The life of artificial intelligence is connected to the lives
of people and also to their livelihoods.
AI is already being used to make processes more efficient.
What could this mean for productivity and therefore economic growth?
And who will benefit from it?
We pose those questions to Geoffrey Hinton.
It will be a wonderful thing for productivity, that's true.
Whether it be a wonderful thing for society is something
else altogether.
In a decent society, if you increase productivity a lot,
everybody's better off.
But here, what's going to happen if you increase productivity
a lot?
The rich and the big companies are going to get
much richer, and ordinary people are probably going to be
worse off because they'll lose their jobs.
AIS ability to reshape productivity could also give new life
to developed economies whose productivity has stalled.
US productivity growth has been significantly lower in the 21st
century than it was between 1930 and 2000.
But for all the talk of the sweeping effects of
AI, economic change has been slow to materialize.
So far, Goldman Sachs reports that only 5% of companies
claim to use generative AI in regular production, with tech
and information businesses leading the way.
When wider adoption does come, Hinton anticipates it will have
very different impacts on different parts of the workforce.
Many people say, you know, it'll create more jobs for
this particular thing.
I'm not convinced of that.
What we're doing in the Industrial revolution, we made human
strength irrelevant.
Now we're making human intelligence irrelevant, and that's very scary.
So there's some areas where demand is very elastic.
An example would be healthcare.
If I could get 10 hours a week talking to
my doctor, I'm over 70, I'd be very happy.
So if you take someone and make them much more
efficient by having them work with a very intelligent AI,
they're not going to become unemployed.
It's not that you're now only going to need a
few of them, you're just going to get much more
healthcare.
Great.
So in elastic areas, it's great.
There's some areas that are less elastic.
Like I have a niece who answers letters of complaint
to a health service.
She used to take 25 minutes to answer a letter.
Now she can just scan the letter into ChatGPT, it'll
give an answer, she'll look at it, check, it's OK.
That's 5 minutes.
Now I suspect they'll need less people like that.
It may be they can just everybody can complain a
lot more, but I suspect they'll need less people like
that.
So some jobs are, some jobs are elastic, others aren't.
The non elastic ones.
I think people will lose their jobs and what's going
to happen is the extra wealth created by the increase
in productivity is not going to go to them.
Hinton's prize in physics wasn't the only Nobel awarded this
year for artificial intelligence.
Two Google DeepMind scientists and a professor at the University
of Washington received nobles in chemistry for using AI to
predict the structure of millions of proteins and even to
invent a whole new protein.
Marty Chavez of 6th St.
says that tool, called Alpha fold, shows the power of
machine learning.
As important a breakthrough as this alpha fold work is,
if you think about the problems in biology, the problems
in biology are so much more complicated, right?
Proteins are the basic building block of life, but then
those proteins organize themselves into organ eels of a cell,
and then cells, which organize themselves into tissues, which organize
themselves into organs, which go to organ systems, human beings,
populations of human beings.
Beyond biology and Health Sciences, generative AI could also have
profound effects on the climate.
For one thing, it will require a lot more electricity
to drive it.
Wells Fargo projects a 550% surge in AI power demand
by 2026.
Ireland is a poster child for the extent of that
demand today.
It's growing number of data centers used up 21% of
the country's electricity in 2023.
The good news is it's not new in our history.
We've been through many periods as a country where we
have had significant energy demand.
But what this is going to mean is AI is
now adding to this.
As we electrify transportation, as we electrify our buildings, we
now have this big near term demand for energy to
power these data centers.
Brian Deese served as the director of the National Economic
Council under President Biden and is now focused on studying
the effects of AI on energy at MIT.
So in the near term, it creates this tension of
how do we bring that additional electricity online and are
we bringing cleaner sources online or dirtier sources online, which
obviously effects emissions.
But the longer term, the bigger picture way these interact
is AI is a technology which will then get deployed
in lots of aspects of our economy, including our energy
system.
And so there are also opportunities where these two technological
developments coming at the same time could actually be 1
+ 1 = 3, help us find more efficient ways
to innovate in the energy space.
For all the good that generative AI could potentially do
for the human race, it also poses some real dangers,
perhaps none so apparent as its application to armed warfare.
Anya Manuel's Aspen Strategy Group published a report on this
very subject.
AI by its nature is dual use because it is
a multi purpose technology.
It's like electricity or the steam engine.
It's going to power everything from the most amazing positive
use cases to the really dangerous ones.
Anya, for all of my lifetime, the biggest geopolitical risk,
terror even, has been the threat of thermonuclear destruction.
There's a recently was a meeting over in South Korea
with an attempt to have everybody agreed that if there
were to be nuclear weapons used, a human had to
be in that chain of command.
And the countries there agreed to it, except for one,
China.
What do we make of that?
We are really on day one of this technology and
how it can be used vis a vis national security.
There have been a number of attempts in the last
couple of years, a lot of them thankfully led by
the United States, to think carefully about AI and nuclear
weapons use 1-2 how much you would allow lethal autonomous
weapons.
And this means really, you know, if you have a
Predator drone, there's still a human directing it and deciding
who to target and who to shoot at.
In the Ukraine war, you're getting very close now to
drone on drone dogfights, drones doing their own targeting and
shooting.
And very soon you have a situation that's very dangerous,
where if the AI weapon is always going to be
faster, the incentive for everyone is to use lethal autonomous
weapons because they'll always win.
And then you'll have huge escalation.
Super dangerous.
Lots of different people are trying to get at this
in different ways.
The Korea conference was one of them.
I'm not surprised that China hasn't signed on.
I would just give you one example.
You use nuclear weapons.
You know, when the US came first in the nuclear
race, we proposed some limited limits on nuclear weapons, I
think as early as 1946.
Then no real arms control treaties were negotiated until after
you had the Cuban missile Crisis.
And we got to the brink and then we had
some pretty good ones.
But what was going on all the time in the
background and what is going on now?
And it's it's super important and bears emphasizing is quietly
behind the scenes.
There are some Chinese scientists and American and Western scientists
talking about these issues.
There are other track twos that are more on the
policy area.
I'm part of one of them quiet conversations with the
Chinese about the dangers of AI.
And the Chinese interlocutors I've talked to are equally worried
about very similar things than the ones you and I
are talking about here.
And for a long time people have realized there's going
to be an arms race who can get the best
lethal autonomous weapons fastest.
All of the defence departments of the people who sell
arms like the US, China, Britain, Israel, Russia, all those
defence departments are busy working on autonomous lethal weapons.
They're not going to stop.
If one of them stopped, the others wouldn't.
What we need for that is not to stop working
on it, but to have a Geneva Convention.
Now you don't get Geneva Conventions, things like the Geneva
Conventions for chemical weapons, until after something very nasty has
happened.
So I think realistically very nasty things are going to
happen with lethal autonomous weapons, and then maybe we'll be
able to get conventions.
So for chemical weapons, the conventions have basically worked.
Putin isn't using them in the Ukraine and they haven't
been used much.
So the basically the conventions worked.
I'm hoping they would work for lethal autonomous weapons, although
I'm less confident.
But nothing's going to happen till after some very nasty
things have happened.
Whether AI works for good or for I'll, a fundamental
question is whether humans can maintain control or at least
influence over it.
Professor Hinton says we shouldn't assume that we can, at
least for long.
As soon as we make agents, which people are busy
doing now, things that can act in the world.
To make an effective agent, you have to give it
the ability to create sub goals.
So if you want to get to Europe, you have
a sub goal of getting to an airport and you
don't have to sort of think about Europe while you're
solving that sub goal.
That's why sub goals are helpful and these big IA
systems create sub goals.
Now the problem with that is if you give something
the ability to create sub goals, it will quickly realise
there's one particular sub goal that's almost always useful.
So if I have the goal of just getting more
control over the world, that will help me achieve everything
I want to achieve.
So these things will realise that very quickly, even if
they've got no self-interest, they'll understand that if I get
more control, I'm going to be better at doing what
they want me to do.
And so they will try and get more control.
That's the beginning of a very slippery slope.
But that suggests time's a wasting, that in fact we
don't have that much time.
We don't to be able to get some control over
generative AI.
We, we control it right now, but we don't have
that much time to figure out how we're going to
stay in control when it's smarter than us.
How do we go about that?
Let's take the United States for example.
Before we go globally.
United States, we have a government.
There are good people, the government, smart people, some probably
less part, but are they up to the job of
really understanding what you're talking about and getting their arms
around it?
I think with the current government, they are taking it
seriously.
It's just very difficult to know what to do.
1 clear thing they should be doing that I believe
they should be doing.
We need many of the smartest young researchers to be
working on this problem and we need them to have
resources now.
The government doesn't have the resources.
The big companies have the resources.
The government, I think, should be insisting that the big
companies spend much more of their resources on safety, on
the safety research and how will we stay in control
compared with what they do now?
Right now they spend like a few percent on that
and nearly all their resources go into building even better,
bigger models.
As you say, big companies don't like to be told
by the government how to spend their money, but they
are often, I mean, you have big accounting departments, for
example, to comply with various regulatory requirements on accounting.
If the government were to say, yes, we're in a,
at least for the very largest tech firms involved in
AI, mandate a percentage of your revenue that will be
devoted towards safety.
What's the right number?
I'm not sure that's the right thing to go for.
It shouldn't be a percentage of the revenue because that's
very complicated and they put all the revenue in some
other country and sheet.
The thing to go for is a fraction of their
computing resources.
The bottleneck here is computing resources.
How many NVIDIA chips or how many Google Tensor chips
can you get?
It should be a fraction of the computing resources.
That's an easier thing to measure.
What fraction?
I think it would be perfectly reasonable to say 1/3
now that's my starting point, and I'd settle for 1/4.
From everything you know, everything you understand.
Is generative AI potentially an existential threat to the human
species?
I think that's what I've been saying, yes, it really
is an existential threat.
Some people say this is just science fiction, and until
fairly recently I believed it was a long way off.
I always thought it would be a very long term
threat, but I thought it would be 100 years before
we had really smart things.
50 years, maybe.
We have plenty of time to think about it now.
I think it's quite likely that sometime in the next
20 years, these things will get smarter than us and
we really need to worry about what happens then.
Coming up, a race for resources South of the border.
We go to Mexico to see how global trade is
evolving in real time.
That's next on Wall Street Week.