- Okay, so, good morning everyone.

I'll just get started.

My name is Shailesh and I give these talks

almost every year so this is a very deja-vu feeling for me.

The only thing different this time

is the stage is slightly thinner.

But great crowd, great list of talks so far.

So, Daniel called me a couple of weeks ago and said

"Why don't you give a keynote again?"

And I said, "You know, I'm running out of things to say now."

I've given four talks at different forums

with The Fifth Elephant and I wasn't sure

what I'm gonna talk about

So, then, one of these days I was talking

to one of my non-geek friends

and he was very excited about what I do

so he said, 'What do you do?'

and I, you know, it was on the phone

and I started talking to him about this, that, and the other.

And for about 45 minutes I was rambling

and this guy was very quiet.

I didn't realize he wasn't a techie

and I was going on and on and after 45 minutes I stopped

and said, "Are you still there? Are you listening?"

And he said, "Yeah, I'm listening.

"Can you tell me what do you do again?"
(audience laughs)

And then I realized, how do I summarize this in two words?

So then I told him, "Hey, I'm building thinking machines."

And that's when he said, "Why didn't you say that before?

"It was so easy to say that, right?"

So that's how the title came by

and obviously we're not building thinking machines

but what I'm gonna talk about is towards thinking machines, right?

So, we have a long way to go.

So I added the word "towards" later.

So what I'm gonna talk about is all over the place.

I'm gonna talk about philosophy, science fiction.

I'll talk about algorithms

and I'm gonna talk about deep learning

and how to think about things beyond deep learning.

And let me give you a perspective and then we'll start.

So I'll take questions at the end.

It's not working.

It's not working, this.

That's fine.

All right, so, I ended my last year's talk on this quotation

So I thought I'll start on this quotation this time.

So I like this quotation because it puts a lot

of things into perspective of what we're doing,

how our civilization got here

and where we are headed.

So it says, "Our technology, our machines, is part of our humanity.

"We created them to extend ourselves

"and that is what is unique about human beings!"

And if you look at chairs, and dogs, and animals, and cats

they don't create machines to extend themselves.

They just have instincts and they follow their instincts.

Right, that's very unique about human civilization.

We've created Taj Mahal, and space flights, and internet.

So we've come a very long way.

So if you think about the tools, right?

The cavemen had tools and now we have

a completely robotic assembly line with no humans

and you could turn the lights off and nothing will happen

the cars will get produced, right?

If you look at our transportation

we have gone from just on-road, bullock carts,

to massive amounts of transportation that we can do now.

If you look at our ability to look further

into space, again...

Since Galileo, we have made a lot of progress.

Recently we saw the news of Pluto flyby

so now we're able to send satellites into space.

If you look at the first computer we built

and where we are today, right?

We have a huge data center, and really

if you look at the whole thing in perspective

we have made an enormous amount of progress

in the last so many centuries, right?

So if you look just at the technical part

the IT kind of intelligent machines

we're not talking about mixies

and other things, just look at what AI

and deep learning and all this stuff has produced.

Today's machines can play chess.

And there's no human on the planet

who can play chess better than the machine.

I want to take a pause and think about where we are.

There's no human on the planet

who can play chess better than a machine.

There's no human on the planet

who can play Jeopardy better than a machine.

And recently, Google came out with automatic cars

so the machines can drive cars and record show

that these cars are better than humans under ideal conditions

And they have much less accident rates

and all the accidents happened because of other humans drivers.

They're not because of cars.

And recently you also saw

how machines are able to create pictures, right?

So this is one of the things

that we saw what deep learning is internally doing.

And now think about all this.

Just think about where machines have gone today.

How many things they can do

which are way beyond our imagination

that machines could have done.

So obviously there's a lot they've done.

But can they do the following?

We would want to stress the limits

so one of the holy grails of AI

is to have a machine have a conversation with a human being.

We all know the Turing test

and the repercussions of this will be huge.

If you think about how we talk to the internet today

we carefully craft three-word, four-word queries, right?

And you know, we allow the internet to make mistakes.

We craft the queries again, we take the suggestions or not.

We talk to the internet like we're talking to a three-year-old.

Now in the day and age needs of massive data computers, NLP

and all this deep-learning stuff, imagine what a shameful thing it is

to talk to a computer like a 3-year-old.

So it's got the capacity of thousands of people

but it can't understand language.

So we need to change that.

Now imagine beyond keywords what can happen.

We can do question-answering

but how do we do question-answering today?

We have created Yahoo Answers, we have created Quora

and people who type questions, we do a match.

Between the questions and the answers

and then we again do retrieval.

We're still not answering questions.

Now think about conversations.

Conversation is an even more complex thing.

If it works out, what are the repercussions?

I don't want to study physics from my physics teacher.

I want to study it from Einstein or Feynman.

We already know all the language and the knowledge of these people.

Can we not have a persona of a person, Feynman or Einstein

and have a conversation with that person?

So, just imagine the future of what will happen

if we are able to just have conversations with the machines.

So, there's a long way to go between

keyword search and conversations.

Can we discover a cure for cancer?

There are a lot of diseases out there.

Now, obviously there is a lot

of research pharma companies are doing.

There's a lot of new initiatives in how

to use the high-end machine learning in pharma research.

But my contention is that I believe that the cure for a lot

of diseases is already out there.

In all the medical literature, if somebody

could actually read them, hold that knowledge

in the brain, in RAM, and do interconnections

we should be able to find a lot of things.

But what is the problem?

A single human expert, even in one field

cannot keep up with that quest of knowledge, right?

We'll forget some things, we won't read certain papers.

And therefore, it's the other problem.

We have too much knowledge and our individual brains

are not capable of forming those connections in the...

Because we can't even read that many documents, right?

But if a machine could do it

the way NLP has progressed

can we not find cures or new medicine?

Can I crack the next IIT Entrance Exam?

You're laughing today, but you never know.

Five years from now, what will happen?

And we should hope that if Watson is a test of intelligence

if Igloo is a test of intelligence

could this not be a test of intelligence?

The ability of AI system to be able

to actually solve an IIT paper and get a rank 1.

Can I search all the video scenes

which only have a goal shot

in the football videos and nothing else.

I don't want to watch the rest of it.

A lot of balls going here and there.

I just wanna see the goal shots.

Today I cannot do that.

Can my machines be intelligent enough

the vision part, that can actually find

this is a goal, this is a goal, this is a goal

the rest of it is something else.

So we can imagine the applications out there.

We were talking about sarcasm a lot

and we all understand sarcasm is a very hard thing to do.

And imagine if you could detect sarcasm, what else can you do?

You're writing an email to your boss

you're angry, you have written a sarcastic comment

and Gmail says, "Hey, are you sure about this?"

In the heat of the moment
(audience laughs)

can I put it this way?

So, like, today we do attachments

can we detect sarcasm and things like that?

And to me the holy grail of AI is not really

all these big things, but a very simple thing.

Can a machine find a joke funny?

Now there are a lot of...

I don't know if you guys watch Star Trek

but Data, in 300 years, 400 years from now

is an android.

He is capable of all these other things.

He's a great supercomputer in a human form

but he's still struggling with humor.

That's how hard the problem is.

So obviously we have a long way to go.

We have come a long way and we have a long way to go.

So this talk is really about the way forward.

So, what do we imagine the future to be?

We want something like this.

Good and bad, hopefully good.

We want a Jarvis, right?

We all want a Jarvis

who'll takes care of the chores

and get rid of whatever

and we all want a Jarvis right?

So if you watch these movies again

after watching this talk

you'll have a very different perspective

on what we need to do to get here.

It's not gonna happen just because we're

gonna make more and more 
Hollywood movies like this.

I mean, Asimov wrote 
"I, Robot" in the 70s

and we're still not there.

It's not gonna happen because we keep 
doing "data science"

And that's one of the reasons I wanted
to do this talk

'cause a lot of people keep

thinking "data science is the end 
of the world"

but there's a lot more to data science

and I want to see how we can go beyond
data science

- and this is not data science.

This is artificial intelligence.

Right? So I want to draw the distinction

and say how we can move
beyond data science

- nothing wrong with it -

but it's, it's a done deal.

Right? We have software you can download,

you can put up whatever you want,

it's a done deal. Data science has been
packaged, already.

Right? If you look at Microsoft Azure,

or some of these other softwares, right?

It has already been packaged

All you have to do is download the right

software, put your data
in the right format,

and you're done. Right? So there's nothing
"great" about data science anymore.

Sorry about that, but, you know,

we need to jolt ourselves out of this
comfort zone, and say

"okay, we are all data scientists"

- that's not it, right?

How do we get here?
How will data science get here?

Alright. So, we'll get here by asking
a lot of deeper questions.

Right? Not the questions like

"Why is this customer
returning from Flipkart?", right? or

"Who's -- what is the next product to
recommend to somebody?", or

"Which movie you're going to ask?"
These are not the questions

that'll take us to the next stage. Right?

So the question that'll take us
to the next stage is

"what is learning?" Fundamentally, 
philosophically.

"What is learning?" We see that we
are learning, children are learning,

everybody is going to school, 
we all are learning.

We think that machine learning
is learning,

but what is learning really, right?

"What is understanding?"

What does that mean?
What does the word "mean" mean?

What is thinking? We keep saying

"Oh -- I'm thinking about this"

What are you doing when you're thinking?

So, today I'm going to show you an
equation of thinking.

Okay? So, it'll be fun -- I don't claim

this is - THE - equation of thinking,

but I'm trying to get to that plot point

where we start thinking about thinking,

and not just think.

"What is creativity?" Now, creativity is,

if you look at an artist, or a musician,
or even a scientist,

we create new inventions

out of the knowledge we have,

and innovation is a manifestation of
the knowledge in a certain form.

Right? A poet creates, 
a musician creates --

so what is creativity?

And the last question I have, here is

"What is consciousness?" Right?

So, ultimately, if you look at movies like
"I, Robot",

the word "I" from the robot is
not really about

the robot's great abilities at
mundane tasks,

but really it's about the "I" in it.

"I am a conscious being", and now what are
the consequences.

Right? So what is consciousness, and

can we have sentient machines at the
end of the day, right?

So, we won't go there today,
maybe we'll see

if we have time we'll watch a video,

but I'll try to cover the bottom three and

see if we can find something interesting.

So, learning. Learning is one of the
most basic things,

we all do learning all the time.

-- at least we all claim to
be learning all the time.

So, really, I'm going to use language and

not vision at first, but language as

the basis for all my examples.

So, learning really is many, many things:

the first thing we learn, so, you know,

the greatest example of a machine
learning system, or an A.I. system

is a human child.

And all you have to do is just observe

how a baby is growing up, how he's
picking language, how he's

picking walking, how he's picking 
swimming, how he's picking tantrums, right?

And you learn so much about A.I. because
you're looking at the real A.I.

So what is learning? I want to use
that example

and see how we pick up language.

If I use the word -- if I start
-- imagine you're reading a novel,

or imagine words are coming at you one
at a time:

you see the word "united" - what do you
think the next word would be?

Right? "United States", 
"United Something" , whatever.

then, [MIC CUTS], predicting. When we're
learning,

we are also simultaneously predicting.

And this is one of the flaws in current
machine learning:

that we keep thinking that learning is
separate, prediction is separate.

We'll learn first, then we'll score.

Right? But the human brain is not like that.

We don't learn for sixty years and suddenly
we start behaving.

We're constantly learning and 
we're constantly applying that learning,

and that is one of the fundamental
reasons why,

you know, I call the current model of
machine learning

like the [inaudible] which is never going
to become a data-flow architecture

ever, right? So that is one of the problems.

So imagine what we're doing now, we are
predicting what will come next.