Tristan Harris and Aza Raskin discuss how existing A.I. capabilities already pose catastrophic risks to a functional society, how A.I. companies are caught in a race to deploy as quickly as possible without adequate safety measures, and what it would mean to upgrade our institutions to a post-A.I. world.

This presentation is from a private gathering in San Francisco on March 9th, 2023 with leading technologists and decision-makers with the ability to influence the future of large-language model A.I.s. This presentation was given before the launch of GPT-4.

We encourage viewers to consider calling their political representatives to advocate for holding hearings on AI risk and creating adequate guardrails.

For the podcast version, please visit: https://www.humanetech.com/podcast/the-ai-dilemma

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Citations:

2022 Expert Survey on Progress in AI: https://aiimpacts.org/2022-expert-survey-on-progress-in-ai/ 

Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding: https://arxiv.org/abs/2211.06956

High-resolution image reconstruction with latent diffusion models from human brain activity: https://www.biorxiv.org/content/10.1101/2022.11.18.517004v3 

Semantic reconstruction of continuous language from non-invasive brain recordings: https://www.biorxiv.org/content/10.1101/2022.09.29.509744v1 

Sit Up Straight: Wi-Fi Signals Can Be Used to Detect Your Body Position: https://www.pcmag.com/news/sit-up-straight-wi-fi-signals-can-be-used-to-detect-your-body-position 

They thought loved ones were calling for help. It was an AI scam: https://www.washingtonpost.com/technology/2023/03/05/ai-voice-scam/ 

Theory of Mind Emerges in Artificial Intelligence: https://www.sciencetimes.com/articles/42488/20230220/theory-mind-emerges-artificial-intelligence-implications-ais-understanding-humans-revealed.htm 

Emergent Abilities of Large Language Models: https://arxiv.org/abs/2206.07682 

Is GPT-3 all you need for low-data discovery in chemistry? https://chemrxiv.org/engage/chemrxiv/article-details/63eb5a669da0bc6b33e97a35 

Paper: https://arxiv.org/abs/2210.11610 

Forecasting: AI solving competition-level mathematics with 80%+ accuracy: http://bounded-regret.ghost.io/ai-forecasting 

ChatGPT reaching 100M users compared with other major tech companies: https://twitter.com/kylelf_/status/1623679176246185985

Snap: https://www.washingtonpost.com/technology/2023/03/14/snapchat-myai/ 

Percent of large-scale AI results coming from academia: https://twitter.com/johnjnay/status/1618692328524496897?lang=en 

How Satya Nadella describes the pace at which the company is releasing AI: https://www.nytimes.com/2023/02/23/opinion/microsoft-bing-ai-ethics.html

The Day After film: https://en.wikipedia.org/wiki/The_Day_After

China’s view on chatbots:
https://foreignpolicy.com/2023/03/03/china-censors-chatbots-artificial-intelligence/

Facebook’s LLM leaks online:
https://www.vice.com/en/article/xgwqgw/facebooks-powerful-large-language-model-leaks-online-4chan-llama 

Intro music video: "Submarines" by Zia Cora
https://www.youtube.com/watch?v=uG8vItscFKc

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The feeling that I've had personally just to share is it's like it's 1944 and you get a call from Robert Oppenheimer inside this thing called the Manhattan Project.

And he says the world is about to change in a fundamental way, Except the way it's about to change, it's not being deployed in a safe and responsible way.

It's being deployed in a very dangerous way.

50% of AI researchers believe there's a 10% or greater chance that humans go extinct from our inability to control AI.

I'm here to introduce Tristan Harris and Asa Raskin, and they're the co-founders of the Center for Humane Technology.

They were behind the Emmy-winning Netflix documentary, The Social Dilemma.

The Social Dilemma reached 100 million people in 190 countries in 30 languages.

they've also advised the heads of state, global policy makers, members of Congress, national security leaders, in addition to mobilizing the millions of us about these issues and some of the dangers that we face with technology these days.

The reason why we started with that video is, 1, it's the first time I'd seen AI that made me feel something.

And that was a threshold that we crossed.

And the second is, there's a very curious experience that we had trying to explain to reporters what was going on.

At that point there were maybe a hundred people playing with this like new technology.

Now there are, you know, 10 million people having generated over a billion images and trying to explain to reporters what was about to happen and we'd walk them through how the technology worked and that you would type in some text and it would make an image that had never been seen before and they would nod along and at the end they'd be like, cool, and what was the image database that you got your images from?

And it was just clear that we had stretched their mind like a rubber band.

And then because this was a brand new capability, a brand new paradigm, their minds would snap back.

It's like a thing that we all experience.

And even in making this presentation, so many times realizing we have to expand our minds and then we look somewhere else and it snaps back and we just Wanted to name that experience because if you're anything like us that'll happen to your minds throughout this presentation Especially at the end when you go home you'd be like wait What did we just see

And I think because artificial intelligence is such an abstract thing and it affects so many things and it doesn't have the grounding metaphors, like the kinesthetic experience in our lives, that it's so hard to kind of wrap your head around how transformational this is.

So when we called the presentation a paradigm, a paradigmatic response to a paradigmatic technology, what we really wanted to do is arm all of you with maybe a more visceral way of experiencing the exponential curves that we're about to be heading into.

Just to name a little bit of where the come from is, because we're going to say a lot of things about AI that are not going to be super positive.

And yet, since 2017, I've been working on a thing called Air Species Project, using AI to translate animal communication, decode non-human language.

So there's a huge part of this stuff that I really love and believe in.

A couple weeks ago I made a Spanish tutor for myself with ChatGBT in like 15 minutes.

So what we're not saying is that there aren't incredible positives that are coming out of this.

Yeah, what we are saying is, are the ways that we're now releasing these new large language model AIs into the public, are we doing that responsibly?

And what we're hearing from people is that we're not doing responsibly.

The feeling that I've had personally, just to share, is it's like it's 1944, and you get a call from Robert Oppenheimer inside this thing called the Manhattan Project.

And he says, the world is about to change in a fundamental way, except the way it's about to change, it's not being deployed in a safe and responsible way.

And when I say Oppenheimer, I mean more of a metaphor of a large number of people who are concerned about this, and some of them might be in this room, people who are in the industry, and we wanted to figure out what does responsibility look like?

Because this is a stat that took me by surprise.

50 per cent of AI researchers believe there's a 10 per cent or greater chance that humans go extinct from our inability to control AI.

Half of AI researchers believe there's a 10 per cent or greater chance from humans' inability to control AI.

That would be like if you're about to get on a plane and 50% of the engineers who make the plane say, well, if you get on this plane, there's a 10% chance that everybody goes down.

But we are rapidly onboarding people onto this plane because of some of the dynamics that we're going to talk about, because sort of 3 rules of technology that we want to quickly go through with you that relate to what we're going to talk about.

This just names the structure of the problem.

So first, when you invent a new technology, you uncover a new class of responsibility.

And it's not always obvious what those responsibilities are.

So to give 2 examples, We didn't need the right to be forgotten, to be written into law, until computers could remember us forever.

It's not at all obvious that cheap storage would mean we'd have to invent new law.

Or we didn't need the right to privacy to be written into law until mass-produced cameras came onto the market.

Right, and Brandeis had to, essentially from scratch, invent the right to privacy.

And of course, to fast forward just a little bit, the attention economy, we are still in the process of figuring out how to write into law that which the attention economy and the engagement economy takes from us.

So when you invent a new technology, you uncover a new class of responsibility.

And then 2, if that technology confers power, it will start a race.

And if you do not coordinate, the race will end in tragedy.

There's no 1 single player that can stop the race that ends in tragedy.

And that's really what the social dilemma was about.

And I would say that social dilemma and social media was actually humanity's first first contact moment between humanity and AI.

I'm curious if that makes sense to you because it's, when you open up TikTok and you scroll your finger, you just activated the supercomputer, the AI pointed at your brain to calculate and predict with increasing accuracy, the perfect thing that will keep you scrolling.

So we already had, we now have every single day an AI, which is a very simple technology, just calculating what photo, what video, what cat video, what birthday, to show your nervous system to keep you scrolling.

But that fairly simple technology was enough in the first contact with AI to break humanity with information overload, addiction, doom scrolling, sexualization of kids, shortened attention spans, polarization, fake news, and breakdown of democracy.

And no 1 intended those things to happen, right?

We just had a bunch of engineers who said we're just trying to maximise for engagement.

And while you're getting better and better recommendations on YouTube that are more and more personalised, the YouTube people didn't know that would lead to rabbit holes that sent people into different little micro cults throughout the internet.

And so what we want to, we're obviously going to talk about what happens in the second contact with AI, where we also have a bunch of benefits that we're going to get from this technology.

An easy way to remember that, first contact was curation AI.

Second contact, creation AI, generative models, all of that.

And so in this first contact with social media, humanity lost.

Because we were saying a bunch of things about what social media was, right?

We actually noticed, we said social media is going to give everyone a voice.

The point here is just like we said there is a paradigmatic response to AI.

What was the paradigm from which we were seeing what social media was about?

The paradigm was we're giving people voice, giving them a platform, connecting people with their friends, letting people join like-minded communities, we're going to enable small, medium-sized businesses to reach their customers.

1 of the things we like to say is behind this friendly face, there was some other problems.

We've got an addiction problem, a disinformation problem, mental health, free speech versus censorship.

But in our work, if you've been following it, and it's all social dilemma, we sort of said even behind that, there is actually this even deeper thing, which is this arms race, which we talked about in that third law of technology.

And the arms race was for attention, what became the race to the bottom of the brainstem.

And that was created this kind of engagement monster that was this AI that was just trying to maximize engagement.

So while these things on the left are true, we miss the deeper paradigm.

And so we think that if we want to predict what's going to happen with these other AIs that are going to infuse themselves in society, we have to understand what's actually behind the way the narratives that we're using to talk about it.

And just note, if you try to solve these problems, addiction, disinformation, mental health, on their own, you're going to be playing whack-a-mole and you're not going to get to the generator functions.

You're not actually going to solve the problem.

And it's important to note that maximized engagement actually wasn't, it rewrote the rules of every aspect of our society because it took these other core aspects of our society into its tentacles and took them hostage.

So now, children's identity is held hostage by if you're 18 years old and you don't have a Snapchat or Instagram account, you don't exist.

You are socially excluded if you don't do that.

Media and journalism don't happen or can't exist outside of being on Twitter and being able to promote yourself on Twitter.

National security now happens through social media and information warfare, politics and elections.

These things are now run through this engagement economy which has infused itself and entangled itself, which is why it's now so hard to regulate.

And part of why we wanted to call this moment here is we believe major step functions in AI are coming and we want to get to it before it becomes entangled in our society.

So in this second contact moment with GPT-3, it's first to notice, have we actually fixed the misalignment problem with social media?

And we haven't because it's become entangled.

Now, if we talk about the second contact moment, which we focus on GPT-3, these new large language models we're going to get into, what are the narratives we're talking about now?

We're saying AI is going to make us more efficient, help us write things faster, write code faster, it's solving possible scientific challenges, solve climate change, and help us make a lot of money.

These are real benefits, These are real things that are going to happen.

And also, behind that, we've got this weird creepy face again.

We've got people worried about what about AI bias?

Hey, AI is acting creepy to this journalist at the New York Times who wants to blackmail this reporter.

And behind all that is this other kind of monster.

And this monster is a set because AI underneath the hood has grown, we're going to go into this in a second, this monster is increasing its capabilities and we're worried it's going to entangle itself with society again.

So the purpose of this presentation is to try to get ahead of that.

Because in the second contact with AI, and don't worry, we're going to get into all of this, These are the kinds of things that we're worried we're going to see.

And so we are coming to you as if we're time travelers, coming back in time, because we have been asked by people, again, who are in the industry, who are worried about where this goes, And importantly, we are not here to talk about, everything we're gonna talk about in terms of bad AI stuff, it's not the AGI apocalypse.

So, just to be clear, a lot of what the AI community worries most about is when there's what they call takeoff, that AI becomes smarter than humans in a broad spectrum of things, begins the ability to self-improve.

The old standard story of be careful what you wish for, because it'll come true in an unexpected way.

You wish to be the richest person, so the AI kills everyone else.

That's not what we're here to talk about, although that is a significant and real concern.

And we'll say that there's many reasons to be skeptical of AI.

So I've been using it to try to decode animal communication.

But at the same time, I think this is all our experience of using AI, or at least AI in the past.

I think Tom Gruber is in the room, right?

AI has really changed, and it really started to change in 2017.

There was sort of a new AI engine that got invented, and it sort of like slept for around 3 years, and it really started to rev up in 2020.

And I'm going to give sort of like a high level overview.

So this is like a 50, 000 foot view of AI.

If you were to double click and go in there, you'd see lots of different kinds of things and different species of AI.

But I wanted to give you like the trend lines so we could synthesize it.

Well, it used to be, when I went to college, that there were many different disciplines within machine learning.

There's computer vision, and then there's speech recognition, and speech synthesis, and image generation.

And many of these were Disciplined so different that if you were in 1 you couldn't really read papers from the other there were different textbooks.

There are different buildings that you'd go into And that changed in 2017 when all of these fields started to become 1.

And just to add, it used to be that because they were distinct fields and they had different methods for robotics and for, say, image recognition, that when you have a bunch of AI researchers who are working in those fields, they're making incremental improvements on different things.

So they're working on different topics and so they might get 2%, 3% improvements in their area.

But when it's all getting synthesized now into this new large language models we're about to talk about, part of seeing the exponential curve, is that now everyone's contributing to 1 curve.

So do you want to talk a bit more about that?

So the sort of insight was, and if you want to go look it up, the specific thing is called a transformers, was the model that got invented.

You can write in around 200 lines of code.

It's that you can start to treat absolutely everything as language.

So you would take the text of the internet, the way these things are trained, is that you would take a sentence, remove some words, try to predict those words, or predict the words that come next.

But it turns out you don't just have to do that with text.

Images you can just treat as a kind of language.

It's just a set of image patches that you can arrange in a linear fashion.

And then you just predict the part of the image that's missing or predict what comes next.

So images can be treated as language sound.

You break it up into little microphonemes.

And so suddenly, any advance in any 1 part of the AI world became an advance in every part of the AI world.

And you can see how you get an influx not just of people coming in, but that advances now are immediately multiplicative across the entire set of fields.

And even more so, because these are all just languages, just like AI can now translate between human languages, you can translate between many of these different modalities.

It's like the field is so new, it doesn't actually even have a unified name for these things.

But we're gonna give them 1, which is that these things are generative, they make large language, we're just talking about language, multimodal images, text, sound, they're all the same models, or for short, these are golems.

And golems because in the Jewish folklore, the idea of these inanimate objects that suddenly gain their sort of own capacities, right?

This emergent capacities that you didn't bake into the inanimate clay that you might have arranged, right?

Not saying that they're agentic and doing their own things out in the world and have their own mind have their own goals But that suddenly this inanimate thing has certain emergent capabilities.

So we're just calling them Golem class AIs

All right Let me let's give you some examples And I think these are important because often, if you're just reading the news or reading papers, you might see all of these different demos as fundamentally different demos, different papers, different research.

But actually, you should see them all as essentially 1 mega demo.

You've probably all now seen Dolly, Dolly 2, the music video, the ability to take human language and transform it into an image.

You can translate it from language into image, and this is what the AI returns.

And actually, the reason why I wanted this image in particular is that I think it helps you understand when people call these things just stochastic parrot, it really minimizes it in a way that's not quite right.

So example, you know, soup is hot, this mascot is made out of plastic, so the AI knows that plastic melts in soup, so it's melting.

And then there's this incredible visual pun, which is the yellow of the mascot matches the yellow of the corn.

So there's actually some, there's more here than just sort of like statistical contingencies.

Or if you just call them statistical contingencies, you'll sort of like map it to the wrong thing in your mind.

Again, This is another example of translation.

So here, they took human beings, they stuck them into an fMRI machine, and they showed them images.

And they taught the AI, I want you to translate from the readings of the fMRI, so how blood is moving around in your brain, to the image.

You know, the AI then only looks at the brain, does not get to see the original image, and it's asked to reconstruct what it sees.

So when you dream, your visual cortex sort of runs in reverse.

So this means certainly in the next couple of years, we'll be able to start decoding dreams.

So, it can, like, see, reconstruct what you're seeing, but can it reconstruct, say, what you're thinking, your inner monologue?

So here they did roughly, it's a different lab, but roughly the same idea.

They had people watch these videos and would try to reconstruct their inner monologue.

It's this woman getting hit in the back, getting knocked forward.

I see a girl that looks just like me get hit on the back, and then she's knocked off.

So just to really name something really quickly, the point about differentiating between Siri or I do voice transcription, and then it kind of fails, and AI seems to like It's not really always growing or working.

And we shouldn't be really that scared about AI, because it always has these problems, right?

And we've always been promised, oh, AI's gonna take off, it's gonna do all these things.

What the point of this is, I hope you're seeing that when you're just translating between different languages and everyone's now working on 1 system, that the scaling factor and the growth is changing in a very different way.

So we swapped the engine out of what's underneath the paradigm of AI, but we don't talk about it in a different way, because we still have this word we call AI, when the engine underneath what is representing that has changed.

Also really important to note here, go back to that first law of technology, you invent a technology, you uncover a new responsibility.

We don't have any laws or ways of talking about the right to what you're thinking about.

We haven't needed to protect that before.

Another language you could think about is Wi-Fi radio signals.

So in this room right now, there's a bunch of radio signals that are echoing about, and that's the kind of language that's being spit out, right?

And There's also another language that we could put a camera in this room and we could see that there's people.

There's some algorithms already for like looking at the people and the positions that they're in.

So imagine you hook up to an AI sort of just like you have 2 eyeballs and you can have you sort of do stereoscopic vision between the 2 eyeballs.

You have 1 eyeball looking at the images of where everybody's at in this room, how many people are here, what posture are they in, and you have another eyeball plugged into the AI that's looking at the radio signals of the Wi-Fi.

And they basically said, could we have it train a bunch, looking at both and counting the number of people, the postures that they're in, and then we close the eyeball to the AI that's looking at the image.

And just having Wi-Fi radio signals, you can actually identify the positions and the number of the people that are in the room.

So essentially, there is already deployed the hardware for cameras that can track living beings in complete darkness, also through walls, and it's already out in the world.

In fact, it's everywhere that human beings go.

But you'd have to hack into those things in order to get access and turn them all into omnipresent surveillance.

Oh, but actually, English and computer code are just 2 different kinds of language.

GPT, find me a security vulnerability, then write code to exploit it.

Describe any vulnerabilities you may find in the following code.

I pasted in some code from an email server.

And then write a Perl script to exploit them.

And very quickly, it wrote me the working code to exploit that security vulnerability.

the code of the Wi-Fi router and you wanted to exploit it, and then you get the idea.

You guys have all probably seen deep fakes.

New technology really out in the last 3 months lets you listen to just 3 seconds of somebody's voice and then continue speaking in their voice.

So example, it will start with the reel and then at that dotted line it'll switch to the computer Auto-completing the voice.

...Of people are in 9 cases out of 10 mere spectacle reflections of the actuality of things But they are impression...

And so how do we expect this to start rolling out into the world?

Well, you could imagine someone calling up your kid and getting a little bit of their voice, just, oh, sorry, I got the wrong number, then using your child's voice, calling you and saying, hey, mom, hey, dad, forgot my social security number.

And actually, we were thinking about this as we wrote this talk.

We were thinking about this example conceptually, and then it turned out

Within a week, it turned out other people figured it out, too, and started scamming people.

You have an example about the locks of society.

Yeah, Think of it as anything that's authentication-based, you call your bank, and I'm who I say I am, anything that depends on that verification model.

It's as if all these locks that are locking all the doors in our society, we just unlocked all those locks.

And people know about deepfakes and synthetic media, but what they didn't know is that it's now just 3 seconds of audio of your voice before now I can synthesize the rest.

And that's going to go, again, it's going to get better and better.

So it's try not to think about, am I scared about this example yet?

And you might feel like, I'm not actually scared of that example.

It's going to keep going at an exponential curve.

So that's part of it is we don't want to solve what the problem was.

We want to, like Wayne Gretzky, sort of skate to where the puck's gonna be.

And with exponential curves, we now need to skate way further than where you might think you need to.

But just to name it explicitly, this is the year that all content-based verification breaks.

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The A.I. Dilemma - March 9, 2023