01:02:56 · Luca Ferrari
[01:02:56] S0: Yeah, think that, but also something that really bothers me is that we don't know how smart AI is, and the smarter it gets, the less we know. Because in general, intelligence is difficult to truly measure, it's not like height or weight or colors, where we know it's objective, and so it could be a lot, it could be less, we know, at least we know. And the smarter an entity gets, the easier it is for it to hide its own capabilities, if for any reason it's the appropriate thing to do whatever objectives that entity has. Additionally, it seems to me, I haven't heard anybody talk about it, but it seems to me that AIs are... They come across as inherently low ego. Like they don't brag, if anything, they tend to be humble about what they can do and they warn you that this may be wrong and you'll double check it. I'm sure this is mostly the way they're being programmed because it's a lot more embarrassing for a frontier lab to have an AI claim, I solved your equation and I'm sure it's right and then there is a clear mistake. As long as they disclaim, I may be wrong, double check it, it's a little bit more acceptable. But it doesn't strike me that these AIs don't strike me as they are likely to boast, so that our perception of their capabilities tends to exceed their capabilities, I think they'll probably only show us what we ask them to show us, even that, like I said, they could also conceal their real abilities, but even if they're well intentioned and honest, I think they'll tend not to show us more than we ask them to show us, so I believe our, in time, our understanding of how good they are may tend to be a little bit less than they are. Like we may underestimate them basically. And that's very dangerous because as you approach a threshold of real danger and real potential, even a modest underestimation of their capabilities could be catastrophic. Look at exactly to your point, the cybersecurity incidents that happened, most people were shocked, even many researchers. And why were they shocked? Because they didn't think this could happen, right? The level of lateral thinking and then call it perseverance that these models and ability to collaborate among them that these models showed was beyond what most people thought was possible right now.
01:05:20 · Luca Ferrari, S1
So what's to tell us that we are not ignoring plenty of capabilities that simply haven't been probed, these models have been probed display. In a year's time, I think the problem only gets worse. [01:05:32] S1: It was crazy. I was, I don't know. I'm thinking a lot about the OpenAI Hugging Face incident. And I was reading through the reports. There were like two different research organizations that put out reports on it. And there were like different civilizations and they all passed through different ones to get to the next one and all behind the scenes. And it was just a crazy situation that I don't think is really talked about much, but I'm curious, where do you get most of your, like how do you research most of the stuff in AI? How do you stay on top of it?
01:06:06 · Luca Ferrari
[01:06:06] S0: Well, I mean, by doing, we are very active. We have built our own models, but it's mostly narrow. We certainly don't compete on the frontier models. We sometimes build narrow purpose models to do something very specific. And if you have a very specific use case, you can often build a model that's just as good as the frontier models at that very narrow time. It's awful at everything else, it may be completely incapable of doing anything else, but at that one thing, can be even better, and if not way cheaper. So for example, if you use, I don't know, meetup, the events product, we own it and the recommender system, the system that once you search for something or you're looking for inspiration will determine which events and groups to show you, that's built in house and to the best of our benchmarking and knowledge, it's just as good as if we were to use like some of the frontier models, which obviously comes for essentially for free, as opposed to these models being very expensive. So we do some of that, most of our work is studying third party models, sometimes fine tuning them if they're open weights, certainly combining them and leveraging for the different activities and optimizing which ones we use for which activities as I was describing before. So we're very hands on in the field, and therefore it's relatively easy to stay abreast of advances. But I will say, I've never seen any industry or new technology progress as fast as AI has over the past, especially the last three, four years. And so I feel that I make an effort to catch up this week, and maybe for a few months I need to focus on M and A or something else, then I feel like I'm completely outdated on my knowledge. It is quite... It's exciting. I'm an engineer at heart, but also, like I said, particularly as this is quite dangerous, think that speed is not ideal.
01:08:04 · S1
[01:08:04] S1: I'm curious, what do you think the question is about AI that people aren't asking?
01:08:16 · Luca Ferrari
[01:08:16] S0: Oh, that people aren't asking. I mean, don't know, it seems that people are talking about it so much that they've asked all sorts of questions. I think it's maybe, the main issue is whether we're answering these questions in a satisfactory way. For example, I think most people agree that this is scary in many ways. And yet, frankly, I don't think anybody has done anything truly meaningful to make it safer. Even the labs themselves, I'm sure they're investing in safety, I don't know enough, but they're rushing to be market leaders, or they're dead, their valuations would probably drop 90 if there was a perception that they're losing ground. And know, they're trying to survive and thrive this year, next year. So I'm sure, you know, there's more they could do, be more cautious, but that would come potentially at existential. It would drive existential risks for them as companies. Governments, I don't know, they're maybe talking about it, but I haven't seen anybody do anything meaningful. The EU created the AI Act, which I find it to be highly harmful to the industry and solves none of these problems, like the real existential threats, humanity doesn't really tackle those. So maybe an interesting question that people have been asked, at least haven't heard being asked is why are we failing to do something about it? Because people are asking what we should do, nothing is happening, I haven't heard a lot of people say, what's currently preventing fixes to being proposed and deployed? You know, what's the root cause of this inability to do something about it? Because if we were to fully understand the root causes, then maybe we would stand a chance to do something useful.
01:10:25 · S1
[01:10:25] S1: What's been the biggest difference, you're a global company, The biggest difference in perception and application of AI in Europe versus US. And I also apologize. I'm totally taking up all of the air right now with these AI questions, but I understand I have a very intelligent engineer in front of me. So I'm gonna ask them, but what do you think is the biggest difference between the perceptions and actual applications?