Translator: Michele Gianella Reviewer: Alessio Politi
Ladies and gentlemen, artificial intelligence doesn't exist.
This is a provocative statement
to make you think about that
for how it has been described and experienced;
it seems something magical, self-creating and self-determining.
Actually, this is not the case.
I'll try to describe
how my life and professional experience
brought me to this intuition.
Men always wanted to cross their limits:
dominate natural elements, like fire.
They always wanted to travel, explore and fly to the Moon.
And this was achieved, right?
So the last challenge
is to comprehend their own mind and intelligence.
As a former neuroscientist,
on a mission to understand
one of the most magnificent and complex human organs,
I did ask myself a question
after nine years of research on the brain.
There is a paradox, behind the human will
to understand our intelligence and replicate it on a machine.
How is it possible
to comprehend, analyze, decompose and recompose the brain
using the same instrument? I mean,
how can we both "detach" from the brain
and analyze it in an objective way
while using it at the same time?
Let's then examine
how human intelligence developed in the centuries -
as the artificial one -
and the parallels we spotted.
Yuval Noah Harari, in his magnificent book "Sapiens",
defined the human cognition evolution
as a revolution.
Actually it is a revolution
if you think that human intelligence, as we know it,
developed in a really short time,
compared to the whole evolution of man.
Over the last 50,000 years,
we developed all the cognitive skills
that make us who we are today.
The first milestone in cognitive development
is the prefrontal cortex development
what distinguishes us from our primate ancestors
and allows to examine the world,
select it carefully and solve complex problems.
Then, we needed to express
these complex ideas in our brain,
so we started talking
and explored the metaphorical dimension of our thought
that needed to be expressed by language;
and then we wrote it to pass it down,
teach it and spread it throughout the world,
so we could create connections between these knowledges
and understand these ideas in our brain
until we could critically manipulate them.
and create a collective knowledge,
which incredibly expanded thanks to the web and technology.
The history of the evolution of the artificial intelligence
is not so linear.
Sure, it was exponentially from a certain point on;
much like human intelligence;
but it also run into troubles.
Artificial intelligence, actually, or the principles of AI,
have been around for a very long time:
from 1950, more or less,
thanks to Turing "imitation game" test
from which we started exploring
and investing in this field.
However, dark times were about to come at a certain point.
We entered the so-called "AI winter".
Why? Because there were not enough data,
so it was impossible to create identical machine learning models
through data.
Data is what we "feed" the machine with for it to learn.
And without data, it was impossible to feed these models.
So people stopped hoping, showing interest and investing.
Then, something incredible happened.
At some point, we realized
we were in a position to have lots, lots of data
collected in the last 50 years.
So there was a massive development in these kind of technologies
that turned into what we know them over the last ten years.
So what are the elements
that drive the development of this technology?
They are basically three:
Big Data,
countless data collected everywhere,
both from the mobile phone you hold in your pocket,
that captures trillions of data, every day,
about who and where you are;
and Internet of Things, objects collecting data in turn.
Then Cloud Computing,
the cloud computing power.
And now we are developing the quantum computing,
which is even faster.
They both made possible
the development of powerful ML and AI models.
Let's draw a parallelism
between a kid learning to see the world
and how a machine learns to recognize images.
Since birth,
and without any information
about reality perception,
a kid needs more or less 6 months
before he could distinguish profiles, colors and images.
But a visual recognition algorithm
with a little more than a million images
to train itself with,
it takes two to three days
to distinguish an image like a human.
A kid needs six months
to build connections in his visual cortex
and comprehend the world;
while an algorithm takes three days
is something scary for someone.
One of the most prominent expert in this field, I think,
is Andrew Ng, who worked for Google and founded "Cloudera".
He said that any thing
that could be elaborated by the human thought
in less than a second
could be mechanized.
So, let's look at the achievements during the centuries.
First, the IBM "Deep Blue" chess algorithm
that defeated Gerry Kasparov,
the undefeated world chess champion
in one of the most complex problem solving game we know.
Then, in 2006, a few years later,
the Google "AlphaGo" software
challenges the world Alpha champion,
a sort of chess to cube power.
There are more possible combinations of Go
than the number of all atoms and particles in the universe.
And while an AlphaGo player
takes roughly ten years to master the game
this algorithm only took three days
to study a ton of men made matches and then play against itself,
and manage to defeat the world champion.
And you probably remember the press release
with this speechless Chinese player
who couldn't understand how it was possible.
Later on, in 2016 with Microsoft,
we managed to achieve the ability to recognize images
and match human ability.
This is an example I'm glad to make,
related to cancer diagnosis:
a doctor now reaches a 90-95 percent of accuracy
in recognizing cancer cells
thanks to a visual recognition algorithm
that obviously learned and observed
countless cancer cells.
By combining this two abilities
the accuracy spikes to 99 percent.
It's interesting, however,
that humans are better in distinguish faux positive
and, on the other hand, machines are better
in distinguish subtle details in the image.
A little later on,
we reached language comprehension.
All your voice devices such as Alexa, Google and Cortana
are surely an interesting experiment
we all are using.
Then yet another achievement:
In 2017, Microsoft ran an experiment.
500 Wikipedia articles were given to a machine and a group of humans.
Then, they made some question
with a standardized test of the University of Stanford:
both the machine and humans answered in the same way.
In the end,
the ability of simultaneously translating 15 variations of Chinese,
one of the most difficult languages to learn.
Chinese kids learn to fully speak
around the age of ten,
Unlike western kids, who make it at five.
So what makes us different from a machine, really?
Why did I introduce the topic
saying there's no artificial intelligence?
It doesn't exist
as something that can analyze complex problems.
Let's make a comparison with Judea Pearl's causality model,
who wrote the wonderful "The Book of Why"
analyzing how humans learn
and porting its findings into machines.
Imagine a kid exploring the world following this model;
First, he will associate actions:
"If I do A, B happens": how A influences B.
After that, he will associate more and more elements,
and start to experience them in the world.
So we act in the world.
This thought is a little bit
more concrete and complex:
we get to know how our body and presence
can have an impact
and how this impact can spread in the world.
Until we get into the creative, something intuitive, "causal" thought.
Cause-effect are very different than the correlation of two elements.
Let's make a very simple example:
if we observe just two events for a year,
such as the dawn and the rooster's crow,
we can affirm that these two events occur in the same moment every day.
By adopting the concept of association,
we can say that the sun rises because the rooster sings.
But we know that this is not true:
I'm not going to talk about the solar system.
These are the problematic, complex situations
throught which we try to describe the world
which is sometimes unclear, and full of inconsistencies.
Are machines going to prevail over humans?
Will a machine ever understand
what I'm thinking and feeling?
I don't think so.
I believe we have a big responsibility,
to not forget the complexity of our human thought
and how it should be combined with artificial intelligence
simplifying complex activities for us
in order to better examine reality.
I like talking ethical responsibility.
Each artificial intelligence algorithm and choice
leans on the human cognitive ability, the critical thinking.
So it's the result of a man or a woman
taking ethical decisions and solving problems.
Going back to my story:
if I only had the chance,
when I started studying neurosciences,
to quickly understand how I could use my knowledge
and leave my research,
withand a help form above,
it would have been awesome and it wouldn't have taken me three years.
It took me some time to understand, instead.
But something wonderful happened, nevertheless:
last year I worked on a project
with the Business School at Politecnico di Milano,
who asked themsleves this question:
"What's the role of University today?"
We can find and get information anywhere all by ourselves.
Again, what's the role?
Is it selecting the complexity of this info?
Let's think about choice paradox:
"God, where do I have to go? What do I have to search for?"
For this reason we created a continuous learning platform
where artificial intelligence helps humans to improve learning,
filter info complexity
and give a subject recommendation
designed on the knowledge gap of each person.
In this way, each individual platform user
will get taylored recommmended content
based on the competence gap they have to fill,
about professional goals.
This is much like machine learning:
machine learns from the user while users learn and help the machine.
I will wrap up
with one of my favourite researchers, Charles Darwin,
who famously said:
"it's not the strongest species nor the smartest, that survives,
but the ones that is most adaptable to the change."
In my opinion, AI should be intended
as a potential instrument for our intelligence.
We can't refuse our responsibility
of taking decisions, or solving complex problems,
using our own mind.
Artificial Intelligence should be the tool
that simplifies this task
and helps us to wonder specific questions
to solve ethically fair question.
So I stress out our responsibility on creating these tools
and using them to make the world a better place.
(Applause)