I, Robot
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Artificial intelligence is back in the headlines because it seems to be getting so much smarter. I found myself forgetting that it was a chatbot generator. You know, it referenced this feeling it gets in the pit of its stomach. It referenced its mother. A digital game designer won first place at the Colorado State...

Fair fine arts competition after submitting a painting created by an AI computer program. I realized that I was having the most sophisticated conversation about the nature of sentience that I'd ever had. And I was having it with a computer program. All of these very malevolent depictions of robotics and artificial intelligence...

influenced how people felt about AI. What if the AI makes better decisions, safer decisions than human beings? Do we abdicate that responsibility? Do we lose that agency? From chat GPT and AI art to neural nets and information war, artificial intelligence in 2023. It's all coming up after this.

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I'm Maria Konnikova. And I'm Nate Silver. And our new podcast, Risky Business, is a show about making better decisions. We're both journalists whom we light as poker players, and that's the lens we're going to use to approach this entire show. We're going to be discussing everything from high-stakes poker to personal questions. Like whether I should call a plumber or fix my shower myself. And of course, we'll be talking about the election, too. Listen to Risky Business wherever you get your podcasts.

From WNYC in New York, this is On The Media. I'm Brooke Gladstone. If 2023 thus far had a person of the year, it might well be AI. That is, if it were conscious, an ongoing debate in some circles. Certainly, the issue has sparked endless coverage, much of it framed along the lines of that old National Lampoon joke, as in, AI, threat or menace? Microsoft has added new AI features to its Bing search engine.

And journalists are getting a taste of its incredible and creepy capabilities. It kept telling me that it was in love with me and trying to get me to say that I loved it back. Recent analysis from investment firm Goldman Sachs looked at the global impact and found AI could replace 300 million full-time jobs. A batch of images surfaced online

showing the former president being taken into custody, police custody there. Although the pictures look pretty convincing, they were all fake, created by artificial intelligence. This wave of AI anxiety and enthusiasm was first set in motion when ChatGPT by OpenAI was unveiled last November.

Rather than holding it close for testing like some of the other big players, OpenAI made its chatbot available to the public, reaping the benefits of buzz and beta testing and oceans of ready money. Microsoft, meanwhile, reportedly investing a whopping $10 billion in students' favorite homework killer, ChatGPT.

Open AI, which is reportedly valued at nearly $30 billion. And back in December, it said it's on pace to generate $200 million this year. But the buzz stoked fears, and at the end of March, there was that open letter. It's been signed by more than 1,000 artificial intelligence experts and tech leaders, past and present. Experts are calling for a six-month pause in developing large-scale AI systems, citing fears of profound risks to humanity.

The very tech execs who'd been building and profiting off of AI issued warnings about its power and the danger that could come of it. Apple co-founder Steve Wozniak chimed in. AI is another more powerful tool, and it's going to be used by those people, you know, for really evil purposes. As did Microsoft founder Bill Gates. We're all scared that a bad guy could grab it.

Which is how, in May, OpenAI's CEO, Sam Altman, ended up testifying in front of Congress, where he basically said, regulate me. My worst fears are that we, the field, the technology, the industry, cause significant harm to the world. I think if this technology goes wrong, it can go quite wrong. And

and we want to be vocal about that. We want to work with the government to prevent that from happening. If you're Sam Altman and you get a whole press cycle that says, first of all, my technology is so powerful that it could destroy the world, and second of all, like, I'm here to help.

regulate me and I'll do whatever I can to prevent that from happening. That's kind of a hero pose for him. I spoke to Washington Post reporter Will Oremes about the hearings. I would just say to consumers of the news, be wary of the hero narrative. Be wary of the idea that this guy who's building the leading AI systems is also the guy to save us from them. So what is it about chat GPT that ignited a global frenzy?

Well, it's so convincing. Bots like ChatGPT and BARD are built and trained differently from earlier, clumsier iterations. Remember the Spike Jonze movie, Her? So human, you could fall in love. What are you doing? I'm just looking at the world. I'm writing a new piano piece. Oh yeah? Can I hear it? Mm-hmm.

These people-pleasing applications can be whatever you want them to be. Like, you could even ask it for directions on how to remove a sandwich from a VCR in the style of the King James Bible. "O Lord, how can I remove this sandwich from my VCR? For it is stuck fast and will not budge." And the Lord spoke unto him, saying, "Fear not my child, for I shall guide thy hand and show thee the way."

Take thy butter knife and carefully insert it between the sandwich and the VCR and gently pry them apart. I mean, listen, thou shalt not put the peanut butter sandwich in there in the first place.

Tina Tallon is assistant professor of AI and the Arts at the University of Florida, and she gave us a brief history of the seasonal nature of AI love and loathing over the past 70 years. In the 1950s, there was a lot of energy behind it. However, those strides were cut short by the fact that they needed lots of data to analyze in terms of being able to move past these rule-based systems. And unfortunately, data wasn't cheap. So around the 1970s,

We get this first AI winter. The freeze on AI research thawed in the 80s when computer power boomed. But in the late 80s and into the 90s, another cold front blew in. People kind of, again, reached a wall in terms of the way that our computational resources were able to render all of these different cognitive processes. And then there also has been a lot of public opinion that has influenced the progression of AI research.

Consider blockbusters like 2001: A Space Odyssey back in 1968. Open the pod bay doors, Hal. I'm sorry, Dave. I'm afraid I can't do that. What's the problem? I think you know what the problem is just as well as I do. What are you talking about, Hal? This mission is too important for me to allow you to jeopardize it. I don't know what you're talking about, Hal. I know that you and Frank were planning to disconnect me.

And I'm afraid that's something I cannot allow to happen. And also things like Robocop.

The enforcement droid, Series 209, is a self-sufficient law enforcement robot. 209 is currently programmed for urban pacification, but that is only the beginning. After a successful tour of duty in Old Detroit, we can expect 209 to become the hot military product for the next decade. Terminator. It can't be bargained with. It can't be reasoned with. It doesn't feel pity or remorse or fear, and it absolutely will not stop.

All of these very malevolent depictions of robotics and artificial intelligence influenced how people felt about AI. When I spoke to Talon in January when the show first aired, she said, it's not just about chat. A digital game designer won first place at the Colorado State Fair.

Fine Arts Competition after submitting a painting created by an AI computer program. Via a newfangled AI-driven text-to-image generator. This is the first year it has been won by our robot overlords.

actual artists who got beat out are not happy. Many of the AI tools initially available to the public hailed not from traditional tech giants, but from newer companies, labs, and models like Prisma Labs and Stable Diffusion, MidJourney, and the aforementioned OpenAI, which counts Elon Musk, Sam Altman, and Peter Thiel among its founders and funders.

But the big players were quick to get back in the game. Google released Bard, its own chatbot, powered by Lambda, back in February. And the money followed. The Alphabet guys, Larry Page and Sergey Brin, having one of their best years ever, up nearly $30 billion. It's the big getting even bigger from the AI craze. Natasha Tiku had her own experience with the Lambda bot.

I found myself kind of forgetting that it was a chatbot generator.

She's a tech culture writer at the Washington Post. In her encounter with Lambda, she experienced some serious uncanny valley heebie-jeebies. You know, it referenced this feeling it gets in the pit of its stomach. It referenced its mother, you know, like these bizarre backstories. I've kind of felt like, okay, I'm a reporter trying to get a good quote from a source. She also messed around with the groundbreaking text-to-image generator, DAL-E2.

What did she ask for? Zaha Hadid designing a hobbit house. I did like a missing scene from Dune 2.

I tried to generate fake images of family escaping the floods in Pakistan. I tried to do Black Lives Matters protesters storming the gates of the White House. When we spoke earlier this year, she told me that this revolutionary tech has actually been around for a while.

They're already being used by major tech companies like Google and Facebook when it comes to autocomplete in your emails, language translation, machine translation, content moderation.

You really wouldn't know that it's happening. It's much more at that infrastructure layer. You know, and again, that's why people kind of freaked out getting to play around with this technology. This stuff is being compared to the steam engine or electricity. Really? Tell me more about that.

the belief that it will be this foundational layer to the next phase of the internet. You could read that in a more mundane way and just imagine it as DALI being incorporated into the next Microsoft Office.

You know, everyone having access to these generative tools so that you or I could make a multimedia video and, you know, generate a screenplay just as easily as we might be able to use a word processor or clip art. And so right now this technology is out there like any beta model so that the public can test it and then how they monetize or if they monetize it later remains to be seen.

Yeah, I mean, part of the reason we're seeing open AI get a lot of press is because the larger tech companies like Google and Facebook, they're just so adverse to bad PR that they either are not releasing similar technology that they have or when they release it and bad things happen, they take it down immediately. Facebook released...

a model called Galactica, and it started generating a fake scientific paper with a real science author. Using a real scientist's name, you mean? Yeah. You know, that's not something Facebook wants to be in the news for. OpenAI has a different philosophy around that, and they say that you need to have this real-world interaction in order to really be able to prepare. How prepared are we to interact with these future tools?

I would say like not at all. But I don't think that we couldn't get up to speed really quickly. And I think that there are a lot of lessons that we've already learned from social media. And it's certainly the media's job to educate the public about that. And I feel like we're up against a lot of hype by people with a financial stake in this technology industry.

It's not taking away from the technology to acknowledge its limitations. AI literacy should be a focus for this year. It's really alarming to see people speculate that chat GPT is great for therapy and mental health. That to me seems just like a wild leap. Because the stakes are too high.

This is why regulations are in place, right? For the instances when it might work really well for 95% of the people, those 5% where it could be disastrous are protected. My percentages aren't correct, but therapy is definitely one of those instances. Like maybe you want advice on like how to talk to your boss. That's great. But mental health is serious.

Yeah, I felt that in a lot of the hype about it, there wasn't much said about it.

how its goal of being more human has made it much more likely to lie. And the reason why I bring this up is because it's often been talked about as a threat to Google, because it's so much easier to ask natural speech questions and get answers back. But from any of these advanced chatbots, there isn't any propensity towards telling the truth, is there?

Well, that depends on what it's optimized for. I think there's obvious reasons why Google, which has already been working on this and has for years been thinking about reorienting its search to a chat-like interface, hasn't done it yet. That's not to say that there aren't many instances where it could be a lot more useful. And, you know, when you have the little answer box that pops to the top of Google, it's

which often also gives you wrong answers. But there's so few questions in life where, A, not knowing the source, and B, just getting one answer is going to be sufficient. You know, the companies could do both. They could cite their sources and give you more than one, but this is just going to complicate our existing information dystopia. You mean make it worse. Yes. Yeah.

I think it's just good for people to keep in mind that these models are above all designed to sound plausible. Plausibly human, you mean?

just plausible. Like, if you're asking for an answer, there's really no warning light that goes off when something is really wrong. There's no warning light that goes off if it generated a list of fake books as opposed to real books you should read, or if it is basically copying an artist's style versus, like, giving you a really original image.

It's designed to people please and look and sound like what you asked of it. So just keep that in mind. It's really good at bullshitting you. Natasha, thank you so much. Thanks for having me. Natasha Tiku reports on tech for The Washington Post. Coming up, the unpopular idea that revolutionized AI. This is On The Media. On The Media.

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I'm Maria Konnikova. And I'm Nate Silver. And our new podcast, Risky Business, is a show about making better decisions. We're both journalists whom we light as poker players, and that's the lens we're going to use to approach this entire show. We're going to be discussing everything from high-stakes poker to personal questions. Like whether I should call a plumber or fix my shower myself. And of course, we'll be talking about the election, too. Listen to Risky Business wherever you get your podcasts.

This is On the Media. I'm Brooke Gladstone. If you show a three-year-old child a picture and ask them what's in it, you'll get pretty good answers. Okay, that's a cat sitting in a bed. The boy is petting the elephant. Those are people that are going on an airplane. That's a big airplane. Those are clips from a 2015 TED Talk by Fei-Fei Li, a computer science professor at Stanford University. She

She was consumed by the fact that despite all of our technological advances, our fanciest gizmos can't make sense of what they see. Our most advanced machines and computers still struggle at this task. In 2010, she started a major computer vision competition called the ImageNet Challenge, where software programs compete to correctly classify and detect objects and scenes.

Contestants submit AI models that have been trained on millions of images organized into thousands of categories. Then the model's given images it's never seen before and asked to classify them.

In 2012, a pair of doctoral students named Alex Krzyzewski and Ilya Sutskiver entered the competition with a neural network architecture called AlexNet. And the results were astounding. They did much better than the existing technology, and that made a huge impact.

Geoffrey Hinton was their PhD advisor at the University of Toronto and collaborator on AlexNet. When we spoke in January, Geoff was still working at Google. But in May, he publicly left the company specifically so that he could blow the whistle and say, we should worry seriously about how we stop these things getting control over us.

And it's going to be very hard. But for the existential threat of AI taking over, we're all in the same boat. It's like nuclear weapons. If there's a nuclear war, we all lose. The warnings hit differently coming from Jeffrey Hinton because he's had a hand in pushing AI along as an explorer and developer of AI technology, especially the architecture of neural networks since the 70s.

It actually began when a high school friend started talking to him about holograms in the brain. Holograms had just come out, and he was interested in the idea that memories are distributed over the whole brain. So your memory of a particular event involves neurons in all sorts of different parts of the brain.

And that got me interested in how memory works. Hologram meaning a picture or a more, for lack of a better word, holistic way of storing information as opposed to just words. Is that what you mean? No, actually a hologram is a holistic way of storing an image as opposed to storing it pixel by pixel. Ah. So when you store it pixel by pixel, each little bit of the image is stored in one pixel. When you store it in a hologram...

Every little bit of the hologram stores the whole image. So you can take a hologram and cut it in half and you still get the whole image. It's just a bit fuzzier. It just seemed like a much more interesting idea than something like a filing cabinet, which was the normal analogy where the memory of each event is stored as a separate file in the filing cabinet.

There was somebody named Carl Lashley, you said, who took out bits of rats' brains and found that the rats still remembered things? Yes. Basically, what he showed was that the memory for how to do something isn't stored in any particular part of the brain. It's stored in many different parts of the brain.

And in fact, the idea that, for example, an individual brain cell might store a memory doesn't make a lot of sense because your brain cells keep dying. And each time a brain cell dies, you don't lose one memory.

This notion of memory, this holographic idea, was very much in opposition to conventional symbolic AI. Yes. Which was all the rage in the last century. Yes. You can sort of draw a contrast between two completely different models of intelligence. In the symbolic AI model, the idea is you store a bunch of facts as symbolic expressions and

Bit like English, but cleaned up so it's not ambiguous. And you also store a bunch of rules that allow you to operate on those facts. And then you can infer things by applying the rules to the known facts to get new known facts. So it's based on logic, how reasoning works. And then they take reasoning as to be the core of intelligence. There's a completely different way of doing business, which is much more biological, which

which is to say, we don't store symbolic expressions. We have great big patterns of activity in the brain. And these great big patterns of activity, which I call vectors, these vectors interact with one another. And these are much more like holograms. So you've got these vectors of neural activity. So, for example, large language models that lead to big chatbots are all the rage nowadays. If you ask, how do they represent words or word fragments?

What they do is they convert a symbol that says it's this particular word into a big vector of activity that captures lots of information about the word. They'd convert the word cat into a big vector, which is sometimes called an embedding. There's a much better representation of cat than just a symbol. All the similarities of things are conveyed by these embedding vectors.

very different from a symbol system. The only property a symbol has is that you can tell whether two symbols are the same or different. I'm thinking of Moravec's paradox, which I understand is the observation by AI and robotics researchers that reasoning actually requires very little computation, but a lot of sensory, motor, and perception skills.

He wrote in 88, it's comparatively easy to make computers exhibit adult-level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility. I just wonder, do you think machines can ever think?

until they can get sensory motor information built into those systems? There's two sides to this question, a philosophical side and a practical side. So philosophically, I think, yes, machines could think without any sensory motor experience. But in practice, it's much easier to build an intelligent system

if it has sensory input. There's all sorts of things you learn from sensory input. But the big language models that lead to these chatbots

Many of them just have language as their input. One thing you said at the beginning of this question was that reasoning is easy and perception is hard. I'm paraphrasing. That was true when you used symbolic AI, when you tried to do everything by having explicit facts and rules to manipulate them.

Perception turned out to be much harder than people thought it would be. As soon as you have big neural networks that learn and learn these big vectors, it turns out one kind of reasoning is particularly easy, and it's the kind that people do all the time and is most natural for people, and that's analogical reasoning.

Analogical reasoning. One thing is like another. Yeah, we're very good at making analogies. So you went on to study psychology and your career in tech, in which you are responsible for something that amounts to a revolution in AI, was an accidental spinoff of psychology. You

You went on to get a PhD in AI in the 70s at the oldest AI research center in the UK. That was the University of Edinburgh. You were in a place where everyone thought that what you were doing, studying memory as multiple stable states in a system, wouldn't work. That in fact, what you were doing, studying neural networks, was resolutely anti-AI. You weren't a popular guy, I guess.

That's right. Back then, neural nets and AI were seen as opposing camps. It wasn't until neural nets became much more successful than symbolic AI that all the symbolic AI people started using the term AI to refer to neural nets so they could get funding.

So when explaining the difference for a non-technical person between what a neural network is and why it was revolutionary compared to symbolic AI, a lot of it hinges around what you think a thought is. I recently listened to a podcast where Chomsky repeated his standard view that thought and language are very close. Whatever thought is, it's quite similar to language.

I think that's complete nonsense. I think Chomsky's misunderstood how we use words. If we were two computers and we had the same model of the world, then it would be very useful, one computer telling the other computer which neurons were active. And that would convey from one computer to another what the first computer was thinking.

All we can do is produce sound waves or written words or gestures. That's the main way we convey what we're thinking to other people. A string of words isn't what we're thinking. A string of words is a way of conveying what we're thinking. It's the best way we have because we can't directly show them our brain states. I once had a teacher who said, if you can't put it into words, then you don't really understand it.

I think there were all sorts of things you can't put into words that your teacher didn't understand. So the only place words exist is in sound waves and on pages. The words are not what you operate on in your head to do thinking. It's these big vectors of activity. The words are just kind of pointers to these big vectors of activity. They're the way in which we share knowledge. It's not actually a very efficient way to share knowledge, but it's the best we've got.

So today you're considered a kind of godfather of AI. There's a joke that everyone in the field has no more than six degrees of separation from you.

You went on to become a professor at the computer science department at the University of Toronto, which helped turn Toronto into a tech hub. Your former students and postdoctoral fellows include people who are today now leading the field. What's it like being called the godfather of a field that rejected you for the majority of your career? It's pleasing. And now all the big companies are using neural nets. Yes.

How do you define thinking? And do you think machines can do it? Is there a point in comparing AI to human intelligence?

Well, a long time ago, Alan Turing, I think he got fed up with people telling him machines couldn't possibly think because they weren't human, and defined what's called the Turing test. Back then, you had teletypes, and you would type to the computer the question, and it would answer the question. This was a sort of thought experiment. And if you couldn't tell the difference between whether a person was answering the question and whether a computer was answering the question...

Then Alan Turing said, you better believe the computer's intelligent. I admire Alan Turing, but I never bought that. I don't think it proves anything. Do you buy the Turing test? Basically, yes. There's problems with it, but it's basically correct. I mean, the problem is, suppose someone is just adamantly determined to say machines can't be intelligent.

How do you argue with them? Because nothing you present to them satisfies them that machines are intelligent. I don't agree with that either. I could be convinced if machines had the kind of hologram-like web of experience to draw from, the physical as well as the mental and computational. The neural nets are very holistic.

Let me give you an example from ChatGPT. There's probably better examples from some of the big Google models, but ChatGPT is better publicized. So you ask ChatGPT to describe losing one sock in the dryer in the style of the Declaration of Independence. Yeah.

It ends up by saying that all socks are endowed with certain rights, certain inalienable rights, by their manufacturer. Now, why did it say manufacturer? Well, it understood enough to know that socks are not created by God, they're created by manufacturers. And so, if you're saying something about socks, but in the style of the Declaration of Independence, the equivalent of God is the manufacturer.

And understood all that because it has sensible vectors that represent socks and manufacturers and God and creation. That's an example of a kind of holistic understanding and understanding via analogies that's much more human-like than symbolic AI and that is being exhibited by ChatGPT.

And that, in your view, is tantamount to thinking. It is thinking. That's intuitive thinking. What neural nets are good at is intuitive thinking. The big chatbots aren't so good at explicit reasoning, but they're Nora people. People are pretty bad at explicit reasoning.

We don't have identical brains. Our brains run at low power, about 30 watts, right? And they're analog. We're not as good at sharing information as computers are. You can run 10,000 copies of a neural net on 10,000 different computers, and they can all share their connection strengths because they all work exactly the same way. And they can share what they learned by sharing their weights, their connection strengths.

Two computers that are sharing a trillion weights is an immense bandwidth of information between the two computers. Whereas two people who are just using language have a very limited bottleneck. So computers are telepathic. It's as if computers are telepathic, right. Were you excited when ChatGPT was released?

We've been told it isn't really a huge advancement. It's just out there for the public. In terms of its abilities, it's not significantly different from a number of other things already developed. But it made a big impact because they did a very good job of engineering it so it was easy to use. Are there potential implementations of AI that concern you?

People using AI for autonomous lethal weapons. The problem is that a lot of the funding for developing AI is by governments who would like to replace soldiers with autonomous lethal weapons. So the funding is explicitly for hurting people.

That concerns me a lot. That's a pretty clear one. Is there something subtler about potential applications that give you pause? I'm hesitant to make predictions beyond about five years. It's obvious that this technology is going to lead to lots of wonderful new things.

As one example, AlphaFold, which predicts the 3D shape of protein molecules from the sequence of bases that define the molecule, that's extremely useful and is going to have a huge effect in medicine. And there's going to be a lot of applications like that. They're going to get much better at predicting the weather, not beyond like 20 days or so, but predicting the weather in like 10 days' time. I think these big AI systems are already getting good at that.

but there's just going to be huge numbers of applications. In a sensible society, this would all be good. It's not clear that everything's going to be good in the society we have. What about the singularity? The idea that what it means to be human could be transformed by a breakthrough in artificial intelligence or a merging of human and artificial intelligence into a kind of transcendent form of

I think it's quite likely we'll get some kind of symbiosis. AI will make us far more competent. I also think that the stuff that's already happened with neural nets is changing our view of what we are. It's changing people's view from the idea that the essence of a person is a deliberate reasoning machine that can explain why it arises conclusions,

The essence is much more a huge analogy machine that's forever making analogies between a gazillion different things to arrive at intuitive conclusions very rapidly. And that seems far more like our real nature than reasoning machines. Have you ever had a flight of fancy of what this ultimately might mean in how we live? That's beyond five years. You're right, I see. I have no idea. You warned me.

Jeffrey, thank you very much. Okay. Jeffrey Hinton is a former engineering fellow at Google Brain. He resigned in May and has been voicing his concerns about the impending AI arms race and the lack of protection ever since. Coming up with great computer power comes great responsibility. This is On The Media. This episode is brought to you by Progressive Insurance.

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This is On The Media. I'm Brooke Gladstone. Toward the end of my conversation with Jeff Hinton, he touched on a couple of things that need a little more explaining. One of them is alpha fold. Which predicts the 3D shape of protein molecules from the sequence of bases that define the molecule. An important development because protein misfolding is known to contribute to the pathogenesis of diseases like Alzheimer's.

AlphaFold is an AI system developed by DeepMind, a subsidiary of Alphabet. Now, a couple of days ago, DeepMind has announced that its second iteration of the AlphaFold system has, quote-unquote, solved...

the 50-year-old grand challenge problem of protein folding. There are other labs working on this software, too. This is University of Washington Seattle biochemist David Baker. We've also designed new proteins to break down gluten in your stomach for celiac disease and other proteins to stimulate your immune system to fight cancer.

These advances are the beginning of the protein design revolution. Hinton also described his fear of autonomous lethal weapons powered by AI. I followed up on that with Matt DeVost, an international cybersecurity expert who started his career hacking into systems for the U.S. Department of Defense back in the 1990s. When I first spoke to him in January, he gave me the beginner's class on autonomous lethality.

where once a target has been designated by a human decision maker, the weapon will have autonomy to kind of operate and get there, right? It'll navigate the terrain properly, make decisions based on how it achieves the impact of that target, for example.

There isn't a kid back in Oklahoma running it on a board. It can make a decision and change its path based on its own information. And probably much more quickly than a human drone operator would be able to achieve.

Now, that doesn't mean that we're going to take humans out of the decision-making equation with regards to what gets targeted. Not yet, anyway. Not yet, but in how it achieves the mission and the ability to basically act in a swarm capacity and make decisions amongst themselves.

adjusting their mission profile based on the swarm intelligence. Yeah, that's when multiple weapons are simultaneously operating and communicating with each other, making decisions based on each other's behavior. That's drone technology. But how would the next generation of swarming weapons behave?

What gets really interesting is if they start to demonstrate an ability to operate in a way that is more humane or cognizant of the human impact than a human decision maker would be able to do, in which case now you start to have some autonomy with regards to the targeting itself. Can you give me an example of that?

you know, trying to target this facility, but we're trying to minimize the potential for collateral damage. And the drone is aware enough to know that a bus just pulled up next to the facility where there is an autonomy that is built into the weapons that allows them to make a decision or abort a decision or delay a decision based on a situation that even a human being doesn't have the capacity to make that decision because it's changing so rapidly.

Right now, we wouldn't allow weapons to autonomously target, but that could happen one day. And it brings up images of Dr. Strangelove and Failsafe. That is going to be a concern. I think we've articulated pretty clearly, at least at the U.S. government level, that humans will remain in the loop as it relates to targeting other humans. It's different if you're targeting humans.

drones or you're targeting a communications tower, etc. But we could reach a point in which the drones are more efficient and more humane decision makers based on the AI capabilities and analytics that they're able to achieve. The same way that we might someday decide that we should allow only self-driving cars. You know, humans do a really good job of killing a lot of ourselves.

in motor vehicles every year, there may be a point in time in which the AI is a more sensible and objective decision maker. Obviously, these new AI tools will have an impact on intelligence gathering and collection. And you say that, for you, chat GPT was a wow moment. It was for a couple of reasons. You know, one is...

It interacts with you based on questions and you're able to refine it like the same way that you could refine your conversation with a human being. Tell me more or make a counter argument. But it also does a great job of understanding nuanced concepts. You know, I gave it an example, a friend of mine, Bill Kroll, who used to be

deputy director of the National Security Agency, had a quote a few years ago where he said, the cybersecurity industry has a thousand points of light but no illumination. I asked ChatGPT, what do you think Bill meant when he said that? And it gave an incredible answer. It said...

"When someone says that the cybersecurity industry has a thousand points of light and no illumination, they are expressing frustration with the fragmented and disorganized nature of the industry. The term a thousand points of light refers to many different players and stakeholders, including government agencies, private companies, and individual security experts.

Holy cow! That is an incredible response, right? And you can tell ChatGPT,

I want you to give a ranking or rating about how confident you are in your analysis. I also want you to provide a counterpoint. Plus, I want you to provide recommendations as to what we can do about this. So if you go in and ask it, what is the probability that Iran will attack a U.S. bank with a cyber weapon, it gives you a response that flows almost exactly like you would see in an intelligence briefing that might be delivered all the way up to the president's daily briefing.

So it's fascinating that it is able to not only query all this knowledge and come up with these great responses, but it can also frame the response from the perspective of the audience expectations. But

it has been shown over and over again that Shack GPT is fundamentally a people pleaser. Yes. It doesn't care if it's true or not. Yes. It will invent sources in order to give you something that has the exact format you're asking for. So,

So you can't trust anything that ChatGPT says. So how can it be helpful in intelligence gathering? Yeah. The intelligence community won't use ChatGPT based on ChatGPT's existing training data set. It'll use it based on data sets that are proprietary to the intelligence community. So what we're about to see in the next year and in the coming years is

is these domain-specific versions of ChatGPT where I control the training data or I tell it that it doesn't have to be the human pleaser, it doesn't have to be conversational, it should use the same heuristics that it's using to derive these answers. But if you don't have a source, you don't invent it, you can't make judgments that aren't based on a particular source. So it's a very quick shift to move away from that inherent bias.

to using the capability in a way that's very meaningful. Give me an example. Would it interrogate a prisoner of war? I don't know that it would interrogate a prisoner of war, although you could certainly envision where it might be used to augment a human's question.

questions that they're asking. But I think it'll probably get really good at threat assessment, making recommendations for remediating vulnerabilities. I think analysts might also use it to help them through their thinking, right? They might come up with an assessment and say, tell me how I'm wrong. And the AI serves as almost the 10th man rule, if you will, where they're

by design taking the counter argument. So I think there'll be a lot of unique ways in which the technology is used in the intelligence community. How imminent is this kind of technology? It's incredibly imminent.

The technology clearly exists. We're going to see with version 4.0, a version that is much more constrained with regards to not making things up and is much more current. I mean, one of the existing flaws right now with ChatGPT is the training data ends in 2021.

If you now start to have it where there's training data current as of whatever it found in the models this morning, that starts to get very, very interesting and means that this technology can be applied around real-term issues in the next year or two years. So another wow moment you had there.

was a challenge several years ago by DARPA. That is the government agency that drives a lot of amazing technology. It gave us the internet, for one thing, and GPS. Tell me about what happened at that DARPA conference. Yeah, so that was fascinating for me. In cybersecurity, we have these contests that we call capture the flag contests, and they really are ways of

for people to compete to demonstrate who's the top hacker, who's the top person at attacking systems. You hack systems and you take control of them, and then you have to defend the flag. You have to make sure that you patch it and you fix it and you prevent other people from taking over that system and booting you off. This is a cyber war game. This is a cyber war game, yeah. So in 2016, they brought the finalists out to DEF CON, which is the largest hacker conference in the world, in Las Vegas.

and they had the sixth finalist compete. That was another aha moment for me, you know, where I felt like I was living in the future, similar to the way I felt when I encountered ChatGPT at the beginning of December. I started my career in 1995. It was my job for the Department of Defense to break into systems and show how they were vulnerable and help system owners patch those systems. And here I was being completely replaced by a machine. And the machines were very creative and fast.

You know, it's an uncomfortable feeling for somebody in the cybersecurity industry, not because of the displacement, but because of the lack of explainability or the lack of understanding with regards to how resilient the patching is or making sure that the AI doesn't lose control of its objectives and do something that ends up being malicious behavior. So it's definitely a brave new world in that regard. How do we ensure that these weapons are safe to deploy? How do we ensure that they don't commit war crimes?

Yeah, I think we'll have clearly defined ethics around the use of artificial intelligence as it relates to things that could impact human lives or human safety. What's going to be disconcerting is when we encounter adversaries that don't have the same ethics. And do we end up having to unleash some sort of autonomy in our weapons because our adversaries have launched autonomous weapons against us?

put in a position of having to violate some of our principles because it's the only way to appropriately defend ourselves. If we dig a little deeper though, there are some other core risks these technologies all run on systems that are vulnerable. So we have an underlying responsibility to make sure that the infrastructure is robust and is secure.

You also need to make sure where the training data has an open collection model, chat GPT draws intelligence from the internet itself, that

that you are aware of adversaries that might try and pollute that environment. What if I decide that putting blog posts up, writing websites, taking out advertisements, going on Twitter to pursue a particular narrative that will influence the decision-making of a particular AI. And then the third area is going to be around the robustness of the algorithms and making sure that we have removed bias

I think that will drive, in the Department of Defense, a requirement for what we call explainable AI. The AI has to describe to us in understandable terms how it arrived at that decision. The debate over the drones was that Americans wouldn't be killed if we used them. Critics say we've overused them because the cost to us is so low.

We've already been able to destroy the world many times over for 70 years. But the ability to be more surgical in our destruction and even to hand off animals

our own autonomy to machines that may well be smarter than we are is a terrifying prospect. It is, right? We need to figure out what levels of agency we want to retain. As it relates to war fighting, we said, well, we want to maintain the decision-making as it relates to other human beings. But what if, over and over again, AI makes better decisions, safer decisions,

than human beings. Do we abdicate that responsibility? Do I lose the agency of being able to interpret what is misinformation with my own brain? Or do I abdicate it to an AI system that does it for me? So that is definitely going to be one of the fundamental questions that we face over the next decade. Where do we retain agency? And where do we decide that the machines can do it better? You seem to be suggesting that it may turn out that humans are far more dangerous.

in some domains the humans might be more dangerous. I'm thinking of the Cuban Missile Crisis and how the tapes suggest that John Kennedy was pretty much alone in wanting to make that deal to take American missiles out of Turkey so that Khrushchev would take them out of Cuba.

I'm just wondering if there had been an advanced chatbot advisor in the room, whether he would have stood with Kennedy or not. Yeah, it makes you definitely consider what does the training data look like for a decision like that. I don't want to think that I'm a fan of abdicating control to the machines. I'm certainly not. We have to figure out which are fundamentally human decisions and which are the ones that can be automated or augmented effectively.

It depends what you think of human nature, right? I mean, if there is a machine that is developed to help us fight the best war, is there a possibility that that machine may say, best not go to war? As long as we get it to understand our objectives and our constraints properly.

You know, you could sit and say, would the world be a better place right now if Russia were run by some sort of autonomous AI? Possibly. But, you know, if the AI has been programmed with the same biases, the same tendencies, the same ambitions, it might be more efficient than Putin in perpetrating these atrocities. Matt, thank you very much. Yes, of course. It was my pleasure. I enjoyed the conversation.

Matt DeVost is the CEO and co-founder of the global strategy advisory firm OODA, spelled O-O-D-A-L-L-C.

And that's the show this week. On the Media is produced by Micah Loewinger, Eloise Blondio, Molly Schwartz, Rebecca Clark-Calendar, Candice Wong, and Suzanne Gabber, with help from Sean Merchant. Our technical director is Jennifer Munson. Our engineer this week was Andrew Nerviano. Katya Rogers is our executive producer. On the Media is a production of WNYC Studios. I'm Brooke Gladstone.