Articles, Blog

Symbolic AI: Crash Course AI #10

November 3, 2019

Back in 1959, three AI pioneers set out to
build a computer program that simulated how a human thinks to solve problems. Allen Newell was a psychologist who was interested
in simulating how humans think, and Herbert Simon was an economist, who later won the
Nobel prize for showing that humans aren’t all that good at thinking. They teamed up with Cliff Shaw, who was a
programmer at the RAND corporation, to build a program called the General Problem Solver. To keep things simple, Newell, Simon, and
Shaw decided it was best to think about the content of a problem separately from the problem-solving technique. And that’s a really important insight. For example, my brain would use the same basic
reasoning strategies to plan the best route to work, school, or wherever I need to go,
no matter where I start. Computers are logical machines that use math
to do calculations, so logic was an obvious choice for the General Problem Solver’s
problem-solving technique. Representing the problem itself was less straightforward. But Newell, Simon, and Shaw wanted to simulate
humans, and human brains are really good at recognizing objects in the world around us. So in a computer program, they represented
real-world objects as symbols. That’s where the term Symbolic AI comes
from, and it’s how certain AI systems make decisions, generate plans, and appear to “think.” INTRO Hi, I’m Jabril and welcome to CrashCourse
AI. If you’ve ever applied for a credit card,
purchased auto insurance, or played a computer game newer than something like PacMan, then
you’ve interacted with an AI system that uses Symbolic AI. Modern neural networks train a model on lots
of data and predict answers using best guesses and probabilities. But Symbolic AI, or “good old-fashioned
AI” as it’s sometimes called, is hugely different. Symbolic AI requires no training, no massive
amounts of data, and no guesswork. It represents problems using symbols and then
uses logic to search for solutions, so all we have to do is represent the entire universe we care about as symbols in a computer… no big deal. To recap, logic is our problem-solving technique
and symbols are how we’re going to represent the problem in a computer. Symbols can be anything in the universe: numbers,
letters, words, bagels, donuts, toasters, John-Green-bots, or Jabrils. One way we can visualize this is by writing
symbols surrounded by parentheses, like (donut) or (Jabril). A relation can be an adjective that describes
a symbol, and we write it in front of the symbol that’s in parentheses. So, for example, if we wanted to represent
a chocolate donut, we can write that as chocolate(donut). Relations can also be verbs that describe
how symbols interact with other symbols. So, for example, I can eat a donut, which
we would write as eat(Jabril, donut) because the relation describes how one symbol is related
to the other. Or we could represent John-Green-bot’s relation
to me, using sidekick(John-Green-bot, Jabril). A symbol can be part of lots of relations
depending what we want our AI system to do, so we can write others like is(John-Green-bot,
robot) or wears(John-Green-bot, polo). All of our examples in this video will include
a max of two symbols for simplicity, but you can have any number of symbols described
by one relation. A simple way to remember the difference between
symbols and relations is to think of symbols as nouns and relations as adjectives or verbs
that describe how symbols play nicely together. This way of thinking about symbols and their
relations lets us capture pieces of our universe in a way that computers can understand. And then they can use their superior logic
powers to help us solve problems. The collection of all true things about our
universe is called a knowledge base, and we can use logic to carefully examine our knowledge
bases in order to answer questions and discover new things with AI. This is basically how Siri works. Siri maintains a huge knowledge base of symbols,
so when we ask her a question, she recognizes the nouns and verbs, turns the nouns into
symbols and verbs into relations, and then looks for them in the knowledge base. Let’s try an example of converting a sentence
into symbols and relations, and using logic to solve questions. Let’s say that “John-Green-bot drives
a smelly, old, car.” I could represent this statement in a computer
with the symbols John-Green-bot and car, and the relations drives, smelly, and old. Using logical connectives like AND and OR,
we can combine these symbols to make sentences called propositions. And then, we can use a computer to figure
out whether these propositions are true or not using the rules of propositional logic
and a tool called a truth table. Propositional logic is basically a fancy name
for Boolean Logic, which we covered in episode 3 of Crash Course Computer Science. And the truth table helps us decide what’s
true and what’s not. So, in this example, if the car is actually
smelly, and actually old, and if John-Green-bot actually drives the car… then the proposition,
“Smelly car AND old car AND John-Green-bot drives the car.” is true. We can understand that sort of logic with
our brains: if all three things are true, then the whole proposition is true. But for an AI to understand that, it needs
to use some math. With a computer, we can think of a false relation
as 0 and true relations as any number that’s not 0. We can also think of ANDs as multiplication
and ORs as addition. But let’s look at what happens to the math
if the car is not actually old. Again, our brains might be able to jump to
the conclusion that if one of the three things isn’t true, then the whole proposition must
be false. But to do the math like an AI would, we can
translate this proposition as true times false times true, which is 1 times 0 times 1. That equals 0, which means the whole proposition
is false. So that’s the basics of how to solve propositions
that involve AND. But what if we want to know if John-Green-bot
drives a car and that the car is either smelly OR old? Like I mentioned earlier, OR can be translated
as addition. So, using our math rules, we can fill out
this new, bigger truth table. The proposition we’re dealing with now is
“Smelly car OR old car AND John-Green-bot drives the car.” For the first row, this translates as (true
plus true), then that result times true, which we calculate as (1 plus 1) times 1. That equals 2 times 1, which is 2, which means
the whole proposition is true! Remember, any answer that isn’t 0 is true. The second row translates as (true plus false), then that result times true, which we calculate as (1 plus 0) times 1. That equals 1 times 1, which is 1, which means
the whole proposition is true again. And we can fill out the rest of the truth
table the same way! Another logical connective besides AND and
OR, is NOT, which switches true things to false and false things to true. And there are a handful of other logical connectives
that are based on ANDs, ORs, and NOTs. One of the most important ones is called implication,
which connects two different propositions. Basically, what it means is that IF the left
proposition is true, THEN the right proposition must also be true. Implications are also called if/then statements. We make thousands of tiny if/then decisions
every hour (like, for example, IF tired THEN take nap or IF hungry THEN eat snacks). And modern Symbolic AI systems can simulate
billions of if/then statements every second! To understand implications, how about we use
a new example: IF I’m cold THEN I wear a jacket. This is saying that if I’m definitely cold
then I must be wearing my jacket, but if I’m not cold, I can wear whatever I want. So if cold is true and jacket is true, both
sides of the implication are true. Even if I’m not cold and I wear my jacket,
then the statement still holds up. Same if it I’m not cold and I decide to
not wear my jacket. I can do whatever since I’m not cold. BUT if I am cold and I decide not to wear
my jacket, then the statement no longer works. The implication is false. Simply put, An implication is true if the
THEN-side is true or the IF-side is false. Using the basic rules of propositional logic,
we can start building a knowledge base of all of the propositions that are true about
our universe. After that knowledge base is built, we can
use Symbolic AI to answer questions and discover new things! So, for example, if I were to help John-Green-bot
start building a knowledge base, I’d tell him a bunch of true propositions. Oh John Green Bot? Alright, you ready John Green Bot? Jabril is a person. John-Green-bot is a machine. Car is a machine. Car is old. Car is smelly. John Green Bot is not person. Jabril isn’t machine. Toaster is a machine. You getting all this John Green Bot? Clearly, at this pace, John-Green-bot would
never be able to build a knowledge base with all the possible relations and symbols that
exist in the universe. There are just too many. Fortunately, computers are really good at
solving logic problems. So if we populate a knowledge base with some
propositions, then a program can find new propositions that fit with the logic of the
knowledge base without humans telling it every single one. This process of coming up with new propositions
and checking whether they fit with the logic of a knowledge base is called inference. For example, the knowledge base of a grocery
store might have a proposition that sandwich implies Between(Meat, Bread), or “IF sandwich
THEN between(meat, bread)”. Meat and Bread are the symbols, and Between
is the relation that defines them. So basically, this proposition is defining
a sandwich as a symbol with meat between bread. Simple enough. There might be other rules in the grocery
knowledge base. Like, for example, a hotdog also implies Between(Meat,
Bread), or “IF hotdog THEN between(meat, bread).” Now, if the grocery store is having a sale
on sandwiches, should the hot dogs also be on sale? Well, with inference, the grocery store’s
AI system can apply the following logic: because sandwiches and hotdogs are both symbols that
imply meat between bread, then hot dogs are inferred to be sandwiches, and the discount
applies! Over the years, we’ve created knowledge
bases for grocery stores, banks, insurance companies, and other industries to make important
decisions. These AI systems are called expert systems,
because they basically replace an expert like an insurance agent or a loan officer. Symbolic AI expert systems have some advantages
over other types of AI that we’ve talked about, like neural networks. First, a human expert can easily define and
redefine the propositional logic in an expert system. If a bank wants to give out more loans, for
example, then they can change propositions involving credit score or account balance
rules in their AI’s knowledge base. If a grocery store decides that they don’t
want to discount hotdogs during the sandwich-sale, then they might redefine what it means to
be a sandwich or a hotdog. Hey Siri, is a hotdog a sandwich? Siri: Of course not Jabril. Do not waste my time with foolish questions. Second, expert systems make conclusions based
on logic and reason, not just trial-and-error guesses like a neural network. And third, an expert system can explain its
decisions by showing which parts were evaluated as true or false. A Symbolic AI can show a doctor why it chose
one diagnosis over another or explain why an auto loan was denied. The hidden layers in a neural networks just
can’t do that… at least, not yet. This, so-called, “good old-fashioned AI”
has been really helpful in situations where the rules are obvious and can be explicitly
entered as symbols into a knowledge base. But this isn’t always as easy as it sounds. How would you describe a hand-drawn number
2 as symbols in a number knowledge base? It’s not that easy. Plus, lots of scenarios are not just true
or false, the real world is fuzzy and uncertain. As we grow up, our brains learn intuition
about these fuzzy things, and this kind of human-intuition is difficult or maybe impossible
to program with symbols and propositional logic. Finally, the universe is more than just a
collection of symbols. The universe has time, and over time, facts
change and actions have consequences. So, next time we’ll talk about these actions
and consequences, and how robots use Symbolic AI to plan out their jobs and interact with
the world. Until then, I’m gonna finish this sandwich. [eats hot dog]. Crash course Ai is produced in association
with PBS Digital Studios! 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 propositional
logic, check out this episode of Crash Course Computer Science.


  • Reply Mohammed Nafisayman October 18, 2019 at 8:15 pm


  • Reply Lonewalker 90 October 18, 2019 at 8:15 pm


  • Reply Mark McHugh October 18, 2019 at 8:15 pm


  • Reply blackcat223364 October 18, 2019 at 8:15 pm


  • Reply Dull Bananas October 18, 2019 at 8:16 pm

    OUR computer

  • Reply justtrolin October 18, 2019 at 8:16 pm


  • Reply Evil Morty October 18, 2019 at 8:16 pm

    Nice Video

  • Reply NATIVE LATINOS Fook TRUMP October 18, 2019 at 8:18 pm

    🍔 I'm like a smart person I know the biggest words believe me I'm a very stable genius I'm the chosen one

  • Reply Flaming Basketball Club October 18, 2019 at 8:20 pm


  • Reply Iosif Zoica Daniela October 18, 2019 at 8:29 pm

    a romanian is the father of cybernetics and PC not this 3 assholes.

  • Reply Antti Björklund October 18, 2019 at 8:40 pm

    01101100 01101111 01110110 01100101 00100000 01110100 01101000 01101001 01110011

  • Reply Zack Reeser October 18, 2019 at 9:01 pm

    A hot dog is a sandwich.

  • Reply krellen d20 October 18, 2019 at 9:13 pm

    Jabril acknowledging that hot dogs are sandwiches makes him my favourite host of all time.

  • Reply Lank Asif October 18, 2019 at 9:13 pm

    I'm honestly blown away by the explanation of "AND" "OR"!! I haven't battled with getting the output correct when using them, but I've never realised what my brain was doing in order to come to the correct result/output. THANK YOU for this amazing channel!!!!!!

  • Reply Pfhorrest October 18, 2019 at 9:40 pm

    Since you're dealing with specifically material implication in this context, a much clearer way of describing the truth table of IF-THEN statements is "either the right is true or the left is false", or equivalently, "it is not the case that the left is true and the right is false". The natural reading of "if…then" has more modal implications about necessity and possibility, which aren't what your AI at this level is going to be dealing with.

  • Reply Adam Augustus October 18, 2019 at 9:45 pm

    isnt a symbol a variable?

  • Reply Adam Augustus October 18, 2019 at 9:47 pm

    Is (Judas) Traitor?

  • Reply Daniel McFadden October 18, 2019 at 9:47 pm

    Me: Siri, am I real?
    Siri: Ofcourse not, do not waste my time with such foolish questions!

  • Reply Thomas R. Stevenson October 18, 2019 at 10:11 pm

    I think you have a logic error when it comes to sandwiches and Hot Dogs. By your logic: Dogs have 4 legs. Cats have 4 legs. Therefore Dogs are Cats.

  • Reply BigSmartArmed October 18, 2019 at 10:27 pm

    So that's how the first child porn server was made, becasue you know, there's at least one pedo in any of those teams.

  • Reply ssmiker October 18, 2019 at 10:32 pm

    Siri is terrible when compared to Google or Alexa. Do Amazon and Alphabet use a different sort of AI or just have a bigger knowledge base?

  • Reply Hamid October 18, 2019 at 11:38 pm

    wow Jabril! what are you doing here! nice to meet you

  • Reply ICE GEL October 19, 2019 at 12:58 am


  • Reply Learn and Grow - Kids TV October 19, 2019 at 1:09 am

    A refreshingly, practical video. That’s why we love this channel!

  • Reply Leo Hao October 19, 2019 at 1:19 am


  • Reply dejohnny2 October 19, 2019 at 1:45 am

    This is dope. I'm going to do something with this.

  • Reply Abhijit Borah October 19, 2019 at 3:12 am

    Always wanted to know about AI (since 1989), thanks to this awesome course a desire is being fulfilled.

  • Reply granworks October 19, 2019 at 5:08 am

    Ah, this really takes me on a memory trip to the very first time I used Prolog — a symbolic language. At this point I had already had passing experience with several functional and procedural languages plus a smattering of assembly. They all "flowed". It's like knowing a Western European language and learning another — they are all in the same conceptual ballpark and so jumping from one to another makes sense.

    But then there was Prolog, a symbolic language. It is NOTHING like the others. It's like knowing Western European languages and encountering Mandarin Chinese. The rules in place and even the way of thinking is so different that nothing you've learned before can really help understanding this new thing. It was quite the mind trip!

    I had fun building up my propositions and making my inferences along with trying to understand some of the complex programs available but there isn't much of a job market for it and so I eventually migrated back to the safer realm of "conventional" languages.

    For a little while, though, it felt like computers were just a little bit magic again.

  • Reply Kenneth Lucas October 19, 2019 at 6:08 am

    1959 what a great year!

  • Reply Meet Patel October 19, 2019 at 6:16 am

    Hey John Green give me like………..🙋🏻‍♂️

  • Reply Yesid Antonio October 19, 2019 at 6:17 am

    i hate how irrationally angry a hotdog being called a sandwich made me i feel so weak pero like at the same time i will fight anyone who defends that position right here and now lmao

  • Reply James Thomas October 19, 2019 at 7:34 am

    Is it okay if I use Hank Green's as my symbol

  • Reply Michael Gainey October 19, 2019 at 8:04 am

    10:54 It starts out as a handshake but turns into some kind of dominance game.

  • Reply Tobias Görgen October 19, 2019 at 8:53 am

    I didn't even know, jabril could speak with an open mouth.

  • Reply ikkedansk October 19, 2019 at 10:16 am

    false/true logic is very outdated and new features like maybe should be implemented

  • Reply Christie Nel October 19, 2019 at 11:09 am

    There are four lights!

  • Reply Adam Augustus October 19, 2019 at 12:39 pm

    because people working on AI are virgins.

  • Reply Маша Кот October 19, 2019 at 9:05 pm

    What are other types of AI?

  • Reply Маша Кот October 19, 2019 at 9:11 pm

    Why did you eat sandwich in the end? You are nice btw

  • Reply Ath Athanasius October 19, 2019 at 9:43 pm

    08:00 shouldn't that be WEAR(ME, JACKET), not just WEAR(JACKET) ?

  • Reply Renan Cunha October 19, 2019 at 10:13 pm

    I didn't really understand the difference between symbolic AI and just a program that does a lot of if and elses with operators. Is a program that calculates my salary based on if/else AI?

  • Reply trejkaz October 19, 2019 at 11:52 pm

    A hotdog has the bread touching the meat on three sides, so is it a taco, not a sandwich.

  • Reply Gina Woolsey October 20, 2019 at 2:52 am

    "the universe is more than just a collection of symbols" symbolic interactionist sociologist: nuh UH

  • Reply labestia October 20, 2019 at 7:30 am

    Jabril: "Jabril is person. John Green Bot is machine. Car is machine. Car is old. Car is smelly. John Green Bot is not person. Jabril isn't machine. Toaster is machine."

    John Green Bot: ………

  • Reply Niko L October 20, 2019 at 11:17 am

    3 minutes in and it's already starting to look a lot like Prolog

  • Reply Niko L October 20, 2019 at 11:27 am

    ok but what if we had a hybrid system?
    one that uses neural networks to make sense of fuzzy data, and a symbolic system to make sense of the concrete data generated by the neural network
    that way one can have the best of both worlds

  • Reply Ken Bell October 20, 2019 at 1:09 pm

    Smelly, Old JohnGreenBot drives a car.

  • Reply PunchKickBlog October 20, 2019 at 5:51 pm

    Baba is you.

  • Reply Shakya Mohanty October 20, 2019 at 11:49 pm

    Damn… that's an awesome video to watch before getting to coding.

  • Reply Alvaro Torres October 21, 2019 at 12:44 am

    graduated high school, not going to college, still watching crash course vids

  • Reply Faultty October 21, 2019 at 1:15 am

    Can you please do a series on forms of oppression. I think we need to know how systems are put in place to control behaviour. Like how we used to beat left handers. There are so so so many. People need to see the code of the Matrix they live in.

  • Reply Dinelle Jeneve October 21, 2019 at 1:45 am

    Next video: crash course on how to play sudoku!!!

  • Reply Justin O'Brien October 21, 2019 at 1:55 am

    Out on a limb here… you always mention donuts. I'm getting an inkling that you like donuts

  • Reply Enjoylife October 21, 2019 at 1:57 am

    Oh man you are smart👍👍

  • Reply Lily October 21, 2019 at 9:49 am

    Can you guys make a crash course for art history?

  • Reply Ethan Avila October 21, 2019 at 10:52 am

    The misuse of the word "Symbol" in this really makes me uncomfortable.

  • Reply Ok Gg October 21, 2019 at 9:45 pm

    Please Arabic transcript

  • Reply Karim Makhlas October 21, 2019 at 11:15 pm

    Can you make videos about maths and modern physics and thanks

  • Reply Ian Buck October 23, 2019 at 6:54 pm

    I get home from work, fix myself a snack, and fire up an episode of Crash Course before my afternoon nap.
    Jabrill gives some examples of if/then statements: "if tired, then take nap. If hungry, then fix snack."
    Am I being watched?

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