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 so sure
what I want to 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 realise 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 2 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 going 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 going to talk about philosophy, science fiction.
I'm going to talk about algorithms
and I'm going to talk about, you know, deep learning
and how to think about things beyond deep learning.
All right?
And let me give you a perspective and then we'll start.
So I'll take questions at the end.
Start working this.
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 civilisation got here, and where we're 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
and 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?
We've 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, 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 the machine.
There's no human on the planet
who can play Jeopardy better than the 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 also saw how machines
are able to create pictures, right?
So this is one of the things
that 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?
So obviously there's a lot they've done.
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.
We could think about how we talk to the internet today.
We carefully craft word-for-word queries, right?
And you know, we allow the internet to make mistakes.
We craft queries again, and we take the suggestions or not.
We talk to the internet like we're talking to a three-year-old.
Now in the daily needs of massive data computers, NLP
and all its deploying staff, imagine how shameful 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've created Yahoo Answers, we've created Quora
where people can type questions
We do a match between the questions and the answers
and then we again do retrieval.
So 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 from Einstein or Feynman.
We already know all the language and knowledge of these people.
Can we not have a persona or a person Feynman or Einstein,
and have a conversation with that person, right? So just imagine the future
of what will happen if we're just able to 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 on how to use the high end machine learning
in pharma research. But my contention is 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 forget some things, we want to 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 docs, right?
But machines could do it, the way, and then there's progress.
Can we not find cures or new medicine too.
Can I crack the next IIT Entrance Exam? You laughing today, but you never know.
Five years from now, what will happen? 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.
What about, 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 want to see the goal shots.
Today I cannot do that. Can my machines be intelligent enough
to vision part, to 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 now.
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 writing an email to your boss You're angry, you've written
a sarcastic comment, and ? says, 'Hey are you sure about this?'
In the heat of the moment, can I put it this way?
So, like, today we do attachments. Can you detect sarcasm and things like that.
And to me the holy grail of AI is not really all these big things,
but a really simple thing. Can a machine find a joke funny?
Now there are a lot of - don't know if you guys watch Star Trek - but data entry
300, 400 years from now, is an android who is capable of all these other things.
He's a great supercomputer in human form but he's still struggling with humans.
That's how hard the problem is. So obviously we have a long way to go.
We've 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.
Golden ? We all want a Jarvis, right?
Who takes care of the chores and gets rid of the whatever and then 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 going to happen just because we're going to make more and more
Hollywood movies like this.