So first of all, let me thank you
Anantha, both for the introduction,
but more importantly for everything
you've done to bring this to this point today,
particularly for your leadership and your collaborative spirit
in bringing the MIT Intelligence Quest a reality.
We owe a great debt to you, Anantha.
Thank you.
My principal responsibility this morning
is to introduce our next speaker.
David Siegel is a computer scientist, entrepreneur,
and philanthropist.
He co-founded Two Sigma investments
where he currently serves as co-chairman.
David received his undergraduate degree
from Princeton and his PhD from MIT,
where he conducted his doctoral work
in the artificial intelligence laboratory.
In 2001, he co-founded Two Sigma on the belief
that innovative technology and data science
could help discover value in the world's data.
Today through the use of algorithms
with its unique culture, Two Sigma
drives transformation in many industries,
including investment, management, and insurance.
As of 2018, the firm manages over $52 billion
in assets with more than 1,400 employees
across offices in New York, Houston, London,
Hong Kong, and Tokyo.
David's philanthropic efforts underscore his conviction
that education, science, and technology
are keys to better world.
He sits on the board of organizations
which promote his convictions, including the MIT
Corporation, Carnegie Hall, and the Scratch Foundation,
which he co-founded.
Before I turn over the podium to David,
I would be remiss if I did not thank him.
David was one of our earliest thought partners
on the Intelligence Quest.
We solicited his opinion and input
when it was still just an idea in its formative stages.
We sent him documents, talked on the phone,
put him in touch with more people from MIT who could
answer questions that we couldn't.
He was curious, tireless, and a diligent partner
in this exercise.
And for that, we're truly grateful.
David's perspective as an industry leader at Two Sigma
gave MIT crucial inputs at a time
when they were most needed, at a moment
when they could have the greatest impact on the Quest
direction.
Our faculty, many of whom Anantha acknowledged,
were also consulted extensively and contributed significantly.
But David brought a unique and valuable perspective.
I feel very confident that the MIT Intelligence Quest will
have tremendous impact here and around the world,
and this success will no doubt have been
influenced by David's guidance.
So thank you, David.
And please join me in welcoming him to the podium.
Good morning.
And first let me thank you, Marty, for those remarkably
kind words.
I really very much appreciate the introduction.
And I have to reciprocate and tell you
how wonderful it was working with the entire team,
putting together the MIT IQ project.
For me, it was nothing but a total pleasure.
And I want to thank you all for being
here on such an incredible day, the launch of MIT IQ.
Before I talk more about the effort,
I want to mention that about 35 years ago today,
I was standing on this very stage talking, of all things,
about the accuracy of invariant moment analysis in computer
vision systems.
I was a junior at Princeton, and I
had a paper accepted at an ASME conference, which
was being held right here in Kresge Auditorium.
And it was the very first conference talk
that I ever gave.
And the stage is actually no different.
What I remember most about when I gave that talk was, number
one, the bright lights would shine right in your face
and you can't see anyone in the auditorium and, number two,
it's an awfully big room filled with very, very smart people,
which is actually quite intimidating.
So my first conference talk you know scared me incredibly.
And I have to tell you, I don't feel really
any different today.
But in any case, it is great to be back here, once again
talking about intelligence, artificial and natural.
I believe that nothing in the universe is, in the end,
more complex than the human mind.
From the start of our history, we
have been trying to grasp how the mind gives rise
to intelligence.
This is not a new question.
We do this and we think about this for the very same reasons
that we think about the origins of our universe
and why we want to understand where all of this came from.
It is an equally important question
to understand where our mind has come from.
Great thinkers through the ages have
wrestled with this question.
Plato, Descartes, Kant the like used philosophy
as a starting point.
Others pursued a more spiritual approach.
In modern times, psychologists and cognitive neuroscientists
have applied the tools of the scientific method
to understanding the problem of the mind.
At the dawn of the computing age,
computer scientists immediately--
it was one of the first things that computer scientists became
interested in--
saw the potential for creating artificial intelligence.
The field is as new or even predates computing.
MIT was one of the pioneering institutions
in establishing this field.
Advances in AI, like deep learning,
have produced remarkably significant breakthroughs.
These approaches are phenomenally
adept at finding patterns in large data sets
and then applying them in useful ways.
However, I do not believe this is
intelligence in any meaningful sense of the word.
Neural networks have interesting and important applications,
but they still far fall far short of the human mind's
ability to think.
Deep learning can identify faces better than a parent--
remarkable.
But we still can't replicate the way a child learns.
There are so many basic questions about intelligence
that we just can't answer.
And I believe that focusing too heavily
on what has been called AI may actually be confusing things.
For example, what do we really mean by artificial?
How is artificial intelligence different
from natural intelligence?
Is there just one notion of intelligence
that can be implemented in different ways?
For all of our efforts to date, we
have yet to crack the code of intelligence.
We may have focused too narrowly on machine learning
and AI, traditional AI approaches,
in search of answers.
There is a good reason why this effort is not called MIT AIQ.
To truly understand intelligence,
I believe we need to get back to basics--
cross disciplinary science framed in engineering terms.
This is what we'll do in the MIT IQ corps.
By combining neuro, cognitive, and computer science,
I believe we have the best hope of making the progress
that we seek.
And now is the time to embark on this mission.
Significant advances are already being made at the MIT Center
for Brains, Minds and Machines, CSAIL, and elsewhere,
both at the Institute and around the world.
Industry is investing heavily in so-called AI,
but they are not so well equipped to make breakthroughs
in fundamental questions of the science of intelligence.
MIT is.
And if we follow this course, I think
at least some important answers are well within our reach.
I believe we are likely to see breakthroughs that will
help us build a better world.
We will have a greater understanding of who we are
and how we operate, and we'll understand
the social and ethical implications of technology
better so that we can help shape public policy.
Of course, these are very ambitious goals.
But MIT does very ambitious things.
Speaking very personally, my interest
in understanding intelligence goes back quite far,
even before I was on the stage here 35 years ago.
As for many people, I've learned,
it started when I saw the film 2001 A Space
Odyssey for the first time as a very young kid.
From then on, I had a real passion for building machines
that could behave like humans.
Later, I learned to program on a supercomputer
when I was only about nine years old in 1970
in the basement of New York University where
my mom had received her PhD in mathematics.
Ultimately, as some of you know, I arrived at the AI lab
here at 545 Tech Square in 1983.
I was lucky enough to work in the presence of pioneers
like Marvin Minsky and Seymour Papert.
Incredible things were happening at the lab.
Tech Square has, not only Minsky,
but many other innovative teams.
Collectively, they develop much of the computer technology
that we use every day today.
Project MAC was the precursor to distributed computing.
The open source movement largely came from that building.
Many of the building blocks of the internet
were developed there--
of course, RSA encryption and the foundations of AI
and robotics.
At Tech Square, I was also one of the first recipients
of the very first computer virus the sendmail worm.
For those of you who remember sendmail, by the way,
you'll understand how hard it was
to distinguish this worm from the constant bugs and glitches
that normally plagued sendmail.
I'll point out that the worm actually originated a mile
down Mass Ave at Harvard.
At the AI lab, I focused on the development of the Utah MIT
dexterous hand.
It served as a testbed for the design
of control systems, tactile sensing,
and other technologies.
But what inspired me the most was the innovation
happening in that building.
It was the challenging and collaborative culture.
It was the wellspring of scientific progress.
I saw that a small group of unconventional thinkers,
given the right environment, the right culture,
could ultimately help to transform the world.
Years later, I would use Tech Square's culture
as a model for the company I co-founded Two Sigma.
Tech Square taught me an awful lot about innovation.
To innovate, you cannot have an overly constrained environment.
You can't put time limits on the problem
because that implies you already know the answer.
That's one reason why it took me eight years to finish my PhD,
and now would be an appropriate time
to thank MIT for not kicking me out any sooner.
Finally, you can't make progress in isolation.
You need to be around smart people who will tell you
what you're doing wrong, who will
push you to think differently, and people
who have diverse skills.
That was and still is and always will be the MIT way.
The atmosphere at Tech Square was truly entrepreneurial.
That was the vision that brought me to MIT in the first place.
And this is the vision MIT is building on and expanding
with its Intelligent Quest.
We have seen this approach work across many different labs
and projects at MIT.
Just to give you one success story--
which I briefly mentioned earlier--
the Center for Brains, Minds, and Machines.
CBMM brings together computer, cognitive, and neuroscientists
to work in close collaboration to further our understanding
of intelligence and through interdisciplinary efforts
in science and engineering.
CSAIL, of course, is another example.
With its roots in LCS and the AI Lab,
CSAIL has leveraged deep fundamental research
to deliver hundreds of innovative discoveries
in computer science and artificial intelligence.
MIT has been a leader in extending
the benefits of cutting edge basic research into the world
in making sure that its research has real impact.
Within IQ, the Bridge will be poised
to link research assets with state of the art AI tools
throughout the university and beyond.
Imagine a future where we can use a deeper grasp of the mind
to build more intelligent and useful machines.
Imagine a future where we use our new knowledge
and our new technologies for the benefit of all,
for the benefit of our nation, and the benefit of the world.
Truly understanding intelligence is of course a daunting task.
But if there is a place to embark upon this quest,
it is here at MIT, where 60 faculty and staff and 29 alumni
have won the Nobel Prize.
Five have won the Pulitzer Prize.
11 have won the Turing Award.
41-- five of whom we'll hear from today--
have won the MacArthur Genius grant.
MIT is the place for this endeavor.
We simply have the best combination of science
plus engineering in the world, and we already
have over 200 investigators working directly
on the science and engineering of intelligence.
We won't have all the answers, of course, in five or 10 years,
or maybe even our lifetimes.
But even modest progress in this quest
will provide enormous benefits to society.
We have been trying to understand
the origin of the universe since the dawn of history.
And we have been on a quest to understand how the mind works
for just as long.
It's a quest to understand basic deep truths about who we are,
and I believe it is the very need to understand ourselves
that makes us human.
The time to start is now.
The objectives of MIT IQ are tremendously ambitious.
But given MIT's long history of tackling big problems,
we must do this.
After all, if not us, who will?
Thank you very much.