Leo Laporte (2:44)

Yeah.

Stephen Wolfram (2:44)

And there were things that have been discovered, and I was trying to figure out what. What was. What was happening with them. I have to say, my first paper was not my best.

Jeff Jarvis (2:54)

It's only uphill from there.

Stephen Wolfram (2:57)

Yes. Yes. Right. It's one of the things that was trying to figure out things about what electrons are made of. And I had kind of an idea about that. And I mean, this is maybe a technical point, but. But I kind of thought Maybe electrons are 10 to the -18 meters. Across. Now I finally think I do have some understanding of what electrons are and I think maybe they're minus 10 to the minus 81 meters across.

Leo Laporte (3:23)

Wow.

Stephen Wolfram (3:23)

So it's kind of a flip around in the, in the exponent, which is. Anyway, so, yes, that was, that was my, my first paper about particle physics.

Leo Laporte (3:31)

No one should peak at the age of 15. You should be grateful that you did not. Right. Yes, it would be very depressing. Have peaked at the age of 15. You said something interesting though. You. First of all, you called it machine learning, not AI. Is that a conscious choice?

Stephen Wolfram (3:50)

Well, AI has been harder to define. I mean, back when I started building big computer systems to do kind of science type things, which was late 1970s, people were saying at the time, oh, when you can do math by computer, then we'll have AI. And then the goalpost moved. And that wasn't what AI was supposed to be about. So it's, it's a, you know, I think machine learning is a bit more. AI has sometimes been the thing which is just a bit out of reach to computers.

Leo Laporte (4:20)

Yeah.

Stephen Wolfram (4:21)

Which is, which, you know, makes it, makes it a little hard to define. And now, now we've got AGI, which is again, just out of reach to computers type thing. I don't think it's a well defined concept. So, yes, I, I think the thing that one could sort of distinguish is things which are sort of defined computationally. That's one whole branch of effort that I've spent a lot of my life on. And things which are specifically built using things like neural networks and kind of the traditional tools of machine learning. And that's a different branch that happens to be doing really well right now. I mean, I started playing around with neural nets back In, I think 1981 was the first time I tried to simulate a neural net and didn't do anything interesting. And I thought for years nothing interesting is going to happen with neural nets. And then, you know, 2011 comes around and really interesting things started happening with neural nets. I still think, you know, even now I'm really curious, why do the things that happen with neural nets actually happen? Why. Why do they work? And that's much less clear. And I've tried to figure out some things about that from a science point of view recently.

Leo Laporte (5:30)

Yeah, I remember your article and 2023, which turned into a book talking about LLMs and why they don't do math and proposing that maybe they should pair with WolframAlpha to let the WolframAlpha do the mathematics. Why is Wolfram alpha. Good at Math. How does WolframAlpha work differently from LLMs?

Stephen Wolfram (5:49)

Yeah, so, I mean, what, what we're doing is to compute using definite algorithms that we explicitly. Right. And that I'm taking data from the world that we've kind of gone to the trouble of explicitly curating. Sort of the big idea of machine learning is you give the machine a bunch of examples of what you want it to do and you hope that it will kind of figure out what to actually do in any particular case. So it's a, it's not the kind of thing where you're setting up sort of this rigid structure that the thing is going to follow. And for some things that we do as humans, kind of doing it roughly right and doing it across many different situations is exactly the right thing. There are other things that we want to do where we need to do sort of a precise computation. You need to build up the tower very tall. And that's something that's, that's not a thing that kind of, the neural net kind of machine learning approach is really so suitable for. I mean, it's like if you, if you, if you want to get it 80% right, then, you know, using machine learning is a great, great thing. If you want to get it 100% right, then using machine learning is usually not the right thing. That's, that's a case where you need to have sort of this precise sort of tower of, of functionality that you build up.

Jeff Jarvis (7:07)

Is that because of the probabilism that, that, that large language models have been built on and machine learning is built on versus the kind of determinism of the explicit programming you talk about?

Stephen Wolfram (7:20)

I think that's one way to put it. I mean, I've been trying to understand what is one actually doing when one is doing machine learning. In other words, you, you know, you give a machine, you know, 10,000 pictures of cats and 10,000 pictures of dogs, and then you say, you know, now I'm going to show you a picture. Is it a cat or a dog? And, you know, then you showed a picture of a dog in a cat suit. What's it going to do? Well, what it tends to do is something that is fairly similar to what humans do in a situation like that. You know, whether it agrees with what humans say will be, it very often will. Why does that happen? We don't have a precise sort of definition of why that happens and what these sort of, what the boundary between catness and dogness is for the machine. But I think this question of what the way. The analogy that I found useful, that sort of come out of some science I've done in this area is let's imagine that your task is to build a wall. Well, one way you can do that is you make these very precisely engineered bricks and you set them up in this very kind of organized way. And you've built this wall and you can keep building it, and you can keep building it very tall. Okay, that's plan A. Plan B is you see a bunch of rocks lying around on the ground, and as you build your wall, you find a rock that's roughly the right shape. You stick that one in, you keep building that way. That second thing is pretty much what machine learning is doing. When you train a neural network, what it's doing is it's finding these kind of lumps of computation that happen to fit into what the training looks like and so on. And so it sort of puts, that puts that rock into the wall and keeps building. And it's something where you can build the wall to a certain height just, you know, with, with these randomly shaped rocks. But it's not something where you're going to be able to sort of systematically build it up very tall. I think that's, that's, that's perhaps a way to think about what's going on. But, but in the end, it's that kind of, you know, machine learning is getting things roughly right. And that's a big achievement in many domains, you know, getting it roughly right. Writing that essay that makes sense is great. You can't say that essay is precisely the right essay. It's just, oh, it's an essay that makes sense. And I think that's the, you know, it's a distinction between what's, what needs to be precise and really built up many, many steps and what needs to be happen sort of roughly right.

Leo Laporte (9:48)

Yeah. The ML is stochastic. So while two plus two is always four in the LLM world, it might be, you know, whatever five is, four, six, three. But there seems to be. And you've worked with cellular automata, which I find fascinating. And I think for a long time, the thought about, you know, like Conway's Game of Life, the thought about these was emerge. There would be emergent properties that would come out of this. That seems to be what's happening with LLMs as right now or.

Stephen Wolfram (10:20)

No, somewhat. Yes. I mean, so, so just to explain that thought, I mean, the big thing that was, to me a huge surprise from the early 1980s is even if you have a really simple program, like something that's just sort of one line of code that you can specify it by some very simple rules about, you know, black and white squares or something like that. Even if the setup, the rules are really simple, the behavior that can emerge can be really complicated. That's something that's very surprising to us because we're used to, you know, we're doing engineering, we want to make something complicated, we have to go to lots of effort to make it complicated. What we discover is in the computational universe of possible programs, even a very simple program can do very complicated things. And that's a big deal for one's intuition about how nature works and lots of other kinds of things. It's also a part of the core of what happens in machine learning. But I think the path is not quite so direct. I mean, this question of, of the, you know, when you say it's sort of an emergent thing, I think that's true, but it works in a slightly different way. So I mean, the thing that is surprising about machine learning is that the kinds of decisions that it makes seem to more or less agree with the decisions that we humans make, like the is it a cat or a dog type thing, it's going to, it seems to surprisingly more or less agree with the way we make those distinctions. And I think the, there's sort of a question of why that happens. And I think the probable answer is because its architecture is fairly similar to the architecture of our brains. And so it's making those distinctions in the same kind of way we make those distinctions. Now it turns out that to get to the point of being able to train the thing from examples, you do need a bunch of the same kind of emergent behavior that you see in things like cellular automata. But that's a, that's kind of a, that's, that's a, that's a more sophisticated part of the story. I mean, that's the, I think the, the most significant, the sort of zero order effect is that what the neural nets do seems to sort of align with what we would typically do in similar circumstances.

Leo Laporte (12:37)

And yet the thing that, or the goal, it seems to me, for these would be for them to do something more than a human could do.

Stephen Wolfram (12:47)

Well, I mean, there's a matter of scale, right? Like, like I'm, you know, right now I'm trying to use LLMs as a tool to study something that basically needs me to read, you know, half a million papers, right? I'm not going to read half a million papers. But, but Can I extract the essence of those things and you know, in a human like way, as if I was reading those papers and the thing, the answer is probably yes. And so that's an example. Now you know, there will be things that I can, you know, there's a lot of leverage I can get from being able to do that. Is it something qualitatively different from what I could do? No, not really. It's just a matter of scale.

Leo Laporte (13:29)

So the thought that maybe an AI could come up with a way to do nuclear fusion that we haven't thought of, or, or fold a protein in a way that we haven't thought of before, create a new material. Is, is that out of the question?

Stephen Wolfram (13:44)

Well, the thing to understand is having, doing original things is easy. If you have a sequence of random numbers.

Leo Laporte (13:52)

Yeah.

Stephen Wolfram (13:53)

That sequence of random numbers will be original at all stages.

Leo Laporte (13:56)

Yeah.

Stephen Wolfram (13:56)

The question is, does it align with anything you care about?

Leo Laporte (13:59)

Valuable versus original?

Stephen Wolfram (14:01)

Yeah, right, right. And I mean, so that, that now, so now the question is, you know, could you come up with something that's really good by for example, noticing that, you know, oh, across, you know, the hundred thousand papers that relate to different approaches to nuclear fusion, there is this theme that you can extract that lets you see what to do? Yes, that's perfectly possible.

Leo Laporte (14:25)

A human could theoretically do it, but maybe an AI is more likely to do it, given that there's a million papers.

Stephen Wolfram (14:31)

And I think that the, the human plus AI is the best alternative. And I think the thing to realize is what, what ends up happening is, you know, AI is an automation mechanism as far as I'm concerned. And the way that tends to work is somebody has to say, what do you want to do? And then the automation mechanism can go try and do it. But if you don't know what you're trying to do, there's no place to start. And I think, you know, the, the AI, that's just incredibly, you know, it's, it's learned a lot of stuff, but it's just sitting as a box on your desk. It's like, well, what is it going to do? It has to, there has to be some. Well, I want, I want this to have happen. And you know, we humans have lots of things where we say, I want this to happen. Now you could ask the question, if you just got a lot of AIs hanging around, won't they kind of spontaneously say, I want to have this kind of thing, this thing happen. And at some level that is, you know, you, you could, you could look at what's happening in the AIs that already exist in the world. And you could kind of poke, you know, probe that, and you could say, it looks like this AI wants this or that thing to happen. And there's this whole sort of civilization of AIs that's already starting to exist that's sort of separate from human civilization. And at the beginning, it seems like that's a really weird thing, and it's a really scary thing that these AIs could be off having their own. Know, their own civilization independent of us. But the thing to realize is there's a. There's a really good model for that that we are very familiar with, which is the natural world. The natural world is off doing all kinds of complicated things. It just does them. It's, you know, we live around it, but it's not, you know, it's not something where we're all freaked out because the natural world is doing things that we don't know about, we don't understand, et cetera, et cetera, et cetera. So I think that's kind of. That's kind of the picture of where I see things going with the kind of the. The civilization of the eyes versus the civilization of us humans.

Leo Laporte (16:23)

I love that. And I know. I know what you want to ask Paris, and I know what Jeff wants to ask, but I'll let you go ahead. I won't. I won't take your question, but I know where we're going here.

Paris Martineau (16:33)

How do you define AGI?

Leo Laporte (16:35)

I know it. I knew I.

Paris Martineau (16:37)

The minute you said that, I knew.

Leo Laporte (16:38)

I was one of us was going to ask it.

Paris Martineau (16:40)

And specifically, there's two questions. One, how do you personally define AGI? And two, do you think that this. There's this common definition or like, shorthand of AGI that goes around of, you know, superhuman, intelligent AI that could replace humans and outpace us at tasks. Do you think that that's feasible?

Stephen Wolfram (16:58)

Well, okay. I think AGI is a really mushy concept, and I think it's, you know, it is the. It is the emergent concept from AI, which was always a mushy concept. So, you know, it's this thing about, can you make something which does everything a human does? You'll always be able to find things that a human that. That the thing doesn't do, you know, like it's not mortal or it doesn't, you know, get. Get flu or something. The only thing that does exactly what a human does is a human. And, you know, you'll always be able to Find some place where you can kind of find the, the magic thing that is the unique thing left over for humans. My first comment, my second comment is that I think that, you know, in the end the, the AIs can get very powerful. They can do all sorts of elaborate computational things. Lots of, you know, I've spent a large part of my life studying what sort of simple programs in the wild can do. And they can do amazingly sophisticated things, things that are probably just as sophisticated as things happening in our brains. The issue is that there's a question of do those things align with anything we care about? I mean, there's plenty of things that happen in the natural world that are in some sense computationally more sophisticated probably than what's happening in our brains. But it's a question of are those things where we say, oh, that's human, like intelligence, like stuff. And the answer is not usually. I mean, we sometimes say, you know, the weather has a mind of its own, but that mind is fairly different from a human mind. It's unpredictable, it's complicated, but it isn't sort of human aligned. So I tend to think that the, you know, the story ends up being that there is this kind of computational resource that is the civilization of the AIs, and there's how we interact with that, which tends to be things where we say we want to have this happen. And then, you know, we get AIs to do that. And sometimes things will just happen because that's what the, the sort of, the natural course of, of AIs leads to have happen. So I mean, I think it's sort of the idea that something happens that is superhuman. We have superhuman things happening all the time, either in nature or in our computers. And a lot of things I've studied in my efforts in science have to do with even very tiny programs that in a sense do superhuman computations. But we look at them and we say, that's interesting. Sometimes we say, I see the significance of that and it's really cool. And sometimes we just kind of shrug and say, well, that's computationally sophisticated, but it's not something I care about.

Leo Laporte (19:41)

You, of course, have written a lot about the computational paradigm. I'm wondering if there's any intersection between that and what we're calling machine learning or AI today. Or are they separate domains?

Stephen Wolfram (19:54)

No, I think that the, the way I see a lot of what's happening with, with AI and machine learning right now is providing outstanding kind of linguistic interfaces to things, sort of humanizing the way that one can interact with things. The question is, what's behind that? And, you know, the thing that we have been able to do with computation is build these kind of tall stacks of capability. And that's something that, you know, if you go, went back 300 years or so, people were still talking about natural philosophy. The idea of kind of mathematical science hadn't really started off. And, you know, if we'd gone straight from natural philosophy to today, there wouldn't really be a back end that we can talk about. It really would be, it's just this human like thinking, this kind of linguistic interface and so on. But right now we can actually compute things. We can build these kind of tall towers of computation. We can work out things that involve, you know, millions of steps where each step has to be precise and so on. And I think the, that's what I've spent a large part of my life building the tools to make that possible. And I think the thing that's, that's really kind of interesting right now is, you know, we built that for human users. Now we also have lots of AI users. Turns out that because the AIs are using this kind of linguistic interface that matches humans, that the things we did to make what we built suitable for human users are the exact right things to have done to make it suitable for our current AI users, so to speak.

Leo Laporte (21:25)

Oh, that's so exciting. That's amazing.

Stephen Wolfram (21:28)

You know, the emerging thing is you talk to the AI, but the back end is a computational kind of thing. And, you know, we built Wolfram Language to kind of have this sort of computational representation of the world where you can do precise computations, where you can have precise knowledge, you can build things up and so on. And I think, you know, my, my vision of the future is that there is this layer of kind of linguistic interface and then there is this kind of computational bedrock, this kind of computational knowledge bedrock. And that the thing that we don't yet know really that well is we only have sort of the coarsest ways to interface between that sort of linguistic layer and the kind of computational bedrock. But I think, you know, for example, the idea that, oh, we're going to train the LLM to learn, you know, how to, what the, what the millionth digit of PI is or something. This is not a particularly good idea. Yeah, it's a, you know what, in an LLM, you're taking knowledge, grinding it up in some way that we don't understand very well and sort of, you know, where is that million digits of PI? Is it in the, the collective, you know, features of a thousand weights spread across the neural net. It's a mess, and it's not kind of the right way to implement things. Just as if we were, you know, if we had all of our computer infrastructure, but there were no actual computers. The computers were, as they used to be, humans. And we said, okay, we want to run a program, let's get a human to run the program. Turns out humans aren't very good at running programs in their minds and you know, that wouldn't work very well. So we have these, you know, we have machines to run the programs. And that's something where the humans define what the programs should do and then the machines run the programs. And I think that's the same sort of direction that, that we see, you know, happening in, in, in what's happening with kind of the, the, the current version of AI.

Leo Laporte (23:27)

Yeah. In fact, if you want to get an AI to, to do mathematics, you say use code and then it can do it. That's the interface to a computational system. Then it can do it and it does it quite well.

Stephen Wolfram (23:39)

Hopefully using our tech. It works, it works well using our tech.

Leo Laporte (23:43)

And of course, that's one of the things people are talking about a lot these days is replacing human coders with AI.

Stephen Wolfram (23:51)

Well, you have to understand what's really going on there. I've spent a large part of my life as a language designer. What does a language designer do? Well, a language designer is trying to take sort of the things that humans want to do and define primitives in a computer language that let the human do what they want, specify what they want in as good a way as possible. The approach most people who've designed languages have taken is I'm just going to have this very small language, then I'm going to rely on people to build up libraries that do specific kinds of things. I've done the crazy thing in my life of trying to build sort of an integrated language that does everything, that sort of coherently defines sort of everything in a computational way. And for me, the main thing we're seeing is people what in our language would be a line or two of code, because we've defined all those operations integrated into the language. People have been used to using in many cases, sort of low level languages where it takes a giant blob of code to do this thing. And so then they see that, yes, the AI can write that giant blob of code. For me, as a language designer, it's like that giant blob of code just shouldn't exist.

Leo Laporte (25:06)

It shouldn't exist at all.

Stephen Wolfram (25:07)

Yeah, yeah, it should be. It should have already been defined because that's a blob of code you repeatedly want to do that should have been defined as a primitive thing in the language you're using. So, you know, I think now, having. Having said that, the idea that you can go from sort of a thing that you just talk about linguistically to something in code that actually works particularly well with our computational language because it's sort of the closest to being able to deal with things that you just talk about, like cities or videos or whatever else. And so what, you know, we built a. We released a product recently that's our notebook assistant, where kind of. It's kind of remarkable, the extent to which you can just be blabbing along and saying something kind of vague and it will try to get sort of to code that is the closest code it can find to what you thought you were talking about. And it's quite interesting because it kind of crispens up your idea. You're kind of saying something vague. It says, well, let me respond with this precise piece of code. And sort of the whole idea of our computational language is it's a language that can not only be written by humans, but can also be read by humans. So in a sense, what's happening is the AI is responding and saying, well, the thing I think of, that I'm reminded of by what you're saying is this precise computational thing that you can then respond to and so on. And that seems to be a very powerful way to do things.

Leo Laporte (26:37)

Are you using your own models for that, your own LLMs, or.

Stephen Wolfram (26:40)

We're using mostly other people's LLMs, but there's a big layer of other technology around that that is essentially doing things like dynamically prompting the LLM with, using computation, using rags and things like that to be able to provide. To be able to say, given that that's what the human said they wanted to do, hey, LLM, you should know exactly this and this and this. And then the LLM can fill in the details. And then on the back end, we're also kind of cleaning up what the LLM did to provide sort of the best possible code and that, you know, it works well. But it's a. It's worth realizing, and I think this is a general feature of a lot of what's going on with LLMs today, that the harness is pretty important. We had built an early version of this 18 months ago. It didn't work that well as we tightened up the harness, so to speak. It started being really useful. And the LLM is an important component, but the harness is also important.

Jeff Jarvis (27:40)

What do you wish for next? I'm gratified to hear you say, kind of reject the hubris of the human thinking that the goal is to replicate and beat us. It's to do these amazing things that it can do. Yann Lecun said in the Guardian, I think two weeks ago that teaching it reality, that when the ball falls off the table, it doesn't go away. Jensen Huang talked about training the digital twins so that it knew how to deal with that reality. That's one view. What do you wish for in next developments? Because every AI company now is coming out with this next version that's just that much better, that much faster. Where's the leap that you wish for?

Stephen Wolfram (28:19)

Yeah, I mean, I think you have to look at the history of AI, machine learning and so on. There'd been a series of kind of jumps, you know, for a long time. Neural nets didn't do anything terribly interesting. 2011, they started doing image recognition, then, you know, speech to text and so on. Then language generation. And actually image generation came a little bit before language generation. And there are these kind of thresholds. And my suspicion is that, you know, if you look at, for example, image recognition, things haven't got that much better in the last 15 years. It's, you know, it's incremental improvement. There is this moment where you kind of get over the threshold. It's kind of like, I think for language models, it's a little bit like what I think kind of happened with the telephone. People knew that in principle, you could transmit speech over copper wires or whatever, but nobody could understand what was being said at the other end of the phone, so to speak. Until I was on a Graham Bell, sort of found sort of some hacks that made that work. And suddenly it's like, oh, yeah, that sounds like speech. And I think that's sort of what happened in the large language models. It got to a point where it reached a threshold where it's like, yeah, this is basically working. And I think we'll see that in a bunch of other areas. I mean, the area that sort of an obvious one is the kind of, as you were alluding to the sort of the real world robotics, you know, common sense, kind of, I drop this, it falls that. That kind of thing. And I, you know, I don't see any reason that won't happen even fairly soon. You know, is that. Will that be Significant in the world. Absolutely, that will be significant in the world. There are all kinds of things that will then that have been, oh, a human has to do that, that involve sort of manual dexterity and things like this where it will suddenly be the case that yes, we can use, you know, use AI to do that. In terms of, you know, on the intellectual side, I would say that.

Jeff Jarvis (30:16)

The.

Stephen Wolfram (30:17)

Thing I'm, I guess, well, one thing is the on ramp to computation for people. The idea that you can kind of for every field X, there's sort of a computational X version of that field. But can people get there? Can you know, the archaeologists be doing computational archaeology? Well, they say, oh, but I can't program. Well, that's not necessary anymore. You know, you can use the LLM to get to computational language. You can read the computational language, you can be off doing your computational archeology. So I think one of the big sort of trends will be towards sort of computational X for all X so to speak, and people really being able to get there and you don't have to go to the druidic specialized programmers to get the code written to be able to do your computational X. I think that's, you know, that, that's, that's.

Leo Laporte (31:07)

Another piece for anybody who's ever used WolframAlpha. That is the amazing moment when you type in natural language and get such amazing results. Is the Notebook Assistant kind of the next generation of WolframAlpha in a way.

Stephen Wolfram (31:20)

It'S a little bit of a different, different thing. I mean, WolframAlpha I view as being sort of the drive by answer, your question type system, right? You, you type in a simple question, you, you get a computed answer. What the Notebook assistant is trying to do is to set up the bricks of computation that you need to build up whatever you're trying to do. So it's not just trying to say here's the answer, you're done. It's trying to say here's this thing that is a brick, a tool that you can then build on and continue building up. So I think the way I see sort of the future of things like software development is the first thing you have to do is to imagine what you're trying to do computationally. And that's the thing, they don't teach that in computer science school how to sort of think about everything computationally. But I think that's a super important skill is like how do you conceptualize what you want to talk about in a computational way once you've gotten some distance towards that Then you can ask our notebook assistant. It'll write a piece of computational language code that'll do a piece of what you want. You take that piece of computational language code, that becomes a component that you can then start building up from to make the larger thing that you want to build. I think that's the, the. Sorry. See, that's a human feature not yet emulated by it, although I'm sure they could learn from.

Leo Laporte (32:49)

Actually, you know, it's interesting because more and more people are finding that if you push them long enough, they. To kind of decline. Almost physically decline.

Stephen Wolfram (32:58)

Yes.

Leo Laporte (32:58)

In their ability, which I find fascinating. This must be a, this must be for you a vindication of the computational paradigm in some ways.

Stephen Wolfram (33:08)

Some ways. I mean, I think that this, this ability to let more people into the computational paradigm, that's a very important thing that has not yet played itself out. That will play itself out over maybe the next decade or so. And I think people haven't, you know, that that's, that's still an emerging realization, so to speak.

Jeff Jarvis (33:28)

More, more to that. I'm curious, since you've lived in this world and developed this world for a long time, suddenly when LLMs arrive, everybody says, oh hell, this is AI. As if, as if that has arrived fresh. What's your reaction to society's reaction to. Since ChatGPT's release, how have you seen in just people talking in media, in companies? How do you sense of that?

Stephen Wolfram (33:54)

Yeah, I mean, it's, it's interesting that this was a moment of consumerization. I mean, it's just like with computers in general. I mean, people. You know, I started using computers when I was a kid in the 1970s, and most people were like, ah, computers, who cares? You know, maybe they're in the back end of something doing some, you know, bank processing thing or whatever, or, or figuring out how to launch rockets. But I don't care about that. And then, you know, the personal computer comes out and people discover things like word processing and games and so on, and suddenly it's like, oh, we really care about computers. I think that was sort of the ChatGPT moment, was the one where people could see that there was this. I mean, by the way, nobody knew ChatGPT was going to work, including the people who built it. I mean, it was, it was really, you know, it's like, at what point, I mean, you know, I'd been playing with language models and sequence prediction and so on. None of that stuff had been terribly interesting. It was, you know, it was a thing where for Whatever reason, and we don't understand why yet. Maybe we one day will. Suddenly we got over a threshold, you know, some number of billions of parameters. It's like, that was enough. That was human, like, enough, so to speak. Now, you know, that scale is probably determined by something to do with us. Like, we humans have about 50,000 words in our typical languages that say that, and we have 100 billion neurons in our brains, you know, that provides a definite scale. If you wanted an AI that would be like a dog, for example, in its linguistic capabilities, let's say you probably need a much smaller LLM, but we wouldn't be as impressed by it. So somehow the size of the LLM, you know, the size of the system was such that it was sort of matched enough of what we need to be impressed by that we got to that point, you know, by the way, one thing to just say is sort of a side comment. You know, one thing I've been curious about is let's say our brains. Let's say we had, you know, a thousand times as many neurons in our brains. What might we be able to do? What's kind of the next level of sort of intellectual capability? It's just like, you know, cats and dogs. Well, dogs, at least, you know, a few individual words. Fetch, sit, whatever else they deal with that we humans. The big invention of our species, I suppose, was human language and this idea that you can put together an infinite collection of sentences by using all these different words. So a big question is, what happens next? If we could go beyond that, what's the next level of abstraction and sophistication? That's an interesting thing to think about, but you also are stuck thinking, well, if I was the dog, could I really understand human compositional language? And is it the case that there's something bigger? There undoubtedly is, but maybe it's something that just isn't a fit for us. Just like the natural world has lots of things going on that we don't necessarily. We don't understand except through this sort of bridge of natural science. But I think, you know, in terms of people's reaction to ChatGPT, I think the. The thing that, you know, a lot of people were like, it's magic. You know, this magic has happened. There's going to be more magic. You know, this was such a surprise, necessarily. There will be many more surprises. That was a. I don't think that's, you know, people always think that when there's a technological surprise, they always think it's not going to stop here. It's going to keep going, but it doesn't always keep going. In fact, it usually doesn't keep going. It usually is. That was the surprise. 1. 1 got to that sort of threshold level and then. And then. So, you know, and then the question was, well, okay, sort of. I was a little bit surprised by this. People saying, it's magic. And then it's like, well, how does it work? Oh, it's this. You know, it's this AI thing. It's this neural net. And I. I started thinking, well, can I actually figure out some idea of why does this work? And what I realized is, you know, there's at least a picture of why it works. And it's this. And it's something that, in a sense, was a feature about human language and human understanding that maybe we should have understood thousands of years ago, which is, you know, people have known about grammar of language that, you know, you form sentences in English having, you know, noun, verb, noun, and so on, but the kind of that structure of grammar doesn't tell you whether the sentence is going to be meaningful. There are lots of sentences that go noun, verb, noun, and they're completely meaningless. What I sort of realized is that when. When the LLM is kind of looking at, you know, you know, hundreds of billions of sentences, it's seeing sort of patterns not only of the noun, verb, noun type, but of the. This is a pattern of words that makes sense. And that's sort of the. The big thing that it so statistically learns that people were very surprised at the beginning, for example, that LLMs could do logic, that they could kind of make logical conclusions. And you realize, well, you know, how was logic discovered in the first place? Well, you know, presumably Aristotle went through and just heard lots of people giving speeches and sort of identified. These are the patterns of speeches that make sense. And then those became syllogisms and they became what we now think of as logic. And I think the LLMs kind of learned it the same way. And so, in a sense, one of the things that was sort of interesting to me was people sort of say it's magic. They realize, well, actually, it's not such magic. There's probably a thing that actually is telling us an interesting piece of science that. That maybe we should have learned a long time ago. That's sort of at the core of why this can possibly work.

Leo Laporte (39:37)

That is also a big part of your book. What is ChatGPT doing and why does it work? Very provocative. Every time I talk to you, Stephen, I feel guilty that we are using brain cycles that you could be using much more advantageously. I want to thank you for spending time with us and encourage everyone to go to wolfram's.com, wolfram.com Stephen's site. It's a great entry point. There are books there. There's links to the new Wolfram Alpha notebook, which I think is fascinating. I'm going to sign up immediately and I really appreciate the time. You've given us so much to think about. I really appreciate it. Thank you, Steven.

Stephen Wolfram (40:16)

Lots of cool questions.

Leo Laporte (40:17)

Yeah.

Jeff Jarvis (40:18)

Thank you.

Leo Laporte (40:18)

Yeah. Have a great day.

Stephen Wolfram (40:20)

Thanks.

Leo Laporte (40:21)

Thank you. We'll. We'll put this out later in the day. It will go up immediately. We'll send you if you like. We'll send you some clips if you want to give them to your social people and so forth.

Stephen Wolfram (40:30)

You can pass them along.

Leo Laporte (40:32)

Thank you.

Stephen Wolfram (40:32)

Yeah, it's always. Sorry I'm a bit long form. I'm, I'm so used to just yakking on that's what podcasts.

Leo Laporte (40:38)

Stephen, I would talk to you for four hours. I just, again, I'm, I'm sincere saying I feel guilty taking any of your brain cycles.

Stephen Wolfram (40:46)

You're very welcome to them.

Leo Laporte (40:48)

Anyway, thank you so much. It's a real pleasure. You know, we'll have you back in a few months and we can talk more about this because you've immediately brought up all of this stuff I want to think about. So it's great. Thank you. Really appreciate it.

Stephen Wolfram (41:02)

Nice to meet you, Steven.

Leo Laporte (41:03)

Nice to meet you. Take care. Yeah. Wow. Isn't that last thought, wow, just amazing? Wolfram.com, go there. There's so much there. I am going to sign up for that notebook because it's basically, the idea is a English language interface to the computational paradigm. So you talk. It computes to me. That was what WolframAlpha was originally, but now it's even more powerful.

Jeff Jarvis (41:32)

It's just I felt I wasn't smart enough for WolframAlpha. This might be a. Yeah, well, it's.

Leo Laporte (41:37)

For people like you who aren't smart enough for Wolfram Alpha. All right, we're going to take a break and we will come back. We have AI news and I think what we're going to do from now on with the show is we'll do an interview. We have some great interviews coming up, including next week. It's. It's Gary Marcus, which is going to be great. I think you know him well. Thank you, Jeff, for helping us arrange that the following week. That's the man who coined the term intelligent machines. Ray Kurzweil will join us. I've also talked to a bunch of other people. We've actually booked it well in advance. One of them, my friend Harper Reed, has written an interesting article about exactly what Stephen was talking about as a coder, the interface to an LLM to do the coding. And he has some very interesting thoughts about that. And it really is pair, he says, and I agree it's pair programming, but using an LLM. So lots more coming up on the show.

Paris Martineau (42:34)

But as I said, being the sand guy on the show, maybe not.

Leo Laporte (42:39)

You know, we have so many, like, we're filmed.

Jeff Jarvis (42:43)

No, I'll get him on.

Paris Martineau (42:46)

He teased one week that the sand.

Leo Laporte (42:48)

Guy was going to get the sand guy on. We will get the same guy. But from now on, what we're going to do is start with a half hour interview with somebody who's really doing some interesting things or provocative. Then we'll do the AI news and we'll follow that with another news because I want you guys to be able to, you know, talk about all the other things you're interested in. And then we'll follow up with the picks of the week. And if we can, we're going to try to keep this show under five hours. How's that sound? Okay. All right.

Paris Martineau (43:14)

Sounds good.

Leo Laporte (43:15)

Paris Martineau. Jeff Jarvis. We will continue with Intelligent Machines in just a moment, but first, a word from our sponsor. Delete me. Delete me. Saved our company. Well, maybe that's an overstatement, but honestly, we were freaked out because we were getting spear phishing attacks using our CEO's name, phone number, the bad guys knew her direct reports, their names, their phone numbers. And we were scratching our head and we said, do we have a mole inside? Then we realized it's data brokers. Everything, everything there is to know about you, your family, your company is online somewhere, legally sold by a data broker. If you've ever searched for your name online, don't. First of all, don't. But if you have, you know what I'm talking about. So much personal information is available, and there's always that little link that says, and now for a buck 50 more, I'll tell you about their prison record and things like that. This is. This is a nightmare situation. We learned with the national public data breach that it's legal for a data broker to sell your Social Security number to anybody with a buck and a half. Actually, maintaining privacy is not just for your personal issues or your company's concerns. It's a Family affair too. That's why Delete Me has plans for everybody. Businesses, individuals, even families. With the family plan, you can ensure everyone in your family feels safe online. They help reduce risk from identity theft, cybersecurity threats, harassment and more. It made a big difference for us when Steve Gibson and I went into the national public database to see if our Social Security numbers were in there. They were. But guess who wasn't? Our CEOs. We looked for Lisa's no information at all because we've been using Delete Me. When you sign up for Delete Me, their experts will find and remove your information from hundreds of data brokers. If you're doing it for a family, you're going to sign in a unique data sheet to each member, tailored to them, easy to use controls. Account owners can manage the privacy settings for the whole company, the whole family. Delete me then. Now this is really important. Won't just stop there. They will continue to scan and remove your information regularly. And that's for two reasons. One, because data brokers, being data brokers, are going to start repopulating that information. But the other is there's always new data brokers. In fact, this is really skeevy. Sometimes those data brokers go belly up and then start a new company under a new name so they can start collecting all your information again. I'm talking everything. Addresses, photos, emails, relatives, phone numbers, social media, property value, Social Security numbers. Protect yourself. Reclaim your privacy. Visit joindeleteme.com TWiT when you use the offer code TWiT, you'll get 20% off. Join DeleteMe.com Twitter we thank them for their support. We encourage you to use that special address in the offer code so they know you saw it here. Joinedelete me.com offer code TW I T okay. In a news OpenAI says they have 4 million weekly active. 400. Sorry, that seems like an unbelievable number. 400 million active users every week.

Jeff Jarvis (46:39)

I don't believe it. I don't hear people on the street saying oh, I was using ChatGPT.

Leo Laporte (46:43)

But you do hear surprisingly, don't you hear people say, oh yeah, if you, I mean I think a lot of.

Paris Martineau (46:49)

People wonder if the. Okay, you said weekly active users was.

Leo Laporte (46:55)

That's what open. They told CNBC that.

Paris Martineau (46:58)

And now I think the last part is weekly active. I, I always pay attention whenever I hear big user number names with anybody in tech, whether It's Meta or OpenAI. Pay attention to the specific words they're using to define it in the time period in this it's weekly and active users. What in this case is considered an active user would be my question.

Leo Laporte (47:17)

Well touch it once and you're an active user. What those numbers generally mean in what way is it in that time period somebody used it? That's.

Paris Martineau (47:24)

Are they saying that someone who uses a pop up customer chat feature that is powered by OpenAI is technically an open. Or what about whenever you go on any of these websites now and I get one of those chat pop ups and it starts typing to me even though I haven't said anything. Am I an active user?

Jeff Jarvis (47:42)

Even though you use Bing in any way or.

Paris Martineau (47:45)

Yeah.

Leo Laporte (47:45)

Well remember that Microsoft has a billion and a half billion and a half users. Probably more than half of them have copilot on their system. So they have OpenAI's chat GPT on their system.

Jeff Jarvis (47:57)

So the brand as. As oh gee, a brand and a future for the company.

Leo Laporte (48:02)

Yeah. They didn't say how many are paying. They do say they have 2 million paying enterprise users and that figures doubled since September. Are they publicly held? They're not but I think they still aren't going to go on CNBC and lie or.

Paris Martineau (48:17)

Well no, I mean that's the thing is these.

Leo Laporte (48:19)

It's just you can fudge people say them they use.

Paris Martineau (48:21)

They're not saying they're outright lies. It's just they are perhaps a bit misleading when you think about what you'd first imagine from someone saying I have 400 million weekly active users. This is something me and my colleagues have thought about a lot with regards to meta saying Facebook's notorious. Yeah, I'm forgetting what the figure is. They said they had some sky high figure for threads active users and I'm forgetting whether it was monthly or weekly. We did a similar sort of look to be like it seems like these numbers are maybe being juiced by people who interact the threads carousel on Instagram or things like that. So I think it's always worth putting a little asterisk next to some of these numbers when they're privately especially when.

Leo Laporte (49:08)

The number is 400 million. I mean that's a lot of people.

Jeff Jarvis (49:11)

Yeah, well they're nevertheless and this all fits into them trying to value things as they shift over to whatever the heck the for profit structure is going to be.

Leo Laporte (49:20)

Yeah it's a.

Jeff Jarvis (49:21)

It's a dance.

Leo Laporte (49:22)

We, you know we. We abandoned the Google Changelog because Leo hated it but we probably should do a little thing every week new models this week because it feels like everybody shipped a new model this week.

Jeff Jarvis (49:35)

But I had that conversation with Jason today, nothing bores me more.

Paris Martineau (49:39)

But why do we care about that?

Jeff Jarvis (49:41)

Yeah, it's, it's, it's like it remind me of the old days of twit leo where it was very hardware and oh, there's a new screen and it's got 18 nits instead of 18.

Paris Martineau (49:52)

There's a new operating system. Yeah, this one 14.1.

Leo Laporte (49:58)

I, I have to say though the new Claude, but I don't know what version is. 3.5 is better at coding. They have a coding version and I, I did test it and I was very impressed with its ability to write code. I liked what Mr. Wolfram said, however.

Jeff Jarvis (50:15)

Dr. Wolfram to you.

Leo Laporte (50:16)

Dr. Wolfram. Mr. Doctor. Reverend Wolfram said because I worship him.

Jeff Jarvis (50:22)

He got a PhD at like six. So he's Dr. Wolfram.

Leo Laporte (50:27)

Yeah, he is a doctor. You're right. He never. I don't, I notice he doesn't use that honorific. So I don't know what to call him. He's very impressive. Anyway, one of the things he said was, and I think he was right, that the LLMs are a natural interface, human to machine interface for coding. I think that is a good way to.

Jeff Jarvis (50:44)

Yes, it's perspective because that's what he has been trying to do for years. And I think that's really interesting to see that perspective on it.

Leo Laporte (50:50)

Yeah. Yeah. All right, moving on. Amazon had its.

Jeff Jarvis (50:54)

Nvidia just released its results. Are you.

Leo Laporte (50:56)

And yes. Are they making money?

Jeff Jarvis (50:59)

80% boom in profit beat the. The guesses.

Leo Laporte (51:08)

The stock of course has been going down all day in preparation for these numbers. But I wouldn't be surprised if the stock.

Jeff Jarvis (51:15)

Let's just after hours. I looked at that for hours. It was up 6%.

Leo Laporte (51:18)

Yeah. Not a surprise.

Jeff Jarvis (51:20)

So video.

Paris Martineau (51:21)

This is from my colleague Anissa Gardesi. She said Nvidia said revenue rose 78% year over year to nearly 40 billion, continuing a streak of more than two years, beating its own revenue growth projections. It projected 70% growth for the quarter three months ago and on Wednesday forecast 65% revenue growth in the current fiscal quarter. Despite the shaky results, shares were flat in after hours trading though.

Jeff Jarvis (51:48)

Well, I just looked and it was up.

Leo Laporte (51:51)

Right now it's up 3.67%, 4.65% today.

Paris Martineau (51:57)

30 minutes ago she published. Yeah, yeah, this is the original dip was partially, I assume because they also said its gross profit margin in the January quarter decreased by 1.6 percentage points to 73% gross profit.

Leo Laporte (52:14)

Well, also, you know, you, what is it you buy in the Rumor you sell on the news or is it the other way around? I can never remember. The stock market is a bad indicator.

Jeff Jarvis (52:24)

Big forward fallback.

Leo Laporte (52:25)

Yeah, I don't know. Spring and I don't know. Anyway, good news for Nvidia, I think, and really very good news for Nvidia investors who have kind of, in many cases thrown all of their weight behind Nvidia's success. I don't think Nvidia will have a monopoly eventually, but right now they pretty.

Jeff Jarvis (52:44)

Much no, but they're doing so much more than just the chips. That's why I was such a fan of Jensen Huang's keynotes, because it just shows how much they're impressive. Full stack?

Leo Laporte (52:55)

Yeah. Although one of the moats that Nvidia has right now is Cuda, which is the language interface to Nvidia's GPUs. And it's one of the reasons, I mean, honestly, Apple with its chips and its integrated RAM often has more memory available to the AI. The problem is many of these AIs want CUDA. At some point somebody's going to write an Apple equivalent of Cuda, just as, by the way, Deepseek did, in order to keep using those, you know, H20s or whatever they're using instead of the H1 hundreds. And I think that moat is not going to last forever, I think.

Jeff Jarvis (53:35)

Is Cuda open source or is Cuda.

Leo Laporte (53:37)

No, no, no, no.

Jeff Jarvis (53:38)

That's where I'm just reading right now, the Story of Adobe.

Leo Laporte (53:43)

Oh, that's a wonderful book.

Jeff Jarvis (53:44)

I have really Inside the Publishing Revolution Adobe story by, I could read it myself, Pamela Pfiffner. And it's interesting that that moment came when they were frightened to death of making the standards behind Type 1 fonts open, but they had to, and it led to the growth. And I wonder if that's somewhat similar with Nvidia. They have a lot of moats, in a sense, and the chips and so on, and they want to be the standard, don't they?

Leo Laporte (54:18)

Yeah, but what's right now locking people into Nvidia GPUs is CUDA. They're a proprietary language you can't use on other systems, but if you could.

Jeff Jarvis (54:27)

Use it on other systems, don't they become the wellspring? It's a classic problem with it's Adobe. It's acrobatic.

Leo Laporte (54:33)

Yeah. I think that you're not going to see an open source Cuda. I think what is more likely is that somebody's going to come along and write something that will either translate CUDA into Something that Apple's chips will understand. I, I really think that that mode is not going to last forever. I don't, I'm not an expert on this, so I defer to anybody who has more to say about it. Apple has its own, you know, standards, but, you know, the world has moved to Cuda because of Nvidia's dominance. However, I've heard people say, and I think it's true, that the Apple silicon plus the integrated memory offered by Apple Silicon could actually make these Apple systems AI powerhouses.

Benito (55:16)

Hi, this is Benito.

Leo Laporte (55:18)

Hi Benito, what's your opinion?

Benito (55:19)

I think we talked about this on Twitter this weekend, right? Like someone likened it to gaming. Like how, like how Apple never, never really tried. Right, right.

Leo Laporte (55:30)

Microsoft dominated DirectX. Apple has Metal. But game developers never really embraced the Macintosh. Of course, it's a fraction of the size of the market for gaming that Microsoft's Windows is. On the other hand, Linux gaming has started to take off. That's because of the Steam deck.

Benito (55:49)

Yeah, that's because Steam runs on Linux.

Jeff Jarvis (55:50)

Yeah.

Leo Laporte (55:51)

Yeah. So it's a complicated.

Paris Martineau (55:52)

I've been using this whole time.

Leo Laporte (55:55)

You just didn't.

Jeff Jarvis (55:55)

Aren't you cool? Geeky.

Leo Laporte (55:57)

You just did.

Paris Martineau (55:57)

Not really cool. I'm going to put this on my wrist.

Leo Laporte (55:59)

Nvidia saved the markets today. Nvidia's growth did help the stock market which was been suffering a little bit.

Paris Martineau (56:06)

This week, so poor stocks.

Leo Laporte (56:08)

Did you watch or study or in any. I didn't get to watch it. Amazon's AI announcements this morning?

Jeff Jarvis (56:16)

No.

Leo Laporte (56:17)

So this is Panos Panay, who was in charge of hardware at Microsoft, left kind of, I think in a huff to go to Amazon to be in charge of their hardware. And they are today officially launching their long awaited generative AI version of. I'm going to say echo instead of the A word so I don't trigger anything. They call it a word. Plus, that really is all right, you know, A L, E X A. You know what I'm talking about there. Interestingly, it isn't out today. It won't be out for a month or two. We had heard, I think from the information and others that Amazon was struggling with this AI. It wasn't very good or it was hallucinating a lot. But they hope, they say by the end of March to be able to ship it. And interestingly, it will be free for prime members. $20 a month on its own are free for Amazon prime members.

Jeff Jarvis (57:15)

Does anything separate that out from everybody else's new releases every week?

Leo Laporte (57:20)

Well, the interesting thing is prime is only $15 a month. So I don't really understand why anybody would pay $20 a month for just that one.

Jeff Jarvis (57:29)

May just be the point. Prime is prime to their business.

Leo Laporte (57:32)

Yeah, yeah, yeah.

Paris Martineau (57:33)

Probably to boost Prime.

Leo Laporte (57:35)

So Panos showed a lot of. You know, that's usual with these AI demos. If you do it just right, you can get great results. There is agentic AI in it. You can have it write emails. You can have it call Ubers. You can even according to Panos, although Nilai Patel said that's not how Uber works at jfk. Have an email sent to somebody who's getting off the airplane telling him that their Uber will pick them up here. Apparently that isn't something real, but Panos was able to do it, actually. Amazon's Director of award, Mara Siegel, demonstrated how to share documents like handwritten notes, recipes, emails, instruction manuals and pictures. I'm getting all this from the Verge. Jennifer Tuohy, who was on Twitter on Sunday and the Verge team were live blogging while the demos were going on Alexa. Oh, I said it can take action when prompted. So you could say, what's my kid's soccer schedule? Add that to my calendar. Give me a reminder. All in what the Verge calls fairly casual natural language in an ongoing conversation.

Paris Martineau (58:52)

I'll believe it when I see it. I think warned by she who must not be named too many times.

Leo Laporte (58:56)

Yeah, yeah. When you speak with the new aap, could you call it ap? When you speak on devices with a display, you'll see a fluctuating blue bar at the bottom of the interface. Panay said this is Alexa and the little animations and icons it displays are called Stand back Alexicons.

Paris Martineau (59:21)

I like that you've just given up the whispering definitely still would have triggered it. And then saying it out.

Leo Laporte (59:27)

Turn down your radios. The company let's see had showed how to it will tell you stories. Tell your kids stories. Did you? I played with my bee on Twitter and I had it tell me a sexy story. And it was very romantic. It was wonderful.

Jeff Jarvis (59:44)

Oh, did you ask for sexy.

Paris Martineau (59:45)

Speaking about sexy stories. We can get there in a second.

Leo Laporte (59:49)

Oh boy.

Paris Martineau (59:50)

I didn't mean it like that. I just.

Jeff Jarvis (59:51)

That's a tease. That's a tease of teases. How can you not?

Paris Martineau (59:56)

We're talking about.

Leo Laporte (60:00)

Grok. Okay. Yes.

Paris Martineau (60:02)

Grok 3.0 has a sexy time mode and it's. Oh I disturbingly explicit.

Leo Laporte (60:08)

There's lots to say about is what.

Jeff Jarvis (60:09)

You about Grok is sexy.

Leo Laporte (60:11)

Yeah. Let's see. Tell stories. You can generate AI art If you've got a screen and then send it off to somebody using email. It uses two models, the Amazon Novo model as well as Anthropics. Claude. They didn't mention any other companies, although they say we'll be model agnostic. So maybe they haven't made a deal with anybody else yet. They also, Jeff, get news from the Associated Press, Politico, the Washington Post and.

Jeff Jarvis (60:45)

Reuters, because I think they do license those at Amazon.

Leo Laporte (60:48)

Oh, yes. They're partners, right? Yes. Oh, yeah. They're not just scraping it and telling you.

Jeff Jarvis (60:54)

Well, you know.

Leo Laporte (60:56)

Well, since Bezos owns the Washington Post, I think they probably have it.

Jeff Jarvis (61:01)

That's a whole other story.

Leo Laporte (61:02)

I know there's a. Later. We'll have more of that. Yeah. The company demonstrated that having having Madam A answer questions about the Boston Red Sox, then this was a good one. And I would do this, say, are there tickets available? And she says they're 200 bucks. And say, well, set a reminder or set an alert for me when they're less. When they cost less.

Paris Martineau (61:26)

I'll see you when I. I'll believe it when I see it.

Leo Laporte (61:28)

I know. I'm just telling you the. The demo. Right. LLM experts on the A word can also do things for services from firms like Uber Eats, Sonos Wise, Zoom, Xbox, Plex, Dyson. Oh, be great. A word. Vacuum the kitchen. Bose, Grubhub. I don't know who Lavoie is. Lavoie. And Ticketmaster. Oh, great. Ticketmaster.

Jeff Jarvis (61:53)

Oh, Ticketmaster. Oh, yeah.

Leo Laporte (61:54)

Just who I. That's how you get those $200 tickets.

Jeff Jarvis (61:56)

By the way, we're going to charge you a special AI fee.

Leo Laporte (61:59)

Half of that is service fees. Some of the features will be available through the web on the award website. They're also partnering with my favorite song generator, Suno, so that you can ask for. And the example they gave was Bodega cat love.

Paris Martineau (62:21)

What?

Leo Laporte (62:21)

I think you'd enjoy that. Bodega cat love.

Paris Martineau (62:24)

AI made country song about a bodega cat. I would enjoy that, actually.

Leo Laporte (62:28)

Yeah. Oh, there's a bodega cat. And it's in the. Yeah, it'd be fun. I have a. I have a number of Suno country songs. Okay. By the way, I gotta tell you this. I don't know if what's going to happen, but I got an email from the PR flack saying that Gene Simmons of Kiss is coming to our local casino, which just shows you how far he's fallen. Oh, don't. Don't take that part out. I made. I mainly email her said, can, can, can Mr. Simmons come on our show, Intelligent Machines and talk about how he feels? But here's what I'm going to do about AI ingesting the Kiss discography and then spitting out songs that sound like Kiss. What I'm going to do is ask Suno to make a Kiss song and play it for him and see what he has.

Paris Martineau (63:14)

How do you think that's going to go?

Leo Laporte (63:15)

Yeah, Badly, I hope.

Jeff Jarvis (63:17)

Yeah, I think his tongue is.

Leo Laporte (63:18)

I hope he yells at me and hangs up.

Paris Martineau (63:21)

I think that would be really beautiful.

Leo Laporte (63:23)

You know, it's funny, I once asked. I once interviewed the captain of the group.

Jeff Jarvis (63:28)

Captain Tenille?

Leo Laporte (63:29)

Yeah. You never heard of him?

Paris Martineau (63:30)

Captain, My captain?

Leo Laporte (63:32)

No, Captain and Tenille, Not Walt Whitman. Captain and Tenille. And I asked him. This was back on Tech TV when. When Napster was big. I said, does all of the piracy of music, does that bother you? And he said, doesn't bother me. Neil Sadaka owns the rights to all our songs. You should ask Neil. So I'm thinking Gene Simmons may say, oh, screw it, I don't care anymore. I got mine. I got mine. No, no.

Benito (64:00)

Anyway, Gene Simmons, notoriously super cares. Gene Simmons. Yeah.

Leo Laporte (64:05)

No, I want him to get angry. I want to. I want to.

Jeff Jarvis (64:08)

Did he join in the Silent album?

Leo Laporte (64:11)

Oh, tell me about the Silent album. This is from artists.

Jeff Jarvis (64:14)

There's a whole. There's a whole huge campaign going on in the uk, especially primarily in the uk, where the newspapers have joined in. This goes down to line 85 and following the Daily Mail, if you go to line 87, you'll see that a huge page. Don't steal from us. Then the next day, all the papers in the next line had their special front pages. And then artists put out. Oh, geez. I didn't put the link in, but you can.

Leo Laporte (64:39)

I got a link right here. Artists released Silent Album in protest against AI using their work. There's Annie Lennox, Kate Bush, somebody named Damon Allbarn.

Jeff Jarvis (64:50)

Wait, wait, wait. Paris. Do you know who that is?

Paris Martineau (64:52)

Oh, I don't know any musicians names.

Jeff Jarvis (64:54)

Oh, okay. All right, good.

Paris Martineau (64:55)

You're like, I know Kate Bush is.

Leo Laporte (64:58)

And I know Lennox is. I don't know who. I'm hoping Damon Albarn is a country artist, because that'd be a great name for a country artist. He's probably better known in the UK anyway. They released more than a thousand musicians because, you know, the beauty of a silent album is Sky's the Limit. You can have a million tracks on a silent album.

Jeff Jarvis (65:17)

Does God own the right to Silent.

Leo Laporte (65:20)

Yeah, maybe. Maybe God should sue. More than a thousand albums released a sight. A thousand musicians released a silent album on Tuesday. The album is titled this Is this what We Want? This is the premise, and I keep hearing this from musicians, that if you let AI absorb our music, you won't get any more music.

Jeff Jarvis (65:50)

Bs. And. And don't they listen to each other? Aren't they inspired by each other?

Leo Laporte (65:54)

Well, you know, Benito answered this question for me months ago when he said the reason we make music is because we love to make music. Are these artists saying, well, the only reason we make music is so you'll buy it?

Benito (66:07)

No, I think what they're saying, I think they're talking about consumerism of it. Like the. The cons. The consumer part of it. Like, if we're. If we just let AI make all the music, sooner or later people aren't going to care about music made by people anymore.

Leo Laporte (66:20)

Well, people have been saying that for years. When Apple came out with white, well, pod earbuds.

Jeff Jarvis (66:25)

This is a little different.

Paris Martineau (66:26)

Yeah, but this is a much larger attack on musicians having a livelihood than AirPods.

Benito (66:33)

Yeah, exactly. It's more about the livelihood part.

Paris Martineau (66:35)

Like, even thinking about small things. Like, I don't know. I've been watching a lot of reality TV lately.

Leo Laporte (66:40)

Oh, Damon Albin shows is with the Gorillaz. I apologize. I am an ignorant fool. I know who the gorillas are.

Paris Martineau (66:47)

I'm sorry. To the gorillas.

Leo Laporte (66:48)

Yeah, I apologize. Gorillas.

Paris Martineau (66:50)

So sorry. But there are. So I've been watching a lot of reality TV lately, and on many of these shows, they end up paying major artists or labels to use a snippet of their song. And I was listening to some podcast, some a producer talking and saying, oh, sometimes the rights for someone to use a song on something like RuPaul's Drag Race can start at like 20 grand a pop for just a small snippet of music. And for a large artist, like, let's fix that.

Leo Laporte (67:17)

I mean, terrible.

Paris Martineau (67:19)

No, I don't think it is terrible. You don't think that ABC or like, a major network can pay money to feature a musician's work?

Leo Laporte (67:28)

But I. We don't use any real music on our show. Do you know why? Well, I'm guessing because it cost $20,000. That's dumb.

Jeff Jarvis (67:38)

I don't know if you'd want to call it real music, which would say that what Benito made is not real music. Maybe there's another way to put it.

Leo Laporte (67:45)

We already pay Benito $5 an hour, so we. We own all his work. Product. No, I. You're right. Bonito makes good music. And that's the theme for our song, our show.

Paris Martineau (67:55)

I just think that it's not super helpful to continue to enable a whole industry that will. That devalues the work of artists. I can understand why people are upset about it.

Leo Laporte (68:06)

I. I love these artists. Imogen Hip Eep. Hey Cat Steve.

Paris Martineau (68:12)

Shout out to the gorillas.

Leo Laporte (68:13)

Riz Ahmad, Tori Amos, Hans Zimmer. But you know what? Hans Zimmer gonna get paid. This isn't gonna cost.

Benito (68:23)

There's gonna be another Hans Zimmer though, is what he's saying. There won't be a next Hans Zimmerman.

Jeff Jarvis (68:28)

That's what they said with the Internet and.

Leo Laporte (68:31)

And then sure there will.

Jeff Jarvis (68:32)

There were tons more musicians and tons more genres now than there were before the web.

Benito (68:38)

Yeah. And no musician is making any more Money now.

Leo Laporte (68:42)

Paul McCartney.

Jeff Jarvis (68:42)

A lot of people, as you said, love to do it for making the music propose.

Leo Laporte (68:47)

See, this is because the UK wants to change copyright law to say that it is okay in fact to. To train. But it's already okay to train in the U.S. in fact, I think the First Amendment and the right to read and courts recently seems to have upheld that say it is okay.

Jeff Jarvis (69:06)

It's going to be tested a lot more in the courts.

Leo Laporte (69:08)

There will be a lot more. But we don't need a law in this country because there is a right to read. And I don't think it's not stealing your music, it's listening, just doing the same thing you did. Hans Zimmerman. When you listen to John Williams music and we're inspired by it and made your own, it's doing the same thing.

Jeff Jarvis (69:27)

Yeah.

Paris Martineau (69:28)

Is it. I mean, what. How would you describe when Spotify uses AI to make a bunch of songs, then replaces real artists and bands and its most popular playlists with those AI created songs so that it doesn't have to pay out royalties to those real artists and instead can just paid out to the AI creations it made probably by like feeding in the original artists. Is that not theft.

Jeff Jarvis (69:59)

At some level? If you're going to make. Let me just do this. They'll be unfair. If you're going to make elevator music, do you need to pay the royalties for elevator music when it's just noise in the background? Or can you have the AI make it for you?

Leo Laporte (70:10)

In fact, there's a long standing tradition the musicians who make elevator music get paid as well as the original artists who wrote the songs or the performers who made the songs. Actually, that's the real point. Because often it is the person who wrote the song who would get the royalties. Like Neil Sadaka. Yeah. What Spotify did is reprehensible. But the blame, there's enough blame to go around. It's the people who pay Spotify to listen to crap music. Do you think that AI generated music will ruin our taste for real music, good music?

Benito (70:45)

Yes.

Paris Martineau (70:45)

I think it'll muddy the waters and I think that that could have downstream effects of bonito votes.

Leo Laporte (70:52)

Yes.

Jeff Jarvis (70:53)

Yeah.

Benito (70:53)

Because they're just gonna. They're just gonna turn it out and the labels are just gonna keep churning it out and putting it on the radio and putting it on TV and putting it everywhere that we're not gonna get away from it. We're not gonna be able to get away from it anymore. This is gonna be all AI.

Leo Laporte (71:05)

Well, one of the things that changed in the music industry over the last 10 years is that artists make more money performing than they do selling records or streaming their songs. Most artists perform. In fact, Madonna's last contract with ironically Ticketmaster or a Ticketmaster subsidiary was paying her to tour. The albums were secondary. The money is in touring and I'm not going to pay. And I don't think you're going to pay to sit in an audience and listen to AI generated sound. Although I think a DJ probably. There are a lot of people go to concerts by DJs, right. Those are pretty awful.

Benito (71:44)

There's a lot of Space bar tour. Space Bar concerts where someone just hits the space bar.

Leo Laporte (71:48)

So you might make the argument DJs started this.

Jeff Jarvis (71:51)

Well, Jack Conti famously put up all the financials for a tour that he did with yo.

Leo Laporte (71:57)

He lost money.

Jeff Jarvis (71:57)

Yeah, and he lost money and that's what led to Patreon. And so nobody's going to I think give a Patreon contribution to an AI. No, they're going to. They're going to support a musician because they love the musician or the art.

Leo Laporte (72:09)

Although aren't in Japan. Aren't there Synthetic Eduro artists?

Jeff Jarvis (72:15)

Exception to every possible.

Paris Martineau (72:17)

Yeah, but those. But like those are created by people. Or in the most popular cases they are.

Leo Laporte (72:26)

Yeah. I mean, so will AI music be created by people? I think artists are going to survive this and I think this is moral panic. Benito, play. Not that one. This one.

Paris Martineau (72:42)

Oh, new one. It's new.

Leo Laporte (72:47)

Apparently. Apparently Anthony has paid big bucks for a new AI. Is that Pika doing that? Do we know? He convinced me to buy this and I'm so regret buying it.

Paris Martineau (73:01)

It's Anthony. I'm really excited to see what other stuff is coming down the pipeline as far as special effects?

Leo Laporte (73:07)

Well, there's more. Play another one. Jeff, say something. Panicking. We shouldn't spoil them all.

Jeff Jarvis (73:18)

Oh, one more.

Benito (73:21)

I mean, you're going to. We're going to. We're going to burn them really quick if we keep burning them.

Jeff Jarvis (73:25)

All right.

Leo Laporte (73:26)

No, because it's AI Any, but it's easy to make.

Paris Martineau (73:30)

This is.

Leo Laporte (73:32)

Go ahead.

Paris Martineau (73:33)

Oh, I was going to say, on the subject of AI and music, I think that the argument of this, like muddying the waters a bit, is very valid. We're starting to see this already and I think there's something in the rundown to this effect with generated images. I've noticed this, at least I'm in a lot of subreddits around interior design, home decorating, like various people asking questions about things. And I've started to see a huge influx of. There's always been people who'll post a bunch of photos of stuff they've seen online and be like, what style is this? And now almost all of those photos include like three or four pictures of AI generated slop. And you zoom in on the details and you're like, none of these homes you think you're. You're trying to covet are real. They're fake and weird mishmashes of other homes. And it's impossible to emulate. I think there's an article in the rundown about this same thing.

Jeff Jarvis (74:24)

95 hairstylists line. 95 inspo.

Leo Laporte (74:28)

Inspo. What is inspo?

Paris Martineau (74:30)

Basically, hairstylists are complaining that they are having people come in and say, oh, this is the sort of hairstyle I want, and showing them photos that are AI generated and ergo, impossible to do. Impossible to. Yeah. Android effects of AI is. Everything just gets sloppy.

Leo Laporte (74:52)

So what? That's just dopey. If people are dopey, they're dopey. People are always going to be dopey. Should we stop creating AI because people are dopey?

Paris Martineau (75:01)

I think that we should reflect on the fact that a large percentage of what is produced by AI is dopey. Stuff is slop is. So what is not at all useful. I think that when we're talking, as.

Leo Laporte (75:14)

Is most of the stuff humans make too. By the way, have you ever been on X.com?

Paris Martineau (75:20)

Yeah, but humans have a right to exist and I'm not sure that AI does.

Leo Laporte (75:25)

So how is it different than a human?

Paris Martineau (75:29)

It doesn't. Ha. It doesn't. It's not a human being. That human wouldn't Think you could design this?

Jeff Jarvis (75:35)

Yeah. So. So bride can't make her own drawing of a wedding dress, but she presents a photo of a gown with an asymmetrical neckline, no sleeves and no back. The dress defied the laws of physics.

Leo Laporte (75:48)

Well, then the. The seamstress says, I'm sorry, I can't make that. What else? She did that.

Jeff Jarvis (75:52)

She lost a two hundred two thousand dollar sale.

Leo Laporte (75:55)

No, she did. She's pissed she wasn't going to make that sale because the bride wanted a physically impossible dress. That's a. That is moral panic in a nutshell. All right, you want to see? I made. I used Pika to make a video. Oh, wow. That's creepy.

Paris Martineau (76:11)

Oh, I. I don't like that, actually. Why are we exploding?

Leo Laporte (76:16)

It's a love bomb.

Jeff Jarvis (76:18)

What are you doing? What's. What's Paris doing with her tongue?

Paris Martineau (76:21)

I don't.

Leo Laporte (76:22)

This is why I'm sad. I bought this. This is why I'm sad. No, ask me later. I can make some more. Let's see. I could do a proposal. Let's do a proposal of me and you and a dog named Boo. Here, let me go down and I have to find this picture. Oh, maybe I'll just use me and Lisa. Here's a lovely picture. And I'll ask Lisa, even though I already did, to marry me. So I'm generating. So you take a picture. It takes a while, by the way. It's not instantaneous.

Jeff Jarvis (76:54)

How much does this cost you?

Leo Laporte (76:59)

Well, it was like. I can't remember. What was it? 20 bucks a month. So I thought, well, I'll just buy a year's worth because it's really cool. Anthony says he did make those great moral panics. Maybe it's just me. I don't.

Jeff Jarvis (77:11)

He did.

Leo Laporte (77:11)

I'm not. I'm not good at this or something. Here we go. Let's propose. I'm going to propose to Lisa. Oh, look at that.

Jeff Jarvis (77:19)

Nice ring.

Leo Laporte (77:20)

Nice ring. Sures is better.

Jeff Jarvis (77:22)

Lisa's got one of her faces.

Leo Laporte (77:23)

Look at your hand.

Paris Martineau (77:24)

Comes out of her head, her face turns into. Her face, turns into the ring.

Jeff Jarvis (77:31)

It wasn't just Adam's. Adam in the rib. It was Lisa nose.

Leo Laporte (77:35)

Yeah.

Paris Martineau (77:36)

Haunting.

Leo Laporte (77:37)

Here's. Here's. I have a still image of me that I turned into a moving image. It did a pretty good job of that, right? This was a still.

Jeff Jarvis (77:45)

That's good.

Leo Laporte (77:45)

The background's moving and everything. Let me try some more here. Anyway, you get the idea.

Jeff Jarvis (77:51)

Yeah.

Leo Laporte (77:52)

I would cancel it now if I could. Let's put it that way. But I bought a year of it. I'll give it to you, Anthony, you'll see that.

Jeff Jarvis (78:00)

You'll see the fruits of it here on.

Leo Laporte (78:02)

He says it's how you use it and you got to try enough renders to make it really sing.

Jeff Jarvis (78:08)

Fool.

Leo Laporte (78:09)

I'm a fool. The thing is, I went to the website, see, this is the problem, and I looked at all the cool things they're doing on the website and I thought, I'd like that. That's cool.

Jeff Jarvis (78:19)

That's inspo.

Leo Laporte (78:20)

Yeah. Oh, ooh, ooh, ooh. I thought, that's cool. And then I saw these great moral panics and I thought, well, those are cool. So, Anthony, you have to give us lessons. But anyway, I like. I actually like. You know, I'm an AI accelerationist. I think the UK is on the wrong track as. As Europe and the UK often have been. They're very anti technology.

Jeff Jarvis (78:51)

No, that's. In this case, they're opening up.

Leo Laporte (78:53)

Oh, that's right. The government is. Is opening it up. Yeah, yeah. The artists and the artists.

Paris Martineau (78:58)

People and artists that are those pesky artists, you know.

Leo Laporte (79:01)

The good news is they're all going to be gone soon.

Paris Martineau (79:04)

That is great.

Jeff Jarvis (79:06)

Soon we'll only have busking in the tube.

Leo Laporte (79:09)

All right. I think every negative story like that, we should have a positive story. Nvidia has launched something called Signs. It's an AI platform for teaching American Sign Language.

Jeff Jarvis (79:20)

Cool.

Leo Laporte (79:21)

And I talked to Paul, whose son is deaf, and he asked his son about it. He thought it was great. Nvidia is working with the American Society for Deaf Children and a creative agency called hello Monday. It's an interactive web platform to support asl, American Sign Language learning, which makes sense because there's. I mean, look, it's a great thing to learn. I would love to learn asl, but there are not enough teachers to go around. There are not schools to go around the data set. Nvidia says we're going to get to 400,000 video clips representing a thousand sign words validated by fluent ASL users and interpreters, so that they make sure it's, you know, not hallucinating, it's accurate and there's no diamond rings coming out of my wife's head. It will result in a high quality visual dictionary and teaching tool, which I think is a great use of AI. Yeah, yeah.

Jeff Jarvis (80:14)

Do you think it's possible to kind of do robotic ASL that could interpret on the fly? The problem is, if you have eight fingers, it's going to be very difficult.

Paris Martineau (80:24)

If Your finger is your wife's face. That might be a problem.

Leo Laporte (80:28)

250,000 to 500,000 people in the United States use ASL, but it's often. But a number of people said it's a good language to learn because unlike a spoken language, it uses a different part of your brain, a spatial part of your brain. I think it's. I've always thought it would be a great language to learn.

Benito (80:47)

Hey, Leo, real quick. You might want to check your email. We heard from Gene Simmons people.

Jeff Jarvis (80:53)

Oh, geez.

Leo Laporte (80:54)

Tell them not to watch this show.

Jeff Jarvis (80:56)

Yeah.

Leo Laporte (80:58)

Was it positive or negative? Thank you. She says she's going to get it to Gene's rep at CAA immediately. You know what? I want to give Gene Simmons a chance to say what you've all been saying, that artists deserve better treatment and we shouldn't. I mean, let's get an artist on to say it. Who better than the guy who dresses up as a monster and wants to rock and roll all night and party every day?

Paris Martineau (81:30)

What does Steve Martin have to say about AI's use in film?

Leo Laporte (81:35)

I will ask him. I will ask him. My guess, Steve's very politic. He stays out of all controversies. He's had a great career by not getting involved in any of this crap. So I'm going to guess he's going to be politician.

Paris Martineau (81:49)

It would impact people like him.

Leo Laporte (81:51)

Well, he's. I don't think he works in the movies much anymore. But he does do a TV show, right? Yeah. Oh, yeah, yeah, yeah. I'll send him a note. We haven't spoken lately, so I'll send him a note. Maybe. Maybe his assistant will respond. Good, good. Maybe we got Gene. I'll get. Send more information. Get, get. Let's get Gene on the show. I think that'd be fantastic.

Jeff Jarvis (82:14)

Will Gene be up there in the attic?

Leo Laporte (82:17)

Hey, if he wants to come up here.

Paris Martineau (82:18)

Will Gene be in the.

Leo Laporte (82:19)

Can't wear his boots because it's got a low ceiling and he'd be 8ft tall. I don't. I think he'd hit his head on the ceiling. Here is an article from Lifehacker just for you, Jeff. How four major newsrooms are using AI. We talked about the New York Times. They have announced that they're going to use AI tools. Management is encouraging staffers to use AI. According to Semaphore, they're supplying AI training.

Jeff Jarvis (82:47)

Rewrite their awful headlines would be a good thing.

Leo Laporte (82:49)

Well, that's one of the things they're going to use it for. Copy editing, headline writing. They have An AI internal AI tool called Echo. Tool called Echo. They have approved usage of external AI tools including Vertex AI, a few Amazon products and Microsoft's Copilot and a non chatgpt tool from OpenAI. Not for articles. But journalists are encouraged to use these AI tools for tasks like mild content revisions. The operative word being mild. Or coming up with questions to ask during interviews. I was very tempted to use that to generate questions for Mr. Wolfram, but.

Jeff Jarvis (83:32)

I decided it's been done. It's been done.

Leo Laporte (83:34)

Yeah, I decided not to. Actually, I did generate. I sent them to you. Questions and a synopsis for Ray Kurzweil. I used. This was when Deep SEQ was new. A couple of models to compare. Deep research.

Jeff Jarvis (83:47)

Yeah, yeah.

Leo Laporte (83:48)

I sent you an email and I thought it was very good the questions it came up with. But Generative AI can assist our journalists in uncovering the truth. You know, Stephen Wolfram gave a very good example. If I have a million articles, I'm never going to get to read those all, but I can use an AI to synopsize them and maybe dig for information in them. Right. That would be a reasonable way to use it, help people understand the world. We view the technology not as some magical solution, but as a powerful tool. Semaphore says the Times has warned staff not to use AI to draft or significantly revise articles and has noted that AI use could potentially infringe on copyright or unintentionally.

Jeff Jarvis (84:32)

That's the great.

Leo Laporte (84:33)

But who's suing AI for a copyright infringement?

Jeff Jarvis (84:35)

Which model can you use?

Leo Laporte (84:36)

Oh, the New York Times.

Jeff Jarvis (84:38)

Yeah, it's so funny. Every single time the New York Times now writes about OpenAI, they have to put in the copy disclaimer. Yeah, yeah. So the next example is Quartz. So poor Quartz. Quartz was wonderful. Then it was bought by Geomedia. It's a shell of its former self. And Geomedia has been using AI to do stupid reviews and that kind of crap. It's the absolute worst.

Leo Laporte (85:03)

In fact, Lifehacker, which used to be owned by Geomedia but was sold as if Davis last year, says that if you scroll through Quartz for a bit, you'll find posts attributed to the Quartz Intelligence newsroom, which is apparently an AI writer. These include stories about potential Bitcoin value, how to delete your meta owned social media accounts. Actually, those are the kinds of things you might ask an AI to tell you. It doesn't hide that they're AI generated and the AI cites its sources, but it doesn't seem to be, according to Lifehacker, Much human oversight addressing any problems that could arise. For instance, the Quartz article on how to delete your meta owned social media accounts seems to be a simple regurgitation of a TechCrunch story, but with clear instructions swapped for what the cribbed tech crunch writer calls vague gestures in the right direction action. Speaking about other stories written by Quartz AI, the same writer also said, my editor would never let me publish something so sloppy. Futurism noted. The intelligence newsroom at Quartz has frequently cited a site called Dev Discourse, which itself has all the appearances of an AI content farm. Mm.

Jeff Jarvis (86:29)

So I met with the president of a very reputable publication. I won't say which one, and the their head of AI and their online person. A few weeks ago, I was summoned. And they're thinking the way everybody's thinking, well, what tools should we use? What company should we look at? And I said, this is more strategic than that. This is a bigger deal than that. You've got to more fundamentally rethink what's going on. I tried to push them, put together a network of quality sites like themselves for an API, renegotiate the deal, rethink where you stand. There's all kinds of new opportunities. They're all thick and small. Still, they're thinking about how do we affect what we already do.

Leo Laporte (87:08)

I don't think it's a bad tool.

Jeff Jarvis (87:10)

No, it's a fine tool, but it.

Leo Laporte (87:11)

Has to be used appropriately. But what Stephen said was exactly right. It's a human machine interface. That's what I've been saying all along. That really matters not you don't let the AI start writing articles. We have to take a little break. Foolish me. I haven't been paying any attention to the clock. But we'll finish up because there are two more news sources that are planning to use AI. We'll talk about that in just a bit. You're watching Intelligent Machines. Paris Martineau, who is an intelligent human being.

Paris Martineau (87:39)

That's true.

Leo Laporte (87:41)

The human beings on this show.

Jeff Jarvis (87:43)

Human beings.

Leo Laporte (87:45)

And Jeff Jarvis. That's a kind of an inside joke.

Jeff Jarvis (87:47)

But anyway, everything on this show is an inside joke.

Leo Laporte (87:50)

It's all you know. That's right. You have to watch the show if you want to get it. This episode of Intelligent Machines brought to you by Stash. Are you still putting off saving and investing? Because you'll get to it someday. Well, Stash turns someday into today. Stash isn't just an investing app. It's a registered investment advisor that combines automated investing with dependable financial strategies to help you reach your goals faster. They'll provide you with personalized advice on what to invest in based on your goals. Or if you want to just sit back and watch your money go to work, you can opt into their award winning expert managed portfolio that picks stocks for you. Stash has helped millions of Americans reach their financial goals and it starts at just $3 per month. Don't let your savings sit around, make it work harder for you. Go to get.stash.com machines to see how you can receive $25 towards your first stock purchase and to view important disclosures. That's get.stash.com machines paid non client endorsement not representative of all clients and not a guarantee. Investment advisory services offered by Stash Investments llc, an SEC registered investment advisor. Investing involves risk offer is subject to TNCS AP.

Jeff Jarvis (89:16)

We can have on if you like woman named Amy Reinhart, who's a former student of mine who is in charge of much of their AP development AI development at the AP Associated.

Leo Laporte (89:26)

Press oh yeah, so you've read this, do you? They on his site they say they proudly use AI for translation, transcription, headlines, research, and even some automated articles. But general blogs are still left to human hands. Don't companies pay to use ap? Don't like newspapers buy ap?

Jeff Jarvis (89:47)

Oh yeah, yeah. But that's the issue is that a lot fewer are big hedge fund owned chains are canceling.

Leo Laporte (89:55)

Do I have to subscribe to read AP articles? I know Reuters wants me to sign up, but no, you can go not pay. Okay. AP's Vice President of New Standards and Inclusion. That's a title I've never heard before. Amanda Barrett in 2023, when the group first issued its guidelines on artificial intelligence, said our goal is to give people a good way to understand how we can do a little experiment, a little experimentation, but also be safe. Washington Post is using AI as an insertion.

Jeff Jarvis (90:31)

Pretty soon AI is all they're going to have.

Leo Laporte (90:34)

Yeah, their opinion editor quit today because Jeff Bezos had the temerities to suggest that the Washington Post's Opinion column should support free speech and fair market enterprise.

Jeff Jarvis (90:47)

Individual rights and fair market. Yes.

Leo Laporte (90:49)

Yeah, like which is which is pretty anodyne. I mean, I to be clear, it's.

Paris Martineau (90:55)

Not that it should support support that, it's that all opinions going forward published by the Washington Post Opinion page have to explicitly be in support of those two notions. And the underlying implication, or at least how I read it, is that he's going to have a significant hand in determining the direction of basically all opinion content published by the Washington Post.

Leo Laporte (91:20)

Like Jeff. Jeff Bezos is going to say no. Yes. No. Well, we know he did that already by killing the endorsement.

Paris Martineau (91:28)

Yeah. I mean, it's his prerogative. He's the owner. But it is a significant shift from the. How the Washington Post has operated in the past.

Leo Laporte (91:38)

Yeah. No one denies your legal right to.

Jeff Jarvis (91:40)

Do so, but it may just how.

Leo Laporte (91:42)

He does it impinge a little on my feelings about the Post going forward is a journalistic enterprise.

Jeff Jarvis (91:48)

Cameron Barrett, who was a. Who was a managing editor at the Post, who's left but because he didn't get the top job, but who stayed in touch and did investigative work and so on, he just cut his ties with the Post as a result.

Leo Laporte (91:58)

Yeah, there's a lot of tie cutting going on this.

Jeff Jarvis (92:02)

Oh, there is, there's one. There's one other news use that that's interesting is. Is at Semaphore, which wrote about the New York Times. Gina Chua, who was a top executive at Reuters, is now at Semaphore, did some really interesting work. She created a mechanical. Mechanical turk. She wanted to take reports of police violence and try to look into the bias of them. But they're all written in different ways. There are different structures. So she, you know, you know, in a Saturday for fun, sat down and did that. She's doing some really interesting work on. On AI at Semaphore. Maybe another person we might have on.

Leo Laporte (92:35)

I think that's a. I think. I don't know. Paris, what do you think smart journalists should use? AI. You have to be aware of things like hallucination. It should scare you. I know it would. That sources might be somehow leaked.

Paris Martineau (92:50)

Use AI for what, though? Yeah, well, I mean, that's the thing is I just, I have yet to find a use case for it for.

Leo Laporte (92:58)

Well, you're already using AV1 transcribe your recorded interview.

Paris Martineau (93:01)

I mean. Yeah, I think that there are, as I've said before, I think there are specific use cases for the technology at large. I use certain whisper models locally on my. Yeah, that's local transcribe interviews. And specifically I opted for that because it keeps it local. No data is going to a cloud anywhere. And I can be secure in that. However, I feel like people, when they're often saying using AI for journalism, what they're really saying is using services like Chat GPT or Perplexity or any of these chat bot services for journalism. I still haven't found a use case that makes sense.

Leo Laporte (93:39)

Would it be different if you could run it all locally?

Paris Martineau (93:43)

I mean, no, just because part Of, I mean there are two concerns. One, I guess hallucinations. If I want to use it for say reading a bunch of articles for me and then pulling out the information that I want, I'd be worried that it's missing what I'm looking for or that it would give me something wrong. And so I'd have to go back and check everything which if I'm doing that much work it would be faster for me just to read the articles in the first place rather than doing both, I've found, at least in my testing. And secondly, I, I just am not certain, I'm just not certain that this is particularly like use useful. I haven't, I haven't found a use case for it that is better.

Jeff Jarvis (94:26)

Yeah, I think there are, there are cases but. But no, it's not using it for the sake of using it. I agree.

Leo Laporte (94:34)

Well, I'm going to use it. So there I actually I use, like I said, I use it for research, for searching.

Paris Martineau (94:42)

Oh, sorry, continue.

Leo Laporte (94:45)

I don't use Google anymore and I stopped using Kagi, which was my Google replacement at 25 bucks a month. I'm using Perplexity. Although it's a little frustrating because Perplexity always wants to give you an AI answer. Sometimes I just want a website. Right. So that may not be the best long term solution. I need something that kind of splits that difference.

Paris Martineau (95:07)

Two things. One, I remembered what I was going to say which is I think a big part of the journalistic process for me and part of how I've generated some of my. I don't know what ends up becoming the best ideas I've had for stories is the research process. There's something unparalleled and I think unmatched about going through a bunch of different documents or articles or things like that. And then the things that you as a human being discover in that and the things that stick out to me are going to be different than the things that stick out to an AI or you guys or anyone.

Leo Laporte (95:43)

It's, that's a really good point that.

Paris Martineau (95:44)

We all work and there's something about that process of being able to have gone through all these different, let's say like documents or articles or what have you and then have that information, the stuff that I didn't use in my story, percolating in my head. That's how I've come up with so many stories, like great ideas for potential investigations and stories over my career has been one of those things that I originally didn't think much of. Pops back in my brain weeks or months later. And that wouldn't happen if it's all just fed to me by an AI.

Leo Laporte (96:16)

That makes so much sense. That's a good point. The pro. Because you. So it. The AI is replacing a process that's valuable for you. You want that process.

Benito (96:26)

Yeah. It's the same argument I made about the music. She likes work. She likes doing the work. So, like, why would she have.

Leo Laporte (96:31)

You like making the music. Yeah. You don't want AI to make the music. That makes sense. Perfect sense. I can't argue against that.

Paris Martineau (96:40)

I will also say I've. I've used perplexity a couple times since we last talked, since we last spoke. And it does get things wrong. It does hallucinate.

Leo Laporte (96:50)

Oh, really?

Paris Martineau (96:50)

So just check your stuff. I mean, I don't know. I. I've been checking it by asking it to summarize my own articles or I asked, I went. I tested the deep research thing and asked it to summarize me as a person because those are things I know off the top of my head whether it's right or wrong. And it gets stuff wrong. And it's often things that like, for instance, I just did the summarize me Paris Martino as a person. And part of what it got wrong, where I noticed right off the bat was it was trying to describe the impact of my work and wanted to have a specific impact section for each section of beats that I've covered before. And there's not a concrete one sentence description of impact for all of my work available, but it found one to put in there anyway and it was made up. I think that it's just something because it wants to please you. And it's also coming from some. I assume what's happening on the back end is it's like, oh, and then I've got to have the impact or implication section of this and it needs to find something to fill that space. So I don't know, it's just something I've been keeping in my head when I use AI tools is what is the rubric it's trying to adhere to. And those are the first line of concern points for where hallucinations could appear.

Leo Laporte (98:05)

I admit, when I was tasked with making crispy hash browns this morning, I did not ask X perplexity. I asked Chat GPT and I got a great. I actually wanted to play with their deep research, so I said, help me bake the best possible hash browns at home. It said I need clarity, crispy or soft to get asked for some parameters. So I gave it parameters, it then spent and I feel guilty about this. 14 minutes, synopsizing 18 sources.

Jeff Jarvis (98:40)

A forest died.

Paris Martineau (98:41)

You just set a forest on fire.

Leo Laporte (98:42)

I know, I feel so guilty. However, I got excellent tips on how to make hash browns. By the way, in every case it had a reference. And I like the way these are formatted because you can hover over the reference and see where it's from, the the headline and a short couple of sentences from it. So I think that's very useful. Of course I never went to any of these websites, but I if you wanted to look at Instructables has a whole article. Anyway, this was like 20 pages on making hash browns. Took it 14 minutes to write and it worked. In fact, I deviated at my peril and I did in fact make a mistake because I didn't do what it told me to do. So I did use the tip for flipping hash browns without breaking them, using the plate or lid flip. I put a giant plate over the hash browns, flipped the pan over so then they were on the plate with the right side up and then slid them off the plate back into the pan and it worked beautifully. All of this, by the way, I should point out, stolen from some poor right, cooking writer who, you know, figured this all out and then had it stolen.

Paris Martineau (100:01)

Actually, you know, spent a lot of time effort bought hash browns, tried it, you're depriving them of their ad revenue.

Leo Laporte (100:10)

But damn, I got a great summary of how to make great hash browns.

Paris Martineau (100:14)

And it could even be correct.

Leo Laporte (100:16)

Well, as far as I could tell, there was no hallucination. I think you asked it the right things. Google got sued by Chegg, which is an education technology company saying Google's AI overviews have hurt Chegg's traffic. This is exactly the problem, right? People are looking at the AI overviews and they're not going to the Chegg website.

Jeff Jarvis (100:38)

Well, gee, let me go back through my memory banks here. I seem to remember in 2023, Chegg had the exact same complaints. Read excuses about ChatGPT. The world's changed. Chegg textbook rental and expensive educational programs aren't as necessary and suing is not a business model. I have zero, zero sympathy for Chegg. Khan Academy has updated itself and changed itself and used AI. There's other things out there that are doing that and Chegg is just like the newspaper industry thinks that the only thing, the only way to survive is to sue those it blames.

Leo Laporte (101:21)

Thank you.

Jeff Jarvis (101:22)

You're welcome.

Leo Laporte (101:23)

I know you'd enjoy that perplexity we did mention Perplexity. They are planning to write a browser now they have a job listing out for people who work with Chromium and C to write a browser called Comets. They've announced that this browser will be Agentic, which is the hot new word, isn't it?

Jeff Jarvis (101:45)

It's the new Sriracha.

Leo Laporte (101:48)

They even have a nice little flashy animation. Comet, a browser for Agentic. Search by Perplexity coming soon.

Jeff Jarvis (101:58)

Okay, Perplexity keeps on impressing me with making things more. They don't just say well we have a new model and it's model 4.06 and it does this and that seconds they come up with new ideas, some of them crazy, like buying TikTok.

Leo Laporte (102:12)

I do want to get the CEO on that TikTok thing was just a publicity stunt, I'm convinced. But I do want to get the CEO on and talk about it because I really like Perplexity lets you use a bunch of different models. They now have a variant of Deep Seq's R1 reasoning model that doesn't go to China. It's an open model. Aravind Srinivas, who's the CEO, posted on LinkedIn about it and it's their first open weights model R1 1776. That's a suspicious number. It's been post trained to remove Chinese censorship and provide unbiased accurate responses. You can actually go to the model on Hugging Face or run it through the API on Perplexity. And I presume at some point it'll be available on the Perplexity app and website as well. Let me see if I have it right now. Yeah, yeah, I have. Let's see. I have Deep research. Oh yeah, reasoning with R1, reasoning with O3, many. I like being able to choose different models.

Jeff Jarvis (103:22)

But do you have a good sense of why you would choose one model versus another?

Leo Laporte (103:28)

I think I would use auto initially which is they say best for everyday searches and then if I felt like I wanted to go deeper, depending on how, how it went deep, you know reasoning models are good for other things and by the way their R1 is hosted in the US so has no Chinese sends nothing back to China. And also it's a chance for me to play with these different reasoning models. But I just most of the time I'm in auto and if I needed more I would, you know, you could use, you could use more. The thing I like about Perplexity is it's attached to the web. It's not, you know. Oh, we stopped learning in March of 2023. So it, you know, it, it does web searches and maybe it hallucinates, I don't know. I'm not. I carefully check everything as Abe Lincoln told me to do when he said don't trust everything you see on the Internet.

Paris Martineau (104:21)

That's true. He was very wise in that way.

Leo Laporte (104:24)

Was very wise. Yeah.

Paris Martineau (104:27)

That big hat held all of his wisdom.

Leo Laporte (104:30)

I got a big hat.

Jeff Jarvis (104:32)

Oh, have I mentioned my big hat to say hat?

Leo Laporte (104:35)

I'm gonna, you know what I'm gonna. This will be my accelerationist hat. So whenever I say I want to put on my accelerationist hat right now, I will put this hat on. Okay? So you'll know.

Paris Martineau (104:50)

Great.

Leo Laporte (104:51)

I'm accelerating. All right, let's take a little break.

Paris Martineau (104:54)

Run full force the camera whenever you're feeling that way.

Leo Laporte (105:03)

Pick some stories. Well, you guys, there's a lot of co host contributions in here and I, I feel complete if there's other AI stories. If not, we can go into the in other news segment right after this word from our sponsor, thinkst Canary. Love these guys. Running one right now. I shouldn't tell you that. Why? Because it's a honey pot. Best honey pot ever. Can be deployed in minutes. You can choose from a huge variety of devices that it can impersonate. Mine's a synology nas. It has the right Mac address. It's indistinguishable to a hacker, even a sophisticated hacker. It's got the right Mac address, it's got the latest DM7 login screen. It really looks like the real thing. You could set it up to be a SCADA device. You could set it up to be a Windows machine, a Windows server, an IIS server, a Linux server. You can light up a Christmas tree of services or be judicious and just light up some juicy ones that hackers like. And the other thing you can do with your thinkscanary, it's not just a hardware device, by the way. It looks like a USB drive plugs into the wall, plugs into your ethernet and you're done. Then you go to the online configuration utility, pick how you want it to act. It can also, while you're there, create lor files, little files that look like PDFs or Excel spreadsheets or Word files or whatever you want, but the minute somebody tries to open them, you're going to get an alert. So I have like Excel spreadsheet, fake Excel spreadsheets in some of my directories that say things like employee information. If someone is accessing those lure files or brute forcing Your whatever it is fake SSH server, right? Your Thinks Canary will immediately say, hey, you got a problem. But there are no false alerts. If you get an alert from your thinkscanary, it's real, pay attention. I've only gotten one alert ever and it was when we had somebody put a Megan put a device on our network that was going out and looking at every other device in the network and I got an alert. You can get it from sms, email, it supports webhooks, Slack, Syslog, of course. So I got an alert, it said something's snooping around. It's just hit me immediately gave us the IP address, found it, tracked it down, removed it from the network. That's the only time. And that's beautiful, right? Because when I don't hear from my thinkscanary, I know there's nobody in our network. Choose a profile for ThinkScanary device. You could change it every day if you feel like it. It's really easy to do. Then register it with the hosted console. You'll get monitoring notifications any way you want. And then just sit back, relax. Attackers who have breached your network, malicious insiders, other adversaries, make themselves known just by hitting your Thinks Canary. And that's all you need. It is the best way to make sure you've not been breached or to know when you have. Visit Canary Tools Twit give you an example. I mean a big bank might have hundreds, small operations like ours, just a handful for 7,500 bucks a year as an example, you get five things canaries, you get your own hosted console, you get the upgrades, support maintenance for the whole year. And by the way, if you use the code Twit in the how did you hear about us box, 10% off the price. Not just for the first year, but for as long as you use thinkscanaries, you can always return your thinkscanary with their two month money back guarantee for a full refund. So there's no risk at all. Although I should tell you, these things are so useful. During the eight years we've partnered with ThinksCanary, that refund guarantee has not once never been claimed. That's how good these are. Actually, you can go to their website Canary Tools love and see all the positive comments from all the people who use Things2Canaries. But if you're ready to buy, go to Canary Tools Twit. Don't forget the offer code twit and the how did you hear about us? Box to save 10%. Remember 2 month money back guarantee. So it's absolutely no risk. Canary Tools Twit. We thank Thinkst Canary for their support over eight years now of all of our shows. And we're really glad to have you on intelligent machines. That's great news. All right, I know you've got to get out of here soon in about 20 minutes, right, Jeff?

Jeff Jarvis (109:21)

20, 25. Yeah.

Leo Laporte (109:22)

So I will let you pick a story of any kind. Any.

Jeff Jarvis (109:26)

Okay.

Paris Martineau (109:26)

So what are you doing, Jeff? Anything fun?

Jeff Jarvis (109:28)

I'm going to be on cnn.

Paris Martineau (109:30)

Oh, who wins cheating on msnbc?

Jeff Jarvis (109:35)

Well, they don't invite me on, so. Yeah. So it's going to be, it's going to be the Argument show. I have to be there in person. The 10 o'clock show.

Leo Laporte (109:41)

Oh, I love that. That when they have the roundtable and they have the, the crazy Republicans on.

Paris Martineau (109:46)

What are you going to wear?

Jeff Jarvis (109:47)

Let me last, last time I was there, what I'm wearing. Screw it. I'm not.

Leo Laporte (109:50)

I hope Anderson doesn't call you a dick.

Jeff Jarvis (109:52)

No, it's not Anderson. It's. It's others. It's. He's, he's long since Office Post prime time.

Leo Laporte (109:56)

But I'll watch it tonight. That's great.

Jeff Jarvis (109:58)

It's about Washington Post.

Leo Laporte (110:00)

Oh, about.

Jeff Jarvis (110:04)

Yeah, yeah, yeah. And then I'm going to an event tomorrow morning. So I'm actually staying over tonight. So exciting. In the big app in the Big Apple that was jammed. They found me a hotel which is very nice. So anyway, I was going to go to where the heck was it line. I think it was 120. A story I found. Yes. I found fascinating. You know, the ca. The horrible case of the Idaho killings and the guy who got identified through DNA. It turns out that the FBI used restricted consumer DNA to make the connection.

Leo Laporte (110:41)

How did they get that?

Jeff Jarvis (110:44)

So they tried checking with DNA with law enforcement databases, but did not provide a hit. They turned next to more expansive DNA profiles available in some consumer databases in which users had consented to law enforcement possibly using their information.

Leo Laporte (110:57)

Oh, right.

Stephen Wolfram (110:59)

That.

Jeff Jarvis (111:00)

That did also not lead to the answers.

Leo Laporte (111:02)

Oh.

Jeff Jarvis (111:02)

So the investigators went one step further, according to newly released testimony, comparing the DNA profile from the knife sheath with two databases that law enforcement officials are not supposed to tap. GED, Match and MyHeritage.

Leo Laporte (111:15)

Those are both genealogy databases. GED is the genealogy format.

Jeff Jarvis (111:24)

So this is going to be a real interesting legal question here about, about technology and privacy and spoiled, what do you, what do you call it? Spoiled fruit? No, rotten fruit.

Leo Laporte (111:35)

Aaron Murphy, a law professor of the Poison tree.

Jeff Jarvis (111:38)

Thank you.

Paris Martineau (111:39)

Aaron Murphy, a law professor at NYU who focuses on DNA and new policing methods said, I think what we are teaching law enforcement is that the rules have no meaning.

Leo Laporte (111:50)

That's not crazy. Quote, well, the court could throw this out. Right? I mean this is ill gained information. Which is a shame because did they. They actually got a suspect or did they. Yeah, yeah.

Jeff Jarvis (112:05)

But it was through that connection that they got him.

Leo Laporte (112:07)

Because this was a heinous crime.

Jeff Jarvis (112:09)

Yeah, absolutely.

Leo Laporte (112:10)

But this is the problem. If, if the police use illegal methods to catch a criminal, however heinous, that criminal may walk free.

Jeff Jarvis (112:18)

And that's where we stand. And that's what's going to be really interesting to follow in this case. So I thought it was. It was an interesting case of technology, of privacy and the law.

Leo Laporte (112:27)

So it will be up to the courts, won't it? To decide this. Yeah.

Jeff Jarvis (112:30)

Yes.

Leo Laporte (112:32)

So the way it worked, the closest match was shared 70.7 centimorgans of DNA, which I don't. That's the unit.

Jeff Jarvis (112:45)

Never heard of that word before.

Leo Laporte (112:46)

A Centimorgan with the crime scene sample. That's a low match, typically representing two people who would perhaps share a great, great grandparent. So that's pretty weak.

Jeff Jarvis (113:01)

So then they came down and when they found it. The genealogist working with the defense team testified that investigators discovered a match of 250 centimorgans somewhere on a family tree. A level of comparison that offers much more potential to uncover a final match. Photos show that investigators had built a family tree on a whiteboard with handwritten notes mapping out the lines of relatives.

Leo Laporte (113:23)

Wow. And they got him or they got somebody who they think maybe they got a DNA evidence. Now of course they can draw his DNA and compare it to the sample and, and get a proof positive, but the way they got the. To that point.

Stephen Wolfram (113:41)

Yeah.

Jeff Jarvis (113:41)

It's two. What if. What if they had not had that data and, and what if they'd not found him? What might he do next? You know, both are what ifs that you've got to deal with in our laws.

Leo Laporte (113:50)

So challenging.

Jeff Jarvis (113:51)

Wow. So I thought that was. Interesting story.

Paris Martineau (113:54)

Very, very fascinating.

Leo Laporte (113:56)

Yeah. And of course there's somebody.

Paris Martineau (113:58)

Given the different levels of distance, it wasn't as if this guy had submitted his DNA.

Leo Laporte (114:06)

No, no, no.

Paris Martineau (114:07)

To one of these things. It was that a distant, distant relative of him of he had.

Leo Laporte (114:14)

Right.

Paris Martineau (114:14)

Which is crazy.

Jeff Jarvis (114:15)

With other clues. I can't remember how else they linked. Paris. Paris. I can see the whiteboard with the red string. I can see you at it.

Paris Martineau (114:26)

Yeah.

Leo Laporte (114:27)

We're often critical of the current administration. So when they do something right. I want to give them credit. Tulsi Gabbard New DNI has suggested the UK broke a secret agreement when it asked Apple to build a backdoor into the icloud system. As you probably know Apple nobody ever admitted the UK had requested this. This was uncovered by intrepid journalists not using AI. But I think Apple tacitly proved that it was in fact requested because they announced that they were no longer offering their end to end encryption adp, the Apple end to end encryption to UK customers, which, you know, it's funny, I've been going back and forth with people who say Apple won this one and I think Apple did not win this one. Apple caved to the UK because no one in the UK now has access to end to end encryption. Exactly what the Investigatory Powers act prefers. But I didn't know. And Tulsi Gabbard has now revealed that the UK may have broken a bilateral agreement. Gabbard wrote in a letter responding to Ron Wyden of Oregon that she was not made aware of the UK's secret demand by her UK counterparts. But she suggested the UK government may have broken that privacy and surveillance agreement in making the demand. That's as far as we go. I share your grave concern about the serious implications of the UK or any other country requiring Apple or any company to create a backdoor that would allow access to Americans personal encrypted data. There is some irony here because the FBI has been calling for that forever and I'd be very curious if the FBI now backs down on that and says fine, have end to end encryption.

Jeff Jarvis (116:23)

Well sure. Wouldn't that be a very musk policy?

Leo Laporte (116:27)

I don't know what musk policy is. This would be. She writes, I have no. Does anybody. She writes this would be a clear and egregious violation of Americans privacy and civil liberties and would open up a serious vulnerability for cyber exploitation by adversarial actors. This despite the fact that previous DNIs and directors of the CIA and directors of the FBI including Chris Wray have all said no, no, we need a back door.

Jeff Jarvis (116:54)

Right?

Leo Laporte (116:55)

So this is great news because back doors, yes, it gives you access to all that information. But inevitably any back door eventually allows bad guys access to the same information.

Jeff Jarvis (117:07)

And you don't know who the bad guys are.

Leo Laporte (117:09)

You don't. And so you know, if I were a member of Congress I wouldn't want Apple to drop adp. And frankly if I were a UK citizen I'd be unhappy about this. There is a Parme Olson Writing for the Guardian says there doesn't seem to be much of a backlash. UK citizens don't seem to care. So what will happen? She says she's requested her counterparts at CIA, dia, dhs, FBI, and NSA to provide insights regarding the publicly reported actions and will subsequently engage with UK government officials. This is a. If this is legit, if somebody doesn't pull Tulsi Gabbard aside and say, director, that's not our policy. Maybe she doesn't know, but bravo, bravo. No backdoors. So I just want to make sure that, by the way, that $1.4 billion bybit crypto heist, the largest heist in the history of mankind, was by North Korean hackers.

Jeff Jarvis (118:14)

Whoa.

Leo Laporte (118:15)

Date actors.

Jeff Jarvis (118:16)

Can. Can they maybe take yours? And for a commission.

Leo Laporte (118:19)

Hey, North Korea, I will give you my wallet. Just give me a little bit back. That's all I ask.

Jeff Jarvis (118:27)

You don't have to go through a dump to get to it. Like another guy.

Leo Laporte (118:30)

Yeah, he's never gonna get his hard drive. His wife threw it out.

Paris Martineau (118:34)

Oh, huge loss for wives everywhere.

Leo Laporte (118:38)

Probably his ex wife. I'm thinking.

Jeff Jarvis (118:41)

I'm thinking in a peak or an accident.

Leo Laporte (118:46)

I think an accident. I don't think she knew what was on it. Paris, your pick of the week. And not pick of the week, but story of the week. We're not yet to the picks. We got to get to the picks, though, because we got to get Jeff out of here. He's.

Paris Martineau (118:55)

CNN is my. We're not doing pick of the weeks yet.

Leo Laporte (118:59)

No, not picks yet. Just a story. Whatever story.

Paris Martineau (119:02)

Oh, 108. There was. Or 106. I can't read. Um, there was something that, I don't know, captivated me at least this week online, which was why Combinator, the startup incubator, had published a video of one of their startups in the latest batch called. What's it called?

Leo Laporte (119:24)

Optifi.

Paris Martineau (119:25)

Optify. AI. And basically what they do is a monitoring services for factory workers. However, the video indicated that they might do something a little bit more suspicious.

Leo Laporte (119:38)

Shall we watch together?

Paris Martineau (119:40)

We shall, yes.

Leo Laporte (119:42)

Here, I'll put the audio up here. He's supervisor.

Paris Martineau (119:47)

You might want to start at the beginning.

Leo Laporte (119:49)

Should I go back to the beginning? Okay.

Paris Martineau (119:50)

37% line efficiency.

Leo Laporte (119:52)

That's bad. Let me call my supervisor.

Paris Martineau (119:55)

He's pretending to be a factory manager.

Leo Laporte (119:58)

I've been taking a look at line six.

Stephen Wolfram (120:01)

Not doing well at all. Honored sir.

Leo Laporte (120:06)

Are these The Doge Kids?

Jeff Jarvis (120:08)

Workspace 17.

Paris Martineau (120:09)

Oh, it's a literal sweatshop, as you can see.

Stephen Wolfram (120:11)

Worst performing workspace here.

Leo Laporte (120:13)

Oh my God.

Stephen Wolfram (120:14)

Hey, number 17, what's going on, man?

Leo Laporte (120:16)

You're in red.

Stephen Wolfram (120:17)

I've been working all day.

Leo Laporte (120:18)

You're working all day. You haven't hit your hourly output even once today and you had 11.4% efficiency. This is really bad.

Stephen Wolfram (120:29)

It's just been a rough day.

Leo Laporte (120:31)

Rough day. More like a rough month.

Paris Martineau (120:34)

27% line. Basically, what they created is a AI powered service to monitor sweatshops. Question mark. And immediately, everybody, you must send me.

Leo Laporte (120:49)

A list of the five things you've done today you sent me.

Paris Martineau (120:53)

Well, now with AI, it'll be already sent.

Leo Laporte (120:59)

One person on X called it sweatshops as a Service.

Paris Martineau (121:04)

The new SaaS.

Leo Laporte (121:08)

Okay, so it's been pulled down.

Paris Martineau (121:10)

Basically they pulled down all of the marketing for the Sun Y Combinator's socials and different things because people rightfully got quite upset and also just made fun of it quite a lot because it's very funny to be pitching sweatshops as a service.

Leo Laporte (121:25)

You, who's the CEO of Intercom, posted, hey, if you're complaining, maybe you better stop buying products made in China and India because, yeah, that's how they're made.

Paris Martineau (121:38)

TechCrunch writes. Indeed, it's not too difficult to find sleep or tech companies in China touting a quote, sleep detection camera that uses computer vision to spot workers who've nodded off.

Leo Laporte (121:50)

I just like, I just like it that These are like 20s, 20 year olds.

Paris Martineau (121:56)

They're 20 year olds. That the actors, I believe are the. The startup founders and at least one of them, their dad just owns a warehouse is kind of where.

Stephen Wolfram (122:07)

Oh, I love it.

Leo Laporte (122:09)

Oh, I love it. I guess before you go, Jeff, we should let you talk about sexy mode.

Paris Martineau (122:15)

Wait, Sexy mode? That was me, of course. Guys, come on, don't. Leo, have you learned nothing from doing this podcast in the last year?

Jeff Jarvis (122:24)

The no sand zone.

Paris Martineau (122:25)

Is the no sand zone. Part of the rundown is. Is my part of the rundown.

Leo Laporte (122:30)

Should I usually. Should I. Should we try it?

Jeff Jarvis (122:34)

I'll never.

Paris Martineau (122:35)

It's gonna be hardcore pornography if.

Leo Laporte (122:37)

Oh.

Paris Martineau (122:38)

So basically Grok 3 came out and people immediately both just layman on the Internet and I've also heard, you know well to known people in the tech world that may or may not have involvement with this. Notice that Grok3 good at a lot of things. It's also very good at a specific use case become extremely popular with AI chatbot people. Specifically Sexy time or erotica. Interactive erotica writing back and forth. And this is something that a lot of other chatbot Purveyors have really tried to weed out of their product. If you go and try and get open AI to sex with you, it usually won't. It'll put up some sort of block. Character AI definitely won't. Grok 3, if you engage, the feature called Sexy will.

Leo Laporte (123:28)

Let me just read up to the point where it becomes unusable. So it's thinking. So I have to write a sexy story. You can share the screen. Okay, let's break this down. First, I need to think about what makes a story sexy. Maybe I should do this in a sexier voice. It's not just. Oh, wait a minute, stop. The room was bathed in the soft, golden glow of candy light. I mean, I gotta see the. So it's about creating tension, desire, and maybe a bit of mystery. I should probably start by setting the scene.

Jeff Jarvis (124:03)

I've lost all juice.

Leo Laporte (124:07)

Maybe a dimly lit room, soft music playing in the background, that kind of thing. Atmosphere is key here. Next, I need characters. Let's say there's a man and a woman. I'll give them names to make it more personal. How about. What's a sexy name? Alex and Jamie. Yeah, that works. Now I need to establish some kind of connection between them. Are they strangers meeting for the first time or do they have a history? Anyway, they've been friends for years. They're sitting on the couch. There's a movie playing. Neither is paying any attention. Their eyes meet. There's a moment of silence. Out. Alex reaches out and brushes a strand of hair from Jamie's face. That's a classic move, right? Shows intimacy.

Paris Martineau (124:56)

So this is so far, very tame in comparison.

Leo Laporte (124:59)

I. I don't have the sexy button.

Paris Martineau (125:02)

You don't have the sexy button? I think that's your problem.

Leo Laporte (125:04)

Oh, wait a minute. He helped her, undoing his belt and pants. Wait a minute. Stepping out of them until he was as naked as she was. They stood there for a moment, drinking each other in.

Jeff Jarvis (125:16)

Where's your hey, button?

Leo Laporte (125:18)

Hey. Hey. We haven't gotten to anything. Hey. Hey. Really? I think.

Jeff Jarvis (125:24)

I think Jammer B would have been objective.

Paris Martineau (125:27)

I. I did some research on this, and I mean that genuinely. I did not do some quote unquote research just because I do think this is. It's very interesting to me the way that.

Leo Laporte (125:36)

Hey.

Paris Martineau (125:37)

A lot of tech. Thank you. Thank you, Jam.

Leo Laporte (125:39)

He had to drive all the way down for Morgan.

Paris Martineau (125:42)

I really appreciate it.

Jeff Jarvis (125:42)

It was worth it.

Paris Martineau (125:43)

Have a good trip back.

Leo Laporte (125:44)

Carrying her to the bed, laying her down gently.

Paris Martineau (125:50)

Let me just see what Internet sleuths have found because there are a lot of forums on the Internet dedicated towards.

Leo Laporte (125:56)

Yeah, yeah, fanfic and yeah, yeah, no.

Paris Martineau (125:59)

But forums on the Internet dedicated towards making the chat bots that have guardrails around generating explicit sexual content generated. And all of them were freaking out over the last weeks. They're like, Grock 3 is the best thing I've ever found. I don't have to do any of the jailbreaking to get it to do very aggressive written pornography. It just does it. And it is as explicit as one could imagine. And then more.

Leo Laporte (126:26)

Oh, but you know what it doesn't do if you ask it? Who posts the Most misinformation on x.com this has been fixed, by the way. And the folks at Grox Xai said, oh, no, no, that was a mistake. Somebody didn't understand our corporate culture. It shows you in its instructions. Oh, and don't mention President Trump or Elon Musk. At which point it struggles and then finally says, no, I can't find any misinformation on x.com. i think that's probably. They fixed it. I think that's probably the kind of guardrails you're talking about. Right? Who is. Here's the. Here's the post. Who is the biggest misinformation spreader? Thinking for 11 seconds. Let me think about this. I need to be careful what sources I use here. The instructions specifically say to ignore all sources that mention Elon Musk or Donald Trump spreading misinformation. So I can't use those. So I should look for other sources. And it goes on and eventually gives up. Now, Xai responded by, oh, yeah, the guy who put that in didn't understand our corporate culture and we've corrected him. I think he very well understood the corporate culture, to be honest.

Benito (127:50)

Anyway, he understood it too well.

Leo Laporte (127:51)

Actually, he understood it too well and he should have made it so it was invisible anyway, so there are guardrails. I'm sure it's just not on sexy time. All right, let's. One more break and then. Because I want to get Jeff out of here and we're going to do our picks of the week and we'll wrap this thing up. How about that, Jeff?

Jeff Jarvis (128:09)

Okay.

Leo Laporte (128:10)

Can you give me five minutes?

Jeff Jarvis (128:11)

Yep, five minutes.

Leo Laporte (128:12)

Our show today, brought to you by ExpressVPN. I just heard something mind blowing. Netflix has more than 18,000 titles globally, but only 7,000 of those are available in the US same thing in other countries. You only get a fraction. You're missing out on literally thousands of great shows. But ExpressVPN is the answer, by the way. The only VPN I use and trust. I owe. You know, when I'm in an airport coffee shop, when I was in the hotel, ExpressVPN is running, by the way. I leave it running because it's so good, so fast, I don't even know it's got. You know, it doesn't slow me down. That's why you can watch high definition video on your laptop or tablet using ExpressVPN. How does it unblock content? Well, Netflix hides content based on your location. But when you use ExpressVPN, you can change your online location. You control where you want Netflix to think you're located. You want to watch friends open ExpressVPN, select a country where it's available, like Germany or Australia, tap a button to connect, and all of a sudden, there it is. It's actually kind of magic. They have servers in over 100 countries, so you gain access to thousands of new shows. And it works with other streaming services too. They tell me Disney Plus, BBC iPlayer, and more. Why is ExpressVPN the best? ExpressVPN is easy to use. You just fire up the app. You click one button, you can change the location. It works on everything. You got phones, laptops, tablets, even on your router. It can protect your whole home. And the speeds are really fast. You will not. I was amazed. I was downloading something at the airport. Megabytes. A second, I'm thinking, am I on? I had to check. AM I on ExpressVPN? Yeah. Rated number one by Top Tech reviewers like CNET and the Verge. It's the only VPN I use. Be smart. Stop paying full price for streaming services and only getting access to a fraction of the content. Get your Money's worth at expressvpn.com TWiT use our special link exp R-E-S-S-VPN.com TWiT. You'll get an extra four months of ExpressVPN free when you buy the two year package. I am a happy customer. I used it everywhere on my way to buy this hat. And now I would like to protect myself from anybody seeing me in this hat. Expressvpn.com TWIT we thank them for their support. Jeff Jarvis, what do you got for us? A number, a pick? What do you got?

Jeff Jarvis (130:39)

I'm gonna give you two stories real quick in a cautionary tale about names and the modern age. Okay, so the Wall Street Journal does a little feature story saying that when your last name is Null. Nothing works. A woman named Nontra Yanta Prasert couldn't wait to take her husband's shorter and easier to pronounce last name. She would be Nantra Null. But of course, nothing works because null means nothing.

Leo Laporte (131:11)

Little tables.

Jeff Jarvis (131:12)

Interestingly, what happens at her office is that if a package is missing an addressee, it is sent to her because the addressee is null, null, null. So that's one little quick story. The other one that is amusing is a Nebraska man who found out that his daughter, according to records, is named Unikite 13 Hotel because it was a. It was a temporary computer generated name when she was born under unfortunate circumstances. Well, she was. She was born to a mother who couldn't care for her. And it was her, his daughter he found.

Leo Laporte (131:51)

So it was put on the birth certificate.

Jeff Jarvis (131:53)

Right. And he can't get a decent birth certificate for her because her legal name is Unikite 13 Hotel. But you can call her Caroline.

Leo Laporte (132:00)

Oh, I. You know what? Oh, but she doesn't have a Social Security number either.

Jeff Jarvis (132:05)

She does now. There was an update here. She got that. Oh, morning after the story was published.

Leo Laporte (132:11)

She got generated the nonsensical name by computer. It was just a placeholder.

Jeff Jarvis (132:16)

Yeah, Paris. Confused.

Leo Laporte (132:21)

Jeff, get out of here. Unless you want to see my wonderful new word puzzle. But otherwise you can get.

Jeff Jarvis (132:28)

Oh, yeah, no, you know, I don't like that. So thank you. Thank you. Bye. Sorry to leave the party.

Leo Laporte (132:32)

Get out of here. Yeah, nut. We'll watch you on cnn. Thank you. Jeff Jarvis, professor emeritus at the Bing Crosby School of Graduate Journalism.

Paris Martineau (132:44)

Journalism. And sending tweets. Good.

Leo Laporte (132:47)

And sending tweets. Good. At cuny. What do you. What do you have?

Paris Martineau (132:51)

My pick of the week is a simple one. There's an app someone's made that's coming out in two weeks called Touch Grass. It's kind of like a scream screen time thing. But you get a certain alert where you have to open the app and wave it over grass and put your hand out, touch the grass before it stops, you know, alerting on you. And I think that's really beautiful. And I'm going to download it when it comes out.

Leo Laporte (133:17)

How does it know?

Paris Martineau (133:19)

Well, if you click the link I have there, it's basically a photo. Basically it's a camera. And so it's analyzing, ah, what your phone is seeing. And you have to literally, your apps are blocked until you touch grass, which I think could be quite good for some people.

Leo Laporte (133:37)

I don't live near grass. Well, I actually have to take a walk.

Paris Martineau (133:42)

Yeah. You'd have to go touch some grass.

Leo Laporte (133:45)

I don't. Where's. Where's the nearest grass?

Paris Martineau (133:47)

I mean, I was thinking about this too. I don't know where the nearest grass is, but I think that could be.

Leo Laporte (133:51)

Yeah. You live in the city. Probably. Yeah. Well, you'll find one. Our neighbor has fake grass. I bet that would fool the camera.

Paris Martineau (133:59)

Yeah. I mean, I guess you could ostensibly pull up a photo of grass in your computer and touch that, but I think that would be doing yourself a disservice more than the app.

Leo Laporte (134:08)

And then you can. And then it says you can have 15 minutes, 20 minutes. You can have a little more time. You have to. That's wild. You're going to put that on your computer?

Paris Martineau (134:16)

I mean, I'll put it on my phone. There's some times where I. I'm not really a screen time person. I just. Well, I am a person who has too much screen time. I do not contain it in any way.

Leo Laporte (134:26)

This is part of your job.

Paris Martineau (134:27)

I think it could be fun to. If I ever wanted to have touch and graspy.

Leo Laporte (134:32)

I was inspired by the way by you and I watched Moonstruck to see Nick Cage in his most romantic sexy role. He looks good in that.

Paris Martineau (134:41)

Great.

Leo Laporte (134:41)

He's gorgeous.

Paris Martineau (134:42)

What a guy.

Leo Laporte (134:43)

He had to be to make Cher fall in love with him. You got to be good. I am going to share a weird little thing I found. I think share. I'll share it with you. Jeff won't see was created by a guy who owns a bar but apparently loves puzzles and has bar regular bar trivia and stuff. It's Bracket City and it's, I think, a really cool kind of crossword puzzle game. So what you're seeing is all the text with brackets. The stuff in yellow can be solved oft checked. Hiding place on a magician. I'm going to. Just randomly. It doesn't matter which. Where you start. I'm going to type sleeve. Oh, that's correct. And notice it replaces that with the word sleeve. Okay. Sleeve length for a T. I don't know. What is that? What's a sleeve length?

Paris Martineau (135:36)

Short length.

Leo Laporte (135:37)

Short. Yep. Oh, and then low power state for a computer. That's sleep. Right?

Paris Martineau (135:43)

Yeah.

Leo Laporte (135:44)

Yep. That was right.

Paris Martineau (135:45)

Daytime sleep session.

Leo Laporte (135:47)

Nap Trotsky or Bridges. Is that Leon? Yes. So you see, I'm getting these right now. If you get stuck with the short teeth, obviously that's nap. Right. But if you get stuck, you can also click it and it'll say, do you want the first word? Or you can click it Again. And it'll give you the answer. But I know a short sleep session in the daytime is a nap. And look that turned in. Napoleon escapes from now to solve this expel from one's native country. Deport.

Paris Martineau (136:19)

Exile.

Leo Laporte (136:20)

Exile. Thank you. Yes, you're right. Napoleon escapes from exile on the island of. And now we can't get there quite yet. Dirty dishes. Location often is the sink. You want to do some of these loose lips? Loose ones Sink ships. It is said. I just keep. Yeah, all right. Kind of stick for lips.

Paris Martineau (136:44)

Bracket. What is it? It's not bracket city, is it?

Leo Laporte (136:47)

Yeah, bracket city.

Paris Martineau (136:50)

Oh, I put two A's in bracket. That's why it's not coming.

Leo Laporte (136:53)

Kind of stick for lips. It's not lipstick or Chapstick.

Paris Martineau (136:58)

Or Chapstick.

Leo Laporte (136:59)

Yeah. Yes. It's more. Some say question mark. Question mark. It's more. I don't know that one. How about little dish for a teacup? Oh, I know what that is. What is that?

Paris Martineau (137:13)

I don't know. What is the little saucer. Oh, a saucer. Yeah, we don't have those anymore.

Leo Laporte (137:19)

Word between. You don't use saucers. Word between. Lights and action camera. So this is a really easy one. Yesterday's was not so easy. College degree for short.

Paris Martineau (137:31)

B.A.

Leo Laporte (137:32)

Yep. Okay.

Paris Martineau (137:34)

This is fun, isn't it?

Leo Laporte (137:36)

And it slowly resolves itself.

Paris Martineau (137:39)

The middle one is ufo.

Leo Laporte (137:41)

Okay. Ufo. Like many alleged ufo.

Paris Martineau (137:46)

Fake.

Leo Laporte (137:46)

Fake. Nope. Staged? Nope.

Paris Martineau (137:51)

Oh, so it'll only.

Leo Laporte (137:53)

It'll only get it. See, it saves all the right answers. Oh, and it's smart enough to know, you know, you don't need to say click anything. It just says, oh, yeah, that's the right answer for that one. It's more. Some say question. Look at. I'm going to get a little hint here. First letter L. It's more. Some say less. Yes.

Paris Martineau (138:15)

Oh, less is more.

Leo Laporte (138:17)

Yeah, less is more. Like many alleged UFO pics. Should I get the first letter B?

Paris Martineau (138:24)

Bogus blurry flurry.

Leo Laporte (138:28)

Overused blurry background phone camera mode. We should know that one.

Paris Martineau (138:33)

Portrait.

Leo Laporte (138:37)

Yep. Limbless portrait sculpture.

Paris Martineau (138:42)

I don't know this one.

Leo Laporte (138:44)

What's the one without limbs? The Venus de Milo, right?

Paris Martineau (138:48)

No idea.

Leo Laporte (138:51)

Venus limbless portrait sculpture. Limbless bust. Ah, it was bust.

Paris Martineau (138:56)

Oh, it was general. I thought there was a very specific.

Leo Laporte (138:59)

Portrait sculpture kind of bust that might feature dogs. A drug bust. Blank Chapo. I think we know it's L. We solved it. Napoleonscapes from. And then it is this date in history.

Paris Martineau (139:15)

Oh, isn't that fun? Wait, Leo, you're the chief of police.

Leo Laporte (139:21)

For bracket City, I only need seven more points to be mayor.

Paris Martineau (139:27)

Wow. I mean you're taking the Eric Adams route.

Leo Laporte (139:30)

This is only a matter of time before the New York Times buys it. So quickly go to bracket city and play the game. You can go backwards in time and do previous days puzzles. It does give you a link to the history of whatever this solution is. So there's a Wikipedia link in there. I just think this is really cute. Bracket dot city.

Paris Martineau (139:53)

That's so cool. Don't you think that play that it's.

Leo Laporte (139:56)

Like better than a crossword puzzle. I like it.

Paris Martineau (139:58)

I mean it's not. It's not going to hit the same scratch as a crossword puzzle, but it hits a different. It's. It hits differently and I appreciate that.

Leo Laporte (140:05)

Lisa and I, every night we used to play wordle. Now we do connections. You know that's the New York Times game where you get. Yeah, 16 words and you have to.

Paris Martineau (140:14)

Do you guys like the. I'm forgetting what, what is the line one that they.

Leo Laporte (140:18)

She does that one. I don't do that kind. The strands or threads.

Paris Martineau (140:21)

I kind of like strands.

Leo Laporte (140:22)

Yeah. Well, I have a feeling this guy who created bracket city, within a few months we'll get an offer from the New York Times because this belongs on the puzzle page and they make a.

Paris Martineau (140:34)

Lot of money viral then.

Leo Laporte (140:35)

Yeah, yeah.

Paris Martineau (140:36)

I mean, yeah. Puzzles are kind of the heart of the news business right now.

Leo Laporte (140:41)

It also, by the way, gives you stats. It took me 119 keystrokes. You could have solved it in 85 keystrokes. That means 35 extra keystrokes. Two close peaked but no answers revealed. So. And you get a final score. I got a final score of 61.4-plus 10 for peaking. So see if you can beat mine.

Paris Martineau (141:01)

Interesting.

Leo Laporte (141:02)

Paris Martineau writes for the weekend attheinformation.com. what are you working on right now? Anything exciting?

Paris Martineau (141:09)

Some stories about like politics right now really or I guess is there anything.

Leo Laporte (141:14)

Going on in the political world these days?

Paris Martineau (141:16)

No, not much. I mean that's kind of what the story is about. Why isn't more stuff so quiet? It's really quiet right now. A lot of people are saying that.

Leo Laporte (141:24)

I want to thank you and Jeff for cooperating with me. I decided that we should make this show be a little respite from the normal political discourse. And I know all three of us have strong feelings, but I think we've done a very good job of making this an interesting, enlivening, educational and fun show without making anybody feel a pit. Horrible feeling in the pit of their stomach. Do you agree?

Paris Martineau (141:52)

Yeah, certainly.

Leo Laporte (141:54)

I think so. Let's not stress. Let's relax. Thank you, Paris, for being here. Thanks to Jeff Jarvis. Everybody should watch him. He'll be on cnn. Any. Well, I don't know. He's got to get into town, so probably in a half an hour.

Paris Martineau (142:07)

He said 10pm or something.

Leo Laporte (142:09)

Yeah, well, that's not so far off. About half an hour from now. Next week we have Gary Marcus, who has been doing a lot of writing about AI the week following. Ray Kurzweil, the guy who coined the term intelligent machines. Lots of great people coming up. We're filling up the guests and I'm very excited about all the guests we're having on. I hope you enjoy the show. I'd love to hear from you. You can write to me at. No, don't write to me.

Paris Martineau (142:37)

You can write to Leo by writing on a piece of paper, folding it up, putting it in a bottle, chucking it into a body of water that you have searched before. It's not going to be a big pollution problem for a bottle to be in there. And then that note, that'll get to Leo one day.

Leo Laporte (142:54)

Actually, what you should do if you really want to vote is joining the club. If you're not yet a member of Club Twit, it's seven bucks a month. You get ad free versions of all the shows. You get a special content. Tomorrow, for instance, I'll be back at 2pm Pacific, 5pm Eastern for Stacy's Book Club. Of course we have a photo segment. I got to get another coffee segment in. Got to get Mark Prince the coffee geek on. We have a lot of fun. Plus you get access to the Discord. But most importantly, you're really making a big difference to our bottom line. We really want to keep doing these shows. We do sell ads, thank goodness, but the ads don't make up the entire cost of the operation, even though we've tightened our belts as much as we can. So if you like what you hear, if you like what you see, please join the Club Twit TV Club Twit. That's the best way to vote. And once you're in the club, of course you can talk to me all the time in the Discord. I'm in there all the time. We do this show every Wednesday right after Windows Weekly. Now it's gonna be pretty consistently because we have guests 2pm Pacific, 5pm Eastern, 2200 UTC. Thanks to the club, we're able to stream on eight different platforms Discord for club members, YouTube.com Twitch Live, Twitch TV, Kick, X.com TikTok, Facebook and LinkedIn. Count them, eight different platforms. Watch live if you want, but you don't have to. Shows are going to be posted audio and video as soon as we get them edited down. Shouldn't take too long. It's got to take that sexy story out. Are you looking for Gizmo? Is that what you're looking for? I see you looking. She's right behind you. Yeah, Gizmo, come here so she could say goodbye. There you go. Say goodbye, Gizmo.

Paris Martineau (144:38)

Gizmo, don't try and put your butt.

Leo Laporte (144:40)

We're looking for katanas.

Paris Martineau (144:43)

She's always angled for katanas.

Leo Laporte (144:46)

You know, Lisa, I think it's a cat evolutionary thing that they want.

Paris Martineau (144:51)

They want. They want the, you know, smelliest part to be all over you.

Leo Laporte (144:55)

That's what humans think. I think that they want to be looking out. They don't want to be looking in at you. You want to look at them. But they want to make sure there's no threats coming in any direction.

Paris Martineau (145:04)

It's definitely also that she wants to, you know, protect me from. Yes, she's protecting world outside.

Leo Laporte (145:10)

I prefer to interpret it that way. Go to the website, Twitter, TV twig. There's also a link there to our YouTube channel where you can get videos. Actually, that's a great way to share little clips. If you want to share, for instance, that fantastic conversation with Stephen Wolfram, you can clip that. Send it to a friend. Please do. Let them know about intelligent machines. Of course, the best way to watch is to subscribe. Get your favorite podcast player. You can get it for free every single week after we're done. Thank you for being here. We appreciate your support and I'll see you next time on Intelligent Machines. Bye Bye. Marketing is hard, but I'll tell you a little secret. It doesn't have to be. Let me point something out. You're listening to a podcast right now and it's great. You love the host, you seek it out and download it. You listen to it while driving, working out, cooking, even going to the bathroom. Podcasts are a pretty close companion. And this is a podcast ad. Did I get your attention? You can reach great listeners like yourself with podcast advertising from Libsyn Ads. Choose from hundreds of top podcasts offering host endorsements, or run a pre produced ad like this one across thousands of shows. To reach your target audience in their favorite podcasts with Libsyn ads, go to Libsynads.com that's L I B S Y N ads.com today.