Turing Test, Artificial Intelligence and Human Silliness | Luca Longo | TEDxVicenza

Luca Longo is currently assistant professor at the Dublin Institute of Technology, where he is a member of the Applied Intelligence Research Centre. His core research interest is in Artificial Intelligence, particularly in Mental Workload modelling. His talk starts with the question: "Can machines think?" and it ends up asking himself: "Does really makes sense for us to let them think?"

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

In 2016, I was awarded a prize by the
National Forum for Teaching & Learning

supported and sponsored
by the Ministry of Education in Ireland,

peculiarly named:
"National Teaching Hero."

The reason for this award

was my availability towards my students

and my capability

to create less formal and more
comfortable educational environments.

Let's imagine this place as
a large university classroom.

I'm used to enter the classroom,

[in the] first ten minutes,
when students enter and take place,

I plug my computer to the speakers
and turn a classical music on.

I think this is the first step

to build less formal and
more comfortable educational environments

and try to keep the attention
of students at a high level.

Unfortunately, this is not always easy.

In my lessons, I employ a method in use
since the ancient times: storytelling!

Pedagogically speaking,

storytelling is a method
based upon the use of narratives,

aimed at transmitting
knowledge to students.

I would start my lesson
exactly with this method,

by explaining, describing a topic
on everyone's lips nowadays:

Artificial Intelligence.

Like every story narrated to children,

I'd like to begin my story with
"Once upon a time."

Second World War, 1942, United Kingdom,

Bletchley Park:

a mansion house in the north of London.

There was a thirty years old guy:
Alan Turing.

Alan graduated at King's College,
Cambridge,

and he obtained
a research doctorate in Logic

at Princeton University, in the USA.

At that time, the Germans made use
of a special machine: Enigma.

It was like a typewriter:

the operator typed some keys,

but, instead of printing those letters
on a paper sheet,

other letters were printed,

according to an encoding mechanically
set under the machine.

The Germans used this machine
to communicate with each other.

Anybody had listened in on this sheet

had in front a meaningless text.

It was encrypted!

Alan Turing was one of the leading figures
at Bletchley Park:

he and his team implemented a machine,

the one you can see behind me,

able to decipher the texts
written by the German.

Due to this invention we believe

that the war terminated two years earlier,

saving many human lives.

After the war Alan Turing continued
his research in Logic

and he is considered
the father of Computer Science,

the father of Artificial Intelligence.

With his Turing machine,
he formalises the concept of computer

even before the computer
was actually built.

In 1950 he published a paper
on the journal "Mind":

"Computing Machinery and Intelligence,"

where he proposed the Turing test.

The question behind the Turing test
is a well-defined one:

Can machines think?

It is at that time that
Artificial Intelligence begun.

Probably most of you have watched
the movie "The Imitation Game":

The game of imitation.

I am going to describe it to you.

Let us suppose that a person is here,
pressing keys on a computer keyboard,

who asks, pose some questions

and on the opposite side of the computer

there is a machine M and an operator O.

Alternately,

the machine M and the operator O
answer to the person P.

It is said that the machine M
passes the Turing test

if the person P is not able to understand

when the answers come from the machine

and when the answers
come from the operator.

This is The Imitation Game.

The machine must have special features

in order to pass the Turing test.

It has to interpret natural language:

the question asked by the person.

It has to represent knowledge
in order to formulate answers.

It has to think in an automatic mode
in order to formulate such answers.

And it has to learn automatically.

There are many approaches

to study Artificial Intelligence.

One of them is the cognitive approach:
it is based upon the human thinking.

According to this approach,

there are two ways to study
the human thinking:

either we try to capture thoughts
right when they occur,

or we try to model thoughts
at a psychological level.

For this reason,
we say that Artificial Intelligence

is closely connected to neuronal and
cognitive sciences and to psychology.

According to this approach,
the assumption is that,

if we can have

a true representation
of the human thought,

then we can transfer it to a machine.

Another approach is the one
based upon the laws of rational thought.

Probably most of you has heard about
the Aristotle's syllogism.

Socrates is a man;

all men are mortal;

[then] Socrates is mortal.

This is a deductive reasoning:

If we have two truthful premises,

we can infer a truthful conclusion.

Deductive logics comes from here.

According to the laws
of rational thought approach,

we try to build deductive arguments
and to transfer them to a machine.

Another approach is
the rational agent approach.

A rational agent, an entity,

has to act,

has to adapt itself to the context,

has to fix goals to itself,
and be able to carry them out,

and it has to act in a rational way.

Therefore, the Turing test
is related to intelligent agents.

By rephrasing the Turing statement,
"Can a machine think?",

we can now say: is it possible to build
a machine, an artificial agent,

able to think,

able to show understanding and rationality?

Artificial Intelligence, therefore,

aims at developing artificial
intelligent entities.

Your mobile phone is an entity.

By developing artificial entities

we try to understand intelligence
as a psychological construct.

Once we have a better knowledge

about this concept,

we try to develop
artificial intelligent entities

to support humans: it's a cycle.

But let's see now whether
machines are able to think.

I'd like to briefly describe you

the state of the art
of Artificial Intelligence

and I'd like to use five classes

to classify artificial agents
according to their abilities:

we have sub-human and par-human agents,

over-human, super-human agents
and then we have optimal ones.

I want to explain them
with some examples.

Optimal agents are the ones
which act better than all the people

and you can't do better than that.

For instance, agents
which solve the Rubik cube,

those that play at "Four in a row"
in the best way,

or at Tic-tac-toe.

Consider that some years ago, a boy,
given an initial state of the Rubik cube,

solved it in 4.73 seconds.

Some months ago an agent,
a robot was developed,

which can solve it in 0.63 seconds.

We have super-human agents
which act better than all the humans,

for instance in the chess game,
in the Scrabble game.

Some years ago the chess Russian
champion Kasparov

was defeated by an artificial agent.

We have over-human agents,

the ones which almost act better
than most of the humans,

for instance in the Texas hold 'em poker,
in answering the Quiz Show questions.

We have par-human agents,

which act almost like all the humans,

for instance in cognitive activities,

such as crosswords or image classification.

Finally, we have sub-human agents,
which act worse than all the humans.

Examples include objects classification,
handwriting recognition,

vocal recognition, translation
from a language into another one.

But if there is something that artificial
agents nowadays are not able to do

is for instance, disambiguation:

Are we talking about the apple as a fruit,

or are we talking about the brand
of the Apple Corporation?

And one thing that agents
are not able to do

is reasoning in the real world
under situations of uncertainty.

These are the main limitations
of Artificial Intelligence,

and because of these, it is believed that
we are far away to pass the Turing test.

Now, let's try to understand [whether]
machines will be able to think.

Some years ago, in America,
a concept has been coined,

named "Technological Singularity":

by Ray Kurzweill, a world-renowned
expert in Artificial Intelligence.

Let's imagine a timeline.

Let's imagine a line indicating
the human intelligence, increasing.

Let's now imagine a red line
indicating the machine intelligence,

with an exponential trend.

This trend follows the Moore's law,

whereby the computational complexity,
for instance,

as measured by the number of transistors
embedded in a chip,

doubles every two years
and quadruples every [three] years.

According to Ray Kurzweill,
in 2010 we should have been able

to use this computational complexity

to emulate the human brain --

I didn't see anything.

In 2020, with 1,000 dollars
we will have access

to this computational capacity.

In 2025, according to Ray Kurzweill,

we will be able to scan our brain
in a very accurate way.

And eventually, in 2029

machines will pass the Turing test!

And then, in 2045,
he refers to that point in time

when the technological singularity
will happen,

when the machines, machine intelligence,

will follow an exponential trend

that will significantly affect
the human intelligence.

In his paper, published
on the journal "Mind,"

Turing not only proposed his test,

but he also suggested nine objections
against his own test.

These objections are nine objections
against Artificial Intelligence.

Some years ago, when I was a student
at the University of Varese,

I attended a course on "Epistemology,
Deontology and Ethics in Computer Science"

held by Prof. Gaetano Aurelio Lanzarone.

Unfortunately, he passed away
some years ago.

One of the assignments we had to do
was to propose a tenth objection

against the Turing test,
against Artificial Intelligence.

I was the only one who proposed
a tenth objection

expressed as a mathematical equation,

that I labelled "human stupidity."

I'd like to explain it in simple terms.

Let's assume we take
the intelligence of all humans

and we put it all together.

The Sum symbol of the equation
on the left hand side.

And we transfer this intelligence
as a whole to a machine.

Then, we get an equality of intelligence.

But in some way the machine
becomes more intelligent than us.

Though, if it is true that were us

who have transferred our intelligence
to a machine

and it becomes more intelligent than us,

it is also true, as well,

that we let the machine become
more intelligent than us.

Then, in order to conclude my story,

and referring back to the initial
question: "Can machines think?",

I'd like to leave you with
an open question:

Does really makes sense
for us to let them think?

Thank you.

(Applause)