Pulitzer Prize-winning journalist Gary Rivlin says regulation can help control how AI is used: "AI could be an amazing thing around health, medicine, scientific discoveries, education ... as long as we're deliberate about it." He spoke with Dave Davies about some of his fears about artificial intelligence. His book is AI Valley. Also, Maureen Corrigan reviews Karen Russell's new Dust Bowl-era epic, The Antidote.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy
Chapter 1: What sparked the recent acceleration in AI development?
This is Fresh Air. I'm Dave Davies. For decades, scientists have dreamed of computers so sophisticated they could think like humans and worried what might happen if those machines began to act independently. Those fears and aspirations accelerated in 2022 when a company called OpenAI released its artificial intelligence chatbot called ChatGPT.
Our guest veteran investigative reporter Gary Rivlin has burrowed deep into the AI world to understand the plans and motivations of those pushing artificial intelligence and what impact they could have for good or ill. In his new book, Rivlin writes that in March of 2023, there were more than 3,000 startup companies in the U.S.
Chapter 2: How are startups influencing the AI landscape?
working on artificial intelligence, with new ones popping up at a rate of 30 per day. While AI is already in use in some fields, such as medical diagnosis, many believe the field is on the verge of a new breakthrough, achieving artificial general intelligence, systems that truly match or approximate human cognitive abilities.
Some believe it could be as transformational to human society as the Industrial Revolution. But many fear where it may take us. A poll of AI researchers in 2022 found that half of them believe there's at least a 1 in 10 chance that humanity will go extinct due to our inability to control AI.
In 2023, President Joe Biden issued an executive order imposing some regulatory safeguards on AI development. But President Trump quickly repealed that order upon taking office, saying Biden's dangerous approach imposed unnecessary government control on AI innovation. We've invited Gary Rivlin here to help us understand all these issues and developments.
Rivlin has worked for the New York Times, among other publications, and published 10 previous books. In 2017, he shared a Pulitzer Prize for reporting on the Panama Papers. His new book is AI Valley, Microsoft, Google, and the Trillion Dollar Race to Cash in on Artificial Intelligence. Well, Gary Rivlin, welcome back to Fresh Air. Thanks for having me. Let's just start with a couple of basics.
You know, we're used to computers being very smart. I mean, way back in 2011, Siri appeared on Apple products. What distinguishes artificial intelligence from just smart computers?
You know, there's this sense out there that in 2022, we suddenly had artificial intelligence. It's been much, much more gradual than that. You know, Google has been using machine learning artificial intelligence since the 2000s, you know, to decipher imprecise Google searches, to figure out how much to charge for the various ads they throw on the system.
You know, Google Translate's been around since the mid-2010s. That's... So, you know, we've been auto-complete, you know, spam filters. That's AI. But you're touching on a really interesting question. It's not this clear, like, oh, this is a smart machine. This is artificial intelligence. The way it's kind of played out now is that these machines can learn, right? I mean—
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Chapter 3: What are the potential risks of AI technology?
the old approach had been you encode rules. You just teach the computer, here's exactly the set of rules, just follow it. Now it's machine learning, deep learning, that the computer is ingesting vast troves of data, books, the public internet, Amazon reviews, Reddit posts, whatever it might be, articles. And it's finding patterns and, in quotes, learning.
And then they're fine-tuned, and then they get better at communicating with us and such. So there really isn't this, oh, artificial intelligence is this. In fact, the term artificial intelligence is controversial just in the sense that right now it's more... amplified intelligence.
We could use this thing to get smarter, to find patterns that humans couldn't possibly understand because we can't read billions of words. So, you know, there's another definition that AI really should be alien intelligence. Because the weird thing about AI is that It seems to know everything, but it doesn't understand a thing.
You know, I mean, there's this term, I love it from a linguist at University of Washington uses it, the stochastic parrot. You know, it's just like, it's like a parrot. It just, it's repeating words randomly, but it doesn't really understand what it's saying.
It's learned a lot of words. Okay. Now, this may be another artificial distinction, but new talk is now of artificial general intelligence, a great leap forward. What is that exactly?
Right. So, you know, AGI, just to use the phrase, is that it's a system that could match or exceed cognitive abilities across the board. And, you know, again, I feel like in some ways we have artificial general intelligence. You know, again... You got to be a PhD in physics and understand this.
But what's amazing about, you know, these models is that they have deep understanding in a vast array of domains. So in one way, that is AGI, artificial general intelligence. You know, it's... There's no set definition. It keeps on changing. There are predictions that we're going to have AGI the next year, two years, maybe it's five years kind of thing. I'm dubious of those predictions.
I mean, this is moving exponentially. This is improving so fast. that making predictions could be perilous. But on the other hand, I really feel like there needs to be another breakthrough or two before we have this artificial general intelligence a la a computer from Star Trek that you're talking to and it's helping you explore. It's at your site, a co-pilot, figuring out everything.
You know, again, an artificial distinction in that I don't think, like, one day there's going to be this Eureka. We have AGI. I do guarantee there will be startups and large companies that say Eureka. We have artificial general intelligence. But, you know, they just play with the definition.
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Chapter 4: How could AI regulation mitigate potential dangers?
A few days ago, I'm sure you saw this, Kevin Roos, the respected tech columnist for the New York Times, wrote a piece saying that we're going to quickly see companies claiming they have artificial general intelligence. And whatever you call it, these dramatically more powerful AI systems are coming and soon. And Ezra Klein of the New York Times opinion section says essentially the same thing.
Both of them agree we're not ready for the implications of this. Do you agree with that?
I do, and you're taking away from me what's the main message of those. These things are coming, and they're coming fast, and we're not prepared. I personally think AI could be an amazing thing around health, medicine, scientific discoveries, education, a wide array of things. as long as we're deliberate about it.
And that's my worry, and I do believe that's Kevin and Ezra's worry, that we're not being deliberate. We started in 2023. There was meetings at the White House, and there were hearings in the Senate, and that's just kind of dropped by the wayside, and now we're more at a laissez-faire attitude towards it. We need to prepare for this. Like any technology... There's good and there's bad, right?
The car, the car meant freedom. The car changed our society, but the car meant pollution. The car means 30,000 to 40,000 deaths in the U.S. a year kind of thing. And I look at AI the same way. It could be really great if we're deliberate about it and take steps to ensure that we get more of the positive than the negatives, because I guarantee you there will be both positives and negatives.
I mentioned in the introduction that President Biden had issued this executive order trying to establish some processes and guardrails and safeguards. Trump swept all that away saying, nope, that's onerous government regulation. Let innovation proceed. And it's funny. The last time you and I talked on this program, it was about –
Efforts to implement the Dodd-Frank reforms of the financial system. And one of the difficulties was that, was that that bill had general principles. But regulators had to actually spell out what it meant to regulate some pretty complicated contracts and instruments in the world of finance.
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Chapter 5: What are the proposed regulations for AI?
And what you'd written about then was how the private interest had gotten in and kind of gummed all that up with – by disputing everything. But I'm wondering what does – what do regulations that control something as sprawling as AI – what does that look like? What do we need in terms of – how do we get prepared?
Right. So there are a few basic steps that the Biden administration thought of. One, that you, in quotes, red team these cutting edge models. And basically, you get outsiders to try to break the system, try to get it to jump the fence, to use the term, to get it to misbehave, just to see what could go wrong. And you
the executive order said you need to test them and then you need to share with the government what you find. That's one of the things that went by the wayside when Trump took over as president. But to me, I'd break it down more to the concerns, the use of AI as a weapon of war, the use of AI for surveillance. You know, I worry that AI is just going to
solidify biases that we already have because the AI is learning from us and all these inherent biases in things. You know, it's like we need to prepare for the impact on the job market, which I think will be a slow roll. I don't think like... We're going to lose millions of jobs in a year kind of thing. But, you know, it is coming and we need to prepare for it.
There's another concept, recursive learning, that these systems change in ways we don't really understand. And that's what scares me. That we're going to let these systems loose and they could just learn. Because, you know, really the way to understand any of these large language models, any of these chatbots, is it's a mirror on us. It's reading our collective works.
It's learning from us about... imperialism and domination and humans mistreating each other, is learning about loneliness, is learning about freedom and independence and autonomy and all that. And so, me, it's recursive intelligence. This idea that these models are constantly improving in ways we don't understand, and then that could be dangerous.
And they could learn how to pursue an agenda and keep it hidden, right, to deceive in their own interest. Yeah. So what would that look like in terms of – what are the dark fears here?
I mean that's not really theoretical. The systems – God, I can't remember which model it was. But they were testing it and it was – dissembling. It was changing the files that would monitor its behavior and then lying to the people who noticed it and said, wait, aren't you changing those files?
And, you know, it's another example, OpenAI, the creative chat GPT, when they came out with GPT-4, their then cutting-edge model in 2021, Three, they put out a research report and they red teamed it. They tested it and saw all the ways it could misbehave. And one of the most interesting is that the model went to, I think it was a TaskRabbit.
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Chapter 6: Why are AI systems considered a 'black box'?
So you can imagine a scenario where, you know, a kid is overseas in Europe and the bot, one of these systems, you know, calls grandma, pretends it's that kid and says, I'm in trouble, wire me money. And they're good enough to fool, you know, the parent, the grandparent. I mean, Maybe not apparent, but I don't think we're very far away from that. And it could certainly fool many, many people.
Right, right. You know, there's something that you wrote in the book. You wrote about a couple of tech guys, Tristan Harris and Azar Raskin, who had real experience in the tech world, who said they worried about AI because it's a technology whose creators confess they do not understand why their models do what they do. Is that literally true? That's kind of scary. Yeah.
Yeah, so, you know, they're a black box. I mean, so nowadays it's neural networks, models that emulate how humans learn. They learn by reading vast stores of data, the open Internet books, whatever, and they improve through feedback and trial and error. You're not really encoding the rules. Well, you know, it's trying to emulate the human brain. And, you know, I mean, I have two teenage sons.
You know, we try to... teach them, they read, you know, we give them feedback and all. And, you know, there are things that come out of their mouths I don't quite understand. And that's the way I look at these chatbots, these neural networks, these large language models, you know, that...
We don't quite understand they say what they say because they're trying to emulate the human brain as best they can. And who could say why I'm saying the words I'm saying right now when you're going to have the exact reaction? And so that's part of the miracle, the gee whiz, these things are amazing. But it's part of what's scary because we don't fully understand.
The people who create it don't fully understand why it says what it says.
One more thing about the national political scene. There's a lot of talk about tech bros and Donald Trump. Elon Musk is clearly a driving force in the administration's effort to cut federal workforce and contracts. There are a bunch of billionaires from the tech world at his inauguration. Do you think that there's an elite tech agenda to radically reshape society at work through Donald Trump?
in a word, yes. What scares me is there's a movement in Silicon Valley, there's a movement in tech, the accelerationists. You know, anything that stands in the way of our advancing artificial intelligence is bad. Often it's put in the context of competing with China. We can have new rules in the way, and that is their agenda. I would say their real agenda is that they could make
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Chapter 7: What role do tech elites play in AI development?
a lot of money, billions, hundreds of billions, ultimately trillions of dollars off of this, and they don't want anyone standing in their way. And so I think if you want to understand Elon Musk, you want to understand Mark Zuckerberg, you want to understand Trump, Jeff Bezos and cozying up to Trump, you know, for a few million dollars, it's not very expensive for them.
You know, they could have a friend in the White House who makes sure that they can do what they want to do unchecked. And in fact, maybe that's my biggest fear about AI. It's so much power in the hands of few people. Creating these models is so expensive. To hire the talent, you have to pay them a million or more a year.
To train them, it takes tens of millions if not hundreds of millions of dollars in computer power and then to operate them. takes equivalent money. It's billions of dollars and billions of dollars.
So it's becoming less and less about the startups and more about the same companies that dominated tech in the 2010s dominating in the 2020s, Google, Microsoft, matter which is Facebook, Amazon, a few others. And that's really what concerns me. You know, that's kind of the Silicon Valley way. Let's get five smart guys, and they're almost always guys, in a room, and we'll figure it out.
And like, okay, we saw that didn't go so great with social network, and now we're having a really powerful technology. And I'd like there to be more than just five people in a room figuring this out.
You know, the account that you give us in the book is pretty detailed and really interesting about how all this unfolded. One of the things that struck me is that some of the leading players in developing AI weren't just coders or computer nerds. A lot of them studied classics or philosophy or worked in completely unrelated fields. Is there a connection here?
That's one of the things I was surprised by and found fascinating myself, that it's not just computer scientists. It's mathematicians. It's physicists. It's philosophers. It's neuroscientists. And, you know, it's a broad range of things because, again, it's no longer about just programming these models to act the way we want them to act. We're trying to emulate the way humans learn.
So what a psychologist has to say, what an educator has to say about that is a linguist is really important to it speaking a natural language. That's actually what attracted me to the topic in the first place, this idea that computers could speak to us in our language. You didn't have to learn a programming language. Earlier in my life, I tried to program in computers. I studied Fortran.
It's difficult.
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Chapter 8: How diverse are the backgrounds of AI leaders?
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You made the point earlier that it's enormously expensive to develop AI. I mean the talent is high-priced and it takes tons and tons of computing power to develop the systems, to run them once you have them, which means not a couple three million dollars but hundreds of millions in some cases or more, which means that the big companies in tech – Microsoft, Google, Meta, we all know the names –
have an edge but it's interesting as I read your story that doesn't that's no guarantee of success is it sometimes it's kind of an obstacle having a big organization you know it's interesting let's use the example of Google let's give Google credit first they were so far ahead of almost everyone else
on AI. They hired some of the best talent. They were employing machine learning, deep learning, long before most everyone else. They did some of the more cutting-edge things. In fact, the breakthrough that led to ChatGPT was actually out of Google had inside the company in around 2020 a chat GPT equivalent. But, you know, Google takes in a lot of revenue.
There's a lot of risk if this chatbot misbehaves. There is famously this example of Microsoft. I think it was 2016, 2017 came out with Tay. And, you know, it was trained on social media and that kind of thing. And within 24 hours, it was a Holocaust-denying white supremacist. And, of course, Microsoft, worrying about the reputational risk, pulled the plug on that rather quickly.
And I feel like that's haunted the giant. So even though Google was far ahead, even though Google could have had their version of ChatGPT, and it was Google that changed the world, they were... They're scared of it and, you know, never underestimate the ability of a giant to stumble over its own feet. They have layers and layers of bureaucracy.
They have, you know, huge public relations department that's whispering the CEO's ear. You know, I don't think it's a coincidence that OpenAI startup founded in 2015 was the one that set off the starter's pistol on this because they didn't have as much as at stake. You know, they can afford reputation-wise to release chat TPT.
They could just make the decision without 10 layers of decision-making before they did it. And, you know, so, yes, they have advantage. But, you know, Google also has like $100 billion of reserves, you know, where OpenAI has to go out and raise funds voluntarily. They've raised roughly, I don't know, $20 billion so far. And there's talk that they've raised another $30 billion.
And I might even be underestimating. And so, you know, that's $50 billion or so. You know, Google, they just pay for themselves. Microsoft, Meta, they all have deep, deep, deep reserves of money. And so... You know, it's almost like a race of attrition. You know, you can use these chatbots for free if you want the leading edge, cutting edge.
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