- 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 the 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.