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  • 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 tothink.”

  • 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, orgood old-fashioned AIas 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 thatJohn-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 isSmelly 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), orIF 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), orIF 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 thatat least, not yet.

  • This, so-called, “good old-fashioned AIhas 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.

Back in 1959, three AI pioneers set out to build a computer program that simulated how

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