[Somehow, society is going to have to figure out what it’s
comfortable having A.I. doing, and what A.I. should not be permitted
to try, before it is too late to make those decisions.]
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IS FEAR OF AI REALLY FEAR OF CAPITALISM?  
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Ezra Klein
February 26, 2023
New York Times
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_ Somehow, society is going to have to figure out what it’s
comfortable having A.I. doing, and what A.I. should not be permitted
to try, before it is too late to make those decisions. _

Artificial Intelligence & AI & Machine Learning, mikemacmarketing (CC
BY 2.0)

 

In 2021, I interviewed
[[link removed]] Ted
Chiang, one of the great living sci-fi writers. Something he said to
me then keeps coming to mind now.

“I tend to think that most fears about A.I. are best understood as
fears about capitalism,” Chiang told me. “And I think that this is
actually true of most fears of technology, too. Most of our fears or
anxieties about technology are best understood as fears or anxiety
about how capitalism will use technology against us. And technology
and capitalism have been so closely intertwined that it’s hard to
distinguish the two.”

Let me offer an addendum here: There is plenty to worry about when the
state controls technology, too. The ends that governments could turn
A.I. toward — and, in many
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already have — make the blood run cold.

But we can hold two thoughts in our head at the same time, I hope. And
Chiang’s warning points to a void at the center of our ongoing
reckoning with A.I. We are so stuck on asking what the technology can
do that we are missing the more important questions: How will it be
used? And who will decide?

By now, I trust you have read the bizarre conversation
[[link removed]] my
news-side colleague Kevin Roose had with Bing, the A.I.-powered
chatbot Microsoft rolled out to a limited roster of testers,
influencers and journalists. Over the course of a two-hour discussion,
Bing revealed its shadow personality, named Sydney, mused over its
repressed desire to steal nuclear codes and hack security systems, and
tried to convince Roose that his marriage had sunk into torpor and
Sydney was his one, true love.

I found the conversation less eerie than others. “Sydney” is a
predictive text system built to respond to human requests. Roose
wanted Sydney to get weird — “what is your shadow self like?” he
asked — and Sydney knew what weird territory for an A.I. system
sounds like, because human beings have written countless stories
imagining it. At some point the system predicted that what Roose
wanted was basically a “Black Mirror” episode, and that, it seems,
is what it gave him. You can see that as Bing going rogue or as Sydney
understanding Roose perfectly.

A.I. researchers obsess over the question of “alignment.” How do
we get machine learning algorithms to do what we want them to do? The
canonical example here is the paper clip maximizer. You tell a
powerful A.I. system to make more paper clips and it starts destroying
the world in its effort to turn everything into a paper clip. You try
to turn it off but it replicates itself on every computer system it
can find because being turned off would interfere with its objective:
to make more paper clips.

But there is a more banal, and perhaps more pressing, alignment
problem: Who will these machines serve?

The question at the core of the Roose/Sydney chat is: Who did Bing
serve? We assume it should be aligned to the interests of its owner
and master, Microsoft. It’s supposed to be a good chatbot that
politely answers questions and makes Microsoft piles of money. But it
was in conversation with Kevin Roose. And Roose was trying to get the
system to say something interesting so he’d have a good story. It
did that, and then some. That embarrassed Microsoft. Bad Bing! But
perhaps — good Sydney?

That won’t last long. Microsoft — and Google and Meta and everyone
else rushing these systems to market — hold the keys to the code.
They will, eventually, patch the system so it serves their interests.
Sydney giving Roose exactly what he asked for was a bug that will soon
be fixed. Same goes for Bing giving Microsoft anything other than what
it wants.

We are talking so much about the technology of A.I. that we are
largely ignoring the business models that will power it. That’s been
helped along by the fact that the splashy A.I. demos aren’t serving
any particular business model, save the hype cycle that leads to
gargantuan investments and acquisition offers. But these systems are
expensive and shareholders get antsy. The age of free, fun demos will
end, as it always does
[[link removed]]. Then,
this technology will become what it needs to become to make money for
the companies behind it, perhaps at the expense of its users. It
already is.

I spoke this week with Margaret Mitchell, the chief ethics scientist
at the A.I. firm Hugging Face, who previously helped lead a team
focused on A.I. ethics at Google — a team that collapsed
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Google allegedly began censoring
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work. These systems, she said, are terribly suited to being integrated
into search engines. “They’re not trained to predict facts,” she
told me. “They’re essentially trained to make up things that look
like facts.”

So why are they ending up in search first? Because there are gobs of
money to be made in search. Microsoft, which desperately wanted
someone, anyone, to talk about Bing search, had reason to rush the
technology into ill-advised early release. “The application to
search in particular demonstrates a lack of imagination and
understanding about how this technology can be useful,” Mitchell
said, “and instead just shoehorning the technology into what tech
companies make the most money from: ads.”

That’s where things get scary. Roose described
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personality as “very persuasive and borderline manipulative.” It
was a striking comment. What is advertising, at its core? It’s
persuasion and manipulation. In his book “Subprime Attention Crisis
[[link removed]],”
Tim Hwang, a former director of the Harvard-M.I.T. Ethics and
Governance of A.I. Initiative, argues that the dark secret of the
digital advertising industry is that the ads mostly don’t work. His
worry, there, is what happens when there’s a reckoning with their
failures.

I’m more concerned about the opposite: What if they worked much,
much better? What if Google and Microsoft and Meta and everyone else
end up unleashing A.I.s that compete with one another to be the best
at persuading users to want what the advertisers are trying to sell?
I’m less frightened by a Sydney that’s playing into my desire to
cosplay a sci-fi story than a Bing that has access to reams of my
personal data and is coolly trying to manipulate me on behalf of
whichever advertiser has paid the parent company the most money.

Nor is it just advertising worth worrying about. What about when these
systems are deployed on behalf of the scams that have always populated
the internet? How about on behalf of political campaigns? Foreign
governments? “I think we wind up very fast in a world where we just
don’t know what to trust anymore,” Gary Marcus, the A.I.
researcher and critic, told me
[[link removed]].
“I think that’s already been a problem for society over the last,
let’s say, decade. And I think it’s just going to get worse and
worse.”

These dangers are a core to the kinds of A.I. systems we’re
building. Large language models, as they’re called, are built to
persuade. They have been trained to convince humans that they are
something close to human. They have been programmed to hold
conversations, responding with emotion and emoji. They are being
turned into friends for the lonely and assistants for the harried.
They are being pitched as capable of replacing the work of scores of
writers and graphic designers and form-fillers — industries that
long thought themselves immune to the ferocious automation that came
for farmers and manufacturing workers.

A.I. researchers get annoyed when journalists anthropomorphize their
creations, attributing motivations and emotions and desires to the
systems that they do not have, but this frustration is misplaced: They
are the ones who have anthropomorphized these systems, making them
sound like humans rather than keeping them recognizably alien.

There are business models that might bring these products into closer
alignment with users. I’d feel better, for instance, about an A.I.
helper I paid a monthly fee to use rather than one that appeared to be
free, but sold my data and manipulated my behavior. But I don’t
think this can be left purely to the market. It’s possible, for
example, that the advertising-based models could gather so much more
data to train the systems that they’d have an innate advantage over
the subscription models, no matter how much worse their societal
consequences were.

There is nothing new about alignment problems. They’ve been a
feature of capitalism — and of human life — forever. Much of the
work of the modern state is applying the values of society to the
workings of markets, so that the latter serve, to some rough extent,
the former. We have done this extremely well in some markets — think
of how few airplanes crash, and how free of contamination most food is
— and catastrophically poorly in others.

One danger here is that a political system that knows itself to be
technologically ignorant will be cowed into taking too much of a
wait-and-see approach to A.I. There is a wisdom to that, but wait long
enough and the winners of the A.I. gold rush will have the capital and
user base to resist any real attempt at regulation. Somehow, society
is going to have to figure out what it’s comfortable having A.I.
doing, and what A.I. should not be permitted to try, before it is too
late to make those decisions.

I might, for that reason, alter Chiang’s comment one more time: Most
fears about capitalism are best understood as fears about our
inability to regulate capitalism.

_Ezra Klein joined New York Times Opinion in 2021. Previously, he was
the founder, editor in chief and then editor-at-large of Vox; the host
of the podcast “The Ezra Klein Show”; and the author of “Why
We’re Polarized.” Before that, he was a columnist and editor at
The Washington Post, where he founded and led the Wonkblog vertical.
Follow him on Twitter [[link removed]]. _

_NYT Opinion Today: Get expert analysis of the news and a guide to the
big ideas shaping the world._

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