Transcriber: Monica Castillo Reviewer: Lucas Kaimaras
Before we start for this story to make any sense,
I have to tell you something personal.
More precisely something personal about my 13 year old self.
As you know, there is this spirit in one’s life
when everything changes for your body and for your mind,
when a whole new world opens up one that’s right up to that moment
you had not paid any attention to.
And you find yourself daydreaming for hours on end
and dreaming about a beautiful future.
As you will ever guess this is, of course,
the ancient story of boy meets computer and falls in love.
And I was so hooked,
I was hooked because a whole new world of endless possibilities opened up.
You see, as a programmer, you may not have a paintbrush,
but you do have a keyboard and an infinitely large playing field
and freedom without boundaries to create something from nothing,
almost like a virtual God.
Programming is creativity without limits.
It's a mix between a science and an art.
I mean, look, we even use many of the same words.
We design, we write, we decorate and compose,
and we comment on beautiful software as if it were poetry.
We see it's elegant, exquisite, sparkling, or a software God greedy.
Booch likes to say, “Software is the invisible writing
that whispers the stories of possibility to our hardware.”
So it’s an art and also a science, and admittedly, I’m a bit of a geek, too.
But what is a geek anyway? Let’s check the dictionary.
Grotesque or depraved acts, biting off the head of a live chicken?
OK, so maybe I’m not like that.
Little bit obsessed and great attention to technical detail?
Yup. This one I can live with.
So let's kick out for a second here.
So on the left, we have the Commodore 64 MOS 6510 processor,
and on the right we have the modern AMD X 5950.
It’s 8 bits versus 64.
It’s 1 core versus 16.
It’s 1 megahertz versus 3,400.
It’s not a fair fight, I know.
Over a period of almost 40 years,
my personal computer became 200,000 times more powerful.
With the same speed up in travel,
you could fly today from Brussels to New York
in less than one second!
One of the reasons artificial intelligence is possible today
is because of all this computer power
and what we already do with it is amazing.
Think, for example, about speech recognition,
about self-driving boats in our harbor, about robots spotting plants.
And let us not forget discovering new drugs,
for example, antibiotics or drugs against COVID.
So that’s all great and it looks like we can live happily ever after.
Unfortunately, as you can guess, it’s a bit more complicated than that.
And I would like to highlight three challenges now.
The first one has to do
with the size of the artificial neural networks that we create.
As you know, in A.I. we are inspired by the human brain
with its neurons and connections between the neurons, the synapses.
And we take those concepts, those neurons and those synapses,
and we recreate them virtually in the computer.
In the last few years, these computer versions have grown exponentially in size.
From 94 million parameters in 2018
to 175 billion in 2020.
Now, the problem is
that the energy needed to train one such neural network is enormous.
In one case, the equivalent of CO2 pollution produced
was the same as a car driving 700,000 kilometers,
which is like driving to the moon and back.
The second challenge is that these huge neural networks
don’t seem to get much more intelligent as they grow in size.
It’s not clear at all that this growth
is bringing us any closer to the dream of real AI.
One popular type, for example, it’s called a transformer.
And one of the things it does is complete text.
So you give it a sentence and it fills in the missing words.
You’ll give it a question and it gives you the answer.
For example, here’s a tough question:
“Which is heavier? A toaster or a pencil?”
A pencil is heavier than a toaster! Rather disappointing, right?
I mean, it clearly has zero understanding
of the meaning of the question and of the text.
It misses completely the real world common sense knowledge
that the young child already has.
So maybe physics isn't its strong suit.
Let’s take a look at another example.
As begin text I now use: “Alan Turing was...”
Now Alan Turing was, of course,
one of the founding fathers of computing as we know it.
But what does our AI write about him?
So now we let it complete the sentence.
It seems to be writing about Alan Turing’s life here.
That looks good, right? Would you not agree?
But let’s go through the facts.
Turing died on June 23rd, not 21st. He was 41 years old.
He died of cyanide poisoning
and he got an award
for his code breaking work during World War II.
So this is really an ideal fake news generator.
The text looks OK at first glance but it cannot be trusted.
This is key.
You cannot trust this system even if the answer looks legitimate,
because it may give you the correct answer or it may not.
You have no way of knowing.
The third challenge with this technology is how and where we apply it.
This illustration by Keker Johnson
perfectly captures our fear of jobs lost to automation.
But there is another other angle here as well,
and that is the impact of the jobs that remain.
For all the good technology can do, we also see some really bad cases.
For example, a global online retailer is using it
to measure and control every movement of their employees in a warehouse.
And something similar is happening to call center employees
that only get the difficult calls all day,
because the easy ones are handled by chatbots.
In this way, people become controlled by machines.
It is as if we work for them
and every joy and satisfaction is taken out of the job
in the name of efficiency.
So that’s all really depressing,
and I must admit it’s something I am struggling with.
On the one hand, technology is fantastic and it can do so many good things.
And on the other hand, there is no way to just ignore the challenges.
Luckily, we are creative problem solvers
and we are addressing our first problem,
the energy/pollution problem in several ways.
And one way is through efficient hardware.
For example, more new computer chips
that perform calculations not in a digital but in an analog way.
Indeed, not with the zeros and the ones,
but by the clever use of the loss of electricity,
to perform a large number of calculations that are needed for these neural networks.
And we also put memory and calculation together
where they are traditionally separate.
And this also makes this chip much more efficient.
It’s a total Win-Win.
What we can do as well is make companies more aware of the energy they are using
and of the pollution that is created by their IT systems.
Specifically with the AI systems, because they use so much,
but I would say more generally, with all kinds of IT systems.
We can do it with any kind of software.
It's also true, the use of software that we are attacking our second problem,
the common sense problem.
And to understand the direction we are taking here,
let’s go back to Alan Turing.
Alan Turing envisioned an abstract machine that manipulates symbols.
And the key point is that these symbols are not just numbers,
it’s not just about calculations.
The symbols can represent any concept.
And let me illustrate this with a game of chess.
In this case, the chess pieces are the symbols
and the knowledge base stores information about the moves that are allowed
and about previous chess games.
And the inference engine takes that knowledge,
applies the rules and determines the next best move
for this particular set up of the board.
So when I studied AI in the early 90s,
the focus was still very much on such expert systems.
The idea was that we would write down all knowledge and rules about the world,
and we would put them in a reasoning engine,
and then intelligence would just pop out.
So people actually tried this by building knowledge bases and huge lists of rules.
But it didn’t quite work out since the world, you know, is too complex.
There are too many rules. The rules change all the time.
They're sometimes conflicting.
So fully describing this common sense in rules,
it turned out not to be possible in practice.
And then the deep learning systems emerged,
the neural networks that are able to learn facts about the world from the data,
for example, from Wikipedia.
And this works pretty well
as long as the information is actually present in the data and it is accurate.
It does not, however, learn how to reason about the world.
So what we are exploring now is a neuro symbolic approach.
It’s bringing together the best of the past with the best of today.
And the deep learning systems of today,
they can discover information in this huge ocean of data,
and with the experts system of the past
we add back the common sense reasoning that we were missing.
Take, for example, the trivial task of cleaning up the house.
I would love to have a robot do that for me,
but for that to work,
the brain of the robot has to know what goes where.
You know, the clothes go in the bedroom closet,
the knives and forks belong in the kitchen and so forth.
And it turns out that it’s way more efficient
if you actually give the robot brain this information
instead of having it learn all of that from scratch, from the data,
which sometimes is even - turns out to be not possible.
Our third problem, the one about the consequences of automation,
is trickier.
Have you heard about the Monty Python saying:
“What have the Romans ever done for us?”
Well, it turns out that automation is surely not all bad.
It increases, for example, the productivity per hour,
which is the core driver of our increased standard of living.
If we would not have automated, we would still, you know,
use horses for transport, you know, weave textile by hand,
and spent countless hours adding and subtracting numbers.
We use machines now for all those tasks
leading to higher speed, better quality and lower cost, all with less effort.
I mean, to illustrate this, think about it,
around the year 1900 the average workweek was approximately 70 hours,
seven zero hours.
And today we are at 40.
And automation also takes away boring and repetitive work
to make room for creativity,
for finding new approaches, for innovation.
And on top, we need it
because our active working population is declining.
So what we need is more automation, not less.
But which kind is it the kind that turns us humans into robots?
Where every second of our workday and every movement we make
is tracked and counted and benchmarked,
squeezing the maximum out of each employee
before they have to quit and be replaced by the next batch of human robots.
Is that the kind we want or do we want to use this opportunity
to create a better life and a better work situation.
Where the boring and repetitive tasks are indeed taken out of the workday,
but are replaced with more interesting work leading to more job satisfaction,
not less.
All of this would allow us to live a fulfilling life
while remaining competitive and building the companies of the future.
And because deep down, all IT people are artists,
I wanted to end with what Pablo Picasso, known for his boundless creativity,
what he said in 1964:
“Computers are useless. They can only give you answers.”
Technology and AI have come a long way since Pablo Picasso made this statement,
and today computers do so much more.
They indeed give us answers and they even point today to interesting questions,
to areas worth exploring, new medicine, energy transition, food supply,
to name just a few of the big challenges ahead.
But the bigger question about how we will use this technology,
about the role we wanted to play, that question -
that one we will have to answer ourselves.
Don’t only think of profit when applying AI.
Look at energy use, look at ethics, look at trust, look at common sense,
and absolutely take into account the well-being of employees.
We ultimately decide how technology is used.
What future do YOU want?
What future will YOU create?