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