked on.) I hated digging around that thing looking for burned out vacuum tubes. Boy am I getting old!!!

Um… Almost all of this was either simple hard-coded logic, or machine learning. Almost none of the stuff you talked about was AI.
….. Do you not know the difference?

I feel like referring to modern day algorithms and programs as "AI" is capitulating to advertisers who wish to make their product seem more advanced than it really is.

Is there going to be an episode where John Greenbot goes haywire, attempts to enact the AI apocalypse, and kill all humans? I can only hope

Distracted by how beautiful it is to see an authentic black dude talking deep science. You rock Jabril. Well done and thank you thank you for all the amazing content.

I think we have a lot of these technologies you said we don't, they're just not shown to the public. But look at the surveillance systems in China (we know more about theirs than our own imo) and how advertising ai can predict behaviour so accurately we believe it's listening to our conversations, because it knows what we want before we do.

Wow, they actually brought Jabril to Crash Course! His videos are awesome, it's even weird to see him actually talking on camera and not dubbing his own footage 😛

This finna be the best crash course, funny af and more nuanced than most of videos on the subject

Oh, I hate that everyone is calling it Artificial Intelligence. All of today's machine learning is nothing more than really fancy pattern matching. There's nothing intelligent about it at all.

I have been working for a company that gives data to apple to help siri pronounce local street names! AI info comes from real people (like me) and has given me a job! Safe to say I am a big fan lol

"I don't understand what's 'Is AI going to kill us all?'" – Siri
That's EXACTLY what an AI that is plotting to kill us all would say!

Is this the first host that doesn’t speak at 98% the speed of light?

I think we’ve been tricked by AI

If technology and ai stop mankind from car crashes, illness, disasters etc, won't the jump in population eventually kill us off?

Inventories are a major component of "economic" statistics. Do the statistical models need to be reconsidered now that algorithms rather than humans (far more susceptible to emotions and groupthink and to the spiral effect) are responsible for a large portion of industrial inventories?

We already have generalized AI, kinda. Those are corporations. They can interpret data, learn from them and use it to adapt and achieve goals. And they will kill us all

What If computers had so much power that they would process all the text written by people via social networks, gather the information needed. Just like a child. But without eyes, years and else.

Great host. Keep him and – of course – John and Hank and leave Nicole (talking way too fast and not being funny) behind the scenes 🙂

I'm super excited to watch this course. I hope that the Arabic translation will be done soon to fully understand this exciting course

Imagine a movie where our main protagonist meets God, that is none other than the role of Hank Green himself. And he like, recognizes him, knowing he met God, to whatever extent the word goes. And after some fotage God was curious and peeks inside him to maybe answer how did he recognize him and after being inspired he gifts him the full extent of his power in radius of Earth. Then you just jam everything we know and make whatever you want with it. Also the gift would be knowledge and some minor powers like teleportation, change time direction, phasing, telekinesis, that in conjunction with his infinite knowledge, he could, with time, change almost nothing. He goes on living for millions of years, technically. He goes on to do every butterfly effect there is, causing disaster and pretty much everything wrong with the world through a hilarious miss happens he caused. Mentioning everything we know today was because a God gave his power to some random guy in 2025 and he went on a happy little time trip and screwed up everything. And at the end of the movie when he figures stuff out and starts to fix those things by doing nothing, they move on just as fine without him.
Movie ends with 2 guys talking in a carriage and one of them yells at you:"Hey you, you're finally awake…"

I've experimented with neural networks and its pretty amazing. Its scary how fast it can learn a task. I usually implement it on unity and recently i rewrote the neural networks to unreal engine. Give it enough information and it will learn pretty fast. Give it too little and it wont really get it. Give it too much and it will get confused. But its really interesting stuff

Wait is that the running gag from the Newsboys movie under the big top where they are talking about donuts and bagels and just appear to be high or somthing because there is really no other reason for the conversation just having virtually not meaning lol. It's not a donut it's bagel "it's baked get it" doughnuts are fried in oil, it's a roll with a hole.

Also I just checked current count of the Athena project and I have about 169 GB of research database application and administration documents to use for what is currently dubbed the Athena project. I'd like to have the information services currently known in drug guides and physicians desk reference + summaries of Implementation and Assessment typed into smaller boxes on medication administration forms for a start use that as my training data and then have a electronic records of any errors in administering medications like being overwhelmed and what licensed staff missed or incorrectly wrote compared against the actual outcomes for patient and use that to write better summarized and sometimes more targeted information on hand reference on the administration sheets but avoid long descriptions that cause alert fatigue. Also because I could not find an information service for that part of a drug guide I had been in contact with F.A. Davis for years but they didn't have it they just asked me to site anything with a footnote so I added that to the form. Neither did another group I can't go into to much detail with here NDA but they can provide the five rights and querying the database a glance possibly drug -drug interactions, and IV compatibility with meds for the each patients cover sheet on the MAR. It was devoid of the common, implementation, assessment fields, as well as labs commonly evaluated when giving meds, simply didn't have the field and came to me unassembled so I could write against the database structure to create my information services links I'm sure someone can remedy this but their notes are a bit foreign to me I live at a higher level overview of a lot of code structures nit even sure why but they didn't even include relationships between tables.??? huh IDK but the stuff I said is there. Other then that the Electronic medical records systems are there but I wrote them with that high level over view tool and I know there are ways to create these basic structures from a bit of formal education in whatever flavor right now c# using Lightswitch but you can use the controls from Componentone to replace any Silverlight controls trees and properties and that made up the screens I can still publish the SQL or if someone knows JAVA then fire up eclipse and I'll show you the "technical readout of this station" cause my brain is toast from mental health issues se prior attempts at earing that formal education. PS I use documents and open them and use OLAP in them to update, arrange, re arrange, add info and loops previously found in the application tier of the database project "cause reasons". Then I use a macro or one I'm trying to work on I almost have it my VBA actually some of the skills I used to make this are gone I've found and yeah I mostly know C# and not VBA with excel objects. So anyways they get auto printed based on the workflow of when they need to be updated for things like Medication, labs, a Kardex of nurse notes more planned and on the way document wise like a treatments book similar to medications but for wound care and so one. These are to improve the delivery of care to the elderly like you mentioned as a whole and were the focus of my creation I started with versions of these in excel as I wanted more information and not had formatting and highlighting to error prone and reduce alert fatigue. We may run into a memory RAM issue with Excel or it's limits of 1 million rows for a power pivot table solvable with custom code if need be (in actuality the issues is with RAM directly and virtual rows when first assembling the table as subtotals with a repeated row for every column). That get out of hand with 2000 medicine names and 5000 diagnosis names being repeated exponentially but if it just never made those virtual repeated rows, in fact I one by one have to turn them off to make a complete working template with al the columns. By far though Excel is still much more familiar to common users and with steps to two fold secure data by encrypting the documents with a password to open and requiring them as blank templates to start they are connected using with the database tier application user permissions login credentials later added by the user and then an DB connection string. under the hood it complex but with a file system command like Process.Start to a data tables folder we install into the universally addressable "special folders" within the front end of the back end it's click to open, type password click macro buttons which loops and prints in the MAR case the same kinds of records a 2 inch binder typically holds in color for use each month. These can be manually updated for minor edits and again because reasons later I can explain its a paper records this is ideal for emergencies in and signature or rather initialing in this environments workflow. So yeah I can set up a copy of the development environment publish an exe but really you need 169 GB of this stuff and well me to explain my creativity where my notes are not clear. Also you need me to install the encryption keys on your systems with my air gaped mind set wink wink. 😉 So am I on the team I know I don't offer this world much value anymore given my severe mental illnesses and physical limitations I don't have enough formal education to work for anyone per say but I do have this it was my exit strategy. along with following other big plans like molten salt reactors and some stuff about farming to restore the atmosphere over the malaria belt I am dubbing Nile 2.0 with that sweeet LFTR power generator and some things form Bill and Melinda Gates + a modern desalinization. =-D

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