Shall we get started?

So thanks, everybody, for coming to this session.

I'm going to be talking about TensorFlow

and particularly TensorFlow from a programmer's perspective--

so machine learning for programmers.

I'd like to show some code samples of using TensorFlow

in some simple scenarios as well as one slightly more

advanced scenario.

But before I do that, I always like to just do

a little bit of a level set.

And if you were at the previous session, sorry,

some of the content's going to be similar to what

you've seen already.

But when I like to think about AI,

and when I come to conferences like this one about AI,

or if I read the news about AI, there's

always stories about what it can do or what it might do,

but there's not a whole lot about what it actually is.

So part of my mandate and part of what I actually

like to educate people around is from a programmer's

perspective, what AI actually is, what it is for you,

what you can begin to learn how to program,

and then how you can apply it to your business scenarios.

But we're also at the cusp of this revolution

in this technology, and lots of people

are calling it like the fourth Industrial Revolution.

And for me, I can only describe it

as like it's the third big shift in my own personal career.

And so for me, the first one came in the early to mid-90s

when the web came about.

And if you remember when the web came about,

we were all desktop programmers.

I personally-- my first job was I

was a Visual Basic programmer programming

Windows applications.

Anybody ever do that?

It was fun, wasn't it?

And so then the web came around, and what happened with the web

then is it changed the audience of your application

from one person at a time to many people at a time.

You had to start thinking differently

about how you built your applications to be

able to scale it to lots of people using it.

And also, the runtime changed.

Instead of you being able to write something that

had complete control over the machine,

you would write something to this sort of virtual machine

that the browser gave you.

And then maybe that browser would have plugins like Java

and stuff like that you could use

to make it more intelligent.

But as a result, what ended up happening

was this paradigm shift gave birth to whole new industries.

And I work for a small company called Google.

Anybody heard of them?

And so things like Google weren't possible.

Anybody remember gophers?

Yeah, so that's really old school, right?

What gophers where were almost the opposite of a search

engine.

A search engine, like--

you type something into it, and it has already

found the results, and it gives them to you.

A gopher was this little application

that you would send out into the nascent internet,

and it would crawl everywhere, a little bit like a spider,

and then come back with results for you.

So for me, whoever had the great idea

to say, let's flip the axes on that

and come up with this new business paradigm,

ended up building the first search engines.

And as a result, companies like Google and Yahoo were born.

Ditto with things like Facebook--

that wouldn't have been possible with the browser.

Can you imagine trying to--

pre-internet, where there was no standard protocol

for communication, and you'd write desktop applications--

can imagine being able to build something

like a Facebook or a Twitter?

It just wasn't possible.

So that became-- to me, the web was

this first great tectonic shift in my own personal career.

The second one then came with the advent of the smartphone.

So now users had this device that they

can put in their pocket that's got

lots of computational power.

It's got memory.

It's got storage, and it's loaded

with sensors like cameras, and GPS, et cetera.

Now think about the types of applications

that you could build with that.

Now it's a case of companies like Uber became possible.

Now, I personally believe, by the way

that all of the applications are built

by introverts because you'll see all of these great things

that you can do nowadays is because they serve introverts.

I'm highly introverted, and one thing I hate to do

is stand on a street corner and hail a taxi.

So when Uber came along, it was like a dream come true for me

that I could just do something on my phone,

and a car would show up.

And now it's shopping.

It's the same kind of thing, right?

I personally really dislike going to a store

and having somebody say, can I help you?

Can I do something for you?

Can I help you find something?

I'm introverted.

I want to go find it myself, put my eyes down, and take it

to the cash register, and pay for it.

And now online shopping, it's done the same thing.

So I don't know why I went down that rabbit hole,

but it's just one that I find that the second tectonic shift

has been the advent of the mobile application

so that these new businesses, these new companies

became possible.

So the third one that I'm seeing now

is the AI and the machine learning revolution.

Now, there's so much hype around this,

so I like to draw a diagram of the hype cycle.

And so if you think about the hype cycle,

every hype cycle starts off with some kind

of technological trigger.

Now, with AI and machine learning,

that technological trigger really

happened a long time ago.

Machine learning has been something,

and AI has been something that's been in universities.

It's been in industry for quite some time--

decades.

So it's only relatively recently that the intersection

of compute power and data has made it possible so

that now everybody can jump on board-- not just university

researchers.

And with the power of things such as TensorFlow

that I'm going to show later, anybody with a laptop

can start building neural networks

where in the past neural networks

were reserved for the very best of universities.

So that technological trigger that's rebooted,

in many ways, the AI infrastructure,

has only happened in the last few years.

And with any hype cycle, what happens

is you end up with this peak of increased expectations

where everybody is thinking AI's going to be the be-all

and end-all, and will change the world,

and will change everything as we know it,

before it falls into the trough of disillusionment.

And then at some point, we get enlightenment,

and then we head up into the productivity.

So when you think about the web, when

you think about mobile phones and those revolutions

that I spoke about, they all went through this cycle,

and AI went through this cycle.

Now, you can ask 100 people where we are on this life cycle

right now, and you'd probably get 100 different answers.

But I'm going to give my answer that I

think we're right about here.

And when we start looking at the news cycle,

it kind of shows that.

We start looking at news.

We start looking at glossy marketing videos.

AI's is going to do this.

AI's was going to do that.

At the end of the day, AI isn't really doing any of that.

It's smart people building neural networks

with a new form of, a new metaphor for programming

have been the ones who've been able to do them,

have been able to build out these new scenarios.

So we're still heading up that curve

of increased expectations.

And at some point, we're probably

going to end up in the trough of disillusionment

before things will get real and you'll be able to really build

whatever the Uber or the Google of the AI generation's

going to be.

It may be somebody in this room will do that.

I don't know.

So at Google, we have this graph that we

draw that we train our internal engineers

and our internal folks around AI and around the hype around AI.

And we like to layer it in these three ways.

First of all, AI, from a high level,

is the ability to program a computer

to act like an intelligent human.

And how do you do that?

There might be some traditional coding in that,

but there may also be something called machine

learning in that.

And what machine learning is all about is instead of writing

code where it's all about how the human solves a problem,

how they think about a problem, and expressing that

in a language like Java, C#, or C++,

it's a case of you train a computer by getting it

to recognize patterns and then open up whole new scenarios

in that way.

I'm going to talk about that in a little bit more.

And then another part of that is deep

learning with the idea behind deep learning

is now machines being able to take over some of the role

that humans are taking in the machine learning phase.

And where machine learning is all about--

I'm going to, for example, show a slide next

about activity detection.

But in the case of activity detection,

instead of me explicitly programming a computer

to detect activities, I will train a computer

based on people doing those activities.

So let me think about it.

Let me describe it this way.

First of all, how many people in this room are coders?

Have written code?

Oh, wow, most of you.

OK, cool.

What languages, out of interest?

Just shout them out.

[INTERPOSING VOICES]

LAURENCE MORONEY: C#.

Thank you.

[INTERPOSING VOICES]

LAURENCE MORONEY: Python.

OK.

I've written about a bunch of books on C#.

I still love it.

I don't get to use it anymore, but it's nice to hear.

So I heard C#.

I heard Python.

C++?

OK, cool.

Now, what do all of these languages have in common?

Ruby?

Nice.

What do all of these languages have in common?

That you, as a developer, have to figure out

how to express a problem in that language, right?

So if you think about if you're building a problem, if you're

building an application for activity detection,

and say you want to detect an activity of somebody walking--

like I'm wearing a smartwatch right now.

I love it because since I started wearing smartwatches,

I became much more conscious of my own fitness.

And I think about how this smartwatch monitors my activity

that when I start running, I want

it to know that I'm running, so it logs that I'm running.

When I start walking, I want it to do

the same thing, and count calories,

and all that kind of stuff.

But if you think about it from a coding perspective, how would

you build a smartwatch like this one if you're a coder?