I, Robot

Summary

Podcast Summary: On the Media – "I, Robot" (July 7, 2023)

Hosted by Brooke Gladstone and Micah Loewinger from WNYC Studios, the "On the Media" podcast delves into the intricacies of the media landscape, scrutinizing its impact on free speech, government transparency, and public perception. In the episode titled "I, Robot," released on July 7, 2023, the hosts explore the rapid advancements in artificial intelligence (AI), its societal implications, and the ethical dilemmas it presents.

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1. The Resurgence of Artificial Intelligence in 2023

Brooke Gladstone opens the discussion by highlighting the renewed prominence of AI in media headlines, emphasizing its rapid advancements:
"Artificial intelligence is back in the headlines because it seems to be getting so much smarter." [00:00]

The conversation quickly transitions to personal experiences with AI, illustrating how sophisticated and human-like these systems have become. Natasha Tiku shares her unsettling interaction with a chatbot that exhibits human emotions:
"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." [00:07]

2. AI’s Artistic and Conversational Capabilities

The episode explores AI's growing influence in creative fields. Max Tawney recounts a digital game designer winning a fine arts competition with an AI-generated painting:
"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." [00:16]

Ben Smith reflects on engaging in profound conversations with AI, questioning the nature of sentience:
"Was having the most sophisticated conversation about the nature of sentience that I had ever had. And I was having it with a computer program." [00:26]

3. The Evolution of AI and Public Perception

Geoffrey Hinton, a seminal figure in AI research, discusses the cycle of AI enthusiasm and anxiety:
"This wave of AI anxiety and enthusiasm was first set in motion when ChatGPT by OpenAI was unveiled last November." [03:07]

The hosts delve into the financial and industrial boom surrounding AI, with Max Tawney noting Microsoft's significant investment:
"Microsoft meanwhile, reportedly investing a whopping $10 billion in students favorite homework killer chat." [03:30]

4. Ethical Concerns and the Call for Regulation

The episode addresses growing concerns from AI experts regarding the unchecked development of AI systems. An open letter signed by over a thousand AI experts calls for a six-month pause in large-scale AI development due to potential risks:
"Experts are calling for a six month pause in developing large scale AI systems. Citing fears of profound risks to humanity." [04:00]

Geoffrey Hinton emphasizes the dangers inherent in AI's rapid advancement:
"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." [04:42]

5. The Technical Advancements and Comparisons to Human Intelligence

The discussion shifts to the technical prowess of modern AI systems like ChatGPT. Geoffrey Hinton explains why ChatGPT ignited global interest:
"Well, it's so convincing... bots like ChatGPT and Bard are built and trained differently from earlier, clumsier iterations." [05:30]

Natasha Tiku illustrates her uncanny experiences with AI-generated content, underscoring the blurred lines between human and machine interactions:
"It referenced this feeling it gets in the pit of its stomach. It referenced its mother. You know, like these bizarre backstories." [10:16]

6. The Historical Context of AI Development

Tina Tallon, an assistant professor at the University of Florida, provides a historical overview of AI's fluctuating fortunes over the past 70 years:
"In the 1950s, there was a lot of energy behind it... but data wasn't cheap." [06:33]

The conversation touches on the "AI winters" of the 1970s and late 1980s-1990s, periods marked by reduced funding and interest due to unmet expectations.

7. Neural Networks vs. Symbolic AI

A pivotal segment discusses the distinction between neural network-based AI and symbolic AI. Matt Devost, an AI expert, contrasts the two paradigms:
"In the symbolic AI model, the idea is you store a bunch of facts as symbolic expressions... Completely different models of intelligence." [22:30]

Matt Devost further elaborates on how neural networks utilize vectors to represent information, enabling more human-like analogical reasoning:
"Large language models... convert a symbol that says it's this particular word into a big vector of activity." [23:33]

8. Defining Intelligence and the Turing Test

The conversation ventures into the philosophical realm, debating whether machines can "think." Matt Devost defends the relevance of the Turing Test:
"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." [30:24]

Brooke Gladstone challenges this notion, suggesting that true intelligence may require sensory and motor integration:
"I just wonder, do you think machines can ever think until they can get sensory motor information built into those systems?" [24:17]

9. AI in Cybersecurity and Autonomous Weapons

Geoffrey Hinton and Matt Devost discuss the integration of AI in cybersecurity and the military. Matt Devost expresses concern over AI's role in autonomous lethal weapons:
"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." [33:52]

They explore scenarios where AI-powered drones could make ethical decisions more efficiently than humans, raising questions about accountability and control:
"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." [40:31]

10. The Future of AI and Human Symbiosis

Looking ahead, Matt Devost envisions a symbiotic relationship between humans and AI, enhancing human competence:
"I think it's quite likely we'll get some kind of symbiosis. AI will make us far more competent." [35:25]

He also touches on the transformative impact of AI on human self-perception and cognition:
"It's changing people's view from the idea that the essence of a person is a deliberate reasoning machine... to a huge analogy machine." [35:25]

11. AI’s Role in Intelligence Gathering and Analysis

Max Tawney shares his "wow moment" witnessing AI's capabilities in intelligence analysis, such as ChatGPT providing insightful interpretations of complex statements:
"When you ask ChatGPT... it gives you a response that flows almost exactly like you would see in an intelligence briefing." [43:45]

Despite acknowledging AI's prowess, Max Devost warns of inherent biases and the necessity for domain-specific training datasets to ensure reliability in critical applications:
"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." [45:04]

12. Ethical Deployment and Safeguards

The episode culminates with a focus on ensuring ethical AI deployment, particularly in weaponry. Max Devost emphasizes the need for robust ethical frameworks and the dangers posed by adversaries lacking such controls:
"We have an underlying responsibility to make sure that the infrastructure is robust and is secure." [49:03]

He also contemplates scenarios where AI could potentially outmaneuver human decision-makers in warfare, underscoring the urgency of establishing clear ethical guidelines:
"We need to figure out what levels of agency we want to retain as it relates to war fighting." [52:00]

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Conclusion

"I, Robot" offers a comprehensive exploration of the current state and future trajectory of artificial intelligence. Through insightful discussions with experts like Geoffrey Hinton and Matt Devost, the episode navigates the multifaceted landscape of AI—from its technical underpinnings and creative applications to the profound ethical and societal challenges it poses. The dialogue underscores the imperative for responsible AI development, robust regulatory frameworks, and a thoughtful examination of the symbiotic relationship between humans and machines.

Notable Quotes:

• Brooke Gladstone:
  "Artificial intelligence is back in the headlines because it seems to be getting so much smarter." [00:00]

• Geoffrey Hinton:
  "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." [30:24]

• Matt Devost:
  "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." [33:52]

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This summary encapsulates the key discussions and insights from the "I, Robot" episode of "On the Media," providing a coherent overview for listeners and non-listeners alike.

Transcript

[Brooke Gladstone]

Artificial intelligence is back in the headlines because it seems to be getting so much smarter.

[Natasha Tiku]

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.

[Max Tawney]

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.

[Ben Smith]

Was having the most sophisticated conversation about the nature of sentience that I had ever had. And I was having it with a computer program.

[Geoffrey Hinton]

All of these very malevolent depictions of.

[Tina Tallon]

Robotics and artificial intelligence influenced how people felt about AI.

[Max Tawney]

What if the AI makes better decisions, safer decisions than human beings? Do we abdicate that responsibility? Do we lose that agency?

[Brooke Gladstone]

From ChatGPT and AI art to neural nets and information war, artificial intelligence in 2023. It's all coming up after this.

[Tina Tallon]

What's the difference between DIY and doing it yourself? It's the difference between a part time passion and a full time business. WIX gives you the power to turn your passion into a money maker with a website that fits your unique vision. Let your ideas flow with AI that guides you but keeps you in the driver's seat. Manage your business from one dashboard and keep it growing with built in marketing features. It's time to turn your daydream into your dream job. Go to wix.com I'm Ben Smith.

[Ben Smith]

I'm Max Tawney and we host Mixed Signals from Semaphore Media. The future of media feels like a moving target, so every Friday we pull back the curtain on the platforms, ideas and people that are shaping the new media landscape. We'll tell you what really matters and try to figure out what's coming next. Plus, we go behind the scenes with the most important players in media right now. Whether you are yourself a media insider or just simply curious about who who or what will be all over your feed next, Mixed Signals from Semaphore Media is the perfect addition to your media diet. Listen wherever you get your podcasts.

[Geoffrey Hinton]

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 la poon joke, as in a threat or menace.

[Max Tawney]

Microsoft has added new AI features to.

[Natasha Tiku]

Its Bing search engine and journalists are.

[Max Tawney]

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.

[Ben Smith]

Recent analysis from investment firm Goldman Sachs.

[Max Tawney]

Looked at the global impact and found.

[Ben Smith]

AI could replace 300 million full time jobs.

[Brooke Gladstone]

A batch of images surfaced online showing the former president being taken into custody, police custody there.

[Max Tawney]

Although the pictures look pretty convincing, they were all fake. Created by artificial intelligence.

[Geoffrey Hinton]

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.

[Max Tawney]

Microsoft meanwhile, reportedly investing a whopping $10 billion in students favorite homework killer chat.

[Ben Smith]

GPT open AI, which is reportedly valued.

[Brooke Gladstone]

At nearly $30 billion. And back in December, it said it's on pace to generate $200 million this year.

[Geoffrey Hinton]

But the buzz stoked fears and at the end of March, there was that open letter.

[Natasha Tiku]

It's been signed by more than a thousand artificial intelligence experts and tech leaders past and present.

[Brooke Gladstone]

Experts are calling for a six month pause in developing large scale AI systems. Citing fears of profound risks to humanity.

[Geoffrey Hinton]

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.

[Max Tawney]

AI is another more powerful tool and it's going to be used by those people, you know, for really evil purposes.

[Geoffrey Hinton]

As did Microsoft founder Bill Gates.

[Max Tawney]

We're all scared that a bad guy could grab it.

[Geoffrey Hinton]

Which is how in May, OpenAI CEO Sam Altman ended up testifying in front of Congress where he basically said, regulate me.

[Max Tawney]

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 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.

[Geoffrey Hinton]

I spoke to Washington Post reporter Will Oremus about the hearings.

[Max Tawney]

I would just say to consumers of.

[Matt Devost]

The news, be wary of the hero narrative.

[Max Tawney]

Be wary of the idea that this guy who's building the leading AI systems is also the guy to save us from them.

[Geoffrey Hinton]

So what is it about ChatGPT 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.

[Brooke Gladstone]

What are you doing?

[Max Tawney]

I'm just looking at the world.

[Matt Devost]

I'm writing a new piano piece. Oh yeah? Can I hear it?

[Geoffrey Hinton]

Mm. 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.

[Natasha Tiku]

Take thy butter knife and carefully insert it between the sandwich and the VCR.

[Geoffrey Hinton]

And gently pry them apart. I mean, listen, thou shalt not put.

[Tina Tallon]

The peanut butter sandwich in there in the first place.

[Geoffrey Hinton]

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.

[Tina Tallon]

In the 1950s, there was a lot of energy behind it.

[Geoffrey Hinton]

However, those strides were cut short by.

[Tina Tallon]

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.

[Geoffrey Hinton]

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.

[Tina Tallon]

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.

[Geoffrey Hinton]

The progression of AI research. Consider blockbusters like 2001 A Space Odyssey back in 1968.

[Max Tawney]

Open the pod bay doors, Hal.

[Matt Devost]

I'm sorry, Dave. I'm afraid I can't do that.

[Geoffrey Hinton]

What's the problem?

[Matt Devost]

I think you know what the problem.

[Brooke Gladstone]

Is just as well as I do.

[Matt Devost]

What are you talking about, Hal? This mission is too important for me to allow you to jeopardize it.

[Geoffrey Hinton]

I don't know what you're talking about, Hal.

[Matt Devost]

I know that you and Frank were planning to disconnect me, and I'm afraid that's something I cannot allow to happen.

[Tina Tallon]

And also things like Robocop.

[Ben Smith]

The Enforcement Droid Series 209 is a self sufficient law enforcement robot. 209 is currently programmed for urban pacification.

[Matt Devost]

But that is only the beginning.

[Ben Smith]

After a successful tour of duty in.

[Matt Devost]

Old Detroit, we can expect 209 to.

[Max Tawney]

Become the hot military product for the next decade.

[Geoffrey Hinton]

Terminator it can't be bargained with.

[Brooke Gladstone]

It can't be reasoned with.

[Max Tawney]

It doesn't feel pity or remorse or fear.

[Geoffrey Hinton]

And it absolutely will not stop, ever.

[Ben Smith]

Until you are dead.

[Tina Tallon]

All of these very malevolent depictions of robotics and artificial intelligence influenced how people felt about AI.

[Geoffrey Hinton]

When I spoke to Talon in January when the show first aired, she said it's not just about chat.

[Max Tawney]

A digital game designer won first place at the Colorado State Fair Fine Arts competition after submitting a painting created by.

[Geoffrey Hinton]

An AI computer program via a newfangled AI driven text to image generator.

[Ben Smith]

This is the first year it has been won by our robot overlords. Actual artists who got beat out are not happy.

[Geoffrey Hinton]

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.

[Max Tawney]

Larry Page and Sergey Brin having one.

[Brooke Gladstone]

Of their best years ever, up nearly $30 billion.

[Ben Smith]

It's the big getting even bigger from the AI craze.

[Geoffrey Hinton]

Natasha Tiku had her own experience with the Lambda bot.

[Natasha Tiku]

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

[Geoffrey Hinton]

She's a tech culture writer at the Washington Post. In her encounter with Lamda, she experienced some serious uncanny Valley heebie jeebies.

[Natasha Tiku]

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 kind of felt like, okay, a reporter trying to get a good quote from a source.

[Geoffrey Hinton]

She also messed around with the groundbreaking text to image generator Dalle 2. What did she ask for?

[Natasha Tiku]

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 protester storming the gates of the White House.

[Geoffrey Hinton]

When we spoke earlier this year, she told me that this revolutionary tech has actually been around for a while.

[Natasha Tiku]

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, people kind of freaked out getting to play around with this technology. This stuff is being compared to this steam engine or electricity.

[Geoffrey Hinton]

Really.

[Brooke Gladstone]

Tell me more about that.

[Natasha Tiku]

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 dall e 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.

[Brooke Gladstone]

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.

[Natasha Tiku]

Yeah, I mean, part of the reason we're seeing OpenAI 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.

[Brooke Gladstone]

Using a real scientist's name, you mean?

[Natasha Tiku]

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.

[Brooke Gladstone]

How prepared are we to interact with these future tools?

[Natasha Tiku]

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, vibration, people with a financial stake in this technology. 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 ChatGPT is great for therapy and mental health. That to me seems just like a.

[Brooke Gladstone]

Wild leap because the stakes are too high.

[Natasha Tiku]

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 how to talk to your boss, that's great, but mental health is serious.

[Brooke Gladstone]

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

[Natasha Tiku]

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 it, the little answer box that pops to the top of Google, 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.

[Brooke Gladstone]

Mm. You mean make it worse?

[Natasha Tiku]

Yes. I think it's just good for people to keep in mind that these models are above all designed to sound plausible.

[Brooke Gladstone]

Plausibly human.

[Natasha Tiku]

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's generated a list of fake books as opposed to real books you should read. Or if it is basically copying an artist 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. It's really good at bull you.

[Brooke Gladstone]

Natasha, thank you so much.

[Natasha Tiku]

Thanks for having me.

[Brooke Gladstone]

Natasha Tiku reports on tech for the Washington Post.

[Geoffrey Hinton]

Coming up, the unpopular idea that revolutionized AI this is on the media.

[Tina Tallon]

No offense, but your brain is a terrible place to keep your big idea. It belongs in the world. But you know that already. You have a calling, a voice that says, this is what I'm meant to do. Create the website your big idea deserves with wix, make it your own with top to bottom customization, AI to help realize your vision and built in business tools to turn your daydream into your dream job. WIX supports every stage of the business journey except one. Your decision to begin ready go to.

[Ben Smith]

Wix.com I'm Ben Smith. I'm Max Tawny and we host Mixed Signals from Semaphore Media. The future of media feels like a moving target, so every Friday we pull back the curtain on the platforms, ideas and people that are shaping the new media landscape. We'll tell you what really matters and try to figure out what's coming next. Plus, we go behind the scenes with the most important players in media right now. Whether you are yourself a media insider or just simply curious about who or what will be all over your feed next, Mixed Signals from Semaphore Media is the perfect addition to your media diet. Listen wherever you get your podcasts.

[Brooke Gladstone]

This.

[Geoffrey Hinton]

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.

[Natasha Tiku]

Okay, that's a cat sitting in a bed.

[Brooke Gladstone]

The boy is petting the elephant.

[Natasha Tiku]

Those are people that going on an airplane.

[Brooke Gladstone]

That's a big airplane.

[Geoffrey Hinton]

Those are clips from a 2015 TED talk by Fei Fei Li, a computer science professor at Stanford University. She was consumed by the fact that despite all of our technological advances, our fanciest gizmos can't make sense of what they see.

[Brooke Gladstone]

Our most advanced machines and computers still.

[Geoffrey Hinton]

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 Krischevsky and Ilya Sutskever entered the competition with a neural network architecture called Alexnet, and the results were astounding.

[Matt Devost]

They did much better than the existing technology and that made a huge impact.

[Geoffrey Hinton]

Jeffrey Hinton was their PhD advisor at the University of Toronto and collaborator on Alexnet. When we spoke in January, Jeff was still working at Google, but in May he publicly left the company specifically so.

[Matt Devost]

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.

[Geoffrey Hinton]

The warnings hit differently coming from Geoffrey 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 and the brain.

[Matt Devost]

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.

[Brooke Gladstone]

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?

[Matt Devost]

No, actually, a hologram is a holistic way of storing an image as opposed to storing it pixel by pixel.

[Brooke Gladstone]

Ah.

[Matt Devost]

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.

[Brooke Gladstone]

There was somebody named Carl Lashley, you said, who took out bits of rats brains and found that the rats still remembered things.

[Matt Devost]

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.

[Brooke Gladstone]

This notion of memory, this holographic idea, was very much in opposition to conventional symbolic AI.

[Matt Devost]

Yes.

[Brooke Gladstone]

Which was all the rage in the last century.

[Matt Devost]

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, 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 is to be the core of intelligence. There's a Completely different way of doing business, which is much more biological, 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'll call vectors, these vectors interact with one another and these are much more like holograms.

[Brooke Gladstone]

So you've got these vectors of neural activity.

[Matt Devost]

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. That is 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.

[Brooke Gladstone]

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?

[Matt Devost]

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 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 languages, 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.

[Brooke Gladstone]

Analogical reasoning. One thing is like another.

[Matt Devost]

Yeah, we're very good at making analogies.

[Brooke Gladstone]

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 went on to get a PhD in AI in the 70s at the oldest AI research center in the UK. That was the University Edinburgh. You are 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.

[Matt Devost]

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.

[Brooke Gladstone]

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.

[Matt Devost]

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.

[Brooke Gladstone]

I once had a teacher who said, if you can't put it into words, then you don't really understand it.

[Matt Devost]

I think there were all sorts of things you can't put into words that your teacher didn't understand.

[Brooke Gladstone]

So the only place words exist is in sound waves and on pages.

[Matt Devost]

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.

[Brooke Gladstone]

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?

[Matt Devost]

It's pleasing.

[Brooke Gladstone]

And now all the big companies are using neural nets?

[Matt Devost]

Yes.

[Brooke Gladstone]

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

[Matt Devost]

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 a question and it would answer the question. This was just 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.

[Brooke Gladstone]

I admire Alan Turing, but I never bought that. I don't think it proves anything. Do you buy the Turing Test?

[Matt Devost]

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? Cause nothing you present to them satisfies them that machines are intelligent.

[Brooke Gladstone]

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.

[Matt Devost]

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, 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, an understanding via analogies that's much more human like than symbolic AI and that is being exhibited by ChatGPT.

[Brooke Gladstone]

And that, in your view, is tantamount to thinking. It is thinking.

[Matt Devost]

That's intuitive thinking. What neural nets are good at is intuitive thinking. The big chatbots aren't so good at explicit reasoning. But then nor are people. People are pretty bad at explicit reasoning.

[Brooke Gladstone]

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.

[Matt Devost]

You can run 10,000 copies of a neural net on 10,000 different computers and they can all share their connection strings 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.

[Brooke Gladstone]

So computers are telepathic.

[Matt Devost]

It's as if computers are telepathic, Right?

[Brooke Gladstone]

Were you excited when Chat GPT was released? We've been told it isn't really a huge advancement, it's just out there for.

[Matt Devost]

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.

[Brooke Gladstone]

Are there potential implementations of AI that concern you?

[Matt Devost]

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.

[Brooke Gladstone]

That's a pretty clear one. Is there something subtler about potential applications that give you pause?

[Matt Devost]

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 it's 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.

[Brooke Gladstone]

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.

[Matt Devost]

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 arrives at 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.

[Brooke Gladstone]

Have you ever had a flight of fancy of what this ultimately might mean in how we live?

[Matt Devost]

That's beyond five years.

[Brooke Gladstone]

So you're right. I see.

[Matt Devost]

I have no idea.

[Brooke Gladstone]

You warned me, Jeffrey. Thank you very much.

[Geoffrey Hinton]

Okay, Geoffrey Hinton is a former engineering fellow at Google Brain. He resigned in May and has been voicing his concerns about the imperial 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.

[Tina Tallon]

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[Ben Smith]

I'm Max Tawny and we host Mixed Signals from Semaphore Media. The future of media feels like a moving target so every Friday we pull back the curtain on the platform, platforms, ideas and people that are shaping the new media landscape. We'll tell you what really matters and try to figure out what's coming next. Plus, we go behind the scenes with the most important players in media right now. Whether you are yourself a media insider or just simply curious about who or what will be all over your feed next, Mixed Signals from Semaphore Media is the perfect addition to your media diet. Listen wherever you get your podcasts.

[Brooke Gladstone]

This.

[Geoffrey Hinton]

Is on the Media. I'm Brooke Gladstone. Toward the end of my conversation with Geoff Hinton, he touched on a couple of things that need a little more explaining. One of them is Alphafold, which predicts.

[Matt Devost]

The 3D shape of protein molecules from the sequence of bases that define the molecule.

[Geoffrey Hinton]

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.

[Ben Smith]

Now, a couple of days ago, DeepMind.

[Max Tawney]

Has announced that its second iteration of.

[Ben Smith]

The AlphaFold system has, quote, unquote, solved the 50 year old grand challenge problem of protein folding.

[Geoffrey Hinton]

There are other labs working on this software too. This is University of Washington, Seattle biochemist David Baker.

[Ben Smith]

We've also designed new proteins to break down gluten in your stomach for Celiac's disease and other proteins to stimulate your.

[Brooke Gladstone]

Immune system to fight cancer.

[Ben Smith]

These advances are the beginning of the protein design revolution.

[Geoffrey Hinton]

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.

[Max Tawney]

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.

[Brooke Gladstone]

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.

[Max Tawney]

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.

[Brooke Gladstone]

Not yet anyway.

[Max Tawney]

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.

[Brooke Gladstone]

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

[Max Tawney]

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.

[Brooke Gladstone]

Can you give me an example of that?

[Max Tawney]

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 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. The capacity to make that decision because it's changing so rapidly right now.

[Brooke Gladstone]

We wouldn't allow weapons to autonomously target, but that could happen one day. And it brings up images of Dr. Strangelove and failsafe.

[Max Tawney]

That is gonna be a concern. I think we've articulated pretty clearly, at least at the US Government level, that humans will remain in the loop as it relates to targeting other humans. It's different if you're targeting drones or you're targeting a communications tower, et cetera. 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.

[Brooke Gladstone]

Obviously, these new AI tools will have an impact on intelligence gathering and collection. And you say that for you, ChatGPT was a wow moment.

[Max Tawney]

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 1000 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. Each of these players brings their own unique perspective and expertise to the field. But the lack of coordination and collaboration among them make it difficult to develop a comprehensive and effective approach to cybersecurity.

[Brooke Gladstone]

Holy cow.

[Max Tawney]

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 at what is the probability that Iran will attack a US 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.

[Brooke Gladstone]

But it has been shown over and over again that ChatGPT is fundamentally a people pleaser. Yes, it doesn't care if it's true or not. It will invent sources in order to give you something that has the exact format you're asking for. So you can't trust anything that ChatGPT says. So how can it be helpful in intelligence gathering?

[Max Tawney]

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 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.

[Brooke Gladstone]

Give me an example. Would it interrogate a prisoner of war?

[Max Tawney]

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 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 counterargulation. So I think there'll be a lot of unique ways in which the technology is used in the intelligence community.

[Brooke Gladstone]

How imminent is this kind of technology?

[Max Tawney]

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.

[Brooke Gladstone]

So another wow moment you had 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.

[Max Tawney]

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 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.

[Geoffrey Hinton]

This is a cyber war game.

[Max Tawney]

This is a cyber war game. Yeah. So in 2016, they brought the finalists out to DEFCON, 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 was being completely replaced by a machine. And the machines were very creative and fast. You know, that'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.

[Brooke Gladstone]

How do we ensure that these weapons are safe to deploy? How do we ensure that they don't commit war crimes?

[Max Tawney]

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. ChatGPT draws intelligence from the Internet itself 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.

[Brooke Gladstone]

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 our own autonomy to machines that may well be smarter than we are is a terrifying prospect.

[Max Tawney]

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 is 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?

[Brooke Gladstone]

You seem to be suggesting that it may turn out that humans are far more dangerous.

[Max Tawney]

In some domains, the humans might be more dangerous.

[Brooke Gladstone]

I'M thinking of the Cuban missile crisis and how the tape suggests 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 a, an advanced chatbot advisor in the room, whether he would have stood with Kennedy or not.

[Max Tawney]

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.

[Brooke Gladstone]

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.

[Max Tawney]

Go to war as long as we get it to understand our objectives and our constraints? 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 effic than Putin in perpetrating these atrocities.

[Brooke Gladstone]

Matt, thank you very much.

[Max Tawney]

Yes, of course. It was my pleasure. I enjoyed the conversation.

[Geoffrey Hinton]

Matt devost is the CEO and co founder of the global strategy advisory firm ooda, spelled OODA llc. And that's the show this week on the Media is produced by Micah Lowell, Eloise Blondio, Molly Schwartz, Rebecca Clark Callender, 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. Since WNYC's first broadcast in 1924, we've been dedicated to creating the kind of content we know the world needs. In addition to this award winning reporting, your sponsorship also supports inspiring storytelling and extraordinary music that is free and accessible to all. To get in touch and find out more, visit sponsorship.wnyc.org.