Chapter 1: What is the main topic discussed in this episode?
Hi, Rory here. Matt Clifford and I are back with another episode in The Rest Is AI.
Chapter 2: Is AI just another normal technology?
This week, we're back to a simple but pretty uncomfortable question, which is, might AI just be a normal technology? something that maybe really doesn't deserve great fear and frenzy. And our guest today is Arvind Narayan. He's the director of Princeton's Center for Information Technology Policy.
Chapter 3: What does Arvind Narayanan think about AI's reliability?
He's a really interesting voice. He's a challenge to Yoshua Bengio, who you heard last week, because he thinks that it's not very likely or that actually we're over-focusing on the end of humanity existential threat. But at the same time, he's a challenge to a lot of the people that are hyping the technology because he's saying, in many ways, this stuff is much less reliable than we think.
And even once it gets more reliable, it's going to take decades for some of these things to be adopted, not a few months. And therefore, the change that AI is going to bring is going to be much more gradual. So it's probably a very, very sane, thoughtful, challenging voice, one that maybe isn't heard as much because it doesn't necessarily suit either the end-of-the-world-is-nigh people or the
AI is going to change the universe tomorrow, people, because he's suggesting that a lot of the issues are just around how humans do or do not adopt technology. Here's a taster. And to listen to the full episode, sign up at therestispolitics.com.
Chapter 4: How does the hype around AI compare to its actual capabilities?
There's a really interesting thing as an outsider that one observes, which is one will get Elon Musk saying there's a 20% chance it's going to destroy humanity, and Sam Altman says it's going to end the world, but in the meantime, it's going to lead to some great companies with great machine learning.
And then suddenly you'll get, you know, Gary Marcus will pop up and say, this is all completely overblown. There's only a 1% chance it's going to eliminate everybody, right? But of course, if one thinks about this, I think Joshua's point was it doesn't matter if it's a 1% chance, a half percent chance, a 0.1% chance. What the hell are you guys thinking about? I mean- You're gambling, right?
At some level, you're taking a risk that you wouldn't take with nuclear waste in your back garden. You wouldn't be reassured for me to say, don't worry, there's only a 0.1% chance that this nuclear waste is going to wipe out your family. You'd be like, what the hell are you doing?
Chapter 5: What are the risks associated with AI according to experts?
Stop, right?
One thing I strongly believe is that we should not be thinking about this in terms of probabilities. At the moment, you're arguing about what the probability is. You're already down a very confusing path that can only lead to, I think, misleading guidance. So I've looked at the most sophisticated efforts that we have for estimating these probabilities.
Chapter 6: Why should we not think in terms of probabilities when assessing AI risks?
It was led by the Forecasting Research Institute. I actually work with these folks. I know them well. They're incredibly smart, and they did... a really well thought out effort to get dozens of expert forecasters to discuss, try to change each other's minds and provide these probabilities. And they put out a 753 page report. And I've read that 753 page report.
And you could have a room with these so-called super forecasters debating, and you could have a room with a bunch of people who are high and debating what the future of AI is going to be, and you can't tell the difference. And this is no slight to the super forecasters. whom I know, they're incredibly smart people.
But the thing is, we have no empirical basis for predicting what these probabilities might be. The arguments that people are giving, it's things like, oh, you know, AI might decide to colonize space instead of Earth. So even if we had super intelligent AI, maybe the probability is not as high as we think.
Or someone else thinks, oh, AI might decide that killing all humans will make the planet cooler, and it helps computer chips work better, and so maybe it will decide to kill all humans.
Chapter 7: What are the implications of having an authoritarian world government for AI regulation?
And so they're listing a bunch of reasons like this, assigning some numbers to each of them, and then multiplying all of them at the end. So this is the best method that we have. So these probabilities are all bogus, and that's my strongly held view. We should not think in terms of probabilities. I do think the risks are potentially real.
I'm not advocating for ignoring the risks, but I think the right response cannot be, let's try to stop all this. There are two big problems with that. One, it's just not going to work. The only way it could work is if you have an authoritarian world government that can control every AI developer everywhere.
Can I pause on that, Arvind, for a second?
Chapter 8: How can policymakers effectively regulate AI technology?
Because that's an empirical claim. I mean, at the moment, these large language models basically can only be run by some of the largest, wealthiest companies on earth. with enormous data centers, with enormous compute power.
So it seems plausible at the moment that if President Trump, driven by Christian nationalists, and Xi Jinping wanted to simply shut down the large language models of two Chinese companies and a small handful of American companies, we would cease to have these LLMs operating pretty quickly.
This is not true at all. It might be true that absolutely the most powerful Frontier models can only run on powerful GPUs, but you have slightly smaller models that are maybe one step below that can run on consumer-grade hardware. And the cost of running these models is dropping by something like somewhere between a factor of 10 to a factor of 100 every year.
the cost is dropping very rapidly, both because the hardware is getting faster per dollar and because algorithmic improvements are allowing us to squeeze more juice out of smaller models.
Again, I suppose to push this argument to the next phase, the claim only needs to be that the existential risk is posed by the frontier models. And if one could shut down the full infrastructure that powers the frontier models, then one has less to worry about from the models that you're talking about.
Now, again, I completely disagree with this. I think historically, this is very easily falsified. When OpenAI built GPT-2, which was two generations before ChatGPT, which was when the world started noticing, they thought that model was so dangerous that they were not going to release it for people to download and use.
And that's something that my grad students can build today just for fun and learning in a day or two. And so historically, when we look back, our ideas around what constitutes the threshold level of danger have kind of been comically off. And I don't think there is really any clear relationship
between the power of a model in terms of how computationally heavy it is and what dangerous things it might potentially be able to do in terms of enabling cyber attack, in terms of various things that worry about in terms of bio risk.
Actually, some of those models can be much, much smaller and faster than these large language models because those biological capabilities are not about language at all.
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