I'll address the planning problem at multiple levels of abstraction.

The other thing we talked about in Unit 4 was how networks work.

In Unit 5 the main new idea we introduced was how to measure cost.

And, then, when you have to draw it, that requires you to take an extra level of abstraction and figure out,

So we fill our cognitive maps with these markers of meaning.

The main new thing in Unit 3 was lists.

And then we introduced the hash table.

You have mechanical systems.

And so, in fact, what we've done is we, humanity, have started abstracting out.

In Unit 2 we introduced procedures.

And something very interesting is going on.

So, the earliest retes, for example, were very simple and primitive.

Evans

We've seen lots and lots of examples of abstraction in this class.

I wish to get to this point up here.

And what's neat about mathematics, and what's neat about algebra in particular is we can focus on this abstraction. we can focus on the abstract here, we can manipulate the abstract here, and what we discover from these ideas, from these manipulations can then go and be reapplied to all of these other applications, to all of them.

We can use a name to refer to some value.

I'm going to formally tell you how to do that.

So let me stretch out that equation.

Let's just say, for the sake of argument, this is 1995.

So the nth term, if this is, you know, if this is 10, it's going to be 1 plus 10 5's.

Instead, what I do and what the designers at Thinking Machines do is we think at some level of abstraction and then we hand it to the machine and the machine takes it beyond what we could ever do, much farther and faster than we could ever do.

But what we want to propose, is that the highest levels of abstraction, things like mathematics, computing, logic, etc. all of this can be engaged with, not just through purely cerebral algebraic symbolic methods, but by literally, physically playing with ideas.

And indeed, this kind of verbiage is not the exception, but the rule.

Then one last one. f of z.

Now, another kind of mind is the pattern thinker.

These are themes that aren't just pervasive throughout this course but are pervasive throughout all computer science.

This is a blue field it's actually an Yves Klein painting.

We recognize them by the experiences and we abstract them into symbols.

I go to the concept of The Lion King and I say, What is the essence of it?

I can lump the bets into small, medium, and large.

Well, we can go back to what we said before.

The first one is abstraction.

Abstract art is something to feel.

It must have been about an hour after this that the front door bell rang.

If the rules for the universe are simple, it's kind of inevitable that they have to be very abstract and very low level operating, for example, far below the level of space or time, which makes it hard to represent things.

It's a Boolean logical distinction rather than on this side a probabilistic distinction.

That way, it makes it much easier to check if new statements invalidate a cached response.

Or in this case, it's not 5.

You're not even capable of thinking in abstractions.

That's a little abstract. What's the target?

Abstraction is all about hiding details.

An Abstract Shooter by Kenta Cho

I became an artist. O.K., and started drawing.

Second we need to reproduce the steps required to reproduce the problem.