This is a KDF, a key derivation function that's built from HMAC.

What can we do with this image? One of the things we can do is to divide up the image into various regions.

And what you see are different people's segmentations of these images.

It's called Pivot, and with Pivot what I can do is I can choose to filter in one particular cause of deaths say, accidents.

Dylan can begin to extract patterns that include faces.

This is a lot of work. As we are going to see.

There are 1.4 billion different ways that a family can find itself.

I think they all involve the same thing we can take small pieces of data and by putting them together, we can generate more value, we can do more exciting things with them.

There are different ways to approach evolutionary medicine, and one way is to extract human DNA from ancient bones.

But we aren't done here.

We can learn by watching other people and copying or imitating what they can do.

We talked about invariances, the type of things that affect the appearance of a feature in the camera image, and we went through methods for extracting edges, for extracting corners, and for extracting fairly sophisticated features like SlFT features.

I've already isolated the virus.

We are going to process them into ricin.

The right hand pane displays details of the references in your library, including all of the meta data that Mendeley extracts.

(Laughter)

So let's simplify each of these expressions and cancel out as much as possible.

What is this thing right here?

Here's the finished code for this problem.

So essentially the salt, you know, you can just bang it the keyboard a couple of times when you generate it but it just needs to be something that's random initially but then it's fixed forever, and it's fine if the adversary knows what it is and nevertheless the extractor is able to extract the entropy and output a uniformly random string K. In some sense the salt is only there to defend against adversarially bad distributions that might mess up our extractor.

And then you can reduce on that to basically reduce that content down to the kind of target output you want.

Extract an essence from the bark of a tree.

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

So now we're ready to get back to the problem of extracting a list that contains all the links in a webpage.

What we do in this case is we use what's called an extractor.

And let's say, we get to the year 2005.

Okay, so we add the post function.

We can generate truly random numbers by measuring random fluctuations known as noise.

So, let's remember the code we had at the end of Unit 1.

And finally of course we can use familiar configuration, we can segment out a face from the background once we have recognized that those pixels correspond to a face.

This right here is 2 and a 3.

Let's talk of when we're in Computer Vision and what type things we can do with images.

We've just abstracted what the retina's doing with a set of equations.

But I believe we're on a path now, when we get to data, that we can distill styles, and templates, and formulas, and all these kinds of things, again, that you've seen happen in the computer graphics world.

There are a few different types of graphics that you can use.

This is called the extract then expand paradigm, where the first step of the KDF is to extract a pseudo random key from the actual source key.

But what you see clearly is that people are able to mark the objects.

We want to keep our code simple and easy to understand.

Here are two of them.

And I'm still on that mission.

Introduction

Extraction

Suppose the value we added brings us to here.

I'm a Lance Armstrong fan, so I'll go ahead and I'll click on that, which reveals, for me, all the issues in which Lance Armstrong's been a part of.

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