TED@BCG Milan
Vittorio Loreto: Need a new idea? Start at the edge of what is known
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"Where do great ideas come from?" Starting with this question in mind, Vittorio Loreto takes us on a journey to explore a possible mathematical scheme that explains the birth of the new. Learn more about the "adjacent possible" -- the crossroads of what's actual and what's possible -- and how studying the math that drives it could explain how we create new ideas.
Vittorio Loreto - Physicist
Vittorio Loreto is passionate about the complexity of the world around us in all its forms and he actively tries to decode it. Full bio
Vittorio Loreto is passionate about the complexity of the world around us in all its forms and he actively tries to decode it. Full bio
Double-click the English transcript below to play the video.
00:14
We have all probably wondered
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how great minds achieved
what they achieved, right?
what they achieved, right?
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And the more astonishing
their achievements are,
their achievements are,
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the more we call them geniuses,
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perhaps aliens
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coming from a different planet,
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definitely not someone like us.
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But is that true?
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So let me start with an example.
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You all know the story
of Newton's apple, right? OK.
of Newton's apple, right? OK.
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Is that true? Probably not.
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Still, it's difficult to think
that no apple at all was there.
that no apple at all was there.
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I mean some stepping stone,
some specific conditions
some specific conditions
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that made universal gravitation
not impossible to conceive.
not impossible to conceive.
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And definitely this was not impossible,
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at least for Newton.
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It was possible,
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and for some reason, it was also there,
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available at some point,
easy to pick as an apple.
easy to pick as an apple.
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Here is the apple.
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And what about Einstein?
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Was relativity theory another big leap
in the history of ideas
in the history of ideas
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no one else could even conceive?
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Or rather, was it again
something adjacent and possible,
something adjacent and possible,
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to Einstein of course,
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and he got there by small steps
and his very peculiar scientific path?
and his very peculiar scientific path?
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Of course we cannot conceive this path,
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but this doesn't mean
that the path was not there.
that the path was not there.
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So all of this seems very evocative,
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but I would say hardly concrete
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if we really want to grasp
the origin of great ideas
the origin of great ideas
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and more generally the way
in which the new enters our lives.
in which the new enters our lives.
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As a physicist, as a scientist,
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I have learned that posing
the right questions
the right questions
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is half of the solution.
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But I think now we start having
a great conceptual framework
a great conceptual framework
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to conceive and address
the right questions.
the right questions.
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So let me drive you
to the edge of what is known,
to the edge of what is known,
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or at least, what I know,
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and let me show you that what is known
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could be a powerful
and fascinating starting point
and fascinating starting point
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to grasp the deep meaning
of words like novelty, innovation,
of words like novelty, innovation,
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creativity perhaps.
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So we are discussing the "new,"
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and of course, the science behind it.
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The new can enter our lives
in many different ways,
in many different ways,
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can be very personal,
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like I meet a new person,
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I read a new book,
or I listen to a new song.
or I listen to a new song.
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Or it could be global,
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I mean, something we call innovation.
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It could be a new theory,
a new technology,
a new technology,
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but it could also be a new book
if you're the writer,
if you're the writer,
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or it could be a new song
if you're the composer.
if you're the composer.
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In all of these global cases,
the new is for everyone,
the new is for everyone,
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but experiencing the new
can be also frightening,
can be also frightening,
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so the new can also frighten us.
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But still, experiencing the new
means exploring a very peculiar space,
means exploring a very peculiar space,
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the space of what could be,
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the space of the possible,
the space of possibilities.
the space of possibilities.
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It's a very weird space,
so I'll try to get you through this space.
so I'll try to get you through this space.
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So it could be a physical space.
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So in this case, for instance,
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novelty could be climbing
Machu Picchu for the first time,
Machu Picchu for the first time,
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as I did in 2016.
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It could be a conceptual space,
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so acquiring new information,
making sense of it, in a word, learning.
making sense of it, in a word, learning.
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It could be a biological space.
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I mean, think about the never-ending
fight of viruses and bacteria
fight of viruses and bacteria
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with our immune system.
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And now comes the bad news.
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We are very, very bad
at grasping this space.
at grasping this space.
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Think of it. Let's make an experiment.
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Try to think about all the possible things
you could do in the next, say, 24 hours.
you could do in the next, say, 24 hours.
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Here the key word is "all."
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Of course you can conceive a few options,
like having a drink, writing a letter,
like having a drink, writing a letter,
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also sleeping during this boring talk,
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if you can.
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But not all of them.
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So think about an alien invasion,
now, here, in Milan,
now, here, in Milan,
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or me -- I stopped thinking
for 15 minutes.
for 15 minutes.
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So it's very difficult
to conceive this space,
to conceive this space,
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but actually we have an excuse.
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So it's not so easy to conceive this space
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because we are trying to conceive
the occurrence of something brand new,
the occurrence of something brand new,
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so something that never occurred before,
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so we don't have clues.
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A typical solution could be
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looking at the future
with the eyes of the past,
with the eyes of the past,
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so relying on all
the time series of past events
the time series of past events
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and hoping that this is enough
to predict the future.
to predict the future.
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But we know this is not working.
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For instance, this was the first attempt
for weather forecasts, and it failed.
for weather forecasts, and it failed.
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And it failed because
of the great complexity
of the great complexity
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of the underlying phenomenon.
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So now we know that predictions
had to be based on modeling,
had to be based on modeling,
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which means creating
a synthetic model of the system,
a synthetic model of the system,
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simulating this model
and then projecting the system
and then projecting the system
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into the future through this model.
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And now we can do this in a lot of cases
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with the help of a lot of data.
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Looking at the future
with the eye of the past
with the eye of the past
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could be misleading also for machines.
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Think about it.
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Now picture yourself for a second
in the middle of the Australian Outback.
in the middle of the Australian Outback.
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You stand there under the sun.
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So you see something weird happening.
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The car suddenly stops
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very, very far from a kangaroo
crossing the street.
crossing the street.
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You look closer
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and you realize
that the car has no driver.
that the car has no driver.
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It is not restarting, even after
the kangaroo is not there anymore.
the kangaroo is not there anymore.
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So for some reasons,
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the algorithms driving the car
cannot make sense
cannot make sense
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of this strange beast
jumping here and there on the street.
jumping here and there on the street.
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So it just stops.
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Now, I should tell you,
this is a true story.
this is a true story.
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It happened a few months ago
to Volvo's self-driving cars
to Volvo's self-driving cars
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in the middle of the Australian Outback.
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06:14
(Laughter)
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It is a general problem,
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and I guess this will affect
more and more in the near future
more and more in the near future
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artificial intelligence
and machine learning.
and machine learning.
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It's also a very old problem,
I would say 17th century,
I would say 17th century,
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but I guess now we have new tools
and new clues to start solving it.
and new clues to start solving it.
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So let me take a step back,
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five years back.
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Italy. Rome. Winter.
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So the winter of 2012
was very special in Rome.
was very special in Rome.
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Rome witnessed one of the greatest
snowfalls of its history.
snowfalls of its history.
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That winter was special also
for me and my colleagues,
for me and my colleagues,
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because we had an insight
about the possible mathematical scheme --
about the possible mathematical scheme --
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again, possible,
possible mathematical scheme,
possible mathematical scheme,
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to conceive the occurrence of the new.
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I remember that day
because it was snowing,
because it was snowing,
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so due to the snowfall,
we were blocked, stuck in my department,
we were blocked, stuck in my department,
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and we couldn't go home,
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so we got another coffee, we relaxed
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and we kept discussing.
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But at some point --
maybe not that date, precisely --
maybe not that date, precisely --
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at some point we made the connection
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between the problem of the new
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and a beautiful concept
proposed years before
proposed years before
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by Stuart Kauffman,
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the adjacent possible.
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So the adjacent possible
consists of all those things.
consists of all those things.
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It could be ideas, it could be molecules,
it could be technological products
it could be technological products
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that are one step away
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from what actually exists,
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and you can achieve them
through incremental modifications
through incremental modifications
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and recombinations
of the existing material.
of the existing material.
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So for instance, if I speak
about the space of my friends,
about the space of my friends,
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my adjacent possible would be
the set of all friends of my friends
the set of all friends of my friends
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not already my friends.
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I hope that's clear.
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But now if I meet a new person,
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say Briar,
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all her friends would immediately enter
my adjacent possible,
my adjacent possible,
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pushing its boundaries further.
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So if you really want to look
from the mathematical point of view --
from the mathematical point of view --
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I'm sure you want --
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you can actually look at this picture.
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So suppose now this is your universe.
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I know I'm asking a lot.
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I mean, this is your universe.
Now you are the red spot.
Now you are the red spot.
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And the green spot
is the adjacent possible for you,
is the adjacent possible for you,
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so something you've never touched before.
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So you do your normal life.
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You move. You move in the space.
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You have a drink.
You meet friends. You read a book.
You meet friends. You read a book.
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At some point,
you end up on the green spot,
you end up on the green spot,
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so you meet Briar for the first time.
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And what happens?
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So what happens is there is a new part,
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a brand new part of the space,
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becoming possible for you
in this very moment,
in this very moment,
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even without any possibility
for you to foresee this
for you to foresee this
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before touching that point.
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And behind this there will be
a huge set of points
a huge set of points
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that could become possible
at some later stages.
at some later stages.
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So you see the space
of the possible is very peculiar,
of the possible is very peculiar,
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because it's not predefined.
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It's not something we can predefine.
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It's something that gets
continuously shaped and reshaped
continuously shaped and reshaped
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by our actions and our choices.
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So we were so fascinated
by these connections we made --
by these connections we made --
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scientists are like this.
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And based on this,
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we conceived our mathematical formulation
for the adjacent possible,
for the adjacent possible,
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20 years after the original
Kauffman proposals.
Kauffman proposals.
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In our theory -- this is a key point --
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I mean, it's crucially based
on a complex interplay
on a complex interplay
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between the way in which
this space of possibilities expands
this space of possibilities expands
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and gets restructured,
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and the way in which we explore it.
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After the epiphany of 2012,
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we got back to work, real work,
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because we had to work out this theory,
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and we came up with
a certain number of predictions
a certain number of predictions
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to be tested in real life.
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Of course, we need a testable framework
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to study innovation.
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So let me drive you
across a few predictions we made.
across a few predictions we made.
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The first one concerns
the pace of innovation,
the pace of innovation,
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so the rate at which you observe novelties
in very different systems.
in very different systems.
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So our theory predicts
that the rate of innovation
that the rate of innovation
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should follow a universal curve,
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like this one.
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This is the rate of innovation versus time
in very different conditions.
in very different conditions.
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And somehow, we predict
that the rate of innovation
that the rate of innovation
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should decrease steadily over time.
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So somehow, innovation
is predicted to become more difficult
is predicted to become more difficult
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as your progress over time.
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It's neat. It's interesting.
It's beautiful. We were happy.
It's beautiful. We were happy.
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But the question is, is that true?
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Of course we should check with reality.
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So we went back to reality
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and we collected a lot of data,
terabytes of data,
terabytes of data,
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tracking innovation in Wikipedia, Twitter,
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the way in which we write free software,
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even the way we listen to music.
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I cannot tell you, we were
so amazed and pleased and thrilled
so amazed and pleased and thrilled
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to discover that the same predictions
we made in the theory
we made in the theory
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were actually satisfied in real systems,
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many different real systems.
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We were so excited.
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Of course, apparently,
we were on the right track,
we were on the right track,
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but of course, we couldn't stop,
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so we didn't stop.
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So we kept going on,
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and at some point
we made another discovery
we made another discovery
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that we dubbed "correlated novelties."
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It's very simple.
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So I guess we all experience this.
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So you listen to "Suzanne"
by Leonard Cohen,
by Leonard Cohen,
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and this experience
triggers your passion for Cohen
triggers your passion for Cohen
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so that you start frantically
listening to his whole production.
listening to his whole production.
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And then you realize
that Fabrizio De André here
that Fabrizio De André here
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recorded an Italian version of "Suzanne,"
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and so on and so forth.
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So somehow for some reason,
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the very notion of adjacent possible
is already encoding the common belief
is already encoding the common belief
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that one thing leads to another
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in many different systems.
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But the reason why we were thrilled
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is because actually
we could give, for the first time,
we could give, for the first time,
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a scientific substance to this intuition
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and start making predictions
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about the way in which
we experience the new.
we experience the new.
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So novelties are correlated.
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They are not occurring randomly.
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And this is good news,
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because it implies
that impossible missions
that impossible missions
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might not be so impossible after all,
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if we are guided by our intuition,
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somehow leading us
to trigger a positive chain reaction.
to trigger a positive chain reaction.
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But there is a third consequence
of the existence of the adjacent possible
of the existence of the adjacent possible
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that we named "waves of novelties."
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So just to make this simple, so in music,
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without waves of novelties,
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we would still be listening
all the time to Mozart or Beethoven,
all the time to Mozart or Beethoven,
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which is great,
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but we don't do this all the time.
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We also listen to the Pet Shop Boys
or Justin Bieber -- well, some of us do.
or Justin Bieber -- well, some of us do.
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(Laughter)
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So we could see very clearly
all of these patterns
all of these patterns
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in the huge amounts of data
we collected and analyzed.
we collected and analyzed.
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For instance, we discovered
that popular hits in music
that popular hits in music
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are continuously born, you know that,
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and then they disappear,
still leaving room for evergreens.
still leaving room for evergreens.
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So somehow waves of novelties ebb and flow
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while the tides always hold the classics.
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There is this coexistence
between evergreens and new hits.
between evergreens and new hits.
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Not only our theory
predicts these waves of novelties.
predicts these waves of novelties.
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This would be trivial.
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But it also explains why they are there,
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and they are there for a specific reason,
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because we as humans
display different strategies
display different strategies
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in the space of the possible.
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So some of us tend to retrace
already known paths.
already known paths.
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So we say they exploit.
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Some of us always launch
into new adventures.
into new adventures.
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We say they explore.
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And what we discovered is
all the systems we investigated
all the systems we investigated
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are right at the edge
between these two strategies,
between these two strategies,
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something like 80 percent exploiting,
20 percent exploring,
20 percent exploring,
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something like
blade runners of innovation.
blade runners of innovation.
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So it seems that the wise balance,
you could also say a conservative balance,
you could also say a conservative balance,
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between past and future,
between exploitation and exploration,
between exploitation and exploration,
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is already in place
and perhaps needed in our system.
and perhaps needed in our system.
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But again the good news is
now we have scientific tools
now we have scientific tools
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to investigate this equilibrium,
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perhaps pushing it further
in the near future.
in the near future.
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So as you can imagine,
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I was really fascinated by all this.
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Our mathematical scheme
is already providing cues and hints
is already providing cues and hints
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to investigate the space of possibilities
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and the way in which
all of us create it and explore it.
all of us create it and explore it.
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But there is more.
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This, I guess, is a starting point
of something that has the potential
of something that has the potential
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to become a wonderful journey
for a scientific investigation of the new,
for a scientific investigation of the new,
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but also I would say
a personal investigation of the new.
a personal investigation of the new.
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And I guess this can have
a lot of consequences
a lot of consequences
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and a huge impact in key activities
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like learning, education,
research, business.
research, business.
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So for instance, if you think
about artificial intelligence,
about artificial intelligence,
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I am sure -- I mean,
artificial intelligence,
artificial intelligence,
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we need to rely in the near future
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more and more on the structure
of the adjacent possible,
of the adjacent possible,
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to restructure it, to change it,
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but also to cope
with the unknowns of the future.
with the unknowns of the future.
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In parallel, we have a lot of tools,
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new tools now, to investigate
how creativity works
how creativity works
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and what triggers innovation.
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And the aim of all this
is to raise a generation of people
is to raise a generation of people
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able to come up with new ideas
to face the challenges in front of us.
to face the challenges in front of us.
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We all know.
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I think it's a long way to go,
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but the questions, and the tools,
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are now there, adjacent and possible.
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Thank you.
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(Applause)
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ABOUT THE SPEAKER
Vittorio Loreto - PhysicistVittorio Loreto is passionate about the complexity of the world around us in all its forms and he actively tries to decode it.
Why you should listen
Vittorio Loreto is a physicist at Sapienza University of Rome and faculty of the Complexity Science Hub Vienna. He is presently director of the SONY Computer Science Laboratories in Paris where he heads the team on creativity, innovation and artificial intelligence. He recently coordinated the research program dubbed KREYON, aimed at unfolding the dynamics of creativity, novelties and innovation. While theoretical modeling and data analysis are his native research tools, in the last few years he has been developing interactive tools, games, installations, to directly involve the public on the very research agenda. He created the KREYON DAYS, a new form of scientific event that tightly entangles research, learning, awareness and fun.
Vittorio Loreto | Speaker | TED.com