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) |

## 评论