The Exchanges

Q I'm not gonna get upset. From a layperson's point of view, it does feel like, oh, okay, now I'm talking to AI. Now AI understands what I think and can actually draw it. Now AI can take my voice and start talking on its own. So why isn't that a step towards the human level intelligence?

A Because the understanding that those, the current systems have of, uh, you know, the underlying reality that language expresses is extremely shallow. So those systems have only been trained with text, uh, a huge amount of text. Um, so they can regurgitate texts that they've seen and, you know, interpolate for new situations, uh, things like that. They can, uh, uh, you know, even produce code and, and, and stuff like that. But, um, they do not understand, they have no knowledge of the underlying reality. They have no, they've, they've never had any contact with, you know, the physical world. Um, you know, if I, uh, Um, if I take a piece of paper, let's say, looking for a piece of paper, um, and I, you know, I, I hold it like this, right? And I tell you, uh, I'm going to lift my, my hand from one side. You can exactly predict what's going to happen.

AI assessment note: “understanding that those, the current systems have... is extremely shallow”

Q Cause Danny brought a list of questions that he wants to ask Jan and I'll just take back seat for that. Uh, but why don't we start here? Jan, I'd love to hear a little bit more about, um, if you could briefly share your ambition to build a thinking machine and like, what does that actually look like? Do you want to replicate the human mind one for one?

A No, I don't want to replicate the human mind. I want to understand intelligence. Um, uh, I think, you know, one of the most, uh, fascinating questions of our time, scientific questions of our time is, uh, what is intelligence? How does the brain work? Uh, you know, the other two fascinating questions of our times are, are what is life all about and how does the universe, how does the universe work? Right? So, you know, these are like, you know, big questions and, um, as a, As, as a, as a scientist and an engineer, I, I, I consider that I don't really understand how something works unless I build it myself. So, or, or, or, or have some understanding of how to build it. And so the, the, the purpose of, of AI is, uh, uh, this is a dual purpose. One is understand intelligence, perhaps, uh, approach models of, of human intelligence if we want. Uh, and the other one of course is to, Construct interesting artifacts that can, you know, help people in their daily lives and make the world better and everything.

AI assessment note: “No, I don't want to replicate the human mind. I want to understand intelligence.”

Q uh, Marshall Marr, and he said the inevitably of the inevitability of intrusive tech is a uniquely American phenomenon. I don't know why American, but anyway, this is what he says. They, they asked you to post baby pictures and are now training lucrative AI engines with your images. This was not disclosed at the time. Pay me now. So when you think about that, what's your, what's your reaction?

A How should we think about it? It's going to be a debate for society to figure out, because I don't think the answer is totally clear. You know, for example, uh, uh, photography, the invention of photography, uh, shrunk the market for, um, portrait, painted portrait by a lot. It's not like portrait Portraitists disappeared. Um, but it certainly, uh, reduced the market for it. Um, recorded music reduced the market for performance, uh, musicians. And in every instance of those things, there was, uh, you know, collectives of artists to say, like, you know, we have to stop this because this is going to kill our business. They were universally unsuccessful. Okay. So you're not going to stop technology, right? The, now the question is, is, is a legal one. So if you, if you assume that current legal, uh, interpretation of copyright, if you want, uh, um, is used, then you cannot let those machines plagiarize. So if you use a generative model that has been trained on whatever, and it produces, regardless of the process, and it produces a piece of art that is too similar to an existing one, The audience that produced that existing one, uh, is entitled to sue the, the, the person who is distributing this, this new piece of art, um, uh, and, and ask for compensation. Now, but what if that piece of art is not copyrighted? That generated piece of art is not copyrighted, so nobody can profit f…

AI assessment note: “It's going to be a debate for society to figure out”

Q Uh, but only 10% or 20% maybe of proof of concepts make it out the door into production because they're, it's either too expensive or it's fallible. So if this is, if we are getting to the top here, uh, what do you anticipate is going to happen with, with everything that's, that, that has been pushed? In the anticipation that it is going to get even better from here.

A Well, so again, it's a question of timeline, right? When, when are those systems going to become sufficiently reliable and intelligent so that the deployment is made easier? Um, but, but, you know, I, I mean, this, this situation you're describing that You know, beyond the impressive demos, actually deploying systems that are reliable is where things tend to falter in, in the use of computers and technologies, and particularly AI. This is not you. Um, it's, it's basically, um, you know, why we, we had super impressive, you know, autonomous driving demos 10 years ago, um, But we still don't have level five self-driving cars, right? Um, it's the last mile that's really difficult, uh, so to speak, uh, for cars. You know, it's, you know, the last few, that was not deliberate. The, the, you know, the last few, few percent of reliability, which makes a system, uh, practical, um, and how you integrate it with sort of existing systems and, and, and, Blah, blah, blah, and, you know, how it makes, ah, users of it more efficient if, if you want, or more, ah, reliable, or, or whatever. Um, that's where, that's where it's, that's where it's difficult. Um, and, you know, this is why, ah, if we take, if we go back several, several years, and we look what happened with, ah, IBM Watson, ok? So, Watson was going to be the thing that, you know, IBM was, was gonna push and generate tons of revenue…

AI assessment note: “Watson was going to be the thing... it was basically a complete failure”