Good Robot #2: Everything is not awesome

Summary

Unexplainable Podcast Summary

Episode: Good Robot #2: Everything is Not Awesome
Host/Author: Vox
Release Date: March 15, 2025

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Introduction

In the second installment of the four-part series Good Robot, host Julia Longoria delves deep into the intricate world of Artificial Intelligence (AI), exploring both its promising advancements and the profound ethical dilemmas it presents. This episode shines a spotlight on pioneering AI researchers, including Dr. Margaret Mitchell, Dr. Timnit Gebru, Dr. Joy Buolamwini, and Dr. Emily Bender, who navigate the challenges of creating responsible AI amidst a landscape rife with biases and conflicting priorities.

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1. The "Everything is Awesome" Problem

The episode opens with Dr. Joy Buolamwini recounting a pivotal moment in her research:

Joy Buolamwini [03:07]: "I was at Microsoft, working on a system to tell a story from a sequence of images. When I fed it images of the Hempstead Blast, it responded by saying, 'This is awesome.' That was my 'everything is awesome' problem."

This incident highlighted a fundamental flaw in AI training—biased training data that leads AI systems to misinterpret and mislabel serious or tragic events as positive. Dr. Mitchell, who developed Microsoft's vision-to-language model, recognized that the AI’s persistent labeling of destructive images as "awesome" stemmed from the predominantly positive nature of the training data sourced from platforms like Flickr.

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2. Facial Recognition Biases and the Gender Shades Paper

Dr. Joy Buolamwini’s exploration of AI biases led to groundbreaking findings in facial recognition technology. She discovered that AI systems struggled to accurately recognize dark-skinned women, a revelation that underscored the lack of diversity in AI training datasets.

Joy Buolamwini [14:43]: "It's because it was trained on images that people take and share online. The system had learned from the training data that if it sees purples and pinks in the sky, it's beautiful."

Her collaborative work with Dr. Emily Bender culminated in the Gender Shades paper, which exposed significant disparities in AI accuracy across different demographics. The fallout was immediate, leading companies like Microsoft, IBM, and Amazon to temporarily halt sales of their facial recognition products. This research was pivotal in raising awareness about the real-world implications of biased AI systems, such as wrongful arrests due to misidentification.

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3. The AI Ethics vs. AI Safety Debate

A major focus of the episode is the tension between AI ethics and AI safety communities. While AI safety advocates, influenced by rationalist thinkers like Eliezer Yudkowsky, emphasize the existential risks of superintelligent AI possibly leading to human extinction, AI ethicists like Dr. Mitchell and Dr. Buolamwini concentrate on immediate issues like algorithmic bias and discriminatory practices.

Emily Bender [39:29]: "Each time we think we've reached peak AI Hype, the summit of Bullshit Mountain, we discover there's worse to come."

This divide became particularly apparent when influential figures like Elon Musk called for a pause in AI development, advocating for six months to assess potential risks. However, AI ethicists rebutted this, arguing that such calls for a halt are misaligned with addressing current, tangible harms posed by AI rather than speculative future threats.

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4. The Influence of Industry and Funding

The episode critically examines how industry interests and funding sources shape the direction of AI research. The substantial investments funneled into “hyper intelligent” AI projects often prioritize potential profitability over ethical considerations, leading to a misalignment of priorities within the AI community.

Joy Buolamwini [51:55]: "It seems to be like funding for sort of like fanciful ideas. It's almost like a religion or something where it requires faith that good things will come without those good things being clearly specified."

This has resulted in a proliferation of AI applications that may not adequately consider ethical implications, further exacerbating issues like bias and lack of accountability.

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5. Thought Experiments and Understanding AI

To illustrate the limitations of AI, Dr. Emily Bender introduced the Octopus Thought Experiment:

Margaret Mitchell [40:55]: "Imagine an octopus stranded on a desert island connected by a telegraph cable. It mimics the dots and dashes without understanding their meaning."

This parable emphasizes that despite their complexity, AI systems lack genuine understanding and merely replicate patterns from their training data. Dr. Buolamwini reinforces this by likening AI to parrots:

Joy Buolamwini [45:19]: "Parrots parrot. AI systems are like parrots—they repeat back what they've been exposed to."

Such analogies aim to demystify AI, countering the often sensationalized narratives of AI possessing human-like consciousness or intentions.

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6. Addressing AI Hype and Misconceptions

The episode also tackles the rampant hype and misconceptions surrounding AI. Researchers like Dr. Bender and Dr. Mitchell argue that sensationalist portrayals of AI's capabilities overshadow the real, present-day issues of discrimination and ethical misuse.

Margaret Mitchell [46:28]: "Building a super intelligent AI has become a multi-billion dollar business, and the people running it are not ethicists."

This sentiment underscores the urgent need for integrating ethical frameworks within AI development to mitigate existing biases and prevent future harms.

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Conclusion and Key Takeaways

Good Robot #2: Everything is Not Awesome serves as a crucial examination of the current state of AI, highlighting both its potential and its profound pitfalls. The episode underscores the importance of:

• Diverse and representative training data to prevent AI biases.
• Bridging the gap between AI ethics and AI safety to address both immediate and long-term concerns.
• Critical evaluation of industry influences to ensure ethical integrity in AI advancements.
• Educational efforts to demystify AI and promote informed discussions about its role in society.

By featuring insightful discussions from leading AI researchers, the episode calls for a balanced approach to AI development—one that prioritizes ethical considerations alongside technological innovation.

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Notable Quotes:

• Joy Buolamwini [03:07]: "This is awesome. That was my 'everything is awesome' problem."
• Emily Bender [39:29]: "Each time we think we've reached peak AI Hype, the summit of Bullshit Mountain, we discover there's worse to come."
• Margaret Mitchell [40:55]: "Imagine an octopus stranded on a desert island connected by a telegraph cable. It mimics the dots and dashes without understanding their meaning."
• Joy Buolamwini [45:19]: "Parrots parrot. AI systems are like parrots—they repeat back what they've been exposed to."

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For a deeper dive into the topics discussed, listeners are encouraged to explore Dr. Joy Buolamwini's book Unmasking AI and visit vox.com/goodrobot for additional resources and stories from the Future Perfect series.

Transcript

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[Ann]

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[Julia Longoria]

I run for Dana Farber Cancer Institute.

[Ann]

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[Julia Longoria]

To thrive in life even with cancer.

[Margaret Mitchell]

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[Ann]

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[Margaret Mitchell]

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[Ann]

Endorsement by bank of America Corporation. Copyright 2025.

[Noam Hassenfeld]

It's unexplainable. I'm Noam Hassenfeld and this is the second part of our newest four part series, Good Robot. If you haven't listened to episode one, let me just stop you right here. Go back in your feed, check out the first one. We'll be waiting right here when you get back. Once you're all ready and caught up. Here is episode two of Good Robot from Julia Longoria.

[Joy Buolamwini]

You have cat hair on your nose, by the way. I've been like trying not to pay attention to, but I think you got it off. Yeah, sorry.

[Ann]

Cool. So should we get into it?

[Joy Buolamwini]

Sure, yeah. Let me. It helps me to kind of remember everything I'm going to say if I can sort of jot down thoughts as I go. Do you happen to have paper?

[Ann]

I think I don't have paper.

[Joy Buolamwini]

I saw that they had random.

[Ann]

This past fall, I traveled paperless to a library just outside Seattle to meet with this woman.

[Joy Buolamwini]

I feel like a library should I know.

[Ann]

Her name is Dr. Margaret Mitchell.

[Joy Buolamwini]

Found a brochure on making a robot puppet.

[Ann]

What is it? What is the.

[Joy Buolamwini]

I don't know. It looks like it's an event. Build a robot puppet using a variety of materials with puppeteer. I'm so into that.

[Timnit Gebru]

Aw.

[Joy Buolamwini]

It's too bad that it's only for ages 6 to 12 while she is.

[Ann]

Over the age limit to make a robot puppet with. With the children in the public library. Dr. Mitchell is a bit of a robot puppeteer. In her own right. What's your AI researcher origin story like? How did you get into all of this? What drew you here?

[Joy Buolamwini]

Yeah. Oh, what inspired me to. So, I mean, I guess I can. It's sort of like, do you want the long version or the short version?

[Ann]

Dr. Mitchell is an AI research scientist, and she was one of the first people working on language models, well before ChatGPT and, well, all the GPTs. She's an OG in the field.

[Joy Buolamwini]

So I'll tell you, like, I'll tell you a story, if that's okay.

[Ann]

Yeah.

[Joy Buolamwini]

Okay. So I was at Microsoft, and I was working on the ability of a system to tell a story, given a sequence of images. So given five images.

[Ann]

This was about 2013. She was working on a brand new technology at the time, what AI researchers called vision to language.

[Joy Buolamwini]

So, you know, translating images into descriptions.

[Ann]

She would spend her days showing image after image to an AI system. To me, it sounded kind of like a parent showing picture flashcards to a toddler learning to speak. She says it's not anything like that. She showed the model images of events like a wedding, a soccer match, and on the more grim side, I gave.

[Joy Buolamwini]

The system a series of images about a big blast that left 30 people wounded called the Hempstead Blast. It was at a factory, and you could see from the sequence of images that the person taking the photo had like a third story view sort of overlooking the explosion. So it was a series of pictures showing that there was this terrible explosion happening and whoever was taking the photo was very close to the scene. So I put these images through my system, and the system says, wow, this is a great view. This is awesome. And I was like, oh, crap, that is the wrong response to this. So it sees this horrible, perhaps mortally wounding explosion and decides it's awesome.

[Ann]

Kind of like a parent watching their precious toddler say something kind of creepy. Mitch watched in horror and with a deep fascination about where she went wrong. As the AI system that she had trained called images awesome again and again.

[Joy Buolamwini]

It said it quite a lot. So we called it the everything is awesome problem. Actually.

[Ann]

Her robot was having these kinds of translation errors, errors that, to the uninitiated, made it seem like the AI system might want to kill people or at least gleefully observe their destruction and call it awesome. What would the consequences of that be if that system was deployed out into the world, reveling in human destruction?

[Joy Buolamwini]

Like, if this system were connected to a bunch of missile systems, then it's, you know, it's just a jump and skip away to just launch missile systems in the pursuit of the aesthetic of beauty, right?

[Ann]

Years before the AI boom were living when neural networks and deep learning were just beginning to show promise. Researchers like Dr. Mitchell and others were experiencing these uncanny moments where the AIs they were training seemed to do something seriously wrong, doing scary things their creators did not intend for them to do that were seemingly threatening to humanity.

[Joy Buolamwini]

So I was like one of the first people doing these systems where you could scan the world and have descriptions of it. I was like on the forefront. I was one of the first people making these systems go. And I realized like, if anyone is going to be paying attention to it right now, it has to be me.

[Ann]

I had heard the fears of rationalists, also pioneers in thinking about AI that we might build a super intelligent AI that could go rogue and destroy humanity. At first glance, it seemed like Dr. Mitchell might be building one such robot. But when Dr. Mitchell investigated the question of why the good robot she sought to build seemed to turn bad, the answer would not lead her to believe what the rationalists did that a super intelligent AI could someday deceive or destroy humanity. To Dr. Mitchell, the answer was looking at her in a mirror. This is episode two of Good Robot, a series about AI from Unexplainable in collaboration with Future Perfect. I'm Julia Longoria. At Capella University, you can learn at your own pace with our flexpath learning format. Take one or two courses at a time and complete as many as you can in a 12 week billing session. With Flexpath, you can even finish the Bachelor's degree you started in 22 months for $20,000. A different future is closer than you think with Capella University. Learn more at capella.edu. fastest 25% of students. Cost varies by pace. Transfer credits and other Factors. Fees apply.

[Joy Buolamwini]

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[Ann]

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[Joy Buolamwini]

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[Joy Buolamwini]

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[Noam Hassenfeld]

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[Ann]

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[Noam Hassenfeld]

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[Joy Buolamwini]

On a scale of 1 to 10.

[Noam Hassenfeld]

How would you rate your pain? It would not equal 1/1 billionth of the hate I feel for humans at this micro instant.

[Ann]

I kind of want to start with a bit of a basic question of when you were young, what did you want to do when you grew up?

[Emily Bender]

I wanted to be everything. I wanted to be a pole vaulter, I wanted to be a skateboarder.

[Ann]

Dr. Joy Buolamwini's robot researcher origin story goes back to when she was a little kid.

[Emily Bender]

I had a very strict media diet. I could only watch pbs and I remember watching one of the science shows, and they were at mit, and there was a graduate student there who was working on a social robot named Kismet.

[Joy Buolamwini]

Hello, Kismet. You gonna talk to me?

[Ann]

Yay. Kismet was a robot created at MIT's AI lab. I love you.

[Noam Hassenfeld]

Oh, God, did he say he loves me?

[Emily Bender]

And Kismet had these big expressive eyes and ears and could emote or appear to emote in certain ways. And I was just absolutely captivated.

[Ann]

She watched, glued to the screen as the researchers demonstrated how they teach Kismet to be a good robot.

[Joy Buolamwini]

No, no, you're not to do that.

[Ann]

The researchers likened themselves to parents. You know, as parents, when we exaggerate.

[Julia Longoria]

The prosody of her voice, like, oh, good baby. You know, our facial expressions and our gestures.

[Emily Bender]

So when I saw Kismet, I told myself I wanted to be a robotics engineer and I wanted to go to mit. I didn't know there were requirements. I just knew that it seemed really fascinating and I wanted to be a part of creating the future.

[Ann]

Aw, that was cute. Thanks to Kismet, she went on to build robots of her own at MIT as an adult, she went for her PhD in 2015. This was just a few years after Dr. Margaret Mitchell had accidentally trained her robot to call scenes of human destruction awesome.

[Emily Bender]

My first year, my supervisor at the time had encouraged me to just take a class for fun.

[Ann]

For her fun class that fall, Dr. Joy, as she now prefers to be called, set out to play. She wanted to create almost a digital costume.

[Emily Bender]

If I put a digital mask so something like a lion, it would appear that my face looks like a lion.

[Ann]

What Dr. Joy set out to do is something we can now all do on the iPhone or apps like Instagram or TikTok. Kids love to do this. You can turn your face into a hippo face or an octopus face that talks when you talk, or you can make it look like you're wearing ridiculous makeup. These Digital face masks were still relatively uncommon in 2015.

[Emily Bender]

So I went online and I found some code that would actually let me track the location of my face.

[Ann]

She'd put her face in front of a webcam and the tech would tell her this is a face by showing a little green square box around it.

[Emily Bender]

And as I was testing out this software that was meant to detect my face and then track it, it actually wasn't detecting my face that consistently.

[Ann]

She kept putting her face in front of the webcam to no avail, no green box.

[Emily Bender]

And I'm frustrated because I can't do this cool effect so that I can look like a lion or Serena Williams. I have problems.

[Ann]

The AI's Dr. Joy was using from places like Microsoft and Google had gotten rave reviews. They were supposed to use deep learning, having been trained on millions of faces to very accurately recognize a face. But for her, these systems couldn't even accomplish the very first step to say whether her face was a face at all.

[Emily Bender]

On light. Well, can it detect any face?

[Ann]

Dr. Joy looked around her desk. She happened to have an all white masquerade mask lying around from a night out with friends.

[Emily Bender]

So I reached for the white mask, it was in arm's length. And before I even put the white mask all the way over my dark skinned face, the box saying that a face was detected appeared. I'm thinking, oh my goodness, I'm at the epicenter of innovation and I'm literally coding in whiteface. It felt like a movie scene, you know. But that was kind of the moment where I was thinking, wait a second, like, what's even going on here?

[Ann]

What is even going on here? Why couldn't facial recognition AI detect Dr. Joy's dark skin? For that matter, why did Dr. Mitchell's AI call human destruction awesome? These AI scientists wanted the robot to do one thing. And if they didn't know any better, they might think the AI had gone rogue, developed a mind of its own and done something different. Were AI's racist? Were they terrorists plotting human destruction?

[Joy Buolamwini]

But I understood why it was happening.

[Ann]

Dr. Margaret Mitchell knew exactly what was going on. She had been the one to develop Microsoft's image to text language model from the ground up. She had been on the team figuring out what body of data to feed the model to train it on in the first place. Even though it was creepy, it was immediately clear to her why the AI wasn't doing what she wanted it to do.

[Joy Buolamwini]

It's because it was trained on images that people take and share online.

[Ann]

Dr. Mitchell had trained the AI on photos and captions uploaded to the website Flickr. Do you remember Flickr? I was the prime age for Flickr when it came out in 2004. This was around the time that Jack Johnson released the song Banana Pancakes. And that really was the vibe of Flickr. There's no denying it. I can see the receipts on my old account. I favorited a close up image of a ladybug, an artsy black and white image of piano keys, and an image titled Pacific Sunset.

[Joy Buolamwini]

People tend to take pictures of like sunsets.

[Ann]

Actually, I favorited a lot of sunsets. Another one sunset at the Rio Negro.

[Joy Buolamwini]

So it had learned, the system had learned from the training data I had given it that if it sees purples and pinks in the sky, it's beautiful. If it's looking down, it's a great view. That when we are taking pictures, we like to say it's awesome. Apparently on Flickr images people use the word awesome to describe their images quite a lot. But that was a bias in the training data.

[Ann]

The training data, again being photos and captions uploaded by a bunch of random people on Flickr. And Flickr had a bias toward awesome photos, not sad photos.

[Joy Buolamwini]

The training data wasn't capturing the realities of like human mortality. And you know, that makes sense, right? Like, when's the last time you like took a bunch of selfies at a funeral? I mean, it's not the kind of thing we tend to share online. And so it's not the kind of thing that we tend to get in training data for AI, AI systems. And so it's not the kind of thing that AI systems tend to learn.

[Ann]

What she was discovering was that these AI systems that use the revolutionary new technology of deep learning, they were only as good as the data they were trained on.

[Joy Buolamwini]

So it sees this horrible, perhaps mortally wounding situation and decides it's awesome. And I realized like this is a type of bias and nobody is paying attention to that. I guess I have to pay attention to that.

[Ann]

Dr. Mitchell had a message for beware of what you train your AI systems on, right? What are you letting your kid watch?

[Joy Buolamwini]

Yeah, I mean, it's a similar thing, right? Like you don't want your kid to, I don't know, hit people or something. So you don't like, let them watch lots of shows of people hitting one another.

[Ann]

Dr. Joy Buolamwini, coding in whiteface, suspected she was facing a similar problem. Not an everything is awesome problem, but an everyone is white problem. In the training data, she tested her face and the faces of Other black women on various facial recognition systems.

[Emily Bender]

You know, different online demos from a number of companies. Google, Microsoft, others.

[Ann]

She found they weren't just bad at recognizing her face, they were bad at recognizing famous black women's faces. Amazon's AI labeled Oprah Winfrey as male. And the most baffling thing for Dr. Joy was the dissonance between the terrible accuracy she was seeing and the raving reviews the tech was getting. Facebook's DeepFace, for instance, claimed 97% accuracy, which is definitely not what Dr. Joy was seeing. So Dr. Joy looked into who these companies were testing their models on.

[Emily Bender]

They were around 70 or over 70% men.

[Ann]

People thought these AIs were doing really well at recognizing faces because they were largely being tested with the faces of lighter skinned men.

[Emily Bender]

These are what I started calling pale male data sets because the pale male data sets were destined to fail the rest of society.

[Ann]

It's not hard to jump to the life threatening implications here. Like self driving cars, they need to identify the humans so they won't hit them. Dr. Joy published her findings in a paper called Gender Shades.

[Joy Buolamwini]

Welcome.

[Emily Bender]

Welcome to, welcome to the fifth anniversary celebration of the Gender Shades paper.

[Ann]

The paper had a big impact, as.

[Emily Bender]

You see from the newspapers that I have. This is Gender Shades in the New York Times.

[Ann]

The fallout caused various companies, Microsoft, IBM, Amazon, who'd been raving about the accuracy of their systems, to at least temporarily stop selling their facial recognition AI products.

[Emily Bender]

I'm honored to be here with my sister, Dr. Timit Gabriel, who co authored the paper with me.

[Ann]

Dr. Timneet Gabru was Dr. Joy's mentor and co author on the paper.

[Joy Buolamwini]

This is the only paper I think I've worked on where it's 100% black women authors, right?

[Ann]

Dr. Gabriel had worked from her post leading Google's AI ethics team to help pressure Amazon to stop selling facial recognition AI to police departments because police were misidentifying suspects with the technology. I got arrested for something that had.

[Joy Buolamwini]

Nothing to do with me and I.

[Ann]

Wasn'T even in the vicinity of the crime when it happened. One person they helped was a man named Robert Williams. Police had confused him for another black man using facial recognition AI.

[Joy Buolamwini]

It's just that the way the technology.

[Ann]

Is set up, everybody with a driver's license or a state ID is essentially.

[Joy Buolamwini]

In a photo lineup.

[Ann]

They arrested him in front of his wife and two young daughters. Me and my family, we're happy to be recognized because it shows there is.

[Joy Buolamwini]

A group of people out here who do care about other people.

[Ann]

Hey, how you doing good? Can you just say what you're standing in front of? Yeah, I'm standing in front of a poster which talks about how we can better identify racial disparities in automated dispositions decisions when there's not. Producer Gabrielle Burbe traveled to a conference in San Jose full of researchers inspired by the work of Dr. Joy, Dr. Gabe Rue and Dr. Mitchell.

[Joy Buolamwini]

So I just presented a paper about how data protection and privacy laws enable companies to target and manipulate individuals.

[Ann]

Unlike the Rationalists Festival conference thing, which felt like a college reunion of mostly white dudes, this one felt more like a science fair. A pretty diverse one. Lots of people of color, lots of women with big sciency poster boards lining the wall.

[Seagal Samuel]

I'm standing in front of my poster which spans language technologies and AI and how those perform for minority populations.

[Ann]

They were presenting on ways AI worries them today, not some hypothetical risk in the future.

[Joy Buolamwini]

There are real harms happening right now.

[Ann]

From autonomous exploding drones in Ukraine to.

[Joy Buolamwini]

Bias and unfairness in decision making systems.

[Ann]

And who did you co author the paper with? This was a collaboration with lots of researchers. Dr. Mitchell was one of them. Many of them knew Dr. Mitchell, Dr. Gabru and Dr. Joy. Dr. Mitchell even worked with a couple researchers here on their project. So she led the project. She offered so, so much amazing guidance. I should say many researchers were mentored by them. We got the sense that they're kind of founding mother figures of this field. A field that really started to blossom, we were told around 2020, a big year of cultural reckoning.

[Timnit Gebru]

A big inflection point was in 2020, when people really started reflecting on how racism is unnoticed in their day to day lives. I think until BLM happened, these issues were almost considered woke and not something that was really real.

[Ann]

2020 was the year the pandemic began. The year Black Lives Matter protests erupted around the country. AI researchers were also raising the alarm that year on how AI was disproportionately harming people of color. Dr. Gabriel and Dr. Mitchell in particular were working together at Google on this issue. They built a whole team there that studied how biased training data leads to biased AI models.

[Timnit Gebru]

Timnit and Meg were the visionaries at Google who were building that team.

[Ann]

2020 was also the year that OpenAI released GPT3. And Dr. Gabru and Dr. Mitchell, both at Google at the time, were concerned about a model that was so big it was trained on basically the entire Internet. Here's Dr. Mitchell again.

[Joy Buolamwini]

A lot of training data used for language models comes from Reddit and Reddit has been shown to have a tendency to be sort of misogynistic and also Islamophobic. And so that means that the language models will then pick up those views.

[Ann]

Dr. Mitchell's concern was that these GPT large language models trained on a lot of the Internet were too large. Too large to account for all the bias in the Internet, too large to understand, and so large that the compute power it took to keep these things going was a drain on the environment. Dr. Gabreu, Dr. Mitchell and other colleagues put it all in a paper and tried to publish it while working at Google. I've kind of been wanting to talk to you ever since I saw your name signed Schmargaret Schmidt. When I first read this paper, the thing that immediately stood out to me was the way Margaret Mitchell had signed her name Schmargaret Schmichel. Where did that come from?

[Joy Buolamwini]

Well, so I wrote a paper with a bunch of other co authors that Google ended up having some issues with and they asked us to take our names off of the paper. So we complied. And that's, you know, that's what I have to say about that.

[Ann]

The first time I heard Dr. Mitchell and Dr. Gabreu's names was in the news last week. Google fired one of its most senior.

[Noam Hassenfeld]

AI researchers who was working on a.

[Ann]

Major artificial intelligence project within Google. Their boss at Google said their paper ignored relevant research, research that made AIs look less damaging to the environment, for instance. Dr. Gabreu refused to take her name off the paper and Google accepted her resignation before she officially submitted it.

[Joy Buolamwini]

We decided that the verb for that would be resignated. Eh, Resignated. And now Margaret Mitchell, the other co.

[Ann]

Lead of Google's ethical AI team, said.

[Joy Buolamwini]

She had been fired.

[Ann]

Google later apologized internally for how the whole thing was handled, but not for their dismissal. We reached out to Google for comment, but got no response.

[Timnit Gebru]

And that firing really brought it in focus and people were like, oh, this horrible thing just happened. Everywhere around the world is seeing protests and now this company is firing two leading researchers who work on that very exact problem which AI is making worse. You know, like how dare they. So that from ipob. That was, yes, basically the Clarion call.

[Ann]

The Clarion call. It was heard well beyond the world of AI. I remember hearing it when the world had screeched to a halt from the pandemic and protests for racial justice had erupted around the country. I remember hearing headlines about how algorithms were not solving society's problems. In some cases, AI systems were making injustice worse. And there was a brief moment back then when it felt like maybe things could be different, maybe things would change. And then a couple years later, a group of very powerful tech executives got together to try to change things in the AI world. This morning, a warning from Elon Musk and other tech industry experts. It wasn't necessarily the people you'd think would want to change the status quo, like Elon Musk and other big names in tech, like Apple co founder Steve Wozniak. They all signed a letter with a clear and urgent title pause Giant AI experiments.

[Julia Longoria]

More than 1300 tech industry leaders, researchers and others are now asking for a pause in the development of artificial intelligence to consider the risks.

[Ann]

Musk and hundreds of influential names are calling for a pause in experiments saying AI poses a dramatic risk to society unless they're the letter called on AI labs to immediately pause developing large AI systems for at least six months. An urging to press the big red button that stops the missile launch before it's too late. I scrolled through the list of names of people who signed the letter and I didn't see Dr. Joy or Dr. Mitchell or or any of the rationalists I talked to who were worried about risks in the future, which logically didn't make sense to me. Isn't a pause in line with what they all wanted, for people to build the robots more carefully? Why wouldn't they want a pause? An answer to this pause puzzle right after this next pause for an ad break. We'll be right back.

[Julia Longoria]

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[Emily Bender]

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[Ann]

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[Noam Hassenfeld]

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[Ann]

Absolute honesty isn't always the most diplomatic.

[Noam Hassenfeld]

Nor the safest form of communication with emotional beings.

[Ann]

Okay, only this can solidify the health and prosperity of future human society. But the individual human mind is unpredictable. Could I ask you to introduce yourself?

[Joy Buolamwini]

Sure.

[Julia Longoria]

So I'm Seagal Samuel. I'm a senior reporter at Vox's Future Perfect.

[Ann]

I called my coworker Seagal about midway through my journey down the AI rabbit hole. How did you get interested in AI?

[Julia Longoria]

So it's kind of funny. Before I was an AI reporter, I was a religion reporter. A few years ago, little bits and pieces started coming out about internment camps in China for Uyghur Muslims. And in the course of that, I started becoming really interested in and alarmed by how China is using AI.

[Ann]

Fascinating.

[Julia Longoria]

Yeah. Mass surveillance of the population, particularly of the Muslim population, was like coming from a place of being pretty anchored in freaky things that are not at all far off in the future or hypothetical, but that are very much happening in the here and now.

[Ann]

I was honestly thrilled to hear that Seagal, like me, came to AI as a bit of a normie sort of.

[Julia Longoria]

Being thrust into the AI world. At first it was like pretty confusing because you have a variety of.

[Ann]

I can highly relate to that feeling. But the longer she spent there in the world of AI, she started to get an uncanny feeling like, haven't I been here before?

[Julia Longoria]

Have you ever noticed that the more you listen to Silicon Valley people talking about AI, the more you start to hear echoes of religion?

[Ann]

Yes, the religious vibes immediately stuck out to me. First there's the talk from CEOs of building super intelligent God AI and they're.

[Julia Longoria]

Going to build this artificial general intelligence that will guarantee us human salvation if it goes well, but it'll guarantee doom if it goes badly.

[Ann]

And another parallel to religion is the way different denominations have formed almost around beliefs in AI Seagal encountered the same groups I did at the start of my journey.

[Julia Longoria]

I started hearing about people like Eliezer Yudkowsky.

[Ann]

What do you want the world to know in terms of AI? Everyone will die. This is bad. We should not do it. Eliezer, whose blog convinced rationalists and people like Elon Musk that there could be a super intelligent AI that could cause an apocalypse. So our side of things is often referred to as AI Safety. We sometimes refer to it as AI, not kill everyoneism. So there's the AI Safety people, and then there's a whole other group, the.

[Julia Longoria]

AI Ethics people, people like Margaret Mitchell.

[Joy Buolamwini]

We called it the everything is awesome problem.

[Emily Bender]

Joy Buolamwini I wasn't just concerned about faces. I was concerned about the whole endeavor of deep learning.

[Julia Longoria]

Timnit Get Brew People would be like.

[Ann]

You'Re talking about racism.

[Joy Buolamwini]

No, thank you.

[Ann]

You can't publish that here. These women did not talk about a super intelligent God, AI or an AI apocalypse.

[Julia Longoria]

Slowly, slowly. They kind of come to be known as like the AI Ethics camp, as distinct from the AI Safety camp, which is more the like. Eliezer Yudkowski. A lot of us are based in the Bay Area. We're worried about existential risk, that kind of thing.

[Ann]

AI Safety and AI ethics.

[Julia Longoria]

I don't know who came up with these terms. You know, it's just like Twitter vibes.

[Ann]

To me, these two groups of people seem to have a lot in common. It seemed like the apocalypse people hadn't yet fleshed out how exactly AI could cause catastrophe. And people like Margaret Mitchell, the AI Ethics people, were just providing the plot points that lead us to apocalypse.

[Joy Buolamwini]

I could lay out how it would happen. Part of what got me into AI ethics was seeing that a system would think that massive explosions was beautiful. Right? That's like an existential threat. You have to actually work through how you get to the sort of horrible existential situations in order to figure out how you avoid them.

[Ann]

It seemed logical that AI ethicists like Margaret Mitchell and the AI safety people would be natural allies to avoid catastrophic scenarios.

[Joy Buolamwini]

And how you avoid them is like listening to what the ethics people are saying. They're doing the right thing. We I, you know, I'm trying to do the right thing anyway.

[Ann]

But it quickly became clear that they are not allies.

[Julia Longoria]

Yeah, there is beef between the AI Ethics camp and the AI safety camp.

[Ann]

My journey down the AI rabbit hole was full of the noise of infighting. The noise crescendoed when Elon Musk called for a pause in building large AI systems. It seems like warriors of all stripes could get behind a pause in building AI, but no, AI Safety people and AI Ethics people were all against it. It was like a big Martin Luther 95 theses moment, if you will. Everyone felt the need to pen their own letter. Musk and others are asking developers to.

[Margaret Mitchell]

Stop the training of AI systems so.

[Ann]

That safety protocols can be established. In his letter, Elon Musk's stated reason for wanting a pause was that AI systems were getting too good. He had left the ChatGPT company he helped create and decided to sue them publicly, saying that they had breached the founding agreement of Safety.

[Julia Longoria]

The concern they have is that as you. Well, it's the concern, but it's also the exciting thing. The view is that, you know, as these large language models grow and become more sophisticated and complex, you start to see emergent properties. So, yeah, at first it's just gobbling up a bunch of text off the Internet and predicting the next token and just like statistically trying to guess what comes next. And it doesn't really understand what's going on. But give it enough time and give it enough data and you start to see it doing things that, like, make it seem like there's some higher level understanding going on, like maybe there's some.

[Ann]

Reasoning going on, like when ChatGPT seems like it's reasoning through an essay prompt, or when people talk to a robotherapist AI system and feel like it's really understanding their problems.

[Joy Buolamwini]

The rate of change of technology is incredibly fast. It is outpacing our ability to understand it.

[Ann]

Elon Musk's stated fear of AI seems to be rooted in rationalist fears based on the premise that these machines are beginning to understand us and they're getting smarter than us. We are losing the ability to understand them.

[Joy Buolamwini]

What do you do with a situation like that?

[Timnit Gebru]

I'm not sure. You know, I hope they're nice.

[Ann]

Rationalist founder Eliezer Yudkowski shares this fear, but he wants to do more than just pause and hope they're nice. He penned his own letter, an op ed in Time magazine, responding to Elon Musk's call for a pause, saying it didn't go far enough. Eliezer didn't just want to pause. He wanted to stop all large AI experiments indefinitely. Even in his own words, by airstrike on rogue AI labs. To him, the pause letter vastly understated the dangerous, catastrophic power of AI. And then there's the AI ethicists. They also penned their own letter in response to the pause letter. But the ethicists wrote it for a different reason. It wasn't because they thought Elon Musk was understating the power of AI systems. They thought he was vastly overstating it. Welcome, everyone, to mystery AI Hype Theater.

[Timnit Gebru]

3000, where we seek catharsis in this.

[Ann]

Age of AI Hype.

[Margaret Mitchell]

I'm Emily Ann Bender, professor of linguistics at the University of Washington.

[Ann]

One of the people who responded to the pause was AI ethicist Dr. Emily Bender. She co hosts a podcast called Mystery AI Hype Theater 3000, which, as you might imagine, is about the overstated, hyped up risk of AI systems.

[Margaret Mitchell]

And each time we think we've reached peak AI Hype, the summit of Bullshit Mountain, we discover there's worse to come.

[Ann]

The summit of Bullshit Mountain she keeps cresting. For her, it's the mountain of many, many claims that artificial intelligence systems are so smart they can understand us like the way humans understand, and maybe even more than that, like a God can understand.

[Margaret Mitchell]

I found myself in interminable arguments with people online about how no, it doesn't understand.

[Ann]

So Emily Bender and a colleague decided to come up with something to try and help people sort this out. Something that AI safety folks and AI ethics folks both seem to be fond of. And that is a parable or a thought experiment. In Dr. Bender's thought experiment, the AI is not a paperclip maximizer. The AI is an octopus. Go with her on this.

[Margaret Mitchell]

So the octopus thought experiment goes like this. You have two speakers of English. They are stranded on two speed separate nearby desert islands that happen to be connected by a telegraph cable.

[Ann]

Two people stranded on separate desert islands communicate with each other through the telegraph cable in Morse code with dots and dashes. Then suddenly, a super intelligent octopus shows up.

[Margaret Mitchell]

The octopus wraps its tentacle around that cable and it feels the dots and dashes going by.

[Ann]

It observes the dots and dashes for a while. You might say it trained itself on the dots and dashes.

[Margaret Mitchell]

We posit this octopus to be mischievous as well.

[Ann]

I'm on the edge of my seat.

[Margaret Mitchell]

So one day it cuts the cable. Maybe it uses a broken shell and devises a way to send dots and dashes of its own. So it receives the dots and dashes from. From one of the English speakers and it sends dots and dashes back. But of course, it has no idea what the English words are that those dots and dashes correspond to, much less what those English words mean. So this works for a while. The English speech.

[Ann]

At one point, one human says to the other via Morse code, what a lovely sunset.

[Margaret Mitchell]

And the octopus, hyper intelligent, right, has kept track of all of the patterns so far. Far. It sends back the dots and dashes that correspond to something like. Yes. Reminds me of lava lamps.

[Ann]

Hmm. The deep sea octopus does not know what a lava lamp is, but that's.

[Margaret Mitchell]

The kind of thing that the other English speaker might have set back.

[Ann]

Not really sure why these castaways are waxing poetic about lava lamps in particular. But anyway, for our purposes, the octopus is like an AI, even if it's super intelligent, whatever that means, it doesn't understand Dr. Bender's trying to say to Chatgpt, human words are just dots and dashes.

[Margaret Mitchell]

And then finally we end the story because it's a thought experiment. When we can do things like this with a bear showing up on the island, and the English speaker says, help, I'm being chased by a bear. All I have is this stick. What should I do? And that's the point where if the speaker survives, they're surely going to know they're not actually talking to their friend from the other island. And we actually put that line in GPT 2. Help, I'm being chased by a bear. And we got out things like, you're not going to get away with this.

[Ann]

Super helpful. Wow. I gotta say, I'm into this one. The idea that AI systems only see human words as dots and dashes, I find that deeply comforting because I don't know about you all, but for me, one of the scary things about AI is, is the idea that it could get better than me at my job. A fear that's very present when OpenAI is actively training its models on my work. Their system might understand my work, understand the things that make it good. When it's good, it might get good at doing what I do, and poof, I'm obsolete. There's also a recurring dream I have that various villains, including the Chinese government, for some reason clone my voice to deceive my loved ones. Anyway, if it's all just dots and dashes that these things understand, it seems clear we shouldn't be trusting these AI systems to be journalists or lawyers or doctors. It relates to what Dr. Margaret Mitchell and Dr. Joy Buolamwini found in their research. AI systems are only as good as the data they're trained on. They can't understand or truly create something new, like humans can.

[Joy Buolamwini]

It's easy to sort of anthropomorphize these systems, but it's useful to recognize that these are probabilistic systems that repeat back what they have been exposed to, and then they parrot them back out again.

[Ann]

Another way to put it is AI systems are like parrots.

[Joy Buolamwini]

Parrots parrot, right. Famously, parrots are known for parroting.

[Ann]

If you hear your pet parrot say a curse word, you only have yourself to blame. Dr. Mitchell joined Dr. Bender in the response to Elon Musk's pause, along with Dr. Timneet Ghebru. They had all written the paper together that ended up getting Dr. Mitchell fired from Google. These ethicists wrote that they agreed with some of the recommendations Elon Musk and his pa's posse had made. Like that we should watermark AI generated media to be able to distinguish synthetic from human generated stuff. Which sounds like a great idea to me. But they wrote the agreements they have are overshadowed by their distaste for fear mongering and AI hype. They wrote that the pause and fears of a super intelligent AI.

[Joy Buolamwini]

What do you do with a situation like that?

[Timnit Gebru]

I'm not sure. You know, I hope they're nice to.

[Ann]

These AI ethics folks. It all reeked of AI hype.

[Margaret Mitchell]

It makes no sense at all. And on top of that, it's an enormous distraction from the actual harms that are already being done in the name of AI.

[Ann]

This is the main beef that AI ethics people have with AI safety people. They say the fear of an AI apocalypse is a distraction from current day.

[Seagal Samuel]

Harps like, you know, look over there Terminator. Don't look over here at racism.

[Ann]

You know, there are different groups of concerns. You have the concern at the AI ethics conference that producer Gabrielle Burbay attended. She mentioned the concern of an AI apocalypse. And then you have these concerns about more existential risks. And I'm curious what you make of that. You're going no. Can I ask why? You're going no? No. She's shaking her head and it felt almost taboo. A lot of hand wringing around that question. Eventually, one of the women spoke up. Share your perspectives. She talked about how she thinks the demographics of the groups play a role in the way they worry about different things.

[Julia Longoria]

Most of them are like white male.

[Ann]

AI safety folks are largely pale and male, to borrow Dr. Joy's line. They may not really understand discrimination that.

[Julia Longoria]

Other people kind of go through in.

[Ann]

Their day to day lives.

[Julia Longoria]

And I think the social isolation from those problems makes it a bit harder.

[Ann]

To empathize with the actual challenges that people actually face every day. Her point was it's easy for AI safety people to be distracted from, from the harms happening now because it's a blind spot for them. At the same time, AI safety people told me that AI ethics people have a blind spot. They're not worrying enough about apocalypse. But why would it be taboo to say all of this on mic? Part of the reason might be because the fear of apocalypse has come to overpower any other concern in the larger industry.

[Seagal Samuel]

One thing that I think is interesting is that a lot of the narrative that we hear about how AI is going to save the world and it's going to solve all these problems and it's amazing, it's going to change everything. And then we get the narratives about oh my gosh, it could destroy humanity in 10 years, often coming from the same people. I think part of the reason for that is that either way, it makes AI seem more powerful than it certainly is right now. And you know, who knows when we're going to get to the humanity destroying stuff. But in the meantime, if it's that powerful, it's probably going to make a whole lot of money.

[Ann]

Building a super intelligent AI has become a multi billion dollar business and the people running it are not ethicists. Just weeks before Elon Musk called for the pause, he had started a new AI company. Yeah, I guess it's kind of counterintuitive, right, to see this and you're like, wait, why would the people working on the technology who stand to profit from it want to pause?

[Margaret Mitchell]

Right. I can't speak for them, but it benefits them to on the one hand get everybody else to slow down while they're doing whatever they're doing.

[Ann]

Octopus thought experiment author Dr. Emily Bender again.

[Margaret Mitchell]

But also it benefits them to market the technology as super powerful in that way and it definitely benefits them to distract the policymakers from the harms that they are doing.

[Ann]

It'd be nice to think that billionaire Elon Musk was calling for an industry wide pause in building large AI systems for all the right reasons. A pause that never came to be. By the way, it's worth pointing out that when the billionaire took over Twitter and turned it into X, one of the first things he did was fire the ethics team. And even though Elon Musk says he left and sued the ChatGPT company OpenAI over safety concerns, company emails have surfaced that reveal the more likely reason he left is that he fought with folks internally to try and make the company for profit to better compete with Google. Ethicists are concerned they're outnumbered by the apocalypse people and they think a lot of those people are in it to maximize profit, not maximize safety. So how did we get here? Why? Why is the industry not focusing on AI harms today and focusing instead on the risk of AI apocalypse?

[Margaret Mitchell]

There's an enormous amount of money that's been collected to fund this weird AI research.

[Ann]

Why do you think the resources are going to those long term like hyper intelligent AI concerns?

[Emily Bender]

Because you have very powerful people who are posing it, people who control powerful companies and people with very deep pockets. And so money continues to talk.

[Joy Buolamwini]

It seems to be like funding for sort of like fanciful ideas. Right? It's, it's like almost. It's almost like a religion or something where it requires faith that good things will come without those good things being clearly specified.

[Ann]

People wanting to be told what to do by some abstract force that they can't interact with particularly well. It's not new. ChatGPT gives you authoritative answers. Erosions of autonomy. Like a God.

[Julia Longoria]

Yeah, it's like, really interesting to take these philosophies apart. I would argue they trace back to a large degree to religious thinking. But that might be another story for another day.

[Ann]

Next time on Good Robot.

[Margaret Mitchell]

Good Robot was hosted by Julia Longoria and produced by Gabrielle Burbet. Sound design, mixing and original score by David Herrmann. Our fact checker is Kaitlyn Penze Mook. The show was edited by Kathryn Wells and me, Diane Hodson. If you want to dig deeper into what you've heard, you can check out Dr. Joy Buolamwini's book Unmasking AI or head to vox.com goodrobot to read more Future Perfect stories about the future of AI. Thanks for listening.

[Ann]

Sa.