Name: 符號人工智能：速成班人工智能＃10 (Symbolic AI: Crash Course AI #10)
Uploaded: 2021-01-14T10:28:14.000Z
Duration: 13 min 22 s
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Back in 1959, three AI pioneers set out to build a computer program that simulated how

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

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.

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.”

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

Modern neural networks train a model on lots of data and predict answers using best guesses

But Symbolic AI, or “good old-fashioned AI” as it's sometimes called, is hugely

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…

To recap, logic is our problem-solving technique and symbols are how we're going to represent

Symbols can be anything in the universe: numbers, letters, words, bagels, donuts, toasters,

One way we can visualize this is by writing symbols surrounded by parentheses, like (donut)

A relation can be an adjective that describes a symbol, and we write it in front of the

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

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

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

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

Using logical connectives like AND and OR, we can combine these symbols to make sentences

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

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

We can understand that sort of logic with our brains: if all three things are 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

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

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

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

For the first row, this translates as (true plus true), then that result times true, which

That equals 2 times 1, which is 2, which means the whole proposition is true!

Remember, any answer that isn't 0 is true.