Chapter 1: What deeper shift does Superfocus represent in AI technology?
We have agents that are capable of replacing, you know, something like 80%, maybe 90% of the calls that come into a call center.
So what is it like when you go out there and you show this technology, particularly to those frontline operatives?
I think they feel about it the way that someone who was a blacksmith or a buggy whip maker felt when they saw their first automobiles rolling down the street, right? They could tell something was happening and they don't like it, but what else can they do?
I think every person who's developing software knows that getting to the first 70% is trivially easy. It's the climb up the last 30%.
You don't just want to turn this thing on and then discover like, oh, overnight it got into some bad loop and pissed off 100,000 customers, right?
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Chapter 2: Will AI models ever overcome hallucination issues?
One of the things that I think the general public doesn't understand is like, how much rigorous statistical testing is required to know what are the tail risks associated with a deployment of autonomy? That could be automated customer service. That could be a driverless vehicle.
Chapter 3: How can AI agents replace a significant portion of call center work?
Let's talk about China. There is this tournament-style competition between mayors of cities.
Chapter 4: What is the experience of customer support workers with AI technology?
They act a little bit like, you know, mini Y Combinator bosses or mini venture capitalists.
Combined with the people there are just more pro-technology. You say like, oh, we're going to have AI at the hospital. The average Chinese person is not thinking like, Oh, what about my privacy? Or what if the AI makes a mistake?
The Chinese people are more like, this is awesome, man. In a future that is full of AI agents, what's something that you might end up doing more of? Again, I'm going to tell you something incredibly crazy. You can break the news here.
Chapter 5: How do China's mayors function like mini CEOs in technology adoption?
Today we are joined by physicist and entrepreneur Steve Hsu. The most loyal Exponential View listeners may remember he was on my podcast back in 2019. Feels like a century ago so much has happened. We talked about biotechnology and the promise of genomic sequencing and I actually had to go and buy a maths book after that podcast to make sense of some of the answers.
So today we're talking about something else, AI agents and what might come next. Steve, it's great to have you here. I want to start with what you're building.
Chapter 6: What factors will determine success in the AI race?
You've co-founded a company called Superfocus, which you've described as solving some of the key limitations in today's LLMs. Give us a quick tour. What are you building and what is the deeper shift that it represents?
So I think everyone who's experimented with large language models knows that they hallucinate. So they will sometimes give you a very confident but completely wrong answer. And that answer will always be typical of the kinds of things that it saw in its pre-training. So if you ask an LLM, is there a flight that reaches Paris from London that lands around four o'clock?
It will give you an answer, but it might not be real.
Chapter 7: How will open source influence the future of AI deployment?
It will seem like a real answer, like, oh, Air France has one that lands at 412, right? And maybe in the past it did, but maybe right now it doesn't, right? Typical large language model hallucination.
Chapter 8: What misconceptions exist about AI tail risks in public understanding?
And so our startup realized early on that in order to make practical applications from these AIs, one would have to solve this problem. One would have to control both the behavior of the AI agent, if you want to call it an agent, and also control its fact base, the sort of core knowledge base that it uses to answer questions or conduct operations.
And that cannot be drawn from the pre-training data. There's just too much junk in the pre-training data. Or even if it's not junk, it may not be relevant to the specific problem that the AI agent is trying to solve for you.
Right. So you're tackling that fundamental limitation in the way that LLMs are built. I've had that experience. The way I think about this is that they are often accurate at a conceptual level, but not necessarily accurate at the word or token level. So they'll sometimes say of me... that I went to Cambridge University, which if you're an alien sitting on Neptune, I went to Oxford.
Well, Cambridge is kind of the same thing. But they don't ever say, I went to West Point and I did SEAL training. I mean, I've never had as wild a hallucination as that. And that really is about the way in which they represent the world in this complex, high-dimensional space, isn't it?
Yes. So there's something called the embedding space. The models are actually working in this abstract space, which is literally a space of concepts. Oxford and Cambridge are very, very close in that concept. And maybe there's no other school that's exactly in that space. But, you know, sometimes the details matter.
So if I'm a fundraiser for Cambridge, I actually care whether you went to Cambridge or Oxford in deciding whether to contact you. Right.
Right.
Yeah, so the details matter, but in some sense, they've still got a useful model of the world that perhaps loses some of its resolution if we push really, really hard for it to be precise. So how do you go about resolving that?
Right, so we actually build systems in which we embed the language models in a larger software platform. And the language model itself, we generally are using mainly only for its language and to some extent reasoning abilities. But the knowledge base is stored separately. The fancy way we describe it is as a kind of attached memory for the AI. The AI can rely on that attached memory.
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