- We live in a society that we learn to admire geniuses:
We write about them.
We read about them.
We watch movies about them.
And in general, the genius label sells.
Typically, everyone whom we label today "genius"
has accomplished something remarkable
by really standing out from among their peers
in a way that really grabs our attention.
They include scientists like Einstein,
musicians and composers like Beethoven and Mozart.
But genius is something more-
it's a story.
We remember the people
who happened to be at the right time at the right place,
and hence, there was a way
of recording their accomplishments.
There are an exceptional number of hidden geniuses
who either have not been at all recorded for posterity,
or we know about their accomplishments,
but we don't know enough for them to enter the canon.
Could we actually use data to predict
who among the scientists will actually be a genius?
That's where network science comes in.
So we are curious:
What really determines the genius label?
And when we compared all geniuses
to their scientific peers,
we realized that there are really two
very different classes:
Ordinary genius and peerless genius.
For example, Einstein,
who turns out to be a truly peerless genius.
When we looked at the scientist working at the same time
or roughly in the same areas of physics that he did,
there was no one who would have a comparable productivity
or scientific impact to him.
He was truly alone.
When we looked at Stephen Hawking,
we label him ordinary genius.
To our surprise,
we realized there were about six other scientists
who work roughly the same area,
and had comparable, often bigger impact
than Stephen Hawking had.
Among them, actually, a woman scientist, Renata Kallosh.
And it turned out,
that there was absolutely no news about her anywhere.
The only article that we find that mentions her
was in the context of her husband.
That raises the question:
Why is it Hawking the genius, and not Renata?
How does really the genius label emerge?
It turns out, that the number of languages
to which a person's Wikipedia page has been translated
was the strongest predictor of the genius label.
We learned that the genius label is a construct
that the society assigns to exceptional accomplishment,
but exceptional accomplishment is not sufficient
to get the genius label-
we always need something more.
You need to be born at the right time.
You need to be in the right circumstances.
Throughout history, remarkable individuals were always born
in the vicinity of big cultural centers.
And everything that is outside of the cultural centers
was typically a desert of exceptional accomplishments.
We have a very strong culture bias towards genius:
typically associated with the vast term "canon,"
and hence, we're losing many,
many exceptional accomplishments
because none of these individuals are really born in vacuum;
they're inspired by some and influence others.
And by unveiling these connections,
you are digging deeper and deeper
into the cultural accomplishments of the society,
and start discovering these hidden geniuses.
It doesn't require much pattern recognition to realize
that I'm past 50,
which made me always wonder,
"Do I still have ahead of me major scientific discoveries?"
To paraphrase Einstein,
"A person who has never made a major contribution to science
by the age of 30 will never do so."
That led to a conception in science
that you have to be young to be creative.
So we were curious, "Is this really true for geniuses,
or it is also true for ordinary scientists?"
We ended up analyzing all scientists out there,
and asking when did ordinary scientists make
his or her biggest discovery?
Was it early in their career or late?
And to our surprise,
the data indicated that indeed it's true.
Most scientists make their biggest discovery
in the first 15 years of their career.
And then after 30 years, the chance of:
"I would make a discovery that would be bigger
than what I did in my thirties,"
would be less than 1%.
When we dig deeper into data,
we realize that we also have to consider productivity.
That is the reason why scientists appear
to be publishing their highest-impact work early
in their career has to do with the fact
that that's when they publish most of their papers.
That is, scientists tend
to have a much higher productivity early in their career
and with time, productivity wins.
Consider, for example, Frank Wilczek,
who got a Physics Nobel Prize
for the very, very, very first paper that he ever wrote
as a graduate student.
But my real hero is not him, but rather John Fenn.
John Fenn was a chemist.
He made the discovery for which 15 years later,
at the age of 80, he got the Chemistry Nobel Prize.
One night, my student came to me and said, "What's next?
What would you like to work on?"
And I said, "Well, how about success?"
And we had a laugh,
but then we looked at each other and said,
"Hmm, that's interesting.
We should study success."
We understood that as a scientist,
we're embedded in a very complicated network
of relationships,
connecting our papers to each other
by citations, by co-authorship-
and we immediately thought
that the availability of all this data may give us a clue
about how scientific success emerges.
What does success depend on?
Productivity is one of them.
How many papers did he or she write during the career?
The more you write, the more visible you are a scientist,
potentially the more impact you make.
But also matters:
What is the impact of the most-cited paper?
What is your biggest discovery?
And how is it situated within your career?
The question is:
How does the quality of the idea that I pick
and the ultimate success
and my ability as a scientist connect to each other?
Your 'Q factor' tells me:
"How good am I of turning an idea into a research paper?"
And when we put the career
of many, many scientists together, by comparing them,
we were able to ask:
How does the Q factor change?
How is it different from one scientist to the other?
And what is the randomness of the idea that we pick?
And the outcomes were very surprising even to us.
We would think that good scientists pick better ideas.
What the math very clearly indicated,
that we are not privy to better ideas
just because we have more experience.
This Q factor, our ability to turn ideas into discoveries,
is really constant throughout the scientist's career.
That more or less, a scientist, when they start publishing,
they come with a certain Q factor
that stays with them for the rest of their life.
Turned out that the Q factor alone,
that is really unique to you, was the strongest predictor
whether you will be really recognized
as a important scientist.
Productivity is the lowest predictor.
If you just publish a lots of papers,
that would not get you any closer
to be recognized as a major scientist.
The impact of those papers really matters,
so does the impact of your biggest discovery.
Big data could help us to discover
those that really had the accomplishment
and deserve the genius level
and put them in the right place-
and education is needed
to make sure that everybody who has the talent
can develop that talent
so that they are contenders to the genius label.