Okay, welcome everybody to the third lecture on symbolic methods for AI.

I've briefly looked into the results.

Was something a problem with the third question?

Did you notice anything?

Very curious that there are no correct answers, which I didn't expect, so probably some technical

problem.

If anybody noticed anything, please tell me, otherwise we'll try to get to the bottom of

this.

And then we'll see.

Okay?

So, are there any questions so far?

We are in the process of looking at mathematical language.

Mathematical language being one of the probably biggest problems that our AI students have

with at least symbolic AI.

So I'm trying to kind of dissect mathematical language and kind of poking your nose to the

things that are weird and wonderful about this particular specialized language.

And I have been trying to show you that this is indeed a language that has adapted to a

need, namely the need of talking precisely and concisely, i.e. precise and short, so

that we have a good overview over the complicated things we're talking about.

And I'm trying to show you some of the mechanisms.

Okay?

So actually, I'm trying to convince you, if you want to, that the shape of mathematical

language is a feature, not a bug.

It's not made to annoy you, but it serves a purpose.

Okay?

So I tried to show you that the formulae, and we're seeing that math is formulae mixed

into natural language.

Formulae are just very compact representations of otherwise complicated stuff.

I showed you the Egyptian way of doing it, which was this long, where we really only

needed this long.

And I was trying to convince you that it somehow has the formulae have to fit with the grammar.

That's basically so that you can actually read it out, and it still sounds like English.

And we looked at the idea that if you give names to things, then you can easily refer

to them precisely.

Okay?

Formulae are something that are precise and concise, whereas names, while not being as

short because they're additional, at least give you precision.

Natural language has it, and they, and he, and she, and all of those kinds of pronouns.

They're less precise than naming.

By the way, variable names is where they come from this practice.

Okay?

We've talked about aggregation.

We've talked about sequences.

Sequences are things we can construct with either putting them down directly or specifying

a formula or just by saying dot, dot, dot.

You do the thinking.

That's something we do quite often in math.

Right?

We make our lives as authors easier by saying, you do the thinking.

You fill in the gaps.