Now, we've made great progress with automobile technology over the last 100 years.

And this next shot here is one of the lunchrooms.

Be fast.

We only burn up a small amount of the uranium that's in there, we take it out and we stick it in a spent fuel pool.

Here. The Pirate Bay. And that's all.

Then it goes forward, then it goes backward, and does so multiple times until it finally has turned around.

There's a way to practice intelligently.

Also, we see how we measure the aerodynamic efficiency.

In general we don't try to please the customer here.

What happens is you have crossing over, and it's a surprisingly organized process.

Half of that runs pumps and fans.

Imagine driving into your garage we've built a system to do this you drive into your garage, and the car charges itself, because there is a mat on the floor that's plugged into the wall.

He's efficient, he's precise.

The tools that we develop in this course are going to be useful across the board here, but we're going to mainly focus on issues of time.

But the heaps that we were talking about the overhead is pretty raw and we get this kind of running time, the elapse should be pretty efficient.

So the total running time ends up being we do (nlogk) operations.

That's gotta be convenient.

It's not about the things that it brings.

And so a matrix is a block of numbers that lets me take all of my data, all of my x's.

Could you enhance certain effects? Probably so.

let's compare that with a number I've just mentioned.

And anyway, to cut the time short so I started

So now we have two reasons why the naive Bayes assumption is a good thing.

So it is better, it is safer it is affordable, it is energy efficient.

They are looking at how ants and bees find their food and their flowers in the most effective way as a whole hive.

Being able to do that means that I can generate energy cleanly, efficiently and cheaply right where I am.

So I'm going to talk about some projects that have explored these ideas.

What SUNDAR stands for, in Hindi, is attractive.

We had knowledge about the electromechanical efficiency and then we can calculate the aerodynamic efficiency.

So that's a little bit of a taste of why someone might realistically, in this case, pay a higher Price to Earnings for safety.

And ever since then, I've wondered why it is that capitalism is so amazingly efficient at some things, but so inefficient at others, why it's so innovative in some ways and so un innovative in others.

you can see now the whole thing takes up far less space.

Maybe a decade, according to my friends in Detroit.

So it needs refurbishment.

But that's a mistake.

So I started to look at passion,

It's what the money motivates, that we're actually terraforming the Earth itself with this kind of algorithmic efficiency.

And, as they do so, they build bigger and bigger cooperative enterprises and find out that their economies are cheaper, more effective, more efficient, work for everybody, and you get the evolution of rainforests, and human bodies made of a hundred trillion cells, working in complete cooperation even though they're so diverse.

despite the obvious intellectual understanding of their safety, my hands still get sweaty on takeoff.

It is important to think over what you will do, to pursue your studies economically, and effectively.

Admittedly, trying to capture all of these effects leads to a lot of work and possibly inefficient shaders.

It happens in a reasonably organized way, which kind of speaks to the idea that it's part of the process.

There's another way to look at this types of value propositions, kind of as a Venn diagram.

I need your help to communicate the gravity and the urgency of this situation and its solutions more effectively.

And that is lead theoreticians at least to find efficient in one particular way sometimes and that is that an algorithm is efficient if it has a polynomial bound on its running time.