619. How to Poison the A.I. Machine

Summary

Freakonomics Radio: Episode 619 - How to Poison an A.I. Machine

Release Date: January 24, 2025

In this compelling episode of Freakonomics Radio, host Stephen Dubner delves into the intricate world of artificial intelligence (AI) and its profound implications on human creativity and economics. The episode primarily revolves around Ben Zhao, a computer scientist at the University of Chicago, and his innovative efforts to safeguard artists from AI-driven exploitation. Through engaging discussions, insightful analyses, and notable expert opinions, Dubner unpacks the hidden dynamics of AI’s intersection with human creativity.

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1. Setting the Stage: The AI Boom and its Discontents

Dubner opens the episode by highlighting the rapid advancements in AI, particularly in San Francisco, the epicenter of the AI revolution. Billboards for AI companies dominate the cityscape, and conferences are abuzz with sessions on the economic implications of AI, its role in finance, and the evolution of generative models like Large Language Models (LLMs).

Eric Brynjolfsson, a leading economist in the AI realm, is introduced as a pivotal voice in understanding AI's potential. Dubner references Brynjolfsson’s appearance on the show where he discusses the optimistic economic projections associated with AI advancements.

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2. Expert Insights: Erik Brynjolfsson on AI’s Economic Impact

During a live show and subsequent conference talk titled "Will AI Save Us or Destroy Us?", Brynjolfsson reflects on Michael Wooldridge's 2021 analysis in A Brief History of Artificial Intelligence. Despite Wooldridge listing several AI challenges—such as understanding stories, automated translation, and interpreting photographs—Brynjolfsson points out that AI has made significant strides in these areas since the publication.

Erik Brynjolfsson [02:54]: "The idea is that AI is doing these amazing things, but we want to do it in service of humans and make sure that we keep humans at the center of all of that."

Brynjolfsson posits that AI could potentially double the Congressional Budget Office’s estimated economic growth of 1.5% per year for the decade, emphasizing that common perceptions of AI are either overly fearful or too narrow.

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3. Introducing Ben Zhao: The Defender of Human Creativity

Transitioning from economic forecasts, Dubner shifts focus to the pressing issue of AI mimicking human creatives without consent. Enter Ben Zhao, a computer scientist at the University of Chicago, who combats AI-driven art forgery.

Dubner describes Zhao as neither a techno-pessimist nor exceedingly optimistic but one deeply concerned with the real-time harms AI poses to artists. Zhao discusses the concept of AI as high-tech forgers that imitate and potentially steal artists' unique styles and creations.

Ben Zhao [05:02]: "On artificial intelligence, there is an exceptional level of hype. That bubble is in many ways in the middle, bursting right now."

Zhao and his team have developed tools designed to "poison" AI systems, thereby preventing them from exploiting artists' work without authorization.

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4. The Poisoning Tools: Glaze and Nightshade

Zhao elaborates on his lab’s primary focus: adversarial machine learning aimed at mitigating the harms generative AI poses to human creatives. He introduces two pivotal tools developed by his lab:

1. Glaze: This tool protects individual artists by making it difficult for AI systems to mimic their unique styles using locally fine-tuned models.

2. Nightshade: A more expansive tool, Nightshade alters images subtly so that while they appear unchanged to humans, AI models interpret them differently, effectively confusing the training process.

Ben Zhao [17:14]: "Glaze is all about making it harder to target and mimic individual artists. Nightshade is a little bit more far-reaching. Its goal is primarily to make training on Internet scraped data more expensive than it is now."

These tools work by introducing minor, often imperceptible alterations to images, ensuring that AI models cannot accurately associate them with the original artistic intent.

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5. The Struggle Against AI Exploitation

Dubner discusses the real-world implications of AI's misuse of artists' work, such as unauthorized scraping of images from online portfolios to train AI models like DALL-E, Stable Diffusion, and Midjourney. This unauthorized use not only dilutes artists' unique styles but also threatens their livelihoods.

Zhao emphasizes that this misuse constitutes a form of identity theft, as it profanes the years of dedication artists invest in honing their craft.

Ben Zhao [15:35]: "Right now, many of these models are being used to replace human creatives. ... artists and teams of artists are being laid off."

The episode draws parallels to the early 2000s music piracy issues, highlighting the imbalance of power between large AI companies and individual artists lacking resources to combat such exploitation effectively.

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6. Economic and Ethical Dimensions

Zhao provides a critical analysis of the economic leverage wielded by massive AI corporations, comparing their influence to past tech giants. He argues that these companies prioritize profit over ethical considerations, often circumventing robust legal frameworks due to their financial clout.

Ben Zhao [17:53]: "We're talking about companies and stakeholders who have trillions in market cap, the richest companies on the planet, by definition. So that completely changes the game."

He underscores the futility of opt-out measures for artists, as enforcing such commitments is technologically and practically challenging given the scale at which AI companies operate.

Zhao advocates for a paradigm shift where AI development complements rather than replaces human creativity, ensuring that artists are adequately compensated and protected.

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7. Ben Zhao’s Motivation and Vision

Dubner explores Zhao’s personal motivations, revealing a deep appreciation for creativity and art stemming from his academic experiences. Zhao expresses a profound respect for artists, whom he views as the torchbearers of human imagination and innovation.

Ben Zhao [53:13]: "Creativity is inspiring. Artists are inspiring. ... creativity, it's the best of humanity. What else is there?"

Zhao positions his work as a protective measure, likening his efforts to providing an umbrella against the storm of unchecked AI advancements. He remains optimistic that while AI poses significant challenges, collective efforts can ensure a harmonious coexistence between technology and human creativity.

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8. The Road Ahead: Optimism Amidst Challenges

In addressing the skepticism surrounding his mission, Zhao remains steadfast in his belief that AI's growth may be plateauing due to inherent data and computational limitations. He contends that despite substantial investments, AI models face diminishing returns, thereby creating a window of opportunity for protective measures like Glaze and Nightshade to make a tangible impact.

Ben Zhao [36:00]: "There's been many papers published on the fact that these generative AI models are well at their end in terms of training data to get better. ... Now recent reports about how Google and OpenAI are having trouble improving upon their models. It's common sense they're running out of data and no amount of scraping or licensing will fix that."

Zhao envisions a future where AI companies adopt legitimate licensing agreements with artists, ensuring fair compensation and fostering a sustainable creative ecosystem.

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9. Concluding Thoughts: A Guardian of Creativity

As the episode wraps up, Dubner reflects on Zhao’s role as a guardian of human creativity amidst the relentless march of AI technology. Zhao's efforts symbolize a broader movement advocating for ethical AI development that honors and preserves the essence of human artistic expression.

Ben Zhao [43:36]: "What's interesting about computer security is that it's not necessarily about numbers. ... I'm trying to cover as many people as possible and shield them from the short term harm."

Zhao embodies the spirit of balancing technological innovation with human-centered values, ensuring that advancements in AI enhance rather than erode the fabric of human creativity.

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Key Takeaways

• Adversarial Machine Learning: Zhao’s work demonstrates how adversarial techniques can be employed to protect artistic integrity against AI exploitation.

• Economic Leverage of AI Firms: The disproportionate power of AI corporations presents significant challenges in enforcing ethical standards and protecting individual creators.

• Ethical Imperatives: The episode underscores the necessity of prioritizing human values and ethical considerations in the development and deployment of AI technologies.

• Sustainable AI Development: Emphasizing collaboration and legitimate licensing, Zhao advocates for a symbiotic relationship between AI advancements and human creativity.

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Notable Quotes

• Erik Brynjolfsson [02:54]: "The idea is that AI is doing these amazing things, but we want to do it in service of humans and make sure that we keep humans at the center of all of that."

• Ben Zhao [05:02]: "On artificial intelligence, there is an exceptional level of hype. That bubble is in many ways in the middle, bursting right now."

• Ben Zhao [15:35]: "Right now, many of these models are being used to replace human creatives. ... artists and teams of artists are being laid off."

• Ben Zhao [36:00]: "There's been many papers published on the fact that these generative AI models are well at their end in terms of training data to get better. ... No amount of scraping or licensing will fix that."

• Ben Zhao [53:13]: "Creativity is inspiring. Artists are inspiring. ... creativity, it's the best of humanity. What else is there?"

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Final Thoughts

Freakonomics Radio presents a nuanced exploration of AI's dual-edged impact on society. Through the lens of Ben Zhao's protective strategies, listeners gain a deeper understanding of the ethical and economic challenges posed by AI. The episode eloquently balances the marvels of technological progress with the imperative to preserve and honor human creativity, offering both caution and hope in the evolving narrative of AI and its role in our lives.

Transcript

[Stephen Dubner]

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[Erik Brynjolfsson]

The idea is that AI is doing these amazing things, but we want to do it in service of humans and make sure that we keep humans at the center of all of that.

[Stephen Dubner]

The day after Brynjolfsson came on our show, I attended one of his talks at the conference. It was called Will AI Save Us or Destroy Us? He cited a book by the Oxford computer scientist Michael Wooldridge called A Brief History of Artificial Intelligence. Brynjolfsson read from a list of problems that Wooldridge said AI was nowhere near solving. Here are a few of understanding a story and answering questions about it. Human level Automated translation Interpreting what is going on in a photograph as Brynjolfsson is reading this list from the lectern. You're thinking, wait a minute, AI has solved all those problems, hasn't it? And that's when Brynjolfsson gets to his punchline. The Wooldridge book was published way back in 2021. The pace of AI's advance has been astonishing, and some people expect it to supercharge our economy. The Congressional Budget Office has estimated economic growth over the current decade of around 1.5% a year. Erik Brynjolfsson thinks that AI could double that. He argues that many views of AI are either too fearful or too narrow.

[Erik Brynjolfsson]

Too many people think of machines as just trying to imitate humans, but machines can help us do new things we never could have done before. And so we want to look for ways that machines can complement humans, not simply imitate or replace them.

[Stephen Dubner]

So that sounds promising, but what about the machines that are just imitating humans? What about machines that are essentially high tech forgers? Today on Freakonomics Radio, we will hear from someone who is trying to thwart these machines on behalf of artists.

[Ben Zhao]

They take decades to hone their skill, so when that's taken against their will, that is sort of identity theft.

[Stephen Dubner]

Ben Zhao is a professor of computer science at the University of Chicago. He is by no means a techno pessimist, but he is not so bullish.

[Ben Zhao]

On artificial intelligence, there is an exceptional level of hype. That bubble is in many ways in the middle, bursting right now.

[Stephen Dubner]

But Zhao isn't just waiting for the bubble to burst. It's already too late for that, because.

[Ben Zhao]

The harms that are happening to people is in real time.

[Stephen Dubner]

Zhao and his team have been building tools to prevent some of those harms. When it comes to stolen art, the tool of choice is a dose of poison that Zhao slips into the AI system. There is another old saying. You probably know it takes a thief to catch a thief. How does that work in the time of AI? Let's find out. This is Freakonomics Radio, the podcast that.

[Ben Zhao]

Explores the hidden side of everything.

[Stephen Dubner]

With your host, Stephen Dubner. Ben Zhao and his wife, Heather Zhang, are both computer scientists at the University of Chicago, and they run their own lab.

[Ben Zhao]

We call it the San Lab, which stands for Security, Algorithms, Networking and Data. Most of the work that we do has been to use technology for good, to limit the harms of abuses and attacks and protect human beings and their values, whether it's personal privacy or security or data or. Or your identity.

[Stephen Dubner]

What's your lab look like? If we showed up, what do we see? Do we see people milling around, talking, working on monitors together?

[Ben Zhao]

It's really quite anticlimactic. We've had some TV crews come by, and they're always expecting some sort of secret lair. And then they walk in. It's a bunch of cubicles. Our students all have standing desks. The only wrinkle is that I'm at one of the standing desks in the room. I don't usually sit in my office. I sit next to them a couple of cubicles over so that they don't get paranoid about me watching their screen.

[Stephen Dubner]

When there's a tool that you're envisioning or developing or perfecting, is it all hands on deck? Are the teams relatively small? How does that work?

[Ben Zhao]

Well, there's only a handful of students in my lab to begin with, so all hands on deck is like, what, seven or eight PhD students plus us? Typically speaking, the projects are a little bit smaller just because we've got multiple projects going on, and so people are partitioning their attention and work energy at different things.

[Stephen Dubner]

I read on your webpage, Ben, you write, I work primarily on adversarial machine learning and tools to mitigate harms of generative AI models against human creatives. So that's an extremely compelling bio line. Like, if that was a dating profile and I were in AI, I would say, whoa, swiping hard left. But if I'm someone concerned about these things, oh, my goodness, you're the dream date. So can you unpack that for me?

[Ben Zhao]

Adverse to machine learning is a shorthand for this interesting research area at the intersection of computer security and machine learning. Anything to do with attacks, defenses, privacy concerns, surveillance, all these subtopics as related to machine learning and AI. That's what I've been working on mostly for the last decade. For more than two years, we've been focused on how the misuse and abuse of these AI tools can harm real people and trying to build research tools and technology tools to try to reduce some of that harm, to protect regular citizens and in particular, human Creatives, like artists and writers.

[Stephen Dubner]

Before he got into his current work protecting creatives, Zhao made a tool for people who are worried that Siri or Alexa are eavesdropping on them, which, now that I've said their names, they may be. He called this tool the Bracelet of Silence.

[Ben Zhao]

So that's from my D and D days. Yeah, it's a fun little project. We had done prior work in ultrasonics and modulation effects. When you have different microphones and how they react to different frequencies of sound, one of the effects that people have been observing is that you can make microphones vibrate in a frequency that they don't want to. We figured out that we could build a set of little transducers. You can imagine a fat bracelet, sort of like cyberpunk kind of thing, with, I think, 24 or 12. I forget the exact number. Little transducers that are hooked onto the bracelet, like gemstones.

[Stephen Dubner]

The one I'm looking at looks like 12. I also have to say, Ben, it's pretty big. It's a pretty big bracelet to wear around just to silence your Alexa or HomePod.

[Ben Zhao]

Well, hey, you gotta do what you gotta do, and hopefully other people will make it much smaller. Right? We're not in the production business. What it does is basically it radiates a carefully tuned pair of ultrasonic pulses in such a way that commodity microphones anywhere within reach will, against their will, begin to vibrate at a normal audible frequency. They basically generate the sound that's necessary to jam themselves. When we first came out with this thing, a lot of people were very excited. Privacy advocates, public figures who are very concerned, not necessarily about their own Alexa, but the fact that they had to walk into public places all the time. You're really trying to prevent that hidden microphone eavesdropping on a private conversation.

[Stephen Dubner]

Okay, that's the Bracelet of Silence. I'd like you to describe another privacy tool you built, the one called Fox.

[Ben Zhao]

Fox is a Fun1. In 2019, I was brainstorming about some dangers that we have in the future. And this is not even generative AI. This is just sort of classification and facial recognition. One of the things that we came up with was this idea that AI is going to be everywhere, and therefore anyone can train any model, and therefore people can basically train models of you. At the time, it was not about deep fakes. It was about surveillance. And what would happen if people just went online, took your entire Internet footprint, which, of course, today is massive. Scrape all your photos from Facebook and Instagram and LinkedIn. And then build this incredibly accurate facial recognition model view without your knowledge, much less permission. And we built this tool that basically allows you to alter your selfies, your photos, in such a way that it made you look more like someone else than yourself.

[Stephen Dubner]

Does it make you look more like someone else in the actual context that you care about or only in the version when it's being scraped?

[Ben Zhao]

That's right. Only in the version when it's being used to build a model against you. But the funny part was that we built this technology, we wrote the paper, and on the week of submission, this was 2020, we were getting ready to submit that paper. I remember it distinctly. That was when Kashmir Hill at the New York Times came out with her story on Clearview AI. And that was just mind blowing because I had been talking to our students for months about having to build for this dark scenario. And literally here's the New York Times saying, yeah, this is today and we are already in it. That was disturbing on many fronts, but it did make writing the paper a lot easier. We just cited the New York Times article and said, here it is already.

[Stephen Dubner]

Clearview AI is funded how?

[Ben Zhao]

It was a private company. I think it's still private. It's gone through some ups and downs since the New York Times article. They had to change their revenue stream. They no longer take third party customers. Now they only work with government and law enforcement.

[Stephen Dubner]

Okay, so Fawkes is a tool you invented to fight that kind of facial recognition abuse. Is Fawkes an app or software that anyone can use?

[Ben Zhao]

Fox was designed as a research paper and algorithm, but we did produce a little app. I think it went over a million downloads. We stopped keeping track of it, but we still have a mailing list. And that mailing list is actually how some artists reach out.

[Stephen Dubner]

When Ben Zhao says that some artists reached out, that was how he started down his current path. Defending visual artists. A Belgian artist named Kim Van Dun, who's known for her illustrations of fantasy creatures, sent Zhao an invitation to a town hall meeting about AI artwork. It was hosted by a Los Angeles organization called Constant Concept Art association, and it featured representatives from the U.S. copyright Office. What was the purpose of this meeting? Artists have been noticing that when people searched for their work online, the results were often AI knockoffs of their work. It went even further than that. Their original images had been scraped from the Internet and used to train the AI models that can generate an image from a text prompt. You've probably heard of these text to image models, maybe even use some of them. There is dall e from OpenAI, imagine from Google Image, Playground from Apple, Stable Diffusion from Stability AI and Midjourney from the San Francisco research lab of the same name.

[Ben Zhao]

These companies will go out and they'll run scrapers, little tools that go online and basically suck up any semblance of imagery, especially high quality imagery from online websites.

[Stephen Dubner]

In the case of an artist like Van Dun, this might include her online portfolio, which is something you want to be easily seen by the people you want to see it, but you don't want sucked up by an AI.

[Ben Zhao]

It would download those images and run them through an image classifier to generate some set of labels and then take that pair of images and their labels and then feed that into the pipeline to some text image model.

[Stephen Dubner]

So, Ben, I know that some companies, including OpenAI, have announced programs to let content creators opt out of AI training. How meaningful is that?

[Ben Zhao]

Well, opting out assumes a lot of things. It assumes benign acquiescence from the technology makers.

[Stephen Dubner]

Benign acquiescence meaning they have to actually do what they say they're going to do?

[Ben Zhao]

Yeah, exactly. Opting out is toothless because you can't prove it in the machine learning business. Even if someone completely went against their word and said, okay, here's my opt out list, and then immediately train on all their content, you just lack the technology to prove it. And so what's to stop someone from basically going back on their word when we're talking about billions of dollars at stake? Really, you're hoping and praying someone's being nice to you.

[Stephen Dubner]

So Ben Zhao wanted to find a way to help artists fight back against their work being either forged or stolen by these mimic.

[Ben Zhao]

A big part of their misuse is when they assume the identity of others. So this idea of right, of publicity, and the idea that we own our faces, our voices, our identity, our skills and work product, that is very much a core of how we define ourselves. For artists, it's the fact that they take decades to hone their skill and to become known for a particular style. So when that's taken against their will, without their permission, that is a type of identity theft, if you will.

[Stephen Dubner]

In addition to identity theft, there can be the theft of a job, a livelihood.

[Ben Zhao]

Right now, many of these models are being used to replace human creatives. If you look at some of the movie studios, the gaming studios or publishing houses, artists and teams of artists are being laid off. One or two remaining artists are being told, here you have a budget, here's midjourney. I want you to use your artistic vision and skill to basically craft these AI images to replace the work product of the entire team who's now been laid off.

[Stephen Dubner]

So Zhao's solution was to poison the system that was causing this trouble.

[Ben Zhao]

Poison is sort of a technical term in the research community. Basically it means manipulating training data in such a way to get AI models to do something, perhaps unexpected, perhaps more to your goals than the original trainers intended to.

[Stephen Dubner]

They came up with two poisoning tools, one called Glaze, the other Nightshade.

[Ben Zhao]

Glaze is all about making it harder to target and mimic individual artists. Nightshade is a little bit more far reaching. Its goal is primarily to make training on Internet scraped data more expensive than it is now, perhaps more expensive than actually licensing legitimate data, which ultimately it's our hope that this would push some of these AI companies to seek out legitimate licensing deals with artists so that they can properly be compensated.

[Stephen Dubner]

Can you just talk about the leverage and power that these AI companies have and how they've been able to amass that leverage?

[Ben Zhao]

We're talking about companies and stakeholders who have trillions in market cap, the richest companies on the planet, by definition. So that completely changes the game. It means that when they want things to go a certain way, whether it's lobbyists on Capitol Hill, whether it's media control and inundating journalists and running ginormous national expos and trade shows of whatever they want, nothing is off limits that completely changes the power dynamics of what you're talking about. The closest analogy I can draw on is in the early 2000s we had music piracy folks who are old enough, remember that was a free for all. People could just share whatever they wanted. And of course there were questions of legality and copyright violations and so on. But there it was very, very different from what it is today. Those who are with the power and the money and the control are the copyright holders. So the outcome was very clear.

[Stephen Dubner]

Well, it took a while to get there. Right. Napster really thrived for several years before it got shut down.

[Ben Zhao]

Right, Exactly.

[Stephen Dubner]

But in that case, you're saying that the people who not necessarily generated but owned or licensed the content were established and rich enough themselves so that they could fight back against the intruders.

[Ben Zhao]

Exactly. You had armies of lawyers. When you consider that sort of situation and how it is now, it's the complete polar opposite.

[Stephen Dubner]

Meaning it's the bad guys who have all the lawyers.

[Ben Zhao]

Well, I wouldn't say necessarily bad guys, but certainly the folks who in many cases are pushing profit motives that perhaps bring harm to less represented minorities who don't have the agency who don't have the money to hire their own lawyers and who can't defend themselves.

[Stephen Dubner]

I mean, that has become kind of an ethic of a lot of business in the last 20, 30 years, especially coming out of Silicon Valley. You know, you think about how Travis Kalanick used to talk about Uber, like it's much easier to just go into a big market like New York where something like Uber would be illegal, and just let it go, let it get established, and then let the city come and sue you after it's established. So better to ask for forgiveness than permission.

[Ben Zhao]

These companies are basically exploiting the fact that we know lawsuits and enforcement of new laws are going to take years. And so the idea is let's take advantage of this time and before these things catch up, we're already going to be established, we already are going to be essential, and we already are going to be making billions. And then we'll worry about the legal costs because really to many of them, the legal costs and the penalties that are involved, billions of dollars is really a drop in the bucket.

[Stephen Dubner]

Indeed, the biggest tech firms in the world are all racing one another to the top of the AI mountain. They've all invested heavily in AI and the markets have so far at least rewarded them. The share prices of the so called Magnificent Seven stocks, Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla rose more than 60% in 2024. And these seven stocks now represent 33% of the value of the S&P 500. This pursuit of more and better AI will have knock on effects too. Consider their electricity needs. One estimate finds that building the data centers to train and operate the new breed of AI models will require 60 gigawatts of energy capacity. That's enough to power roughly a third of the homes in the US in order to generate all that electricity and to keep their commitments To Clean Energy OpenAI, Amazon, Google, Meta and Microsoft have all invested big in nuclear power. Microsoft recently announced a plan to help revive Three Mile Island. If you want to learn more about the potential for a nuclear power Renaissance in the US we made an episode about that number 516 called Nuclear Power. Isn't. Is it good enough? Meanwhile, do a handful of computer scientists at the University of Chicago have any chance of slowing down this AI juggernaut? Coming up after the break, we will hear how Ben Xiao's Poison works.

[Ben Zhao]

We will actually generate a nice looking cow with nothing particularly distracting in the background. And the cow is staring you right in the face.

[Stephen Dubner]

I'm Stephen Dubner. This is Freakonomics Radio. We'll be right back. Freakonomics Radio is sponsored by Sling tv. With the news constantly evolving, it can be hard to stay up to date. But with Sling, you get access to the essential news channels that keep you informed. Get all your favorite news channels in one place from Fox News, cnn, MSNBC and more. And it's not just news. Sling has the live sports and entertainment channels you love and less of the ones you don't. Sling lets you customize your channel lineup so you can choose channels you actually like, and Slings cloud DVR lets you record your shows to watch on your schedule. There's no complexity technology, no long term contracts, no hidden rigmarole. Get rewarded for watching your favorite news channels. Sling lets you do that. Visit sling.com now to learn more and get started. That's sling.com now. Sling.com now.

[Heather Zhang]

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[Stephen Dubner]

At Amica, you will receive coverage with compassion. When you choose Amica, they'll take the time to explain your options for auto, home and life insurance. You can feel confident knowing that they'll protect what matters most to you. Amica will provide you with peace of mind. Go to ameca.com and get a quote. Today in his computer science lab at the University of Chicago, Ben Zhao and his team have created a pair of tools designed to prevent artificial intelligence programs from exploiting the images created by human artists. These tools are called Glaze and Nightshade. They work in similar ways, but with different targets. Glaze came first.

[Ben Zhao]

Glaze is all about how do we protect individual artists so that a third party does not mimic them using some local model. It's much less about these model training companies than it is about individual users who say, gosh, I like so and so's art, but I don't want to pay them. So in fact, what I'll do is I'll take my local copy of a model, I'll fine tune it on that artist's artwork and then have that model try to mimic them and their style so that I can ask the model to output artistic works that look like human art from that artist, except I don't have to pay them anything.

[Stephen Dubner]

And how about Nightshade?

[Ben Zhao]

What it does is it takes images. It alters them in such a way that they basically look like they're the same. But to a particular AI model that's trying to train on this, what it sees are the visual features that actually associate it with something entirely different. For example, you can take an image of a cow eating grass in a field, and if you apply it to Nightshade, perhaps that image instead teaches not so much the bovine cow features, but the features of a 1940s pickup truck. What happens then is that as that image goes into the training process, that label of this is a cow will become associated in the model that's trying to learn about what does a cow look like. It's going to read this image, and in its own language, that image is going to tell it that a cow has four wheels, A cow has a big hood and a fender and a trunk. Nightshade images tend to be much more potent than usual images, so that even when they've just seen a few hundred of them, they are willing to throw away everything that they've learned from the hundreds of thousands of other images of cows and declare that its understanding has now adapted to this new understanding that, in fact, cows have a shiny bumper and four wheels. Once that has happened, someone asking the model, give me a cow eating grass. The model might generate a car with a pile of hay on top.

[Stephen Dubner]

The underlying process of creating this AI poison is, as you might imagine, quite complicated. But for an artist who's using Nightshade, who wants to sprinkle a few invisible pixels of poison on their original work, it's pretty straightforward.

[Ben Zhao]

There's a couple parameters about intensity, how strongly you want to change the image. You set the parameters, you hit go, and out comes an image that may look a little bit different. Sometimes there are tiny little artifacts that if you blow it up, you'll see. But in general, it basically looks like your old image, except with these tiny little tweaks everywhere in such a way that the AI model, when it sees it, will see something entirely different.

[Stephen Dubner]

That entirely different thing is not chosen by the user. It's Nightshade that decides whether your image of a cow becomes a 1940s pickup truck versus, say, a cactus. And there's a reason for that.

[Ben Zhao]

The concept of poisoning is that you are trying to convince the model that's training on these images that Something looks like something else entirely, Right? So we're trying, for example, to convince a particular model that a cow has four tires and a bumper. But in order for that to happen, you need numbers. You don't need millions of images to convince it, but you need a few hundred. And of course, the more the merrier. And so you want everybody who uses Nightshade around the world, whether they're photographers or illustration or graphic artists, you want them all to have the same effect. So whenever someone paints a picture of a cow, takes a photo of a cow, draws an illustration of a cow, draws a clipart of a cow, you want all those night shaded effects to be consistent in their target. In order to do that, we have to take control of what the target actually is, ourselves, inside the software. If you gave users that level of control, then chances are people would choose very different things. Some people might say, I want my cow to be a cat. I want my cow to be the sun rising. If you were to do that, the poison would not be as strong.

[Stephen Dubner]

And what do the artificial intelligence companies think about this Nightshade being thrown at them? A spokesperson for OpenAI recently described data poison as a type of of abuse. AI researchers previously thought that their models were impervious to poisoning attacks. But Ben Zhao says that the AI training models are actually quite easy to fool. His free Nightshade app has been downloaded over 2 million times. So it's safe to say that plenty of images have already been shaded. But how can you tell if Nightshade is actually working?

[Ben Zhao]

You probably won't see the effects of Nightshade if you see it in the wild. Models give you wrong answers to things that you're asking for. But the people who are creating these models are not foolish. They are highly trained professionals. So they're going to have lots of testing on any of these models. We would expect that effects of Nightshade would actually be detected in the model training process. It'll become a nuisance. And perhaps what really will happen is that certain versions of models post training will be detected to have certain failures inside them, and perhaps they'll have to roll them back. So I think really that's more likely to cause delays and more likely to cause costs of these model training processes to go up. The AI companies, they really have to work on millions, potentially billions of images. So it's not necessarily the fact that they can't detect Nightshade on a particular image. It's the question of can they detect Nightshade on a billion images in a split second? With minimal cost, because any one of those factors that goes up significantly will mean that their operation becomes much, much more expensive. And perhaps it is time to say, well, maybe we'll license artists and get them to give us legitimate images that won't have these questionable things inside them.

[Stephen Dubner]

Is it the case that your primary motivation here really was an economic one of getting producers of labor, in this case artists, simply to be paid for their work, that their work was being stolen?

[Ben Zhao]

Yeah, I mean, really, it boils down to that. I came into it not so much thinking about economics as I was just seeing people that I respected and had affinity for be severely harmed by some of this technology in whatever way that they can be protected. That's ultimately the goal. In that scenario, the outcome would be licensing so that they can actually maintain a livelihood and maintain the vibrancy of that industry.

[Stephen Dubner]

When you say these are people you respect and have affinity for, I'm guessing you being an academic computer scientist, you also have respect and affinity for. And I'm sure you know many people in the AI machine learning community on the firm side though, right?

[Ben Zhao]

Yes, yes, of course. Colleagues and former students in that space.

[Stephen Dubner]

And how do they feel about Ben Zhao?

[Ben Zhao]

It's quite interesting, really. I go to conferences, same as I usually do, and many people resonate with what we're trying to do. We've gotten a bunch of awards and such from the community. As far as folks who are actually employed by some of these companies, some of them, I have to say, appreciate our work. They may or may not have the agency to publicly speak about it, but lots of private conversations where people are very excited. I will say that, yeah, there's been some cooling effects, burn bridges with some people. I think it really comes down to how you see your priorities. It's not so much about where employment lies, but it really is about how personally you see the value of technology versus the value of people. And oftentimes it's a very binary decision. People tend to go one way or the other rather hard. I think most of these bigger decisions, acquisitions strategy and whatnot, are largely in the hands of executives way up top. These are massive corporations, and many people are very much aware of some of the stakes and perhaps might disagree with some of the technological stances that are being taken. But everybody has to make a living. Big tech is one of the best ways to make a living. Obviously, they compensate people very well. I would say there's a lot of pressure there as well. We just had that recent news item that the young whistleblower from OpenAI just tragically passed away.

[Stephen Dubner]

Zhao is talking here about Suchir Balaji, a 26 year old former researcher at OpenAI, the firm best known for creating ChatGPT. Balaji died by apparent suicide in his apartment in San Francisco. He had publicly charged OpenAI with potential copyright violations and he left the company because of ethical concerns.

[Ben Zhao]

Whistleblowers like that are incredibly rare because the risks that you're taking on when you publicly speak out against your foreign employer, that is tremendous courage. That is an unbelievable act. It's a lot to ask.

[Stephen Dubner]

I feel that we don't speak so much about ethics in the business world. I know they teach it in business schools, but my feeling is that by the time you're teaching the ethics course in the business school, it's because things are already in tough shape. Many people obviously have strong moral and ethical makeups, but I feel there is an absence of courage. And since you just named that word, you said you have to have an enormous amount of courage to stand up for what you think may be right. And since there is so much leverage in these firms, as you noted, I'm curious if you have any message to the young employee or the soon to be graduate who says, yeah, sure, I would absolutely love to go work for an AI firm because it's bleeding edge, it pays well, it's exciting and so on, but they're also feeling like it's contributing to a pace of technology that is too much for humankind right now. What would you say to that person? How would you ask them to examine, if not their soul or something, at least their courage profile?

[Ben Zhao]

Yeah, what a great question. I mean, it may not be surprising, but as a computer science professor, I actually have these kind of conversations relatively often. This past quarter I taught many second year and third year computer science majors and many of them came up to me in office hours and asked very similar kind of questions. They said, look, I really want to push back on some of these harms. On the other hand, look at these job opportunities. Here's this great golden ticket to the future and what can you do? It's fascinating. I don't blame them if they make any particular decision, but I applaud them for even being aware of some of the issues that I think many in the media and many in Silicon Valley certainly have trouble recognizing. There is a level of ground truth underneath all this, which is that these models are limited. There is an exceptional level of hype like we've never seen before. That bubble is in many ways in the middle of bursting right now.

[Stephen Dubner]

Why do you say that?

[Ben Zhao]

There's been many papers published on the fact that these generative AI models are well at their end in terms of training data to get better. You need something like double the amount of data that has ever been created by humanity. And you're not going to get that by buying Twitter or by licensing from Reddit or New York Times or anywhere you've seen. Now recent reports about how Google and OpenAI are having trouble improving upon their models. It's common sense they're running out of data and no amount of scraping or licensing will fix that.

[Stephen Dubner]

Bloomberg News recently reported that OpenAI, Google and Anthropic have all had trouble releasing their next generation AI models because of this plateauing effect. Some commentators say that AI growth overall may be hitting a wall. In response to that, OpenAI CEO Sam Altman tweeted, there is no wall. Ben Zhao is in the wall camp.

[Ben Zhao]

And then of course, just the fact that there are very few legitimate revenue generating applications that will even come close to compensating for the amount of investment that VCs and these companies are pouring in. Obviously I'm biased doing what I do, but I thought about this problem for quite some time and honestly, these are great interpolation machines, these are great mimicry machines, but there's only so many things that you can do with them. They are not going to produce entire movies, entire TV shows, entire books to anywhere near the value that humans will actually want to consume. And so yeah, they can disrupt and they can bring down the value of a bunch of industries, but they are not going to actually generate much revenue in and of themselves. I see that bubble bursting. And so what I say to these students oftentimes is that things will take their course and you don't need to push back actively. All you need to do is to not get swept along with the hype. When the tide turns, you will be well positioned, you will be better positioned than most to come out of it having a clear head and being able to go back to the fundamentals of why did you go to school, why did you go to University of Chicago and all the education that you've undergone to use your human mind because it will be shown that humans will be better than AI will ever pretend to be.

[Stephen Dubner]

Coming up after the break, why isn't Ben Zhao out in the private sector trying to make his billions? I'm Stephen Dubner. This is Freakonomics Radio. We'll be right back. Freakonomics Radio is sponsored by LinkedIn. Growing your small business in 2025 all comes down to how well you can hire. LinkedIn has the strongest hiring data and insights to help you identify the right candidates so you can start the new year off hiring smarter. LinkedIn knows hiring is a big deal for small businesses because every hire is crucial for a growing company. That's why LinkedIn pairs you with the best candidates, using data you won't find anywhere else, from unique skills and interests to the connections you have in common. LinkedIn also lets you go beyond candidates who are actively applying but are open to new opportunities. So hire smarter in the new year. Find your next Great hire on LinkedIn post your job for free at LinkedIn.com free that's LinkedIn.com freak to post your job for free. Terms and conditions apply.

[Heather Zhang]

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[Stephen Dubner]

By NerdWallet when it comes to finding the best financial product, have you ever wished someone would do the heavy lifting for you with NerdWallet's 2025 Best of Awards? That wish has come true. The nerds already did the work for you, reviewing over 1100 financial products like credit cards, savings accounts and more to bring you only the best of the best. Check out the 2025 Best of Awards today at NerdWallet.com awards It's easy to talk about the harms posed by artificial intelligence, but let's not ignore the benefits. That's where we started this episode, hearing from the economist Erik Brynjolfsson. If you think about something like the medical applications alone, AI is plainly a major force, and just to be witness to a revolution of this scale is exciting. Its evolution will continue in ways that, of course, we can't predict. But as the University of Chicago computer scientist Ben Zhao has has been telling us today, AI growth may be slowing down, and the law may be creeping closer to some of these companies, too. OpenAI and Microsoft are both being sued by the New York Times. Anthropic is fighting claims from Universal Music that it misused copyrighted lyrics and related to Zhao's work, a group of Artists are suing Stability, AI Midjourney, and DeviantArt for copyright infringement and trademark claims. But Zhao says that the argument about AI and art is about more than just intellectual property rights.

[Ben Zhao]

Art is interesting when it has intention, when there's meaning and context. So when AI tries to replace that, it has no context and meaning. Art replicated by AI, generally speaking, loses the point. It is not about automation. I think that is a mistaken analogy that people will oftentimes bring up. They say, well, you know, what about the horse and buggy and automobile? No, this is actually not about that at all. AI does not reproduce human art at a faster rate. What AI does is it takes past samples of human art, shakes it in a kaleidoscope, and gives you a mixture of what has already existed before.

[Stephen Dubner]

So when you talk about the scope of the potential problems, everything from the human voice, the face pieces of art, basically anything ever generated that can be reproduced in some way, it sounds like you are, no offense, a tiny little band of Don Quixote is there in the middle of the country, tilting at these massive global windmills of artificial intelligence and technology overlordship. And the amount of money being invested right now in AI firms is really almost unimaginable. They could probably start up a thousand labs like yours within a week to crush you. Not that I'm encouraging that, but I'm curious. On the one hand, you said, well, there is a bubble coming because of, let's call it, data limitations. On the other hand, when there's an incentive to get something for less or for nothing, and to turn it into something else that's profitable in some way, whether for crime or legitimate seeming purposes, people are going to do that. And I'm just curious how hopeless or hopeful you may feel about this kind of effort.

[Ben Zhao]

What's interesting about computer security is that it's not necessarily about numbers. If it's a brute force attack, I can run through all your PIN numbers, and it doesn't matter how ingenious they are, I will eventually come up with the right one. But for many instances, it is not about brute force and resource riches. So, yeah, I am hopeful. We're looking at vulnerabilities that we consider to be fundamental in some of these models, and we're using them to slow down the machine. I don't necessarily wake up in the morning thinking, oh, yeah, I'm going to topple OpenAI or Google or anything like that. That's not necessarily the goal. I see this as more of a process in motion. This hype is A storm that will eventually blow over. And how I see my role in this is not so much to necessarily stop the storm. I'm more, if you will, a giant umbrella. I'm trying to cover as many people as possible and shield them from the short term harm.

[Stephen Dubner]

What gives you such confidence that the storm will blow over or that there will be maybe more umbrellas other than what you pointed out as the data limitations in the near term? And maybe you know better than all of us. Maybe data limitations and computing limitations are such that the fears that many people have will never come true. But it doesn't seem like momentum is moving in your favor. If it seems it's moving in their.

[Ben Zhao]

Favor, I would actually disagree. But that's okay. We can have that discussion, right?

[Stephen Dubner]

Look, you're the guy that knows stuff. I'm just asking the questions. I don't know anything about this.

[Ben Zhao]

No, no. I think this is a great conversation to have. Because back in 2022 or early 2023, when I used to talk to journalists, the conversation was very, very different. The conversation was always, when is AGI coming? What industries will be completely useless in a year or two? It was never the question of, like, are we going to get return on investment for these billions and trillions of dollars? Are these applications going to be legit? So even in the year and a half since then, the conversation has changed materially because the truth has come out. These models are actually having trouble generating any sort of realistic value. I'm not saying that they're completely useless. There's certain scientific applications or daily applications where it is handy, but it is far, far less than what people had hoped them to be. And so, yeah, how do I believe it? Part of this is hubris. I've been a professor for 20 years. I've been trained or I've been training myself to believe in myself in a way. Another answer to this question is that it really is irrelevant because the harms are happening to people in real time. And so it's not about will we eventually win or will this happen eventually. In the end, it's the fact that people's lives were being affected on a daily basis, and I can make a difference in that, then that is worthwhile in and of itself, regardless of the outcome.

[Stephen Dubner]

If I were a cynic or maybe a certain kind of operative, I might think that maybe Ben Zhao is the poison. Maybe, in fact, you're a bot talking down the industry, both in intention and in capabilities, and who knows for what reason. Maybe you're even shorting the industry, in the markets or something. I kind of doubt that's true. But you know, we've all learned to be suspicious of just about everybody these days. Where would you say you fall on the spectrum of makers versus hardcore activists, let's say? Because I think in every realm throughout history, whenever there's a new technology, there are activists who overreact and often protest against new technologies in ways that in retrospect are revealed to have been either shortsighted or self interested. So that's a big charge I'm putting on you. Persuade me that you are neither shortsighted nor self interested, please.

[Ben Zhao]

Sure. Very interesting. Okay, let me unpack that a little bit there. The thing that allows me to do the kind of work that I do now, I recognize as quite a privilege the position in being a senior tenured professor. And honestly I don't have many of the pressures that some of my younger colleagues do.

[Stephen Dubner]

You have your own lab at the University of Chicago with your wife? When I read about this, I think, how did you get the funding? Did you have some kind of blackmail material on the UChicago budget people?

[Ben Zhao]

No, I mean all of our grants are quite public and I'm pretty sure that I'm not the most well funded professor in the department. I run a pretty regular lab. We write a few grants, but it's nothing earth shaking. It's just what we turn our time towards, that's all. There's very little that drives me these days outside of just wanting my students to succeed. I don't have the pressures of needing to establish a reputation or explain to colleagues who I am and why I do what I do. So in that sense, I almost don't care in terms of self interest. None of these products have any money attached to them in any way, shape or form. And I've tried very, very hard to keep it that way. There's no startup, there's no hidden profit motive or revenue here. So that simplifies things for me when.

[Stephen Dubner]

You say that you don't want to commercialize these tools. I assume the University of Chicago is not pressing you to do so.

[Ben Zhao]

No, the University always encourages entrepreneurship, they always encourage licensing, but they certainly have no control over what we do or don't do with our technology. This is sort of the reality of economics and academic research. We as a lab have a stream of PhD students that come through and we train them, they do research along the way and then they graduate and then they leave for things like Fox where this was the idea. Here's the tool, here's some code. We put that out there. But ultimately we don't expect to be maintaining that software for years to come. We just don't have the resources.

[Stephen Dubner]

That sounds like a shame. If you come up with a good tool.

[Ben Zhao]

Well, the idea behind academic research is always that if you have the good ideas and you demonstrate it, then someone else will carry it across the finish line, whether that's a startup or a research lab elsewhere. But somebody with resources who sees that need and understands it will go ahead and produce that physical tool or make that software and actually maintain it.

[Stephen Dubner]

Since you're not going to commercialize or turn it into a firm. Let's say you continue to make tools that continue to be useful and that they scale up and up and up. And let's say that your tools become an integral part of the shield against villainous technology. Let's just call it. Are you concerned that it will outgrow you and will need to be administered by other academics or maybe governments and so on?

[Ben Zhao]

You know, at a high level? I think that's great. I think if we get to that point, that will be a very welcome problem to have. We are in the process of exploring perhaps what a nonprofit organization would look like, because that would sort of make some of these questions transparent.

[Stephen Dubner]

That's what Elon Musk once said about OpenAI, I believe, correct?

[Ben Zhao]

Well, yeah, Very different type of nonprofit, I would argue. I'm more interested in being just the first person to walk down a particular path and encouraging others to follow. So I would love it if we were not the only technology in the space. Every time I see one of these other research papers that works to protect human creatives, I applaud all that. In order for AI and human creativity to coexist in the future, they had to have a complementary relationship. And what that really means is that AI needs human work product or images or text in order to survive. So they need humans, and humans really need to be compensated for this work that they're producing. Otherwise, if human artistry dies out, then AI will die out because they're going to have nothing new to learn on and they're just going to get stale and fall apart.

[Stephen Dubner]

I'm feeling a strong Robin Hood vibe here. Stealing from the rich, giving to the poor. But also what you're describing, your defense mechanism, it's like you are a bow, but you don't have an arrow. But if they shoot an arrow at you, then you can take the arrow and shoot it back at them and hit them where it really hurts over.

[Ben Zhao]

The last couple years, I've been practicing lots of fun analogies. Barbed wire is one. The large Doberman in your backyard. One particular funny one is where the hot sauce that you put on your lunch. So if that unscrupulous coworker steals your lunch repeatedly, they get a tummy ache.

[Stephen Dubner]

But wait a minute, you have to eat your lunch, too. That doesn't sound very good.

[Ben Zhao]

Well, you know, you eat the portion that you know is good, and then you leave out some stuff that.

[Stephen Dubner]

Got it. Got it. Can you maybe envision or describe what might be a fair economic solution here? A deal that would let the AI models get what they want without the creators being ripped off?

[Ben Zhao]

Boy, that's a bit of a loaded question because honestly, we don't know. It really comes down to how these models are being used. Ultimately, I think what people want is creative content that's crafted by humans. In that sense, the fair system would be generative AI systems that stayed out of the creative domain, that continued to let human creatives do what they do best to create really truly imaginative ideas and visuals, and then use generative AI for domains where it is more reasonable. For example, conversational chatbots seem like a reasonable use for them as long as they don't hallucinate.

[Stephen Dubner]

I'm just curious why you care about artists. Most people, at least in positions of power, don't seem to go to bat for people who make stuff. And when I say most people in positions of power, I would certainly include most academic economists. So of all the different labor forces that are being affected by AI, there are retail workers, people in manufacturing, medicine, on and on and on. Why go to bat for artists?

[Ben Zhao]

Certainly I know what it's not because I'm not an artist, not particularly artistic. Some people can say there's an inkling of creativity in what we do, but it's not nearly the same. I guess what I will say is creativity is inspiring. Artists are inspiring. Whenever I think back to what I know, art and how I appreciate art, I think back to college. You know, I went to Yale, and I remember many cold Saturday mornings I would walk out and there's piles of snow and everything would be super quiet and would take a short walk over to the Yale Art Gallery and it was amazing. I would be able to wander through halls of masterpieces. Nobody there except me and maybe a couple of security guards. It's always been inspiring to me how people can see the world so differently, through the same eyes, through the same physical mechanism. That is how I get a lot of my research done is I try to see the world differently and it gives me ideas. So when I meet artists and when I talk to artists to see what they can do, to see the imagination that they have at their disposal that I see nowhere else. You know, creativity, it's the best of humanity. What else is there?

[Stephen Dubner]

That was Ben Zhao. He helps run the Sand Lab at the University of Chicago. You can see a lot of their work on the Sand Lab website. While you're online, you may also want to check out a new museum scheduled to open this year in Los Angeles Angeles. It's called Dataland and it is the world's first museum devoted to art that is generated by AI. Maybe I will run into Ben Zhao there someday and maybe I'll run into you too. I will definitely be in LA soon. On February 13, we are putting on Freakonomics Radio live at the gorgeous Ebel Theater. Tickets [e-mail] live. I hope to see you there. Coming up next time on the show, are you ready for some football? The super bowl is coming up and we will be talking about one of the most undervalued positions in the game, the running back. Why are my boys being paid less when these quarterbacks who aren't nearly as tough as running backs, are being paid more? But wait a minute. Running backs used to to be the game's superstars and they were paid accordingly. What happened?

[Heather Zhang]

This is a classic example of multivariate causation.

[Stephen Dubner]

Okay, that doesn't sound very exciting, but the details are. I promise we will hear from the eggheads, the agents and the players.

[Heather Zhang]

You're telling me that you'd be a great difference maker and I can't get paid the right value for my position?

[Stephen Dubner]

And we'll ask whether this year's NFL season has marked a return to glory for the running back. That's next time on the show. Until then, take care of yourself and if you can, someone else too. Freakonomics Radio is produced by Stitcher and Renbud Radio. You can find our entire archive on any podcast app. [e-mail] where we publish transcripts and show notes. This episode was produced by Teo Jacobs, the Freakonomics Radio Network Artwork Staff also includes Alina Coleman, Augusta Chapman, Dalvin Abu Aji, Eleanor Osborne, Ellen Frankman, Elsa Hernandez, Gabriel Roth, Greg Rippon, Jasmine Klinger, Jeremy Johnston, John Schnars, Morgan Levy, Neal Carruth, Sarah Lilly, and Zach Lipinski. Our theme song is Mr. Fortune by the Hitchhikers and our composer is Luis Guerra. As always, thank you for listening. When I don't have a shredder around and I need to put something in the trash that I don't want anyone to see, I just put some ketchup on it. The Freakonomics Radio Network the Hidden side.

[Ben Zhao]

Of Everything Stitcher.

[Unknown]

What'S your New Year's resolution? To improve your relationships? Find a better job. We often make these commitments without first exploring a deeper question. How do I define a life well lived? Hidden Brain will do just that with our new January series, Wellness 2.0. Start the new year on a strong foot. Listen and subscribe to Hidden Brain wherever you get your podcasts.