Jeff Jarvis (2:01)

Fake. The guests, big gadget.

Paris Martineau (2:03)

Before they even go, before you even introduce them.

Leo Laporte (2:07)

I threw it in the air so you can wear it around your neck as a pendant. You can wear it as a clip on. I like to wear it as a bracelet. It's always with me. Plus, it fools people because it looks like it's a Fitbit or some sort of fitness thing. What is it really? It is a set of microphones. Very good microphones attached to a device that then streams to my phone and then sends it from my phone to an AI for analysis. Everything that I do say happens in my day gets sent to this AI.

Jeff Jarvis (2:39)

For summer, any summer, is a summary from your rock trip did pick up other conversations.

Leo Laporte (2:44)

Yes, yes.

Jeff Jarvis (2:46)

It's leaving me on day today.

Leo Laporte (2:48)

One thing I have to do, it has a button you can push to turn it off to have it stop recording. And I keep forgetting to do that during TV shows. So occasionally he'll say things like, good luck with your rehearsals for Richard iii.

Ethan Sutton (3:02)

We have an update on that when we can talk about it.

Leo Laporte (3:05)

Let me introduce our guests. It's great. We've got the founders of B Dot Computer. They had their coming out party in January at ces. Maria, I'm going to do your name, I think correctly, Delor de Thoyo.

Marie Delores Zoyo (3:19)

So it's Maria de Lourdes. Zoyo is her name. Yeah, South America.

Leo Laporte (3:25)

So your first name is Maria de Lordes.

Marie Delores Zoyo (3:27)

Yeah.

Leo Laporte (3:27)

And then Zoyo is your surname. Okay. And you're from South. You're not from Spain, you're from South America.

Marie Delores Zoyo (3:33)

Born in Caracas, grew up in Italy. I'm a mix and I really appreciate the pronunciation because I think like basically no people understand that.

Leo Laporte (3:41)

It's O. I love it. And I saw that you went to school in Italy and I thought, yeah.

Marie Delores Zoyo (3:46)

Yeah, I got it.

Leo Laporte (3:46)

That's definitely a Spanish name. So that's great. And with Maria, her co founder, Ethan Sutton. Am I saying Sutton?

Ethan Sutton (3:52)

Right, Sutton.

Leo Laporte (3:54)

Sutton. Either way, you guys met your previous startup was Squad, right?

Ethan Sutton (4:00)

That's right, yes.

Leo Laporte (4:01)

Which then was purchased by Twitter and became Spaces.

Ethan Sutton (4:05)

Yes.

Leo Laporte (4:07)

So you're the founders of Spaces.

Ethan Sutton (4:09)

Well, you know, don't want to take full credit for Spaces.

Leo Laporte (4:13)

Esther Crawford also, right, yeah, yeah, yeah.

Ethan Sutton (4:16)

But we joined and launched it on Android. We interestingly worked on a lot of speech to text for the captioning of that.

Leo Laporte (4:26)

So you both have a little bit of experience in AI, but tell me how you started B B Computer is the website, by the way.

Ethan Sutton (4:36)

Yeah, I will keep it really brief. But there's some relevance to our last company actually with, with Bee, because we actually started in 2016, the company we were all together at with Esther that we sold to Twitter. We actually started it as a personal AI company. So me and Esther were always kind of really obsessed about what it would be like if we could have a computer, a machine, have like a really good model and understanding of ourselves, of humans, like personality, beliefs, attitudes and thoughts and what value would come from that. We actually started that and launched it, but it was too early because it was before Transformers existed, before the Transformers paper, before large language models. And I don't know if you remember, there was like a brief, very short lived chatbot boom. In 2016.

Leo Laporte (5:28)

Yeah. With Tay. Microsoft's Tay, the racist chatbot. Rest in peace. Yeah.

Paris Martineau (5:35)

Yeah.

Leo Laporte (5:36)

We all thought chatbots would be the big thing and it turned out they were terrible.

Ethan Sutton (5:40)

Yes.

Leo Laporte (5:40)

Yes.

Ethan Sutton (5:40)

And we were. We were excited about it. And so, like, we were one of the terrible chat bots at that time.

Leo Laporte (5:45)

Oh, dear.

Ethan Sutton (5:46)

But, like, we were too early. But it, you know, I think that obviously that all changed with. With LLMs. So anyway, we pivoted and we went through Y Combinator and we built Squad and we worked at Twitter. But then, you know, now it's kind of, you know, if you would have told us in 2016 the kind of natural language processing that's possible now, I don't think anybody working in the field would have really believed that we would have gotten. So far.

Leo Laporte (6:13)

It's been remarkable. I don't. We certainly wouldn't. And we've kind of jumped on it. Transforming a show that used to be about the hot technology of 2016, Google, and it's. And is now about the hot technology of 2025, which is obviously AI the sequence. I'm just curious about what. I know Jeff's going to say Esther Crawford was the woman who slept in her sleeping bag at Twitter after Elon took over. But you guys did. Were you under the. You didn't stay for Elon, right?

Marie Delores Zoyo (6:43)

No, no, we left before.

Ethan Sutton (6:45)

Not related to Elon, but left before. So did not get to experience any of the Elon.

Jeff Jarvis (6:50)

That was my question. Yes, I knew.

Leo Laporte (6:52)

I knew where Jeff was going.

Jeff Jarvis (6:54)

I just had to know. I had to know.

Ethan Sutton (6:56)

Yeah. Yeah. So. So no, no, no. No Elon stories and no sleeping in the office stories, but I was about.

Paris Martineau (7:03)

To say B isn't sleeping in sleeping bags in the office.

Leo Laporte (7:08)

Well, we.

Marie Delores Zoyo (7:09)

We're for sure sleep deprived.

Ethan Sutton (7:10)

Yeah. Yeah.

Paris Martineau (7:12)

But hopefully just a normal amount where you can be sleep deprived and sleep in a bed.

Jeff Jarvis (7:16)

Yeah.

Leo Laporte (7:16)

Let me show you. I have my iPhone on the screen. This, by the way, only works with iOS currently, I'm sure.

Ethan Sutton (7:22)

Well, Android is actually in the Play Store now.

Jeff Jarvis (7:24)

Oh, yeah.

Ethan Sutton (7:25)

Under preview. So it's in beta testing. We're still telling people that it's like the full support's on iOS, but the Android's full featured. And as soon as we, you know, Android is a little trickier to iron out the bugs with the device fragmentation and everything, but within about a week, we'll probably do the full release.

Marie Delores Zoyo (7:44)

Yeah, I like that you just did the announcement.

Leo Laporte (7:46)

Congratulations. Thank you for holding that announcement for us.

Ethan Sutton (7:50)

It was in preview beta, but you can install it on Android now. And it works.

Leo Laporte (7:54)

Okay, good. Amazingly, it's currently this is the kind of Explorers Edition $50 and there's no subscription. Now that's obviously going to change. In fact, I'm here to lobby you for that to change because I want to make sure that you get a regular income for this and I want to make sure we have access to the best AI models. Let me just, before we go any further, show people what I'm getting from this thing. Here's my bee app. It does a few things. It records all the time, so it's recording right now. It also has previous days, summarizes the daily. And by the way, the summaries are fun because it's not only it has a certain personality, which we'll talk about in a bit. And I stipulate this is not for everyone. I mean, there are probably a lot of people who say you have a microphone on at all times. What do the people around you think about that? It gives me a summary of the day, event highlights, notice it's got a map. They even offer didn't today but often offer links associated with the things you were talking about. It also keeps track of things that I'm talking about that I might have a to do around. So it suggests these to dos. This is from today. And if I think, oh yeah, actually I do want to do that, I can press plus to add it to my actual to do list. Otherwise I can delete it. I can then go to my to do list and you see it's been recording. I have 34 to dos, but these are all legit things I want to do. I haven't typed any of this. This is all from listening, transcribing, analyzing, and then presenting to you.

Jeff Jarvis (9:41)

Do you ever instruct me and say, oh, don't forget to type.

Leo Laporte (9:44)

You can. We'll get to that in a second. I'll also show you it is already 477 facts about me. The facts are often trivial. Oh, and I get to approve them all. So let me review some. I'm associating with people discussing topics like MacBook Pros and Bitcoin. Yes, I've been receiving significant residual income from Megaphone and Libsyn. Yes, I don't know how it knew that I enjoy dark roast coffee with a preference for rich chocolate and bitter flavor. Okay, so this is one thing we'll talk about. That was Lisa. Now I can discard it, but it's actually not wrong. It was my wife, so sometimes it makes mistakes that's true. I have an interest in AI topics. Would you say that's true? Yeah. All right. I would say I like live events and shows in New York. That's. Oh, by the way, that's because I was watching Saturday Night Live.

Ethan Sutton (10:35)

Yeah.

Leo Laporte (10:35)

You're going to tell me about that pet cat named Sammy. True Sammy. Leo's cat is black fur coloring with a white nose and distinctive marking that represents a Hitler mustache. How did you know? Yes. So I can go through all of these. I won't go through all 49, but it is over time and I think this is very smart. Built up this fairly long list of 485 things it knows about me. Trivial little facts, but certainly things that could inform an AI to give me better results. So that's just a quick tour of the interface. Let's just see what it thinks of the current conversation. I am particularly focusing on the integration of AI into daily. Yeah, so it's, it's old as you know, that I'm talking to its parents, but we'll get. It'll get that probably. So a couple of questions right off the bat and then I'll let you tell us, tell us more about this. Where is this going? Where is my recording going?

Ethan Sutton (11:33)

We don't store it so that there's no audio recording. Well, where is it going? It's going from the device through Bluetooth low energy to your iPhone.

Leo Laporte (11:43)

Okay.

Ethan Sutton (11:43)

The iPhone to the cloud and transcribed in real time. And then the audio is not. That's the end of its life.

Leo Laporte (11:52)

So that's the privacy angle, at least so far. And by the way, that does answer a question I had about two party recording in California. I have to ask somebody before I can record a conversation with them.

Ethan Sutton (12:04)

California is a two party consent state and depends on the expectation of privacy by the other party. So in public you wouldn't. In private, yes. You know, it's so.

Leo Laporte (12:17)

So we're a little bit skirting the law here. Rewind. With its. Its pin won't record unless you get an explicit. By the way, I don't have the pin yet. So it's.

Paris Martineau (12:26)

I was gonna say. Does that actually work? I'd be curious to see.

Leo Laporte (12:29)

They say they ask for explicit approval and then we'll record and then the plot doesn't record unless you press a button. Presuming the plot is presuming that you will ask before you record. This doesn't do any of that. And frankly I think it's better because of it. But it might be a little extra legal.

Ethan Sutton (12:47)

Well, I Think there's you know, it depends obviously on the jurisdiction. But you know, also I think a little untested around. Yeah. Is the, is the summary and we target the summary to focus on the user through the speaker identification. Like I don't think if you went through a lot of, you know, your memories.

Leo Laporte (13:09)

Yeah, I don't have a transcript of things other. Well, I sort of do of things other people have said.

Ethan Sutton (13:14)

I don't really have any personal information. I mean I think this is like the key challenge we have to navigate is to kind of establish what the right norm is. I think that you would probably agree that it's going to be the future to have like AI understanding us. And so you know the, the expectations are currently, you know the way they are now thinking about like oh somebody's recording me and listening to me. But if the expectation was my AI is just learning about what I did, purposely not learning about anybody else.

Leo Laporte (13:50)

Right.

Ethan Sutton (13:50)

People will become more, more attuned to that.

Leo Laporte (13:54)

So where is. You don't reveal on the website or in your terms of service, you don't reveal the LLMs you use. Do you want to tell us now?

Ethan Sutton (14:03)

There's a variety of LLMs we use. So all of the pipelines have different kind of requirements. So summary doesn't require necessarily the largest model. But for example the assistant, you know, you want a very large model. So you know we, we use self hosted models and then we, we do use commercial ones and you know, under the agreement of no retention, no training. So that's for to be very clear. No, no, no training on. On customer data at all from. From us or any commercial model we use.

Leo Laporte (14:45)

Okay.

Ethan Sutton (14:45)

And so I can say, you know the. From a provider that you probably like.

Leo Laporte (14:57)

How are you going to pay for this? This has got to be expensive.

Marie Delores Zoyo (15:00)

Yeah. Well, we will introduce soon the subscription.

Leo Laporte (15:04)

Good.

Marie Delores Zoyo (15:05)

I want you to $12 per month. It will be a freemium model. So the basic features as for example memory transcripts, all of that will be free for the users. But the integrations as for example you already have some of them right now for free. But Gmail, Google Calendar, how actually to give should be the digital side of you. That would be under the subscription model in the future. And we are already testing them, we are testing agents and those as well. They will be under the subscription model.

Ethan Sutton (15:40)

And we focus a lot on.

Jeff Jarvis (15:44)

When.

Ethan Sutton (15:44)

We introduce a subscription to having a tier where people can kind of experience the base of memory building of understanding and learning. And you think it's very expensive. But with the Right, Optimization. Because it's like, oh, it's all time. It's really around voice activity. So it can vary how much inference is needed depending on how much you talk, how much voice activity. And then with the premium, you will. And you kind of alluded to this, have like the more accurate models, the larger models, the models that take more GPU to run and then like you can still. And we can talk about this later. We have, we have a version we're testing where everything runs on your iPhone. So this has both, you know, for people who are very concerned about control of the data, but also, you know, there's no inference cost to us if everything is running on the iPhone. And that won't be as accurate, obviously as a large model that you couldn't run on the phone. But actually for a lot of use cases, it's still very useful.

Leo Laporte (17:01)

So it sounds like you got a lot of secret sauce in the AIs you're using. I don't want to press you. I mean, is the transcription done locally on the phone? Is it done in the cloud?

Ethan Sutton (17:16)

The transcription is currently done in the cloud.

Leo Laporte (17:18)

Okay, your cloud or somebody else's cloud?

Ethan Sutton (17:23)

Our cloud.

Leo Laporte (17:24)

Okay, well. Oh, interesting.

Ethan Sutton (17:25)

Not our, not our data center, but.

Leo Laporte (17:28)

But you do have your own local models. Yes, interesting.

Ethan Sutton (17:33)

We don't. We, like I said, we do use some commercial models and some of the pipelines, but on the edge, on the mobile phone now there is a model running. It's very small model for, but it is a neural network for around detecting voice activity. We have a fully working version. So every part of it, there's five different models that are needed to do it on the phone. And in that situation it works on airplane mode.

Leo Laporte (18:07)

Oh, really?

Ethan Sutton (18:08)

The data never leaves your phone. So there's activity. There's the speech to text, there's the voice embeddings for speaker identification, there's text embeddings for retrieval, and then the LLM for the summary and assistant. So all of those can work on the phone. Obviously they're in like the 3 billion parameter range for the LLMs.

Leo Laporte (18:28)

Yeah, they're too small, those models. Really, I think they're good.

Ethan Sutton (18:32)

They're not as good. But the summary, we field test it and you can still kind of experience the value of the summaries. I think for the assistant it's a little rough. It's not so smart. But for things like summarization and even generating insights and to dos, it's surprisingly how good things are getting. We have Gemma, we have Phi, we have really good small models now. But you can see the trajectory going where those models will be as good as some of the larger models now. But the best models will probably always remain on the cloud because once those get so good, that ones we can run on larger machines will be even better than they are now.

Leo Laporte (19:20)

Let me show you folks the settings page. Now on this, if you show my screen Benito. So I'm going to go into settings. Show my screen Benito. Do you have my screen?

Paris Martineau (19:35)

Am I. I'm seeing your screen.

Leo Laporte (19:38)

You're seeing it. Okay, I have.

Paris Martineau (19:39)

Well, I'm seeing your screen on the camera thing, but it's not.

F (19:42)

Yeah, I'm sorry.

Paris Martineau (19:43)

On Twitch.

F (19:44)

Trouble here.

Leo Laporte (19:45)

Vinito is having some trouble switching. I just want to show you the choices you have as the user. Some of the things in here are really kind of interesting. So here's my settings. You do train it on your voice. Obviously you can connect accounts. I actually created special Google Gmail contact and Calendar accounts that extract from my real Gmail and my real mail so that I get rid of some of the stuff that I don't want you to worry yourself about. But you could connect it if you. Again, I know not everybody would do this, but I like it. You give it location. I'm giving it everything. Location, reminders, notifications, photos, contacts, all the health information from Apple Health. This is kind of fun. You have a choice of. You can name your assistant and a choice of some voices. Oh, you've added quite a few more. Holy moly. That's pretty new. Yeah.

Paris Martineau (20:45)

Do you guys have a favorite voice?

Jeff Jarvis (20:47)

Yeah.

Leo Laporte (20:48)

What voice do you.

Marie Delores Zoyo (20:48)

The users love the voice.

Leo Laporte (20:50)

Yeah. Because you're going to talk to it. This is one of the things I think is the most fun. Is there a button on the thing and you can choose what happens for what is it a double tap. Single tap mutes it. Is that right? And the double tap picks one of these actions.

Ethan Sutton (21:07)

Yeah. So it's like a multipurpose button. So the interface on the device is kind of intentionally pretty minimal. It should be.

Leo Laporte (21:15)

That's perfect.

Ethan Sutton (21:16)

Yeah, yeah, yeah. So single button is to toggle, mute. Double press is like a customizable action button.

Leo Laporte (21:22)

Look at this. I have it on fact checker. So it's listening. You and I are talking. Jeff, you say something preposterous. If I double tap this, my phone will then say what? I've never used it. I've never dared. Jeff, you ignorant. That's wrong. Interesting anecdote. Is that about you or is it just a random anecdote?

Ethan Sutton (21:48)

It should be topical.

Leo Laporte (21:49)

To what process? Current conversation, which is probably the one most people would pick, which means this conversation. I want a full summary of relationship advice. Yeah, yeah, that's interesting. And then roast. I did try the roast and it was mean.

Ethan Sutton (22:06)

Yeah, we actually. We need to remove that.

Leo Laporte (22:12)

Don't take it out. If somebody chooses roast, they deserve what they get.

Ethan Sutton (22:16)

Yeah, this is like that one uses a local, uncensored model. I think it's not.

Leo Laporte (22:22)

It's pretty. It's like grok. It's pretty.

Ethan Sutton (22:24)

But yeah, this is like kind of an underdeveloped area that we are.

Marie Delores Zoyo (22:30)

We are testing around that.

Leo Laporte (22:32)

But that's what I like. This is the early version, right?

Ethan Sutton (22:35)

Yeah. Oh, yes.

Leo Laporte (22:37)

So you can also. You do get transcripts and you can assign names. So as I've been talking to people, I've assigned names to them so that there are some names, but it's not. There are better models for this kind. Like, Claude is very good at this kind of thing. I don't know what models you're using or whether that's even important to you, except that, as I mentioned, occasionally it confuses.

Ethan Sutton (22:59)

Yeah, it is very important to us. And so this is actually one of our primary focuses, improving.

Leo Laporte (23:05)

But I understand this is a, you know, Explorer edition. This is like, I only paid 50 bucks for this thing. And by the way, Maria, five bucks a month. That's still. I'm paying 20 for perplexity, paying 20 for chatgpt, 20 for Claude. Everybody else is 20. That's a good deal.

Jeff Jarvis (23:27)

What use cases? I can imagine many, but what are the. A, most prevalent, but then B, any surprising use cases you're seeing?

Marie Delores Zoyo (23:36)

Yeah, so some of them. We are super excited about them. Students. Students in particular, students with ADHD that they use be to have in the transcripts of their lectures, understanding what areas they should be focused on. Study as well. The neurodivergent use case.

Ethan Sutton (24:00)

Yeah, that really surprised us. A lot of people have wrote to us around not just adhd, but also people who've had, you know, brain injury or a stroke. Yeah. So like, we don't really even market it all to that. But, you know, I think there was just, you know, people looking for tools and. But the, the really, like, amazing thing is they've wrote and they're getting benefit from it, which is not anything we had really planned for.

Leo Laporte (24:28)

In your discord, I've talked to several people who. Yeah, no, who say, this is great. There's a blind user who loves it. It's really. It is absolutely an Accessibility device.

Ethan Sutton (24:42)

And I'm an old guy, we really want to kind of expand, like really dig into that.

Leo Laporte (24:50)

But ultimately to me, and so this is like a very futuristic use of AI, but ultimately to me that's we're all disabled in various ways. And as I get older, my memory fades and, and things. Yeah, I think this is a daily journal, if nothing else.

Marie Delores Zoyo (25:08)

Yes, absolutely.

Ethan Sutton (25:10)

The reflective, like self understanding reflective part of, you know, a lot of users kind of overlap with that. I think that applies to people generally. Like, you know, one of the other kind of use cases that emerge are like people who are salespeople or talk, you know, a lot for their job and they want feedback but like not only do they, they've given us feedback on that, but then they're like, oh, you know, make this, this, you know, this insight use case more. You know, they have feature requests for that. And so it's kind of crossing between work and reflection.

Jeff Jarvis (25:44)

Yes.

Marie Delores Zoyo (25:44)

Yeah, that's. I think it's one of the things that I found like the most interesting is that some people are coming for professional, but after they're asking parental advices or you know, like almost like the holistic coach aspect of the product. How can I be better on something? How can I have more personal insights about myself, patterns on my relationship, things like that.

Leo Laporte (26:09)

Yeah, there are people in the discord who are asking, so you can, you can talk to the assistant and say you can ask it for things. This one guy says, I want all your answers to sound like it's Bill Burr. I don't know how, how permanent that is, but he says it's very funny. But there are some people who say, hey, listen to this next conversation I'm having with my wife and let me know if I'm doing something that is non productive or whatever, if I'm being mean. And I guess that's the relationship advice button too. Yeah, yeah, yeah.

Ethan Sutton (26:45)

So I mean you can, in the assistant it will. And I kind of measure my life in tokens now in terms of tokens I produce. So 250,000 tokens is my informational output per day. But that builds up over time. And so the assistant can actually traverse, in my case, 50 million tokens. So if I ask it to kind of analyze my behavior and trends, it can look historically and then kind of coordinate that with things that happened in my email and calendar and then kind of try and connect the dots. And I think this is something that's really useful and will only get better. The more signals we can understand and the better we can kind of allow the assistant to think about it, to iterate across and pull things that when it discovers something then it can fetch relevant pieces and then continue on until it has something that's kind of substantial to the, that's kind of surprising to me.

Leo Laporte (27:49)

That's the point. Right. Is the more you give, it's like rag. The more you give the AI of your life, the more insights you can ask, the more information you can get from it. Yeah, I think it's, I think this is. Now there's a privacy concern and people want to make sure. But just to reiterate, you're not saving. What are you, what, what, what do you have? What data do you save?

Ethan Sutton (28:16)

Well, we don't save any audio so that, that's processed in real time and there's never any audio saved to perform the, the, the functionality of the assistant. You know, we have the, the conversation history to be able to then perform the AI on it. And we do a lot to make sure that it's only ever decrypted at the point, at the time the AI needs to, to run inference. And so I mean I think you, you probably are, are aware like the Apple intelligence, you know, they have a hybrid.

Leo Laporte (28:57)

Yeah. And it's pretty stupid to be honest with you because I think they're unwilling to breach people's privacy to some degree. You got it? You, you kind of know stuff to, to, to understand stuff.

Ethan Sutton (29:08)

Yeah. So I mean I think.

Leo Laporte (29:09)

And they have a hybrid. I know what you were. I'm sorry, I, I went off on a tangent. They have a local model and they also will go to their particular servers in the cloud and then even further if, if they can't answer it, they'll refer you to chat GPT. But then they'll warn you, you know, we're about to send you somewhere where we don't control. So that's roughly the model you're using.

Ethan Sutton (29:31)

Yeah, roughly. I mean we, we, we, you know, to provide the service, you know, we, we use the models in the cloud but we, we take every safeguard to make sure that like the, the data is encrypted up until the point and that it's, it's basically unreadable up until the point where it needs to be decrypted AI and we're always trying to find, you know, we, to get to like for example, to get the Google scopes, we had to go. Well, we voluntarily went through a third party security audit, so.

Leo Laporte (30:02)

Oh, that's good.

Ethan Sutton (30:03)

Yeah, we take it very seriously and like we're committed to doing it every six months is like a best practice. And we come from an information security background too. You know, we worked at Twitter Payment. We worked on like the first Twitter blue, if you remember that.

Jeff Jarvis (30:17)

Blessed soul. Blessed. Tony Hale. We love Tony Hale. What, what about this? The discoverability in subpoenaed. If it's, if it's. Where, where is it vulnerable?

Leo Laporte (30:31)

Oh yeah, Paris is a reporter. You wouldn't want to wear this to a interview with a, a source, would you?

Paris Martineau (30:37)

I mean, yeah, I think that's certainly a question. Is, is this, are the, does the data exist in some place that hypothetically a recording of your entire life could be subpoenaed?

Ethan Sutton (30:48)

So this is a great question. When you say recording. Well, there's no recording, so we wouldn't be able to comply with any requests asking for audio because it doesn't exist. Right, the metadata and the summaries, yes, we are vulnerable to that now because, you know, we would fight, you know, any court order, but the, because we have to decrypt it to be able to run the AI on it. We cannot do full end to end encryption because then unless you're running on local model mode. So our plan around this is kind of multi step. So like we're going to be releasing very soon the fully local mode. So you can turn it on and then anything that's all the transcription, all the AI is all happening on your device. So the data is on your device. So that would not be vulnerable because we couldn't comply with anything because there's no data that's possible to comply with that. Again, they're not as accurate models, but they still provide pretty good results and they only get better over time as what you can run on the edge improves. And then there's like the second option which we're working on now, which is where. And not to get too, too technical, but like if, if we have the encryption key. Well, you guys understand, but like if you have the encryption keys that you control on your phone and then the data is encrypted where you would like ephemerally grant access to run some AI, like when you're using the assistant, you know, you would give us a decryption token that has a certain 12 hour, 1 hour life and then so we wouldn't be able to decrypt the data unless you explicitly allow it and that's time limited. And then like if we don't have your permission at that point and you can delete the key, revoke the key at any Time then, then there's no way to comply.

Leo Laporte (32:58)

Yeah, I think there, look, I don't, I no longer care about privacy so I'm a perfect guinea pig for this and I'm not worried about it. But obviously before it became a real consumer product people would want to know that that was the case and there's going to have to be a certain amount of trust. They're going to say, oh yeah Maria Nathan, I trust them to do the right thing with my data. But I also.

Paris Martineau (33:20)

How do you guys think just generally broadly about the idea of us transitioning from a world where I don't know, you can say something or have a conversation with someone and the only two people that know are you two, to a world where this is all increasingly recorded or searchable in some way. I mean obviously this is a quandary bigger than your company, but I assume you've given this quite a bit of thought. So I'm curious as to your thoughts on it.

Ethan Sutton (33:46)

Yeah, I mean this is I think core to what we want to navigate and like, I mean I think that we really believe that the value of what you can do with AI when it has this context about you is going to be so undeniable that not just us, but it will be kind of normalized and so there will be some expectation changes. But I think that core is if you want to have an off record conversation, obviously that's always should be.

Leo Laporte (34:22)

You could press that button and it's off the record. Yeah.

Ethan Sutton (34:25)

You know, but we do want people to, to be responsible and, and you know, but I think that like if you think about the future and Google's promoting always on AI and like it meta, like with the Ray Bans, you know, I think increasingly, you know, it will become kind of normalized. Like you know, maybe when ring came out you have a doorbell camera. Like that was kind of quite controversial at the time but like it just became kind of more normalized because there's a lot of value to be able to like have some ability to. Yeah. See who's at your door. But like the, the key thing that we need to do is like a, like you said, build trust, make sure we can do it in a, in a privacy centered way. But also you know, I think the value of having personal AI is just going to be more or less universal and I mean the trade offs there are real but they can be like maximizing the benefit and minimizing any downsides.

Jeff Jarvis (35:23)

Yeah.

Marie Delores Zoyo (35:23)

I also believe that we are in a transitional time. So right now it's a bit challenging. And you know, we are a startup, so there are like many things that we need to take care about that we should care about. But I think it's just a transition and will be the norm. And that's what makes me excited, like one day, actually. What AI can do for me now that knows me so well. What are the actions, how actually, you know, I can be free in a way.

Jeff Jarvis (35:49)

That raises another question then of kind of the portability of this. Is there an ecosystem that exists where if someone comes up, do you envision an ecosystem where someone can create a specific application on top of what you have? You know, I want to create a romance advisor, or I want to create a job advisor or I want to do a negotiation coach. Can you imagine that this is either portable, that I could take some kind of concatenation of what data is collected somewhere else, or that someone can plug an application in on top of what you have. Is that, is that in the future, you think?

Ethan Sutton (36:34)

Yeah, absolutely. Well, we have a developer API currently.

Leo Laporte (36:37)

Yeah. Where does that? Because I heard about that now I'm thinking that's really cool.

Ethan Sutton (36:41)

Developer B computer.

Leo Laporte (36:43)

Okay.

Ethan Sutton (36:44)

And so, I mean, I think that's definitely a priority for us because, like, both in terms of data portability, yes, you can, you can export data, but also like, what, what, you know, what kind of use cases that, you know, are beyond what we would build, like, can be empowered by this.

Jeff Jarvis (37:02)

Right?

Ethan Sutton (37:03)

About like, you know, the future and like, where there's different ways to exchange signals. Maybe there will be some open standard even that will allow like, even more facilitated kind of exchange between AI systems where it's like, you can authorize this AI system to have this, you know, explicit part of my context and, and there would be some standard where they can just talk to each other. And I think, like one example, you know, this is all unreleased yet from Apple, but like they talk about the semantic engine on the iPhone where like Siri can query apps about context. So you could see like, you know, Siri could query Bee and be like, I want to know about this part of Ethan's life. And then if you had authorized it, then B can return like context from. In real life that Siri wouldn't have.

Jeff Jarvis (37:54)

What about the agentic strategy? You mentioned the beginning that you're, you're starting to play with that.

Ethan Sutton (37:58)

Yeah, I mean, I think that that's a, like our frontier and like a key, like, key thing we're excited about because, like, one of the most valuable things, you know, that we could do is actually like proactively help and take action. So like you know, for example, a simple one is like if there's a to do that that we could help with, the be could help with, like there should just be, you know, you could just say yes, go do it as all of your preferences. It doesn't need to ask you every little thing, you know, especially at the beginning there'd be the appropriate amount of human in the loop, you know, so it, you know, has your, your kind of sign off it. But like you know, for an example, like you know, a to do is referencing like, you know, oh, you need to follow up with this person from something that be learned. You know, could just have a button just like do you want me to draft the follow up for you and send it? And it can do that. But larger, more complex multi step kind of things it can be doing for you.

Leo Laporte (38:57)

Maria, can you talk a little bit about the roadmap? What I can look forward to.

Marie Delores Zoyo (39:02)

That's something that I wanted to mention as well. Speaking about agents, one of the things that I envision for the future is how agents can speak with each other. Meaning how actually personal AI can speak with my personal AI and how they can take actions on our behalf. We did some experiments where Ethan's agents spoke with my agents and they chose for us nice French restaurant nearby our locations. And actually it was quite good.

Ethan Sutton (39:30)

That's wild.

Marie Delores Zoyo (39:32)

Basically they're both agents that they knew our preferences in terms of food, they knew our location, they knew when we were free based on our calendar, and they figured that out for us. And that's where I want to go. Understanding how actually the user should not do things. But it's more like your personal AI that does those actions for you. And I feel that will make us even more connected as humans.

Leo Laporte (39:56)

I wish I'd had this my whole life. It would be really fun to go back and look at different days of my life. It saves it all forever. Does it roll off at some point?

Ethan Sutton (40:07)

No, no. So it's, you know, everything is there unless you explicitly. It's all there unless you explicitly remove a memory.

Leo Laporte (40:16)

So this is an incredible journal.

Marie Delores Zoyo (40:19)

Yeah, indeed. Like the user. The other thing that amazed me is the fact that some users were sad or upset if they don't bring the bee with them. Because after a while they get used to it.

Leo Laporte (40:29)

Yeah, you get hooked.

Marie Delores Zoyo (40:30)

You know, like they're like, oh my God, like I lost my day or you know, I want to understand what happened, but now I cannot because there is a point of Self reflections that be helps you to do so.

Leo Laporte (40:41)

Yeah.

Ethan Sutton (40:42)

And the journal part, like one of the fun things that like at the around Christmas when the Spotify rap came out, like I just asked be like make a life wrapped, you know, in the Spotify style for my life. And it was like, oh, you visited three, you know, had stats like three countries. Here are your key achievements.

Marie Delores Zoyo (40:59)

Yes.

Leo Laporte (40:59)

See, I feel like in a way I'm making an investment in the future of AI. Like I'm now storing as much as I can, knowing that sometime this will be a treasure trove. Maybe it's a little trivial right now. It's actually already quite useful, but I can only imagine what it will become down the road.

Ethan Sutton (41:16)

Yeah.

Marie Delores Zoyo (41:17)

And future. Future. There is one of the things that sometimes we wonder around the vision of the product. So right now it's just audio. But for future, there might be a vision, a camera component in the product as well.

Leo Laporte (41:32)

A camera of some kind. Yeah. Oh, man. I really want you guys to succeed. I think this is a fantastic.

Jeff Jarvis (41:41)

He's your best agent. I am very enthusiastic.

Leo Laporte (41:44)

I love it. Yeah, you could. I talk about it all the time right now in the app I can request an agent upgrade. Is that just a nonsense setting or.

Ethan Sutton (41:54)

No, no, we, we can. We can enable it for your account. I mean there's. We want to be very careful, careful with the agents because we don't want any harm at all, you know, around.

Leo Laporte (42:09)

So assume that I'm the person who you can harm all you want. Okay. Well, no, I'm just all in on the potential for this and I've got nothing to lose at this point.

Paris Martineau (42:19)

Wait, I'm confused with the Asian upgrade. What does that entail?

Leo Laporte (42:23)

What do I get?

Ethan Sutton (42:24)

Well, yeah, we.

Leo Laporte (42:25)

The double O number.

Ethan Sutton (42:29)

All right, so we have agents that run in multiple execution environments. So just to give a brief kind of outline of how it works. So the LLM can do kind of tool calling. So for things you already have, there's kind of already a little bit of agent. You can ask the assistant to send an email to look up. Oh, some information. It can send an email. So that's kind of one way where we have API access to take actions. We will use that. But as you know, like we can't integrate with everything. A lot of things don't have public APIs. So we have two other agent execution environments. The one that you saw in the. The request screen. So, you know, basically you get a lot of stuff done probably on your phone. Most people's digital life, it revolves around the Phone. So, like, the concept is if you get a lot done on your phone and we're trying to give everybody a personal AI. What if we gave their personal AI their own phone? Right. That it could do things for you and had all the same apps that you do. And so we actually have an emulated cloud phone for users who are enrolled in the agent, and it can use its cloud phone to perform actions for you. And that gives it, like, through computer use, the ability to use any app that exists. And it can do multiple steps. We could try and give you a demo. We don't have the phone, unfortunately. I think we can't share the phone screen. But I'd love to give you a demo or follow up. We can enable it on your account.

Leo Laporte (44:06)

Just turn it on my account and I'll end up demoing it on the next show, probably most likely by accident. So if you go now to B DOT computer, you've sold out. How many? Well, you probably don't want to talk about numbers, but how many?

Ethan Sutton (44:22)

We have the next shipment arriving in on soon.

Leo Laporte (44:29)

Yeah, 24th.

Jeff Jarvis (44:30)

It's 24th.

Ethan Sutton (44:31)

Yeah. It's shipping tomorrow. Yeah, we'll arrive to then be shipping to customers on the 24th, so.

Leo Laporte (44:41)

And again, I don't think it's for everybody, but it's only $50 if you want to play with it. And currently there is. Go ahead, Maria.

Marie Delores Zoyo (44:50)

Yeah, one of the things that we are seeing is that some of our customers are buying them. They're buying beef or, you know, after they try it for family or friends.

Leo Laporte (44:59)

Yeah, I'm going to need a new one pretty soon. I can tell. I'm delaminating it already because I never take it off.

Jeff Jarvis (45:06)

You don't need it, for Christ's sakes.

Leo Laporte (45:09)

But the data is on my account, not this device, so. Yeah, yeah. And when you come up with one that I could put in my ear or on my glasses that has a camera, I'll be getting that one, too. I think the idea is brilliant and the execution is surprisingly good considering how early days we are with all of this. And now that I've talked to you, I think one of the things that wasn't apparent to me, and I read through all the material I could on this, is the complexity and the depth that you guys have put into the software. The back end of this is actually kind of interesting. It's very impressive. A lot of thought has gone into this. I wish you the best.

Ethan Sutton (45:48)

Thank you so much.

Leo Laporte (45:49)

Thank you for the good work. Talk to us, Maria. Delores Zoyo. It's great to have you, Ethan Sutton. Great to have you. Founders of B computer on the Web. Yeah. Can you turn on my. Give me all the things. I want it to be smart. I don't care if it's dangerous.

Ethan Sutton (46:12)

Yeah, yeah.

Paris Martineau (46:12)

Leo really finished this interview so that he could get an upgraded feature.

Leo Laporte (46:16)

That was the whole point, frankly. Yeah. It's just to get an upgrade. No, I was very impressed by this. And I really have thought for a long time that the future of AI is personal AI, one of the many, many futures. But personal AI has such an interesting implication in our lives. Yeah. Thank you, Maria. Thank you, Ethan. Really appreciate it.

Ethan Sutton (46:35)

Thank you.

Marie Delores Zoyo (46:35)

Bye.

Leo Laporte (46:36)

Take care. You're watching intelligent machines getting more intelligent all the time. Our show today brought to you. We'll get to the AI news in just a bit and we've got some fun stuff for the back of the book as well. Our episode brought to you by Zscaler this week, the leader in cloud security, as you probably know, if you've been following this and certainly if you listen to security now, it's obvious that enterprises have been spending billions of dollars on perimeter defenses, firewalls. Then of course, you have to get VPNs so that employees can use your network and your apps. Has this made everything safe? No. Breaches continue to rise. In fact, ransomware is up last year, 18% year over year. $75 million record payout in 2024. Clearly, what we're doing isn't working. These traditional security tools expand your attack surface with public facing IP addresses that are exploited by bad actors. And of course, the bad actors are getting worse because they're using AI now to craft their attacks, which means they can operate faster and faster. And you know, the assumption on this is the really, it's almost a bad mindset because you think, I've got the perimeter defenses. So the assumption is anybody inside our network works for us or is supposed to be there, Right? But we know now that's not the case. The breaches are going on, like all the time. What do you do? Well, if you, if you don't have, you know, some sort of protection, you're allowing them lateral movement anywhere in your network, they can find stuff before they even trigger the ransomware. They look through your emails, they look for incriminating emails, they exfiltrate customer information, they blackmail you with that. We're gonna reveal everything, all because they have full access once they're inside. And of course, part of the problem with this perimeter defenses is the bad guys. When they start exfiltrating. They encrypt their outbound traffic. And these VPNs and firewalls cannot really analyze this encrypted traffic at scale. It just allows. It allows all that data out. Hackers, bottom line, are exploiting traditional security infrastructure, using AI to outpace your defenses. It's time to rethink your security. You can't let these bad actors win. That's why you need Zscaler zero trust plus AI. Those are two concepts that you really need. Zero trust hides your attack surface. Your apps, your IPs are invisible. There's no lateral movement. Users are only allowed to connect to specific apps. There's no assumption that, well, you're in the network, you're good. No, you have to have permission to do anything. And it continuously verifies every request based on identity and context. Zscaler simplifies security management using AI powered automation. And it uses AI to analyze over half a trillion daily transactions. Looking most of those are fine. But looking for those suspect transactions that are going to become threats over time. It's really remarkable. Hackers can't attack what they can't see. Protect your organization with Zscaler Zero Trust Place AI. You can learn more at Zscaler.com Security that's Z S C A L E r z s c-a l e r.com security. We thank him so much for supporting the show. We thank you for supporting the show by using that address. Zscaler.com/security. So there's an API. They seem like they have pretty good data security, at least intentions. You think you'd buy one of these things? Paris.

Jeff Jarvis (50:18)

Paris. You were the most dubious, I would say.

Leo Laporte (50:22)

Oh, you're muted.

Jeff Jarvis (50:25)

Even B can't hear you.

Paris Martineau (50:27)

I took. I muted it so that I could eat a biscuit and then I forgot.

Jeff Jarvis (50:31)

Now, is that a biscuit? Biscuit. Or is that a cookie calling itself a biscuit?

Paris Martineau (50:34)

It's a biscuit. It's a biscuit.

Jeff Jarvis (50:35)

Because.

Paris Martineau (50:35)

Biscuit.

Jeff Jarvis (50:36)

But last week you said spanner. You're trying to act all.

Leo Laporte (50:40)

She getting Anglo.

Jeff Jarvis (50:41)

She is.

Paris Martineau (50:42)

No, no, no.

Leo Laporte (50:43)

Get off the list.

Paris Martineau (50:45)

Something written by an Australian. And he said spanner. To be clear, my answer to the B question. One brief aside, though. This is a biscuit, but they call it a scone at the bakery. I get. So it's all kind of all a problem. My B answer is it's not personally a technology or tool for me, but I'm happy you're happy. You seem very happy about it.

Leo Laporte (51:08)

What is it? About it that you would. That turns you off.

Paris Martineau (51:11)

I'm not certain that there's a. I don't see a use case for it. And I'm not certain. Like I was thinking during that conversation, I was like, all right, how would I use this if I wanted to try it out? I'm like, I. Because I think the way to get some sort of use out of it like you are describing, like they were describing would lie in connecting it to everything as you demonstrated, connecting it to your reminders, your calendar invite, having it in your apps that it can scoop up all that data and make intelligent ish inferences from it or at least records of it. But given my line of work, I'm just not certain that that's tenable in any way. Like I use. I would have to somehow contain it to personal devices versus work and in a way that I'm just not certain that it would. But even outside of that, the only compelling use case that I. I mean, I think that it reminds me of that Black Mirror episode that I'm gonna poorly describe. I haven't seen it because I haven't seen it, but I've heard someone describe it. So it reminds me of that Black Mirror episode where it's something like there's a technology that can record all of your memories or every fabric waking life and it utterly destroys all of the characters lives and sanity. Because I think that there is. And maybe this is an outdated notion, but I think that there is something beautiful and messy about humanity and human existence to where I don't have a record of every embarrassing thing I've ever done, nor do all of my friends or enemies. And I'm just not sure that humanity is built to have that suddenly, even though that seems to be where we're going.

Leo Laporte (52:57)

Yeah, that's an interesting. Yeah, that's an interesting point. That it's probably good to forget a lot of this or have an imperfect memory. How about you, Jeff?

Jeff Jarvis (53:07)

It's a gadget. I'm like you. I have the same disease you have. Leo. It's a gadget. I want to buy it. Right. It's a connected gadget that can do things that I can't even see. I'm fascinated as I think it through. I mean, if I were teaching full time, I could really see the use.

Leo Laporte (53:21)

Of it for student or teacher. Valuable. You know, Anthony Nielsen saying, I don't talk much because he's, you know, he works at home. If you didn't have any audio, you wouldn't be that useful. I agree Right.

Jeff Jarvis (53:31)

That's the thing. I'm sitting here writing my book.

Leo Laporte (53:33)

It's somewhat less useful for me for that reason because I, I don't, you know, the only person I'm ever having any conversations with is my wife. I don't go outside. So. But for a teacher. Yeah, a student. Yeah.

Jeff Jarvis (53:45)

But some teachers get quite freaked at the idea of students recording them.

Leo Laporte (53:49)

Really?

Jeff Jarvis (53:49)

Yeah.

Leo Laporte (53:50)

Wow.

Jeff Jarvis (53:50)

Yeah.

Paris Martineau (53:52)

No, this is actually a huge problem right now. It has been for years with the. I'm not going to go into details. With the turbulent political climate people are experiencing all over the world. You could imagine a situation where, and I've heard anecdotally from professors over the past, you know, 10 years being like, I'm worried that I'm going to make some offhanded comment in one of my classes. A student is going to record that, leak it to some media outlet and then I'll lose my job. I just think there's a lot of strange consequences that are opened up where suddenly everyone is recording everyone all the time. And that's not even considering the national security implications.

Leo Laporte (54:31)

Like, yeah, I recognize I'm in a somewhat privileged position where I'm not at risk. I'm not employed by anybody. I'm a white man, I'm older white man. I'm not really in a high risk environment. I'm a privileged environment.

Jeff Jarvis (54:46)

The other interesting thing about it is so profess some of them, not all of them. So I'm very open and I don't. I put my stuff up online and I'm fine with that. But some have. Are copyright hawks of their own sort.

Leo Laporte (54:57)

Right.

Jeff Jarvis (54:57)

And they don't want their stuff recorded for that reason. And then there's, then there's an interesting thing about being caught in. So I had a minister once, back when I was a decent human being and actually went to a church and he never let us put. I was on the board of the church, believe it or not, and he never let us put his sermons online. And he said he thought someone would steal them. And I thought, well, frankly, sir, they're not very much worth stealing. But then as I thought about it, he was. I realized, I'm pretty sure he was projecting that he was borrowing sermons.

Leo Laporte (55:31)

Yeah.

Jeff Jarvis (55:32)

And he didn't want to be discovered.

Leo Laporte (55:33)

If you think about it, writing a new sermon for some is quite a challenge. There are AI tools for writing sermons now.

Jeff Jarvis (55:41)

There are.

Leo Laporte (55:41)

Now, Father Robert told me about this. This. Yeah. So I understand. And you don't want to write a sermon and have somebody else use it. I guess.

Jeff Jarvis (55:50)

And I can see if I were, let's say, an executive and I'm dealing in legal matters, personnel matters, that kind of stuff, or a dean, and I'm dealing with similar kinds of things. I can imagine my organization say, don't you dare.

Leo Laporte (56:05)

Right.

Jeff Jarvis (56:06)

And so it'll be interesting, but obviously, you know, we've talked about this in terms. Well, let me ask. What about your mother? Would this be useful for her?

Leo Laporte (56:13)

Yeah, she's Alzheimer's. Yeah. Problem is, right now you have to be fairly technical to use it. So I don't.

Jeff Jarvis (56:20)

But if the place were using it, yeah, maybe. Right.

Leo Laporte (56:24)

I mean, certainly that's an application. And as I get older, my memory deteriorates. I think there's a lot to be said for that. I think that it's interesting. I think this is a larger conversation, which is, society isn't crazy about these tools. The people who are, I think, may have an advantage, do you think, in the long run over people who.

Paris Martineau (56:47)

In what way?

Leo Laporte (56:51)

More insight, more information.

Paris Martineau (56:53)

How does your phone knowing that your cat has a Hitler mustache provide you an advantage?

Leo Laporte (56:59)

Well, if at any point there is a surgical solution for that, they may let me know. I don't know. No, you're right. A lot.

Paris Martineau (57:08)

A lot of gizmo has a half a Hitler mustache. And I'm gonna leave it on her face.

Leo Laporte (57:12)

The worst, the worst thing about this is you realize 99% of your conversations in life are completely trivial.

Jeff Jarvis (57:19)

Are absolutely trivial. Well, I was thinking about this, but my wife is very private. I could think her thinking, are you knocking futz? No absolute way, though I can imagine this use case, Leo, in which she says, give me that thing. I'm gonna play back. I did too tell you about this.

Leo Laporte (57:34)

Right.

Paris Martineau (57:35)

I was about to say my immediate thoughts is this would be useful in arguments. And I'm not certain that it would actually be useful.

Leo Laporte (57:42)

Would it be good? It would be useful, but it'd be good is another. Yeah, entirely. But I think, you know, keeping track of agreement. One of the things I think is useful is keeping track of the things I've said I'm going to do or I've agreed to do or want to do as a to do list, that's pretty valuable. I've tried to do that in my own life, and it's hard. I, you know, I remember that you, you know, to do that, the fact that it picks these up. But the disadvantage is it can't read my mind. It doesn't know unless I say it out loud.

Paris Martineau (58:10)

So, you know, hey, Neuralink is coming.

Leo Laporte (58:14)

Just around the corner for you. Just around the corner. Scarlett Johansson urges the government to limit AI after a fake video of her opposing Kanye goes viral.

Jeff Jarvis (58:27)

I never saw the video itself. It was a bunch of celebrities.

Paris Martineau (58:30)

It's a bipartisan, partisan issue that enormously affects the immediate future of technology. Is very funny to say in response to an AI video of yourself going viral.

Leo Laporte (58:41)

Yeah, yeah, it's a little. I mean, look, I'm a big fan of Scarlett Johansson. She's the most powerful woman in Hollywood.

Jeff Jarvis (58:48)

She was being used by OpenAI, let's not forget. I think she's a bit bruised in that her voice was being used there.

Leo Laporte (58:53)

But this is an example of kind of knee jerk anti AI movement, of which there will be a lot, I'm sure. I don't think the government should limit AI because there was a fake viral video of her. No, that seems like a lot. It's like a big ask.

Jeff Jarvis (59:08)

Well, and I think there's a First Amendment issue there too. If I want to. If I. If I.

Paris Martineau (59:13)

If I wanted.

Jeff Jarvis (59:14)

If I wanted to hire actors to do exactly what that video was. I have a right to do that. Just because I use the machine to do it. I can't.

Paris Martineau (59:23)

Well, but she Machine trained on her voice.

Leo Laporte (59:27)

Yeah. She has a right of.

Jeff Jarvis (59:29)

Yes, of publicity.

Leo Laporte (59:30)

Publicity. Not that you not use her image. And she could pursue it along those lines. That's fine for her. Then to say no, a government's gotta ban AI is another.

Jeff Jarvis (59:39)

No, no, no, no.

Leo Laporte (59:40)

That's a bridge too far. Yeah, all right, just thought I'd bring that up.

Paris Martineau (59:46)

So, Jeff, would you get a B? And what would you use it for?

Jeff Jarvis (59:52)

You see, I had, for a brief time the disease that. That Leo has, which was Kickstarter disease.

Leo Laporte (60:00)

I had. I had to stop that.

Jeff Jarvis (60:02)

Yeah, he had a bad. He had a bad case. I had a late case. So I would buy stuff because I'm fascinated with it. You know, I have a Newton back there in my closet.

Leo Laporte (60:15)

Oh, I had several, but I got rid of them when I closed the studio.

Paris Martineau (60:17)

I got rid of the Isaac Variety.

Leo Laporte (60:19)

No Isaac Newton. Oh, she doesn't know what a Newton is.

Jeff Jarvis (60:23)

Oh, my God. Oh, she's so young. She's so.

Paris Martineau (60:26)

What's a Newton?

Leo Laporte (60:27)

Before the Palm Pilot, Apple released a handheld device that you could write on.

Jeff Jarvis (60:31)

In a certain kind of. It was a very certain kind of writing you had to do.

Leo Laporte (60:35)

It was. It was a flop. You don't. You don't know about the Newton. That's interesting.

Jeff Jarvis (60:41)

Hold on. We got to show Her. You do have to show me Doonesbury Newton.

Paris Martineau (60:46)

Doonesbury Newton.

Leo Laporte (60:47)

So Doonesbury did a famous comic commenting on the very poor handwriting recognition of the Newton.

Jeff Jarvis (60:56)

Here's one. I'll put it up in the. In the chat.

Leo Laporte (60:59)

In the discord.

Jeff Jarvis (61:00)

In the discord.

Leo Laporte (61:01)

Okay, let me go to the discord. So that. But then this is from the. This looks like Simpsons. So. So Bart is writing Beat up Martin and it says, eat up. Eat up, Martha. But, you know, this was a long time ago. It wasn't. It was before the Internet. It was before cell phones. So there were. This is not unusual that you have inventions that eventually will become a big deal, but that technology, it's way too early. It was just too early.

Jeff Jarvis (61:33)

That's all. So I would love to have gadgets.

Leo Laporte (61:36)

What was your first pda? First Paris. I gotta know. Not public display of affection. What was your first handheld?

Paris Martineau (61:46)

The answer. Was it the smartphone of some sort? Yeah, I can't recall. It might have been an. And no, it may have been an early iPhone. I don't know. I'd have to. I have to do.

Leo Laporte (61:57)

But it was a phone, probably.

Paris Martineau (61:58)

It was a. It was a phone with all screen, no tack, like, I guess, like, no keyboard. But I remember my mother having a Zune. I really. I really want.

Leo Laporte (62:11)

My mom had a Zune. Jeff.

F (62:13)

My mom did have Jeff.

Leo Laporte (62:15)

We're so old.

F (62:16)

That wasn't a pdr.

Leo Laporte (62:17)

That's not a PDF.

Jeff Jarvis (62:20)

Yeah.

Paris Martineau (62:21)

Or no? What am I thinking of? There was like a little switchy guy. I'll think it was probably a Palm.

Leo Laporte (62:26)

Pilot or a Cleo.

F (62:27)

A Sidekick.

Leo Laporte (62:28)

Sidekick. You think she had a Sidekick? That's pretty fancy.

Paris Martineau (62:32)

Sidekick.

Leo Laporte (62:33)

Mom had a Zune.

Jeff Jarvis (62:37)

Leo. We were at south by Southwest at the Digg event and Microsoft was a sponsor and they tried to give away a Zune and nobody wanted it.

Leo Laporte (62:46)

Nobody would take it.

Paris Martineau (62:47)

What I'm thinking of is the Verizon chocolate phone. She also had one of those. And I don't know why that lies in my head.

Leo Laporte (62:55)

Prior to all of this, there was a whole bunch of different. They called them personal digital assistants. The Newtons started it, but they were all flawed. Palm Pilot was pretty successful, but because they didn't have a phone in them, it wasn't until you could get the Internet on them and the phone in them that they really became useful. And now this is our primary computing device, isn't it?

Paris Martineau (63:17)

Yeah.

Leo Laporte (63:18)

Like this is.

Jeff Jarvis (63:18)

Oh, yeah.

Leo Laporte (63:19)

A lot of people don't even have a computer anymore. This is. They have a phone that's psychotic.

Paris Martineau (63:23)

But I understand.

Leo Laporte (63:25)

Well, you need one because you're a writer. But if you didn't have to write.

Paris Martineau (63:28)

But it feels right. Sometimes you need to do like. Sometimes I feel like if I'm making a large purchase, it needs to be on a computer rather than a phone. It feels too trivial to do it on the small screen.

Leo Laporte (63:39)

When you bought your tv, did you do that on the.

Paris Martineau (63:42)

I did, yes, because I, you know, I had.

Leo Laporte (63:44)

How do you like that? Are you happy with it?

Paris Martineau (63:47)

Oh, we talked about a little bit last week. It's fantastic. It's so. It's like, jawed. You were right that I should have gotten the 65 inch. I did that on your recommendation. And it is like.

Leo Laporte (63:58)

Yeah, you don't have to go to the movies ever again.

Paris Martineau (64:00)

I don't.

Leo Laporte (64:01)

It's so LG oled.

Jeff Jarvis (64:04)

Oh, no. She followed your instructions.

Paris Martineau (64:06)

I got. I got a Samsung SD QD oled. Wow.

Leo Laporte (64:12)

You got the best screen.

F (64:14)

There's no going back from that.

Paris Martineau (64:16)

It's really. It's also so thin. It is like I was shocked when I took it out of the box. I had to ask my upstairs neighbor to help me lift it up there because I foolishly thought I could put it.

Jeff Jarvis (64:28)

Oh, no, no, no.

Paris Martineau (64:29)

My thing myself. No, not allowed to do that. But the thing that I've really liked the most is I got a Blu Ray player as well.

Leo Laporte (64:36)

Oh, good. And it's been delightful that you watched.

Paris Martineau (64:41)

Wild at Heart, which was the movie that started that.

Leo Laporte (64:43)

That's what you wanted.

Paris Martineau (64:44)

Yes, but now I've been watching all of Twin Peaks on Blu Ray and I really love it because I started watching Twin Peaks while I was waiting for the disc to arrive on whatever streaming service that's available. That was fine. But watching it on Blu Ray is totally different because they have the log lady intros, which I guess aired when Twin Peaks was airing, but now I get to watch those and they're great.

Leo Laporte (65:06)

This was a woman that carried around, as I remember, a log.

Paris Martineau (65:09)

Yes. But she, at the very beginning of. She's just kind of a side character in Twin Peaks. But something I didn't realize is when they were airing them on TV originally, they have these little intros that play before the show starts where the log lady is sitting at a table holding her log and does a direct to camera monologue kind of about nothing, but also about strange things that kind of tangentially relate to the episode. And it's beautiful. Would really recommend the log 4k.

Leo Laporte (65:38)

There's nothing like it.

Paris Martineau (65:40)

There's Nothing like it.

Leo Laporte (65:41)

Well, I'm glad you like it. I think you did the right thing. You got to. Right.

Paris Martineau (65:44)

Before I move on, I have one question for you guys, which is I have a strange compulsion which has been brought up by this conversation. We just had to get old technology and frame it or, like, put it on my wall in some way, maybe in a shadow box or just hold a thought about this with the, you know, corded phones before. If I was to do this, the PDA. What, what, what? You know, early 2000s phone. Should I do what looks interesting or is notable trio?

Jeff Jarvis (66:11)

A trio is wonderful thing. Let me see if I get mine.

Leo Laporte (66:14)

Oh, he's gonna go get his trio, ladies and gentlemen. See, this is where getting rid of the studio was such a wonderful thing for me because I really got rid of a lot of this stuff I have. I kept very little of it.

Jeff Jarvis (66:27)

You're gonna cavell.

Leo Laporte (66:29)

Oh, isn't that beautiful? See, it's really beautiful, actually. It really want to have it dismantled and framed, or you want to have it just framed as is.

Paris Martineau (66:38)

Framed as is your shadow box?

Jeff Jarvis (66:40)

Yeah, I think you want.

Leo Laporte (66:41)

Want that because, you know, that's one thing people do now is they dis. Disassemble these things and have it all arranged nicely in a shadow.

Jeff Jarvis (66:47)

I have a box downstairs next week. I can bring it upstairs. I have a. I have a. A Newton. I have. There was another device that you clipped on your belt was this really dorky thing. And it. It. It dealt with a vertical blanking interval and got a constant feed of information. Would pick up some. So you'd get. It was like a beeper, but just for your stock prices or news headlines. It was a way you were going to get news.

Paris Martineau (67:11)

And do you have that in a box?

Jeff Jarvis (67:13)

I have that downstairs. Yeah.

Paris Martineau (67:15)

You got to bring that next week. I want to see that.

Jeff Jarvis (67:17)

I have a. I have an intel now. That Intel's about to get broken up and die. I had a Intel, worked on an early, early tablet just about the time Bluetooth came out. It wasn't Bluetooth. It was an intel proprietary. It never came to market. I had the prototype because I worked on it. And the idea was you would sit on your couch and it would replicate your laptop. There were no laptops. It would replicate your desktop screen and your den behind you so that way you could surf on your couch. I have a news pyramid. Did you ever have that one, Leo?

Leo Laporte (67:55)

No.

Jeff Jarvis (67:55)

So it also worked on the vertical blanket.

Leo Laporte (67:57)

Oh, it lit up a little bit and.

Jeff Jarvis (67:59)

No, this is different. It worked vertical Blanking interval. And it would. So that was a constant stream of information and you programmed it to just pick up what you wanted.

Paris Martineau (68:08)

It's a news pyramid, but it's not the pyramid structure used in news. It's a different.

Jeff Jarvis (68:14)

No, it's news. Exactly.

Leo Laporte (68:15)

It's a physical pyramid that has an antenna. And it turns out the public broadcasting stations all over the country would. They had in the broadcasting technology, there was a little extra space that you could send data, but very, very slowly out over their antenna. So there were devices made that would pick up this data and display it. And that's what he had. And it's called a newspy News pyramid.

Jeff Jarvis (68:45)

Yeah, I've got that downstairs. There's also Google Glass.

Paris Martineau (68:49)

Oh yeah, you can find this on Google. I just did a couple.

Jeff Jarvis (68:51)

You want me to run downstairs now and get the box?

Paris Martineau (68:54)

I mean, you don't have to.

Leo Laporte (68:56)

No, no, no. Let me show you though. This is what we were talking about. The disassembled, you know, kind of blown up. This is an iPhone 6. These are on Etsy. So it's kind of cool. A lot of people do these. A lot of our people in our audience do make their own. They'll disassemble something and frame it. Yeah, I think it's kind of an interesting way to show old technology. You might as well do that with your news pyramid, because public broadcasting stopped send data in the vertical blank a long time ago.

Jeff Jarvis (69:25)

Did I ever tell you about my use of public broadcasting with the India Journal?

Leo Laporte (69:28)

No.

Jeff Jarvis (69:29)

So the first task I did when I got to Advanced Publications in Jersey City, as Steve Newhouse, my boss, said, we were in the shadow of the Statue of Liberty and all these waves of immigrants come in, and the time Italians and English, I mean, in Irish and German. And so when we were there working, there was Indian and Pakistani. And so we wanted to serve them. And one. One story from all of India and the newspaper every week wasn't going to do much. So we started the India journal. This is 1990. This is pre web, just pre web 1994. And so we found a whole bunch of old computers and put them out in the neighborhoods. And people there would send in reports and would come into a bulletin board we had. And then every day, in what we called the Black Hole of Calcutta, which was below the press room in the building, Sriram Krishnaswamy would turn out a two page news bulletin about both local and diaspora news from India and Pakistan.

Leo Laporte (70:28)

In English?

Jeff Jarvis (70:29)

Yes. And it went out over the vertical blanking interval into the NBC signal. Down into a TV antenna over the Shivam Deli on Newark Avenue, down into a kitchen microwave cart that I put together. And in there was a laptop and a big printer with a big red button. We worked with MIT Media Lab, and the assistant had to have a big red button. And you hit it, and out would come the day's India Journal. In the midst of the Indian neighborhood, it was killed. You want to guess why? You want to guess who killed this project?

Leo Laporte (71:04)

Who?

Jeff Jarvis (71:05)

The Teamsters.

Leo Laporte (71:06)

Oh.

Jeff Jarvis (71:07)

Because it was seen as an altered delivery of the newspaper.

Leo Laporte (71:11)

Oh, man.

Jeff Jarvis (71:12)

And that vertical blanking interval was a. Was a handy way to. To transmit a constant flow of information. And was used. Was that. Was that not the basis of teletext and video text?

Leo Laporte (71:23)

It was. That was teletext.

Jeff Jarvis (71:24)

Yeah, yeah, yeah.

Leo Laporte (71:26)

Teletext over VBI. But it wasn't even 300 baud. It was pretty slow.

Jeff Jarvis (71:30)

Yeah, yeah.

Leo Laporte (71:31)

So. Oh, this is such a rat hole.

Paris Martineau (71:35)

What a beautiful world.

Leo Laporte (71:39)

So, weirdly, one of my first computer programs back in the 90s was a demon dialer. I had a bulletin board system. And so you had to dial into it, Right. And it only had two lines.

Paris Martineau (71:52)

A demon dialer.

Leo Laporte (71:54)

Well, it was very hard to get in. There were only two phone lines, Right. So I wrote a Mac program that would run on the vertical blank interval because there is a moment in time on all CRTs when the cathode ray tube. Right. There's a gun that's firing. What is it? I don't even remember now. Some sort of electrons at the cathode ray tube to light it up. It goes all the way up, and then there's a moment in time when it repositions that. It's not doing anything. So the computer is kind of waiting for the gun to go back down to start drawing the next screen. So that gives you a very small millisecond length of time that the computer's just sitting there. And what I wrote was one of the first multi. I think it might be the first multitasking program for the Macintosh. During that vertical blank interval, it would stuff dial commands into your modem. It took quite a few of them, but by the end of it, it would fill it up, and then you could dial the modem, and if it got a busy signal, would start over, but if it didn't, it would beep allowed.

Jeff Jarvis (72:55)

I think I might have advanced past. We didn't go back far enough for Paris here. The old billet and bulletin boards. Paris, you weren't bulletin boards. I got.

Leo Laporte (73:04)

I've got you Know, BBS is okay.

Paris Martineau (73:06)

I know bulletin boards. You know, everything else you just mentioned I was trying to hold in my head. I wish that there was a book that I could read that I would understand all.

Leo Laporte (73:14)

Let me share this to me. Yamming CRT yolks. Do you know that in the old days they didn't have flat thin screens like your tv. They had a cathode ray tube. And this is the little thing that's firing at the gun. That's firing to align these is called yamming them. And it's very difficult. So it turns out we've only recently learned how they did automated deflection yoke yamming. Here's a video from Thompson. This is a video from Thompson Electronics on how we aligned the yolks in a mass way in the factory. The article starts, when I worked in a TV repair shop. The first thing they told you is never touch the yoke for two reasons. One, it could electrocute you. But two, once it's out of alignment, it's almost impossible to get back in alignment. So here's Bill from Thompson Consumer Electronics. He's talking about how they came up with an automated way to do the yamming. I don't know when this is recorded, but look at that screen.

Paris Martineau (74:21)

Oh, look at that screensaver. So good.

F (74:27)

They managed to not have a refresh line on that screen.

Leo Laporte (74:30)

Oh, yeah. Well, you have to have a special camera that you could adjust the shutter speed. So just as we've done here. So that you don't get. Because, you know, you notice I. You don't have this refresh problem with LCDs. So my LCDs don't. But this Mac is a CRT. In fact, was it you, Benita, who got this working?

F (74:50)

No, it was Russell or Russell.

Paris Martineau (74:52)

What is that fun? Is it a screensaver on that or is that flying?

Jeff Jarvis (74:56)

That was the big. That was the funny. Everybody had the same sense of humor at the same time. We had a universal sense of humor. And toasters flying was funny to everyone.

Leo Laporte (75:04)

Everyone. Anyway, this Mac, which Jammer B gave me before he left, is the original Mac. It has a cathode ray tube and cathode ray tubes because of the VBI will have a line going across them. Because unless you can get the frame rate of the camera to be exactly the frame rate of the tube. And that's what Russell or somebody did. Maybe it was Burke. I think Burke did it.

F (75:26)

It was Burke or Russell.

Leo Laporte (75:27)

He figured out how to get this frame rate of my very fancy Sony FX30 camera to adapt so that we didn't have. We used to have a line going up and down on that. So he fixed it, which is pretty cool. Okay, so let's go back to AI.

Jeff Jarvis (75:43)

I was just about to put up a picture from my memory lane, but go ahead.

Leo Laporte (75:46)

Oh, go ahead, go ahead. No, do it, do it, do it. I don't want to stop you.

Paris Martineau (75:49)

I'm just. I'm writing down all these terms that I can educate myself later, guys.

Leo Laporte (75:55)

I had never heard of CRT yamming before.

Jeff Jarvis (75:58)

No, I haven't either.

Leo Laporte (75:59)

That was brand new.

Paris Martineau (76:00)

I was going to say. Okay, good, because I still don't fully understand it.

Leo Laporte (76:04)

You want to jump into the future? Microsoft has announced a new quantum chip, the Majorana one chip that they say is going to transform quantum computing. Now, we've been talking a lot about quantum computing.

Paris Martineau (76:18)

Wait, is this the thing that they're saying, they've created a new form of matter?

Leo Laporte (76:24)

Yeah. Well, it's not a new form because it's been around since matter began.

Paris Martineau (76:29)

Well, yeah, that was going to be my point. You can't really create a new one. Well, you could find.

Leo Laporte (76:34)

It's not that kind of new frame of matter. It's a very subatomic particle that was discovered in 1937 by Ettore Maioragna, an Italian who then disappeared mysteriously, by the way, at the age of 31. Anyway, the. This is a breakthrough in quantum computing. Is it going to. Is it going to make suddenly quantum computing happening? No, no. But it is a way of getting more qubits onto a single chip.

Jeff Jarvis (77:05)

And it's not electrical. Can you explain that to me? I heard something about that.

Leo Laporte (77:09)

It's not using electrons. It's using electrons.

Jeff Jarvis (77:12)

Okay.

Leo Laporte (77:13)

It's using this Majorana particle that was hypothesized about in 1937, but I think CERN probably discovered. Maybe not CERN. Anyway, some super particle accelerator discovered it. And this is. Microsoft has created what they call the world's first topo conductor, a new type of material that can not only observe, but can control majorana particles to create more reliable qubits. I never thought in my life I'd say that sentence.

Jeff Jarvis (77:46)

Well, Leo, if we want to go even further into the future. Line 117.

Leo Laporte (77:52)

Oh, boy.

Jeff Jarvis (77:53)

I put up just before.

Leo Laporte (77:55)

Yes, A.

Jeff Jarvis (77:57)

For the first time, according to Wired, a team at University of Oxford has succeeded in getting two quantum processors to connect to each other and transmit the same information using particle entanglement.

Leo Laporte (78:07)

Again, a sentence I never.

Jeff Jarvis (78:09)

Yes. So they weren't actually connected.

Paris Martineau (78:13)

Explain that. For some reason, this Is the weird.

Jeff Jarvis (78:16)

This is the weird thing about quantum.

Leo Laporte (78:18)

So, you know, traditionally in order to get from point A to point B, you have to cover every point in between.

Paris Martineau (78:26)

Yes.

Leo Laporte (78:26)

Right. Well, it turns out quantum particles somehow managed to get from point A to point B without doing that.

F (78:33)

No, it's not. It's not. It's not movement. It's too.

Leo Laporte (78:36)

They're not moving.

Jeff Jarvis (78:37)

No, it's communication.

F (78:38)

Are connected to each other and they're.

Leo Laporte (78:40)

Connected without any connection.

Jeff Jarvis (78:42)

Right.

F (78:43)

So they're transition faster than the speed of light in theory.

Jeff Jarvis (78:47)

Right, right, right, right. Well, right. One will change a state and the other one will change too. But they're.

Leo Laporte (78:52)

The reason I say travel is because one thinks of data traveling between those two.

Jeff Jarvis (78:56)

But it has right to correct you. Because that's the old way to think. Yeah, that's the past.

Leo Laporte (79:01)

Yeah. In the experiment, light particles remained in the same place, but because they were entangled, the computers could see the information and work in parallel even though they were not next to each other. Well, they're next to each other, but not in the same spot.

F (79:19)

And there's entangled qubits. Is that what it is?

Leo Laporte (79:22)

Yeah, it sounds like it pretty much.

Jeff Jarvis (79:24)

It's entangled. Yeah, it's quantum entanglement. A pair of linked particles, even at a distance, can share the same state and therefore transmit the same information. If one changes state, the other instantly reflects it. Instantly, in our view because it's faster than light reflects it.

Leo Laporte (79:41)

And that's what's interesting because you're implying that there's some sort of communication. When we're communicating, we are traveling as electrons down a pipe. And so there's traveling involved. And that takes time. It's the speed of light, but it still takes time. This takes no time because it's simultaneous. And it's a puzzlement because how does that. How.

F (80:06)

The greatest mindset is trying to figure out how to use this to make an ansible.

Jeff Jarvis (80:10)

Yeah.

Leo Laporte (80:10)

Basically an ansible is a sci fi concept of instant trans.

F (80:17)

Instant travel faster than light communication.

Jeff Jarvis (80:20)

Yeah, yeah. That's different. This is not.

Paris Martineau (80:22)

I was gonna say it would be communication, not travel.

Leo Laporte (80:25)

I'm sorry, I keep bringing the word travel into it.

Jeff Jarvis (80:27)

Yes. This is what wired. Wired makes for. Just for you, Leo. Wired said this. This is not to be confused with the conventional idea of teleportation which involves a hypothetical immediate exchange of matter in space. In the experiment, the light particles remained in the same place, but entanglement allowed the computers to see each other's information. As you said. Yes.

Leo Laporte (80:46)

So big Breakthroughs in quantum computing in the last. Google also has a quantum chip, Willow, that solved a benchmark that they say solved it in five minutes. It said conventional computer would have taken 10 quadrillion years to solve. Do we live in interesting times? I have to say we do. Coming up on intelligent machines. We are. We've got Ray Kurzweil, the guy who coined the term, wrote the book the Age of Intelligent Machines. His new book is coming out soon. It is, frankly. He says we will merge with AI. I can't wait to talk to Ray Kurzweil. That'll be in a couple of weeks. We. We've also communicated with Stephen Wolfram and he has a very good book on how AI works. He's been very good actually writing about AI. So Stephen Wolfram will be joining us as soon as we can work out a time that works for both of us. So we've got some good guests and.

Jeff Jarvis (81:41)

Some other people in the proverbial pipeline.

Leo Laporte (81:44)

Smarter people than I who can explain this. You know, maybe we should get somebody on quantum computing on.

Jeff Jarvis (81:50)

It would be good because it confuses the hell out of me.

Paris Martineau (81:53)

I'm literally looking up quantum entanglement as defined in the outer Wilds game because I remember when I played that I kind of understood it briefly.

Leo Laporte (82:05)

I asked one of the AIs, I think it was Deep Seek to explain quantum comp. Quantum entanglement to a dog. And it said something like, well, imagine there were a million tennis balls and you could catch them all at the same time. You know what? Maybe I'm not as smart as a dog. Anyway, let's see what else, what else in the AI news?

Jeff Jarvis (82:30)

There's lots of stuff. I enjoyed some information reporting in line 25.

Leo Laporte (82:37)

Oh, I wanted to talk about that.

Jeff Jarvis (82:38)

This is really well done, I think, by. Who was it? Aaron Wu.

Leo Laporte (82:42)

Aaron Wu. She's talking about Google. Google's AI efforts marred by turf disputes. And there's a picture of Demis Hasibis, Sir Demise Hasibis, who's the CEO of Google's DeepMind. Google, she writes, scored a rare hit with AI powered Notebook LM, which of course we talked about. We had Richard Gingrich. Was it Richard on that?

Jeff Jarvis (83:06)

No, no, no. We have Stephen Johnson on that.

Leo Laporte (83:07)

Stephen Johnson, that's right.

Jeff Jarvis (83:08)

Who will get back? I think we're.

Leo Laporte (83:10)

Yeah, let's get him back. Yeah, people love it. That was the one that not only summarizes your document.

Jeff Jarvis (83:15)

I think everyone that I've talked to who's used Notebook LM says this is cool.

Leo Laporte (83:19)

It was one of the AI breakthroughs or a products that made a lot of people say, oh, maybe there is something here. Yeah, particularly the feature that would let you make a podcast out of your, you know, your documents. After the product's release, Sundar Pichai praised the small, dedicated team that built Notebook lm. But the path to launch wasn't quite so smooth as he made it sound, writes Aaron Wu in the Information with an exclusive. Before Notebook LM launched, staffers working on it in the company's labs clash with their counterparts at Workspace, which runs Gmail Docs. According to four people with knowledge of the situation. She's got some sources. Workspace employees argued NotebookLM will conflict with our plans for existing apps like Google Docs. They even tried to shut it down. This was a big problem at Microsoft back in the day, where the Office team would say, no, you can't do that, or the Windows, you can't do that. It's going to hurt our business. In the end, leaders of both teams stuck with NotebookLM and it launched as a standalone website, although it is now a service and Workspace.

Jeff Jarvis (84:30)

But I think if Workspace had won and said, no, no, we're going to own this, they would have concatenated, however, you say that, down to features that fit within Workspace. Whereas Notebook LM was important on its own and hiring Steven Johnson, he's editorial director of Workspace. I'm sure that title exists nowhere else in Google. The team allowed it to exist to serve writers like Stephen.

Leo Laporte (84:56)

Some of the tensions flow from how Google is structured. Demisa Seabis runs Google DeepMind. Thomas Currian runs Google Cloud. Differing priorities between the two units have caused some tensions. Top managers at Google Cloud are debating how to improve their Unit's relationship with DeepMind. Yeah, I mean, look, it's pretty clear that, by the way, Gemini 2.0 is pretty good, right? In fact, let me just see the AI arena score, which has got, you know, it's. This is. This is a people test and vote on which AIs are better. And it's kind of the most useful way of. What's. Am I at the wrong spot? Maybe I'm at the wrong spot. It's the. It's trans look. It's tracking my mouse. That's the. That's not the tracker I was thinking of. That's not the AI arena I'm looking for.

Jeff Jarvis (86:01)

I like it.

Leo Laporte (86:03)

What did I do wrong? Where did I go wrong? Anyway, here we go. It ranks. It's actually really useful because it ranks the AIs and it's changing constantly. But Gemini for a while was on top. I haven't checked it recently. I think I'm still on the wrong site. No, that's the wrong place. Anyway, I think Gemini's pretty good. I think DeepMind got a lot of people's attention. You know I did, I. Did you guys watch the video I sent you?

Jeff Jarvis (86:36)

I got a third of the way through it, Leo. It is very informative.

Leo Laporte (86:40)

Yeah.

Jeff Jarvis (86:40)

However, I've never had a big best, better way to go to sleep. The affect is everything. Well, we took out your kidney and then we're going to take out your heart.

Leo Laporte (86:57)

Simple things like multiplication addition. I found it gripping but it's exactly.

Jeff Jarvis (87:03)

This voice for the entire time.

Leo Laporte (87:05)

This is Andre Karpati who is one of the founders of OpenAI and he's currently working at an AI education startup.

Jeff Jarvis (87:12)

How long is it Leo?

Leo Laporte (87:13)

Three and a half hours. But it's everything you need to know about how modern models are created, including this breakthrough in reinforcement learning. This is actually the relevant portion where they're talking about AlphaGo, which is the blue line here. Using reinforcement learning actually got better than the red line supervised learning, which means they got better than the best humans. This is the breakthrough. See the problem with the current common models using supervised learning never get better than a human because they're basically learning from what humans have done. They're remixing human intelligence. But this new thing that Deep SEQ is doing is reinforcement learning is actually potentially giving us or AIs the ability to exceed human capabilities. And it did it within go very early on. Any event worth highly recommended. It's a deep dive into LLMs like Chat GBT if you're interested. It's the first thing I found that I really makes sense to me it's a primer. It's a very useful.

Jeff Jarvis (88:17)

Yeah, it is good. But, but oh, the voice.

Paris Martineau (88:21)

Just a soft spoken man.

Leo Laporte (88:23)

He's got a nice.

Jeff Jarvis (88:25)

No, no, no, it's fine. I'm not criticized that criticism.

Leo Laporte (88:27)

It's just we're never gonna get him on now.

Jeff Jarvis (88:30)

No, no, no, he's brilliant. He's explaining everything I've been trying to get him on. Yeah, that's what I'm saying. He explains everything so calmly that it's comforting and I, I was trying to.

Paris Martineau (88:42)

Watch Jeff of what he normally watches to go to sleep which is diners drive ins and dives as we learned last week.

Leo Laporte (88:49)

What Guy Fieri puts you to sleep.

Paris Martineau (88:52)

He falls asleep to Guy Fieri regularly. And the thing that I'VE been thinking about since you said that, Jeff. This is just what goes on in my head as I'm going on my day is one day I was walking around, I was like, that means Jeff's wife must fall asleep. To Guy Fieri.

Jeff Jarvis (89:06)

We fall asleep.

Paris Martineau (89:07)

What a woman.

Leo Laporte (89:08)

Oh, oh, she's already asleep. So here's the LM LLM arena that I was looking for. Grok3, which has just came out. We're going to talk about that in a second. Is currently ranked 1. But look, Gemini Flash is number 2. Gemini 2.0 Pro number 3. Then chat GPT4O Deep Seek.

Paris Martineau (89:30)

How is this being graded? What are the greenish scores?

Jeff Jarvis (89:33)

Yeah, this is such a game.

Leo Laporte (89:35)

You have one against another. Humans are doing it and then giving them the score. It makes it a little volatile. You see the number of votes. For some reason, the Gemini stuff was getting a lot of votes all of a sudden because it was very low and the. It suddenly went up. And so I think there's. I think it's a little bit of gaming going on, but it is the closest thing. It's hard to judge. You know, I use them all and, you know, I just find some are better at some things than others. I'm really happy with Perplexity, which gives me a good variety of models.

Jeff Jarvis (90:06)

I was saying, I think, huh, we.

F (90:09)

Should have a LEO score for like I should.

Leo Laporte (90:11)

I should have LEO Arena. That's too much trouble.

Paris Martineau (90:17)

Rank all of us against one another.

Leo Laporte (90:18)

Yeah, right.

Jeff Jarvis (90:20)

It's a hard thing to do. I know that.

Leo Laporte (90:23)

All right, we're going to take a little break. You're watching Intelligent. The age of intelligent machines has come. This is the brand new rebranded this week in Google IM we call it Intelligent machines. Focusing on, I think, the most exciting area in technology ever. More than the invention of the computer, the microprocessor or even the Internet. We're talking about AI, but not just AI, robots. The smart little widgets and gadgets you have all around your house that seem to, you know, have a mind of their own. Paris Martineau and Jeff Jarvis and I, and we're so glad you are here. Have you tried Grok 3 yet?

Paris Martineau (91:04)

I have not.

Leo Laporte (91:06)

So this is Elon Musk's Moonshot. Spent a lot, a lot of money to build what I think most people consider the most powerful AI computer ever. Oh, absolutely.

Jeff Jarvis (91:25)

Hardware.

Leo Laporte (91:25)

The hardware, hardware X has built with Elon's money, a data center in Memphis containing, ready for this? 200,000 Nvidia GPUs to train Grok 3. That's Musk claimed. Now, I don't know if Musk's making it up or not, but it was 10 times the computing power of the process of the predecessor GROK2. And this is, you know, this comes back to that article we talked about some time ago, the Bitter Lesson. Remember this? You're not paying attention at all, are you? Rich Sutton, who's an AI professor in Canada, wrote this six years ago. In summary, the lesson is it's not how the model is made, it's the amount of computing you can assign to it that there's a direct correlation. He says one thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. In other words, the LLMs get better and better and better without modification if you throw more hardware at it. Two methods that seem to scale arbitrarily in this way are search and learning. He also says, by the way, this was an important insight. The second general point will be learned from the bitter lesson is the actual contents of minds. Human minds are tremendously, irredeemably complex. We should try stop trying to find simple ways to thinks about the contents of minds.

Jeff Jarvis (93:02)

Amen.

Leo Laporte (93:03)

That's not what we're trying to build is another mind. We want AI agents that can learn and discover like we can. And that's the. To me, this is the whole key and the breakthrough that's starting to happen is getting machines to think of things that aren't just remixed human content. Does that make sense?

Paris Martineau (93:25)

Well, they're not thinking, but no, no, and don't.

Leo Laporte (93:28)

And that's again, you know, we talk about AGI and why it's a terrible term. No, but they could come up with new novel ideas like new proteins or new things.

Jeff Jarvis (93:38)

Well, they could inspire. Google just started a new assistant for scientists not to do the work for them, but to be kind of on the side and say think about this too. Think about that too. I think that yes, it can do that, but I think this whole war, Leo, I think we're in a commodification. Ben Evans said of grok. Congratulations to Grok on spending a lot of money to be yet another company on the leaderboard for a commodity technology with no defensibility that we know of. And I think that this me tooing. Wait a second. This me tooing of these models as they stand, I don't think no one's leaping ahead. They're a little faster for a hell of a lot of money. And I'm more interested and more excited about A, applications like we had the first half of the show, but B, about working on different visions for how these things work. And I think that's where Yann Lecun we talked about two weeks ago, talks about teaching them reality is the next step. There's next steps. But the fact that Musk is spending this fortune to say, oh, I'm a bazillion fraction of a second faster than that model, I'm more powerful, doesn't excite me.

Leo Laporte (94:46)

It's not about speed.

Jeff Jarvis (94:47)

Well, whatever the measurement is.

Leo Laporte (94:49)

It's about. Well, I'll tell you what the measurement is. It's about coming up with insights that are beyond the human.

Jeff Jarvis (94:56)

Oh, you're getting near AGI Sand. Sand time.

Leo Laporte (94:59)

You don't think that's possible?

Jeff Jarvis (95:00)

Sand.

Leo Laporte (95:01)

I. I raise move 37. Here's a move and go that deep that AlphaGo did after, by the way, the way AlphaGo learned was the current hot thing in AI which is reinforcement learning. It learned. And if you would watch the video, you would know this.

Jeff Jarvis (95:19)

I won't finish it. I just need to have a little.

Leo Laporte (95:21)

It learned by playing billions of games against itself. Instead of, in other words, ingesting all the existing human knowledge about the game. It taught itself.

Jeff Jarvis (95:32)

It's a new way to learn. It's a new way to learn.

Ethan Sutton (95:34)

Yes.

Leo Laporte (95:34)

Well, this is reinforcement learning. And by doing that exceeded the human capability. And this is move 37, a move that the experts at the time, the Master Go players, said, that's a terrible move. Everybody knows that's a terrible move. Why did it do that? That's a mistake. It turned out it was a huge insight and it played a non human move. And that's what we want AI to do, is come up with things that aren't remixes of existing content. That's what most AI is right now.

Jeff Jarvis (96:03)

But I don't think that comes out of necessarily more power.

Leo Laporte (96:06)

More power. Well, that's what the bitter lesson says. It does. It does. It also comes out of reinforcement learning.

Jeff Jarvis (96:13)

Deep Seek. I think there's other methods in other ways. I think we're an American nacho.

Leo Laporte (96:18)

You got to watch this video. So Deep SEQ is exactly the same as everything else right up to the very end. You know, it does. This builds the models, does the tokenization, it does the fine tuning. But at the very end, they're doing this new step, which OpenAI immediately said, oh, we have that too. We knew about that. Deep Seq was the first public model to do it. Publicly and more importantly, I think do it openly so that people could see what it was doing. It used reinforcement learning. It actually used a punishment and reward system. And by doing that, and that's also what AlphaGo did. In effect, by doing that, can exceed, can go beyond what is essentially just rehashing existing human content. It's as if you had a textbook, a physics textbook. The first stage of fine tuning would be reading the content, adding it to your token base. Right. Your LLM model. Second stage would be. All right, now here's the problems in the book. Solve those. And that's, that's the, the reinforced. What is it called? It's supervised fine tuning, I think, where it. But it never goes beyond what a human's already done. Right. Because it's just learning the solutions to those problems. This most recent step, and if you get to the end of the three and a half hour video, he talks about this, this is what Deep Seek did. Is this reinforcement learning. And that's where the capability can go beyond what's in the book.

Jeff Jarvis (97:45)

Yeah, we're already there with things like protein folding. We're beyond what a human can do. It's raising the ceiling, raising the floor, and that's what we need, the ceiling and we can.

Leo Laporte (97:53)

That's when it gets interesting.

Jeff Jarvis (97:54)

Yeah, we're there for the many functions, just not this AGI bs.

Leo Laporte (97:58)

Yeah. By the way, Karpathy said I was given early access to Grok 3. We should mention he's a former head of AI at Tesla. I was given early access to Grok 3.

Jeff Jarvis (98:11)

Controversy around him. Yes.

Leo Laporte (98:13)

Yeah. Making me think one of the first, I think one of the first few who could run a quick vibe check. He says First Grok 3 clearly has an around state of the art thinking model and did great out of the box on my Settlers of Catan question. And he gives this question. Few models get this right reliably. The OpenAI thinking models, the ones you pay 200 bucks a month for, get it too. But all of DeepSeek R1, Gemini 2, Flash and Claude do not. However, GROK did not solve my emoji mystery question where I gave a smiling face with an attached message hidden inside Unicode variation selectors. Even when I give a strong hint on how to decode it in the form of rust code, the most progress I've seen is from DeepSeek R1, which once partially decoded the message. So that's a negative. And then there's a question mark. It solved a few Tic Tac toe boards. I gave it With a nice chain of thought. So I asked it to generate three tricky tic Tac toe boards, which it failed on, but then so did O1 Pro.

Jeff Jarvis (99:17)

Can I take you to line 112 and Gary Marcus's contrarian as his usual view of Croc.

Leo Laporte (99:24)

Yeah, and I'm trying to get Gary on the show because I think he'd be great to have on anyway. Well, just to finish before we move on, Karpathy's summary is as far as a quick vibe check over two hours this morning. Grok 3 + thinking feels somewhere around state of the art territory of OpenAI's strongest models and slightly better than Deepseek, R1 and Gemini 2.0 flash, which is quite incredible considering the team started from scratch a year ago. That's the other thing that all of this compute power gives them is, you know, OpenAI has been working on this for how long? Five years? In one year, in less than a year, they can get to state of the art.

Jeff Jarvis (100:02)

Call me skeptical of claims, but we'll see about how they're doing it.

Leo Laporte (100:06)

Yeah, I think we know how Grok's doing it. It's not. R1 is a little bit more of a question mark because it's kind of behind the Iron Curtain or what do you call them?

Jeff Jarvis (100:16)

Curtains.

Leo Laporte (100:17)

Curtain. Anyway. All right, what line again?

Jeff Jarvis (100:22)

112. If you go to Gary Marcus, who's a wonderful curmudgeon but an intel, but, you know, one who knows his stuff.

Leo Laporte (100:33)

I DM'd him and if you would as well, we should get him on the show.

Jeff Jarvis (100:37)

Yeah, I've. I've connected with him.

Leo Laporte (100:39)

Okay.

Jeff Jarvis (100:39)

My Davos thing.

Leo Laporte (100:41)

Yeah.

Jeff Jarvis (100:42)

So he starts off. I don't know if we want to go into this or not, but. Well, Elon Musk praising how it criticizes the information. But we can skip that.

Leo Laporte (100:52)

Before we do, though, let me just point out nobody has been able to reproduce this. And when I give Grok3 the same question, it says the information is very reliable. It gives me a great summary of this. So this is probably made up or.

Jeff Jarvis (101:06)

It'S him doing it.

Leo Laporte (101:07)

It's fake.

Jeff Jarvis (101:07)

Go on for that's why I'm joke. Go further down where he gives its own tests to Grok. My favorite is draw a picture of five different basic geometric shapes and label each one.

Leo Laporte (101:19)

Okay.

Jeff Jarvis (101:20)

The triangle is a turgle. The circle is an. Is queer. The square is a rectangle. The circle is a rectangle. And the hexagon. Hexagon is a pentacle.

Leo Laporte (101:32)

Now these other ones are what OpenAI or.

Jeff Jarvis (101:35)

No, it's all him. He's asking. It's four.

Leo Laporte (101:38)

Oh, it's four different efforts. So in some cases it's correct. No, none of them are 100% correct.

Jeff Jarvis (101:46)

Go down, draw me a calendar of this month circling today's date. The calendars look quite nice, but the circling is blindfolded. Pin the tail on the donkey. Right, and this is AGI8 around the corner. Folks, I think we need voices like Gary here to be ballast.

Leo Laporte (102:08)

I'm going to bring back a voice from a little bit longer ago. I'm going to bring you a. You know who Richard Feynman is. Great physicist, Long, long gone, by the way. He was a physicist at Caltech, I think. Brilliant guy, long gone. Nobel Prize winner, I believe.

Jeff Jarvis (102:29)

Very, very, very Ted, very Ted Talky.

Leo Laporte (102:32)

Asked in 19. He was in Oppenheimer.

Paris Martineau (102:36)

Okay, then I do know of him.

Leo Laporte (102:38)

He was asked in 1985, do you ever think there will be a machine that will think like human beings and be more intelligent than human beings? This is. I think this is about four or five minutes. I'm going to play it, though, and I'll stop it before it's all the way through. You should go watch this.

Jeff Jarvis (102:54)

As long as it's not three and a half hours. Leo.

Leo Laporte (102:56)

No, but listen, first of all, Feynman is very compelling. He's also.

F (103:00)

I actually saw this, like when we first started talking about AI, I saw this, and this kind of formed the basis of my philosophy of AI.

Leo Laporte (103:09)

Ah, Benito says. So let's listen. Let's listen to Feynman's answer.

G (103:16)

First of all, they think like human beings. I would say no, and I'll explain in a minute why I say no. And second, that they be more intelligent in human beings is a question. Intelligence is to be defined. If you would ask me, are they better chess players than any human being possibly can be. Yes, I get you. Someday, someday, better chess players than most human beings. Right now. One of the things, by the way, that we always do is we want the darn machine to be better than anybody, not just better than us. If we find a machine that can play chess better than us, it doesn't impress us much. We keep saying. And what happens when it comes up against the masters? We imagine that we human beings are equivalent to the masters in everything, right? The machine has to be better than a person in everything that the best person does at the best level. Okay? But it's hard on the machine. But with regard to the question of whether to make it to think like a Machine. My opinion is based on the following idea that we try to make these things work as efficiently as we can with the materials that we have. Materials are different than nerves and so on. If we would like to make something that runs rapidly over the ground and we could watch a cheetah running, we could try to make a machine that runs like a cheetah. But it's easier to make a machine with wheels, fast wheels, or something that flies just above the ground in the air. When we make a bird, the airplanes don't fly like a bird. They fly, but they don't fly like a bird. Okay. So they don't flap the wings. Exactly. They have in front another guy of a gadget that goes around. Or the more modern airplane has a tube that you heat the air and squirt it out the back. A jet propulsion. A jet engine has internal rotating fans and so on. And it uses gasoline. It's different. Right. So there's no question that the later machines are not going to think like people think in that sense. With regard to intelligence, I think it's exactly the same way. For example, they're not going to do arithmetic the same way as we do arithmetic, but they'll do it better. Let's take very elementary mathematics, arithmetic. They do arithmetic better than anybody. Much faster and differently. But it's fundamentally the same because in the end the numbers are equivalent. Right. So that's a good example of we're never going to change how they do arithmetic to make it more like humans. That would be going backwards.

Leo Laporte (105:46)

So you're looking for a machine that can circle a date in the calendar because you have that as a model of human.

Jeff Jarvis (105:52)

I'm just saying.

Leo Laporte (105:53)

But I could tell you right now, Emacs could make me a calendar with the right date on it 40 years ago. Right. When Richard Feynman was doing that talk in 1985. But that's a phony test. It's a paper tiger.

Jeff Jarvis (106:08)

Listen, I constantly argue that two things. One, AGI is bs. Two, that to try to make something human is the wrong scale. That's a species hubris. And that's not the goal. And I certainly argue that the machine can do tons of things that we cannot do today. But I think that. But this is. I'm always going to fight this AGI, bs. I'm going to fight it from. And the people who push AGI are the same ones who believe in the philosophies of test real. I'm doubly dubious of them as a result. Let's talk about it in terms of its actual capabilities, what we push it to do what we can't do. Fine. But this.

Leo Laporte (106:54)

Don't be the guy that says, oh, humans will never fly because we can't flap our wings fast enough. It's not. AGI isn't. And you're right. Maybe some people who are talking about AGI are thinking in those terms, but that's not really.

Jeff Jarvis (107:07)

Yeah, they want to replace human beings and all that, and that causes all kinds of fears, and that's why that's leaking. Exact. Wrong way to talk about this. We're actually agreeing here because I'm arguing that that's limiting. To talk about the machine. If we limit it to human capabilities, that. That meaningless. Sorry, Paris, you were starting.

Paris Martineau (107:22)

I was just saying I agree with you. And I think also when we're talking about any new technology, but especially a technology that is in the peak of its hype cycle, it would behoove all of us to, I don't know, keep our conversations grounded and firmly planted in reality, because ultimately, that's more interesting and, I think, useful, and it will result in, I don't know, more productive and interesting conversations.

Jeff Jarvis (107:51)

Where do you want our feet, Paris? Firmly in the sand, Ideally in the air, please.

Leo Laporte (107:57)

More importantly, how do you pronounce cheetah?

Jeff Jarvis (108:02)

That's. That is the best part of it.

Paris Martineau (108:04)

That was really good.

Jeff Jarvis (108:05)

Yeah.

Paris Martineau (108:05)

That video. You think?

Leo Laporte (108:07)

But you know what? It's Richard Feynman, so I'm gonna guess that's the real way to pronounce cheetah.

Jeff Jarvis (108:12)

Let's find out.

Leo Laporte (108:14)

I don't know how to. How to find that out.

Jeff Jarvis (108:16)

Well, you go to. You go. You just go to Google and say cheetah.

Leo Laporte (108:20)

Oh, I don't do Google.

Paris Martineau (108:23)

The only perplexity is.

Leo Laporte (108:25)

Now I just. Let me see if. Perplexity. If I say how the word cheetah is pronounced. Cheetah.

Jeff Jarvis (108:33)

Let me do. I'm doing.

Leo Laporte (108:34)

She rhymes with C. T. Sounds like T and table. So I don't know why he said sheeta, but I'm guessing, knowing it was Feynman, that that's the African pronunciation.

Jeff Jarvis (108:44)

Wait, wait, wait, wait. I'm doing the British now. Hold on a second here.

Leo Laporte (108:47)

Cheetah.

Jeff Jarvis (108:48)

No, cheetah. Still. Still heartsy.

Leo Laporte (108:50)

Well, what's the New Jersey pronunciation? Because that's where Feynman was from.

Jeff Jarvis (108:54)

Run.

Leo Laporte (108:57)

What the hell is that?

Jeff Jarvis (108:59)

Get the hell out of here.

Leo Laporte (109:01)

All right, I was smart. I did not buy the humane pin. I admit. I have bought the pin.

Paris Martineau (109:09)

I did really think about asking the bee people about their thoughts in the Humane pin.

Leo Laporte (109:14)

I bought many dumb AI devices. I'm wearing some right now. I even have those, those glasses with the rubber nose. But I did not buy an AI, human AI, because I saw the video and I thought, that is not going to. That's not. And you know what?

Jeff Jarvis (109:32)

You were right.

Leo Laporte (109:33)

It's a $700 brick of e waste. Jason Keibler, writing at 404 Media, Only 10 months after it's released, the Humane AI pin, some would say killed by Marques Brownlee, who said it was the worst product he's ever reviewed.

Jeff Jarvis (109:48)

I killed.

Paris Martineau (109:50)

Was killed. Before that, it already had dozens of reviews saying it was truly terrible.

Jeff Jarvis (109:56)

Yeah.

Leo Laporte (109:56)

The company sold its IP and software to Hewlett Packard and says, we're turning it off on the 28th, which is nine days away. The server will be turned off. Nearly every function of the AI pin will stop working on fit.

Jeff Jarvis (110:13)

Well, it'll still check your battery.

Leo Laporte (110:15)

Yeah. I want to show you the FAQ because it says some functions will continue. You could tell what the battery level is, but the Humane servers are gone. Center access will be retired. If you bought in the last 90 days, you're going to get a refund. The rest of you, hey, congratulations. You now know. Will my AI PIN work with another cellular provider after the shutdown? No.

Jeff Jarvis (110:41)

Nope. It's not a real.

Leo Laporte (110:43)

Can I port my number to another device? No. Can I use my AI PIN for offline features? Yes. After February 28, 2025, AI PIN will still allow for offline features like battery level, but will not include any function that requires cloud connectivity, like voice interactions, AI responses and center access.

F (111:03)

Couldn't even think of a second thing. After battery level.

Leo Laporte (111:07)

It was just battery level, it was.

Paris Martineau (111:08)

Just battery level, etc. Which I think is very funny.

Leo Laporte (111:15)

I feel kind of bad for them. I mean, why?

Jeff Jarvis (111:19)

I saw somebody on, On Twitter on one of the socials, said, yeah, well, don't. Don't dance on their grave. And somebody came back and said they danced on the grave of the iPhone. Of the. Of the. Of the phone.

Leo Laporte (111:28)

Yeah. They thought the phone was gone.

Paris Martineau (111:30)

How many millions of dollars? They're fine.

Leo Laporte (111:34)

Do you think they made money? Like, they, they certainly.

Paris Martineau (111:38)

The founders certainly got paid for their time.

Jeff Jarvis (111:43)

What was the juicer thing?

Paris Martineau (111:45)

Juicero?

Leo Laporte (111:46)

Yeah, this is.

Jeff Jarvis (111:47)

This is Juicero.

Leo Laporte (111:49)

It was pretty apparent, and actually the story of the company, it was that they pivoted. They really were just trying to come up with a projector screen, and then they AI became hot and they. So they added AI on top. Top of it, but it never.

Jeff Jarvis (112:00)

So now they're going to. Now they're going to add things into meetings for hp.

Leo Laporte (112:04)

You're watching Intelligent Machines. Jeff Jarvis, Paris Martineau. I'm Leo Laporte. We'll have more in a moment. Just. Did you see the. The piece about these AI assistance at meetings and how terrible it is? Let me see if I can.

Paris Martineau (112:19)

I don't know what piece you're talking about, but I will say it's truly the bane of my existence when I get in a specifically like a company zoom meeting and someone has their little otter AI thing there. It's like, we're not allowed to record our company meetings, guys.

Leo Laporte (112:33)

Oh, you're not?

Paris Martineau (112:34)

No, it's been a like whole thing. They've had to make announcements.

Leo Laporte (112:38)

It was a whole. Bloomberg's weekend essay from Chris Stokel Walker, please stop inviting AI note takers to meetings. Using artificial intelligence to summarize meetings raises questions about etiquette, privacy, and the purpose of the meeting in the first place. It sounds like the information.

Jeff Jarvis (112:56)

Some of them aren't just notetakers. My wonderful former student of mine named Joe Mdis, who's at Montclair State, he plays with all the tools, so his will will tell you who's speaking the most in the meeting. I think that's good Tone is in the meeting.

Leo Laporte (113:10)

I think that's great.

Jeff Jarvis (113:11)

All kinds of things added in. So we did it one time with. With the. The dean of the school. He was like all nervous, like, oh no, I'm talking too much.

Leo Laporte (113:20)

Well, if I were a woman at meetings, I would definitely run that thing to say, look how many.

Jeff Jarvis (113:26)

Or podcasts. Right. Paris, we could use it here.

Paris Martineau (113:29)

Certainly.

Leo Laporte (113:34)

And again, he raises the issues. Or actually one of the interview subjects raises the issues of hallucinations, which I think is not as much of a problem as it used to be, especially when you're working off a corpus of. Of knowledge, like a transcript of a meeting.

Jeff Jarvis (113:48)

Well, this goes to the piece, if I may.

Paris Martineau (113:49)

No, I don't know. Especially with depending on what transcription service you're using or what model.

Leo Laporte (113:57)

Oh, there could be areas can definitely.

Paris Martineau (113:59)

Like I've noticed this with Mac Whisper because you can pick from like dozens of different models and there are some that it has like a very specific hallucination bugs that are kind of.

Leo Laporte (114:11)

Margaret Mitchell, the author. Co author of the famous Stochastic Parrot's piece. I want to get her or and. Or Tim. Tim Netgebru on let's make a note of that. They would be great.

Jeff Jarvis (114:22)

They were Margaret, I think, has a brand new book out, so that can be more reason to.

Leo Laporte (114:25)

She's chief ethics scientist at Hugging Face. We asked the CTO of Hugging Face to join us, and he said he doesn't do. He only does in person calls.

Paris Martineau (114:37)

Only one where you can hug his face.

Leo Laporte (114:39)

I said, I think that means no, but I don't know. Unless you want to host him. I think it's a creative way to say no. Anyway, she says using an AI note taker is much more like recording a meeting than taking your own notes. Quote, some of the etiquette around recording applies. For instance, anyone using an AI note taker should let others in the meeting know. Well, of course. And give them the right to veto the AI's attendance. Zoom has it built in. You can push a button on a Zoom call. Do we do that? Could I do that on this call? Yeah. AI companion. Request access for AI companions. Ask the host. I'm asking the host right now. Are you the host, Benito?

F (115:21)

Yeah, it's me. It's asking me.

Paris Martineau (115:22)

Do we need an AI companion in here?

Leo Laporte (115:25)

Well, we could at least. Okay, so we just turn it on. So I'm not going to push the catch me up button. Oh, look at questions you can ask. Was my name mentioned? Are there any action items? So it's really good for somebody who's sleeping through the meeting, what topics have been discussed.

Paris Martineau (115:47)

It really is for someone who wasn't paying attention.

Leo Laporte (115:49)

Yeah. So I'm going to leave that on. And at the end of the show, we can say, hey, did they mention.

Jeff Jarvis (115:54)

Me, by the way, the book, Just for the record, the third of three authors, the first one listed for the Paris paper is Emily Bender from University of Washington. She has a new book out called AI Con how to Fight Big Tech's Hype and Create the Future We Want.

Leo Laporte (116:13)

So I think she doesn't.

Jeff Jarvis (116:14)

She's not weighed down by sand. I would say Paris.

Paris Martineau (116:17)

Yeah. Sandless.

Leo Laporte (116:19)

I'm going to defend myself, you know, because the first guest, Zach Cass, was very pro, was an accelerationist. And I think what we have to do and what we plan to do is get all points of view on this.

Jeff Jarvis (116:31)

Yeah, he really was.

Leo Laporte (116:32)

I will. I will paint myself as an accelerationist. I know I am. I don't know, Paris. You're kind of in the middle, I would say. Jeff, are you. Are you a negative? Are you. Are you more like Emily Bender and Zoomer? It's all a big hype.

Paris Martineau (116:48)

I would say my natural position towards all things is skepticism.

Leo Laporte (116:53)

Yeah.

Paris Martineau (116:53)

That is the nature of my job.

Leo Laporte (116:55)

That's, you know, you can't go wrong. You might have a miserable life, but you can't.

Paris Martineau (117:00)

Oh, I definitely will.

Jeff Jarvis (117:01)

That's where the nihilism comes from.

Paris Martineau (117:03)

Such is. Such is the curse I've been tasked with.

Leo Laporte (117:07)

And Jeff, you're a little bit more positive, aren't you?

Jeff Jarvis (117:09)

I think I'm positive, but I'm, I'm. People think that I'm all. Because I wrote a book about Google and such, that I'm all accelerationist. I'm not. I mean, you are.

Paris Martineau (117:21)

When they try to pay you for that.

Jeff Jarvis (117:22)

They. They don't pay me, but. Well, that's about. That's about my stupid industry. That's about newspaper publishers being so jerks in this. No, I'm skeptical. And I think on, just on today's show, I've been arguing more than you about how AGI is BS and how the boys are dangerous and how we have to be careful. So, no, I'm no accelerationist and I'm.

Leo Laporte (117:50)

Damn the torpedoes, full speed ahead. I can't wait until our AI overlords are in charge.

Jeff Jarvis (117:57)

I'm an optimist. Well, I think there, you know, the, the eu, sorry, the uk, in its current discussions about regulation of AI, has been surprisingly, in my view. I didn't, I didn't expect this at all. I think enlightened, where they're saying their, their starting point on training is that reading text online is a right of the machine to learn.

Leo Laporte (118:18)

Good, good. Whoa.

Jeff Jarvis (118:19)

I didn't expect that.

Leo Laporte (118:20)

Kathy Gallis has argued for that. The right to read.

Jeff Jarvis (118:23)

And after the Paris AI Summit, I think what we've seen is a shift in the attitude in Europe is that, well, being the biggest regulator in the world hasn't gotten us very far.

Leo Laporte (118:33)

Exactly.

Jeff Jarvis (118:34)

And so I think they're more open to things, but they're still going to be by nature cautious.

Leo Laporte (118:39)

Yeah, I think that's okay.

Jeff Jarvis (118:42)

There's a mix. We need that.

Leo Laporte (118:43)

I think there's a realization, though, when it comes to national authorities that if you don't do it, somebody else might. And so we are in a arms race of AI. And so to be too cautious or too safety focused might be a mistake from a national priority point of view. Mitchell goes on to say, talking about the AI assistance, as we seek to have more automation in traditionally human tasks, we cede our control, power and privacy to AI systems and the companies that own them. That's good point. I think a critical thing that AI note taking lacks, which human note takers have, are the Non verbal cues from the speakers. A sarcastic note joke, rather said in a meeting could become an action point when cranked through the compute power of an AI system. That's the kind of hallucination that probably.

Jeff Jarvis (119:34)

Could go to hr.

Paris Martineau (119:36)

Yeah. I do think it's worth noting that again, having a record of stuff like this and having in some cases perhaps sarcastic joke transcribed, literally. Literally. And put in your meeting notes at work could be a problem for a lot of people.

Leo Laporte (119:54)

The piece concludes. This is Chris Stokel Walker talking. If workers trade in person engagement for AI read backs and colleagues curb their words and ideas for fear of being exposed by bots, what's left if the humans step back? All that remains is a series of data points and more AI slop polluting our lives.

Jeff Jarvis (120:18)

That's a little harsh. I think that's trying to find too much meaning in it. I think Paris's attitude is right. I don't want a record of this. This is company stuff. It's a little rude. Stop. It's more that etiquette level than it is the oh my God, it's ruining humanity level.

Leo Laporte (120:42)

So I'd counter that.

Paris Martineau (120:43)

Stretch it to make an essay, baby.

Jeff Jarvis (120:46)

Exactly. Exactly.

Leo Laporte (120:48)

I counter that with the piece from Ars Technica. Researchers have made a significant breakthrough in 2025 using AI to design a multi step enzyme capable of breaking down certain plastics, potentially revolutionizing efforts to combat plastic pollution. I think that's interesting. This was published in Science, a significant advancement in AI driven enzyme design. Now that's. To me, this is an example of going beyond what humans have been able to come up with to something. To something new.

Jeff Jarvis (121:22)

Yeah.

Paris Martineau (121:22)

I will also note though, you are reading Perplexity's summary of this article. You're not reading the actual article.

Leo Laporte (121:30)

Yeah, I don't want to give Ars Technica too much. All right, I'll read the source. Actually, Ars Technica was. Just to be honest and fair, Ars Technica was just summarizing this scientific piece from Biorxiv Preprints. Computational design of serine hydrolysis preprint server. Yeah, yeah, it's a preprint server. So this is a. I mean they were just interesting. But. So he's continuing to synthesize the information and say, look, it looks like maybe not, but it looks like an AI may have come up with a new enzyme that we otherwise didn't know about. That's. That's a good use. Yeah. Nicola, the electric autonomous truck company has filed for bankruptcy. There's one of them.

Jeff Jarvis (122:27)

Bye. Bye.

Leo Laporte (122:30)

Oh, you know, we could have A AI graveyard segment. We could just things that didn't make.

Jeff Jarvis (122:37)

It, you remember after.

Paris Martineau (122:39)

It could be the sort of stuff that washes up on the beach.

Leo Laporte (122:42)

Would you guys leave off? I'm gonna get this guy on the show. You can decide. No, it wasn't.

Paris Martineau (122:48)

We'll play for him. All of the, you know, your little journey into being a radical.

Leo Laporte (122:55)

I, I, I've. I have like a good AI synthesized all of the different data points, including those that you and Jeff have proposed and have come up with. My theory of the future.

Jeff Jarvis (123:09)

Is the sandman a real person, Leo?

Leo Laporte (123:11)

Yes, he is.

Jeff Jarvis (123:12)

Okay. You sure?

Paris Martineau (123:17)

Hey, is he tall? I imagine being tall. I imagine. I imagine you looking down at the sand and there is only one set of footprints and that's because he's carrying you. Is that what happened?

Leo Laporte (123:29)

That'd be good. That. Oh, that was good. That was. Jesus. I think that did that right. I watched.

Paris Martineau (123:37)

Was that who you were walking with?

Leo Laporte (123:38)

No, some guy.

Jeff Jarvis (123:40)

AI Jesus.

Leo Laporte (123:42)

Google Lens has in Chrome for iOS they didn't put that circle to search thing in, but this is kind of like that.

Jeff Jarvis (123:51)

You could put the changelog in front of this one if you want.

Leo Laporte (123:54)

It's the Google change log. Google's more deeply integrating Lens visual search in the Chrome. Weirdly though, for iOS, I guess, because you already have it in Android with circle to search, so you don't really need that. So if you're using Chrome on your iPhone or iPad, you can now search screen with Google Lens. As you're browsing the web or watching a video, you don't have to do a screenshot. There's a new shortcut in the 3dot overflow menu and it will then give you an AI result based on. There it is. Search this screen based on what it's seeing. Google Credits Advanced AI models is allowing Lens to go much further and provide information on the contents of more novel or unique images. This is really the successor to search, right? Like image search.

Paris Martineau (124:50)

I mean image search. They've had image search for a while, for a long time.

Leo Laporte (124:55)

Is this better? This is better, isn't it?

Jeff Jarvis (124:56)

Well, because it does more than search. You can query about what it's a way of saying. I remember many years ago, first of all, I went to MIT Media Lab and it was a whole big deal. One of their first demonstrations was a big screen and some and some shapes on it and the instruction set of saying, put that there. So it understood the antecedent. What is that? What are you pointing to? That's what the circle really means, is you can, you can say, I'm talking about this. And then you can ask a question about it. It's just another UI, but it's more than search.

Leo Laporte (125:29)

I think YouTube has added AI to YouTube. Right. There's now a, an AI button.

Jeff Jarvis (125:35)

Does everybody got to have YouTube?

Leo Laporte (125:36)

Everybody got to have YouTube. I think that's cool. I guess. I mean, it's Google. Let's see what Google. This is a little Inception moment. Can I do AI on our show?

Jeff Jarvis (125:50)

There's a, there's a, there's a button that shows you.

Paris Martineau (125:54)

Interesting.

Leo Laporte (125:55)

Maybe because it's live, it can't very meta. I can't do that. Maybe.

Jeff Jarvis (125:59)

Yeah, but go to last week's show. There's that, there's the, there's that.

Leo Laporte (126:04)

I could do it there. Oh, okay. And. Oh, okay. That's interesting.

Jeff Jarvis (126:07)

Don't go to last week's show. Go to a few weeks ago.

Leo Laporte (126:11)

Why shouldn't I go to last week's show?

Jeff Jarvis (126:13)

Because you know what it is?

Leo Laporte (126:14)

What are you talking about?

Jeff Jarvis (126:16)

Things you don't want to talk about.

Leo Laporte (126:19)

What are you saying? Intelligent machines. Let's go to the last episode, which is 806. All right. Well actually we got episode one, which is 805. Yeah, that's all right. So here it is. Now where's the AI?

Jeff Jarvis (126:37)

No, Maybe it's in the mobile app. I don't know. That video will show you where the button is.

Leo Laporte (126:43)

I know. I should have watched the YouTube video. Look, intelligent Machine store. I didn't know that you can shop the Intelligent Machine Store.

Paris Martineau (126:52)

That's fun. Maybe it's down there.

Jeff Jarvis (126:58)

Okay, so I. So on my phone.

Leo Laporte (127:00)

Yeah.

Jeff Jarvis (127:01)

Put up Karpathy's video.

Leo Laporte (127:03)

Okay.

Jeff Jarvis (127:04)

And now I can ask about the video. So what?

Leo Laporte (127:06)

Oh, that's cool. So instead of watching a three and a half hour video.

Jeff Jarvis (127:09)

So if you go to your phone, there's an ask button. You can do that.

Leo Laporte (127:12)

Nice.

Jeff Jarvis (127:13)

Please tell me what the point of this video is. Please tell me what the point of this video is. Is I'm not being a smart ass. I think it's really brilliant.

Paris Martineau (127:21)

I like the loud voice you use to talk machine.

Leo Laporte (127:26)

Hello, machine. I think this is a brilliant life hack. A ring that adds an extra finger to your hand so that any picture of you will appear to be AI generated.

Paris Martineau (127:43)

That's great. People have been saying you should wear that if you're going to do crimes.

Leo Laporte (127:48)

If you're going to do crimes, wear the finger.

Jeff Jarvis (127:50)

So we're going to get past official podcast position. So the, the, the YouTube I'll turn down the video audio here. The YouTube comes back pretty quickly. It says the video is all about explaining large language. LLMs like chat, GPT, exclamation point. Why the exclamation point? I don't know. We need to go through the steps involved in building an LLM. This is the cool part. Here are some of the highlights of the video. 12 seconds in. The goal of the video, 34 seconds in. You'll learn about these models. One hour, seven in. Oh, wait, is that one minute? Yeah, I guess one minute. It only lasted two minutes, 42 seconds that it came up. Oh well, well, it tried, it explains Common crawl. But I think if you went through this, that would be a very useful thing to come up with its own highlights.

Paris Martineau (128:39)

I mean, again, my question is are these hallucinations though? I mean, I'd be curious if you have it go through a whole three hour video. There's probably going to be some things come up that they didn't actually say.

Jeff Jarvis (128:50)

They're much like there's two things.

Leo Laporte (128:51)

No, there's not. No, this. Watch the video, you'll understand what a hallucination is and why it's not going to happen there.

Jeff Jarvis (128:57)

Well, there's also randomness that you. Are you going to get the same answer twice in a row? No, no. Can I go to the story I want to go to on line 110? Yeah, you'll like it.

Leo Laporte (129:06)

I think the Overton window of safety and hallucinations has shifted. Which line?

Jeff Jarvis (129:12)

109 on probabilism.

Paris Martineau (129:14)

It's a real problem. I had one of these services. I had Gemini summarize an article two weeks ago on the show. It provided a very succinct well done summary with all the stuff I wanted in it. And then I went to go check any of the details it gave me and like half of them were made up.

Jeff Jarvis (129:31)

Oh well that's not, that's still the case. That's still the. Yeah, absolutely.

Paris Martineau (129:34)

That's Gemini, which I assume is powering this.

Jeff Jarvis (129:36)

And if you do it the second time you're going to get a different answer.

Paris Martineau (129:38)

Yeah.

Jeff Jarvis (129:41)

But the, the issue here is that we expect computers to be deterministic. That like a spreadsheet, you put data.

Leo Laporte (129:49)

Yeah.

Jeff Jarvis (129:49)

You put, you put a question in and then you get an answer and what, and, and what this argues. It's a really neat piece that then came, that was deterministic to deterministic to deterministic. Then came deterministic to probabilistic to deterministic things like targeted ads. You Put in a specific question, it did some stuff and you don't really know what. And it came back with an answer, and it may be the right answer. Maybe there was no right or wrong answer. Then where we are now is probabilistic to probabilistic to probabilistic, where it's all kind of just close enough for jazz and generative. AI and it's going to change every time. But what we want to get to is probabilistic. I can ask a vague question to probabilistic. It can do its thinking in all kinds of ways. No, doesn't think. But to deterministic, where it's going to give me an answer that I can rely on. We are not there yet. And paired with this is a story from our friend Brett Taylor, who line 110, who we ought to have on as well. Brett, I think, is now the chair of OpenAI, right?

Leo Laporte (130:57)

Yeah, I think so. I think that's.

Jeff Jarvis (130:59)

And he's CEO of Sierra, and he was at Salesforce and was at Meta. So what he's saying here is that artificial intelligence agents, yeah, they're not quite right. The other gonna get stuff wrong, but kind of lump it. Don't wait for them to be 100, because you're never going to get there. Except that it is imperfect. He says, rather than say, will AI do something wrong? Say, when does it do something wrong? What are the operational mitigations that we put in place to deal with it? So what I hear Paris say is, that's not acceptable. That's. That's too much effort and too much risk. And Leo is saying, yeah, accelerate. Let's see what it can do. It can do amazing things. So where's your tolerance for whether you call it hallucination, whether you call it randomness, whether you call it just being wrong?

Leo Laporte (131:44)

You should be. I think it's fair to say you should be able to trust a summary, Summary of a document to be accurate to the document. I'm very shocked that there was hallucination, to be honest with you, because it's supposed to be directly from the document.

Paris Martineau (131:57)

Typed in to Gemini. Hey, this anecdote you gave me, where'd you get it from? And it said, oh, I made it up and gave a little description of it. We talked about this in the show. And I think this is just my constant frustration with these tools is, yeah, I think they could be really useful. But if I am with some frequency encountering problems like this, then I can't really trust them to do tasks that I need them to do. Which would be stuff like, hey, an article that I meant to read that I want to talk about in the show. Can you give me a high level summary of it? Because I've only read half of it and point out any interesting anecdotes. It can't do that, so I'm not going to use it for that. And then it's like, where does the buck stop if I have to be? Because I mean, then my next logical step in thinking about this goes to, well, if I want to use something like this for the purposes I want to use it, I have to go through and check everything it gives me before repeating that to other people. And that's just not particularly useful. I'll just read the article myself.

Leo Laporte (133:00)

Yeah, no, I would agree with you. It's not been my experience that when working with an AI on documents that I've given the AI that it hallucinates on those documents. So that's troubling to be sure. I haven't seen that, but I still do. You still see, huh?

Jeff Jarvis (133:19)

Yeah. Okay, again, I keep on saying this. I'll say it again. The other issue to me is the randomness. AI has built in randomness. So you don't get the same answer twice. That alone is an issue when it comes to reliability and credibility.

Paris Martineau (133:33)

And the thing that I think it makes this particularly difficult is the way that these tools work is they're trying. They're trying to produce something that sounds right, they're trying to produce something that is right, but it doesn't know really the difference between sounds right and is right. And so in the case of that article, it gave me exactly what I was looking for anecdote about. I think it was that article we were talking about about tech companies removing tampons and period products from men's restrooms and kind of a squabble. And it gave me an anecdote about, oh, I remember this between a manager and an underling about set a comma. A pithy comment like, oh, well, if you need pads so much, bring your own. And I'm just like, right, what a strange little detail to make up and insert in it. But it's the sort of thing that if my natural inclination was not to check everything. Yeah, I would have just assumed that was in the article.

Leo Laporte (134:33)

And if you have to do that, then it's not that useful. I completely agree with you. I understand if you're using it for.

Jeff Jarvis (134:39)

Like in the Google example for a scientist, if it's to your side and it says have you thought of this? Have you tried that? Why don't you think of this? That's okay. Because it's up to the human then to decide what to pursue. And if it does inspire some new path, cool. They're going to do it in their scientific method and, and, and they'll have confidence. But if you use it to replace your task because it's better than us.

Leo Laporte (135:04)

Let me ask, did you just give it a URL or did you paste the article in in and ask it for that?

Paris Martineau (135:10)

I gave it a URL, but then I tried again with pasting the article.

Leo Laporte (135:15)

And it wasn't better with pasting something.

Paris Martineau (135:17)

I mean, both times it gave me something that wasn't right.

Leo Laporte (135:20)

Okay. I feel like that's a flaw in Gemini, to be honest with you. You shouldn't do that.

F (135:29)

It might be paid versus free version because if you're not seeing that you're paying for all those services. I'm not sure if Paris is paying for those services.

Paris Martineau (135:36)

Yeah, I'm not paying for them.

Leo Laporte (135:37)

Yeah, I pay for everything.

Paris Martineau (135:38)

But I mean, I think that's also kind of.

F (135:40)

So that's kind of a problem too.

Leo Laporte (135:43)

I want the best AI. Yeah.

Paris Martineau (135:45)

Yeah. But the average person is going to. If they have any exposure to these tools, it's going to be for a while. As a free user. And especially if kind of the end game for these products is a use case like yours, Leo, where you're not using search engines like Google, you're not going to read the Ars Technica write up or the preprint. You're just trusting these services for all your information, then we get to a kind of dangerous place. If they're very adept at making things that are not quite right but sound very right, which is kind of their whole thing. I will say, though, and I've said this before on the show, my favorite use case for AI remains it. If I ever can't think of a synonym or a specific word, chatgpt can find it every single time. And it's fantastic.

Leo Laporte (136:35)

Yeah. Because it has basically a giant thesaurus built in. That's really what it is.

Paris Martineau (136:39)

But it's a giant thesaurus. But then also I can just describe it in the most in the shoddiest way possible. Like it's like this word, but not. But the vibes are slightly more positive and it'll get right there. It's great.

Leo Laporte (136:53)

I'll have to look at the synopses that I'm getting and see if I can find issues. Maybe it's because I don't use Gemini. But I mean, I don't have this problem, I don't think with the perplexity, but maybe I do. And I just, I think it could.

Paris Martineau (137:09)

Also be related to, I mean, if I'm thinking of that specific use case. I asked for, you know, a summary, asked for, you know, an overview of like any main points, arguments or counter arguments and then like an overview of any interesting anecdotes. Gave me all of this.

Leo Laporte (137:26)

So it was trying to help you?

Paris Martineau (137:27)

Yeah, he was trying to provide everything I asked for and it doesn't know.

Jeff Jarvis (137:32)

How to say sorry. No, there's nothing interesting.

Paris Martineau (137:35)

Yeah.

Jeff Jarvis (137:36)

What would please you?

Paris Martineau (137:38)

I mean that would please me if they're just like, yeah, not there. Okay, I move on.

Leo Laporte (137:45)

Let's take a break and get the picks of the week as we wrap things up on this issue of Intelligent Machines. We don't yet have a. A guest for next week, right, Benito?

F (137:55)

We still working on it.

Leo Laporte (137:57)

We will fill that hole. We have some good. Quite a few. So yeah, so we will get somebody.

Jeff Jarvis (138:04)

Know if you need me to go to some of the people my name's on, let me know.

F (138:07)

Okay, thanks.

Leo Laporte (138:09)

I'd like to get Martin Dave Marcus on. Gary Marcus. Gary Marcus, that would be great.

Jeff Jarvis (138:17)

And I think that. Who did I just mention? The author.

Leo Laporte (138:22)

Co author Margaret Mitchell.

Jeff Jarvis (138:23)

No, Margaret. Well, that's one. Yes.

Leo Laporte (138:26)

Anyway, we've got some ideas. We've got some ideas and if you're an AI wizard, let us know. Email us. Oh, Emily Bender. Yeah, yeah, yeah, I put her on.

Paris Martineau (138:37)

Daniel Oberhaus, a former reporter at Vice and Wired, just has a book out on.

Leo Laporte (138:43)

Oh, good.

Paris Martineau (138:44)

AI and AI powered therapists.

Jeff Jarvis (138:47)

Could be.

Leo Laporte (138:48)

Oh, and we'll, in just a moment, check with Zoom to see what it's learned from this show. You're watching Intelligent Machines with Jeff Jarvis and Paris Martineau. All right, let me, let's see, let me see. Was my name mentioned? Let's see. It's analyzing now. And yes, Leo Laporte's name was mentioned multiple times throughout the meeting. Transcript. Leo Laporte appears to be one of the main participants in the discussion, contributing to various topics including AI technology, recent developments and personal views on AI enhancement. Are there any action items? Analyzing. This is Zoom's AI companion, Leo, researcher. Reach out to Emily Pender to invite her as a guest. Investigate potential hallucinations and AI summaries from Perplexity. Benito, continue working on securing. Yes. For next week's show. Jeff, assist Benito in reaching out to potential guests if needed. Very nicely done. Very nicely. Done. Were there any. Let's see. Catch me up. I don't want to. Well, all right. Too late. I clicked.

Jeff Jarvis (139:52)

Were there any disagreements?

Leo Laporte (139:54)

Oh, that's good. Let's see. Were there any disagreements? Analyzing. Yes, there were some disagreements. During the meeting, Paris expressed skepticism about the reliability of AI generated summaries.

Jeff Jarvis (140:16)

Well, good.

Leo Laporte (140:17)

Yep. Citing experiences with hallucinations in Gemini's outputs. Leo was surprised by this, as it hadn't been his experience with other AI tools. Jeff acknowledged seeing similar issues with AI generated content. The group also had differing views on the pace of AI advancement, with Leo positioning himself as more of an accelerationist, while Paris maintained a more skeptical stance. See this? This is great.

Paris Martineau (140:43)

It's true.

Leo Laporte (140:43)

How could you not love that, Chris, if you were in the strongest? I think the action items is the most useful. I think that's great. Let's get our picks of the week. I've got one. Can I start? I don't usually do a pick of the week. I want to give you guys all the time in the world for this, but it happens. This is an old friend of ours. Gina Trapani, who was the original host of this show, has just posted something@ginatrepani.org called My Life in Weeks. This is apparently something people do now.

Jeff Jarvis (141:19)

It's just the kind of beautiful thing that she would make.

Leo Laporte (141:20)

Yeah. And she actually. The software is available. She talks about where she got the idea. She adapted the code from Buster Benson. So what it does. In fact, if you go to the Buster Benson, you enter in. You can do this and you. Oh, well, she's actually doing a page. Let me see if I can find the. Yeah, here it is. You can enter it in. There are pages where you can enter in your birth date and it will make the chart and then it's up to you to fill it in. So I was born. And then you see, the first few years, nothing happened. Then there's kindergarten, the tiny tot schoolhouse where Miss Mary and Miss Ninny were my teachers. Reagan was inaugurated elementary school, first computer. What was Gina's first computer? Ah, he bought her an IBM PC. Junior. That's many people's first computer. Anyway, it goes on and on. Saw my first webpage, graduated college elevators, first professional IT job at the elevator company where my dad and brothers worked. I didn't know Gina was an elevator engineer.

Jeff Jarvis (142:26)

No.

Leo Laporte (142:27)

Might have asked her about that. Here's her founding of Lifehacker. She started Lifehacker. I do believe we are in here. The first podcast, April 1, 2009, launched this week in Google with Leo and Jeff later co hosted all about Android with Jason in beta with Kevin Purdy and the post light podcast Catalyst started Think Up. She talks about getting married, having her child. I think this is really cool.

Jeff Jarvis (142:56)

Yeah.

Leo Laporte (142:57)

It is my life in weeks. And of course it. The last entry is made this February 5th and then there's a lot more weeks left. You could fill in. Do you think you would do this?

Jeff Jarvis (143:11)

I'm not that organized. I started and I need to do it.

Paris Martineau (143:16)

Oh yeah, yeah. That would require a lot of effort on my part. B would be perfect for this though.

Leo Laporte (143:22)

Yeah. By the way, probably put too many.

Paris Martineau (143:25)

Personal things about your life in there.

Leo Laporte (143:27)

On her 100th birthday, she has a to do item. Hug her partner and her daughter at the beach. That's a good goal for your 100th birthday. That's very gen in the year 2017.

Jeff Jarvis (143:39)

Ms. Eugenia, come say hi.

Leo Laporte (143:41)

Very cool. Paris, your pick of the week.

Paris Martineau (143:45)

I've got two very different picks of the week. One is a delightful long, long published by the New York Times. That is a look into the Murdoch's succession drama that has got more twists and turns than the show succession. And it does have a literal succession. The TV show side plot in it. It is such a fascinating. It honestly flies by. It's really. I don't know, I just really enjoyed it. Would recommend it if you're looking for something to do. And then I will also recommend.

Jeff Jarvis (144:16)

While you're on that. While you're on that. May I sit for a second? The companion piece to this, the bookmark which I just put there is the Atlantic McKay Coppins interviews James Murdoch.

Leo Laporte (144:27)

James is on the outs, right. Because Lachlan is going to run the empire.

Paris Martineau (144:33)

Well, no, no, no.

Leo Laporte (144:34)

I know there's a court. There's a court case going on.

Jeff Jarvis (144:37)

Well, the New York Times piece, which is really wonderful. We should give credit to the reporters there because it was a great report.

Paris Martineau (144:41)

The New York Times piece, they somehow. This is a sealed court case. They somehow got all of the documents. 3,000 discovery, 3,000 pages. Which basically shows all of the dirty laundry discussions between all of the Murdochs during this entire period.

Jeff Jarvis (144:55)

I read it straight through.

Paris Martineau (144:57)

Same. I literally. I thought I was going to take it and read it on the train. I.

Jeff Jarvis (145:01)

No, no, no, no, no. Jim. Jim Rudenberg and Jonathan Mahler. Credit to you. It's really good reporting. This is the best of the New York Times.

Leo Laporte (145:09)

So is it not the case that Lachlan beat out James for the.

Paris Martineau (145:14)

Well, you know, Lachlan and his daddy Tried to.

Leo Laporte (145:18)

Oh, they lost all of.

Jeff Jarvis (145:19)

Change the trust.

Paris Martineau (145:20)

They tried to change the trust to get out. Basically everyone, including James. That is not Lachlan. They lost.

Leo Laporte (145:29)

I didn't know they lost. I knew this was ongoing and that they had sealed it. We did not know they lost. The theory was, well, you can change the trust if everybody benefits from the change. And. And Murdoch's contention was, well, having Lachlan run Fox will be much better than having James runs Fox because he shares my political voice.

Paris Martineau (145:50)

One of the small things in this that I particularly loved was all of the project and code names they use, which are just like the plan to kick his three children out of the trust that they are entitled to and give it to his other son is called Project Family Harmony.

Jeff Jarvis (146:12)

If I may, let me give you this from. From the Atlantic story, because this is just. You got to read both of them. So in the. In the deposition, Rupert's attorney asked James a series of withering questions. Have you ever done anything successful on your own? Why were you too busy to say happy birthday to your father when he turned.

Leo Laporte (146:31)

Geez, this.

Jeff Jarvis (146:33)

Does it strike. Yeah. Does it strike you that in your account, everything that goes wrong is always somebody else's fault? At one point, the attorney referred to James and his sisters as white, privileged, multi billionaire trust fund babies. James did.

Leo Laporte (146:46)

And Lachlan's not exactly.

Jeff Jarvis (146:49)

Well, Rupert is. James did his best to concentrate, but he couldn't help stealing glances at his father. Rupert sat slouched and silent throughout the deposition, staring inscrutably at his younger son. Every so often, though, he would pick up his phone and type. Finally, James realized why, quote, he was texting the lawyer the questions to ask. Oh, geez, how effing twisted is that? Oh, they're the two stories together.

Paris Martineau (147:15)

Oh, that's what I'm doing after I. Oh, yeah.

Ethan Sutton (147:17)

Oh, yeah.

Leo Laporte (147:18)

Can I just have the AI and analyze it? And you don't want to.

Paris Martineau (147:21)

You want every detail, you want every word of this.

Leo Laporte (147:24)

We love watching it.

Paris Martineau (147:26)

You want to see the text go by. They've got a great graphic with it. It's fun.

Jeff Jarvis (147:29)

Yeah.

Leo Laporte (147:29)

We loved watching Succession, and I knew that Succession was at least partly based on the murder.

Jeff Jarvis (147:35)

It in turn, as it plays a role here.

Leo Laporte (147:38)

Interesting.

Paris Martineau (147:39)

You know, there's a big happening in succession that. I guess there's also succession spoilers in this article. Yeah, yeah. Because a big happening in the. The last season of Succession inspires real world events and drama.

Jeff Jarvis (147:57)

They all deny watching succession, but it had an impact.

Leo Laporte (148:00)

Apparently they. They heard about it that's what.

Paris Martineau (148:04)

Well, my other pick of the week is very different. It's a recipe from the New York Times for roasted chicken thighs. Hot honey and lime. And it's changed my. I don't know. I've been eating it all week. It's really.

Leo Laporte (148:15)

I live.

Paris Martineau (148:16)

I live on 10.

Leo Laporte (148:17)

I live on chicken thighs.

Paris Martineau (148:18)

I don't know how I'd been sleeping on chicken thighs now, but now I'm gonna be living on them. They're.

Leo Laporte (148:23)

Anything you do with a chicken thigh is gonna be good.

Jeff Jarvis (148:25)

Yes, I'm.

Paris Martineau (148:26)

Chicken thighs after this. It's gonna be great.

Leo Laporte (148:28)

Oh, yeah.

Jeff Jarvis (148:29)

Way better than breasts.

Leo Laporte (148:30)

Yeah. Breasts are dry and boring. They're white.

Paris Martineau (148:32)

Terrible. Yeah, yeah. And this, you could pot it in. You pop it in the little oven. It probably will be done before you finish reading the Murdoch article. So, you know, enjoy.

Jeff Jarvis (148:45)

All right, so it's my turn.

Leo Laporte (148:46)

Oh, I'm making this. This is so good.

Paris Martineau (148:49)

It's really good.

Leo Laporte (148:52)

Okay. And very simple, by the way.

Paris Martineau (148:54)

It's very simple, healthy, reasonable calories, you know.

Leo Laporte (149:03)

Thank you for that. I'm putting that. My recipe box.

Jeff Jarvis (149:06)

Can you send it to me since I don't subscribe to the food?

Leo Laporte (149:08)

Oh, yeah. Oh, yeah. You know what I do? I have a program called Paprika that will go to any webpage and scoop it up, scoop it up, make it input in my recipe box.

Jeff Jarvis (149:21)

Oh, I see.

Leo Laporte (149:22)

Oh, so here, I'll show you. Watch this. So I just copied the URL for the recipe, and I'm going into paprika, into the browser, paste in the URL, and then I say, yeah, yeah, yeah. No one wants me to log in. All right, well, I'll log in later. So. But I would download that, and it makes a beautiful little recipe card out of it with the picture and everything.

Paris Martineau (149:46)

So I put your link in the discord. Anybody can get the gift link in there.

Leo Laporte (149:50)

Oh, you did a gift link. Aren't you sweet?

Jeff Jarvis (149:52)

Thank you.

Leo Laporte (149:53)

Aren't you kind? Jeff Jarvis, you have our game.

Jeff Jarvis (149:59)

Through time has been trying to get Leo to. To spend money on the show.

Leo Laporte (150:03)

It's not a hard. It's not a difficult game, is it?

Jeff Jarvis (150:06)

It's not hard. He didn't get the humane pen. He didn't get that.

Leo Laporte (150:09)

Did you try to get that?

Jeff Jarvis (150:11)

If you go to line 144.

Leo Laporte (150:14)

No.

Jeff Jarvis (150:14)

Aired to translate the video on line 144.

Leo Laporte (150:18)

Okay. Okay. So first.

Jeff Jarvis (150:20)

Yeah.

Leo Laporte (150:21)

This is so complicated.

Jeff Jarvis (150:22)

The sound starts low, but.

Leo Laporte (150:23)

Okay, there's the video. And do I have to do something with. It's in German.

Jeff Jarvis (150:27)

That's so. For over 50 years, the Thermomix has been the heartbeat of the kitchen. 8. Millions of people love it. Every day to do their own cooking. 1971 looks like a blender.

Leo Laporte (150:41)

Looks like a blender with a base. Oh, now it's looking like the Thermomix 2004.

Paris Martineau (150:47)

That one looks fun.

Leo Laporte (150:49)

That's the one I have.

Jeff Jarvis (150:50)

2014, the Music Comes up.

Leo Laporte (150:52)

No, that's the one I have. Sorry.

Jeff Jarvis (150:53)

2019.

Leo Laporte (150:55)

Yeah. Is there a new one this year?

Jeff Jarvis (150:58)

The heartbeat gets even faster.

Leo Laporte (151:00)

Oh, no. Please tell me no. This is very German here.

Jeff Jarvis (151:10)

They need now, like never before.

Leo Laporte (151:16)

Oh, my God, the music.

Jeff Jarvis (151:17)

This is the interstellar in service like never before.

Leo Laporte (151:23)

The only bad Thermomix, besides the fact that it's $1,500, is there's a monthly subscription.

Jeff Jarvis (151:28)

Intuitive like never before. Beyond. I forget what that is. Like before. Inspired. Inspiring as never before. There it is, Leo.

Leo Laporte (151:43)

Ah, I don't need a new one, but. Oh, it looks better. Look at that.

Jeff Jarvis (151:48)

Oh, it is. It is, Leo. It is.

Leo Laporte (151:49)

Oh, it is better. Look, the peppers fly.

Jeff Jarvis (151:52)

Experience like never before.

Leo Laporte (151:54)

Look at the kiwis fly. Look at the raspberries.

Jeff Jarvis (151:57)

Raspberries. The onions not together.

Leo Laporte (151:58)

So really, it's a blender within pot than ever before.

Paris Martineau (152:02)

Yeah, Jeff, I just want to watch.

Jeff Jarvis (152:05)

You translate German all day than ever before.

Leo Laporte (152:11)

In. Oh, it does look prettier. Oh, it is a lot prettier. Yeah. Tmz.

Paris Martineau (152:32)

I didn't know you spoke German.

Leo Laporte (152:35)

That's his thing, man.

Paris Martineau (152:37)

I mean, makes sense, given all that I know about what is a Thermomix?

Leo Laporte (152:42)

Wow.

Jeff Jarvis (152:44)

Oh, Leo's little.

Leo Laporte (152:45)

Oh, it's my. You know. Stacy got me to get one.

Paris Martineau (152:48)

Hey, you could get this new one and then give your old Thermomix to me.

Jeff Jarvis (152:53)

Stacy wanted.

Leo Laporte (152:54)

That's a good idea.

Jeff Jarvis (152:56)

She's gone now, so you're. You get it.

Leo Laporte (152:58)

That's a very good idea. I don't mind that. So it's a food processor, kind of. But really what it is, it's a blender with a heated thing. And then it's got inside. It has a scale. So what it does. And then it's got this Android recipe tablet. So you pick a recipe, and then it says, okay, put 600 grams of potatoes in, and it tells you how many grams you've put in. And then do this and do that, and it heats it and cooks it. It makes an incredible risotto, incredible mashed potatoes, incredible soup. Yeah, I've never ate bread in it. I guess you could. I mean, Yeah, I guess you could.

Paris Martineau (153:40)

Interesting.

Leo Laporte (153:42)

It's a ridiculous kitchen appliance. But I tell you what, most of my kitchen appliances have been relegated from the counters because Lisa likes kind of a clean lines. That one's on the counter because we use it a lot.

Jeff Jarvis (153:58)

I found this because Dite online did a story. The headline is the Redeemer. The New Thermomix Steams all the world problems.

Paris Martineau (154:08)

I love the drama.

Jeff Jarvis (154:09)

I'm reading the Google translation for ease right now. Since life is often an unfortunate affair, mankind has decided to cook something nice for itself from time to time. Over the centuries, the machinery in the kitchens of this delightful planet has grown quite enthusiastically, even if people hardly have time to cook the most part.

Leo Laporte (154:26)

You can make yogurt with it. You can. I mean, there's a lot you can do with it here. Here is an example of a poor housewife house vow who is doing just all the work and then she disappears. You don't need the housewife anymore. You've got yogurt making. Actually, try making yogurt with it. That's cool.

Jeff Jarvis (154:49)

Yeah, the big. We're on the food. I haven't even. I couldn't listen to this because I discovered it while we're on the show. But the next line, 145. It looks funny. I read. I read it. The new Australia. Australian lamb commercials are renowned for being funny for the.

Leo Laporte (155:06)

Okay, here we go. Australian lamb.

Jeff Jarvis (155:09)

You want to start back the beginning? Of course.

Leo Laporte (155:10)

This is for 2024.

Jeff Jarvis (155:12)

Is the sound on no.

Leo Laporte (155:14)

9. All right, now everybody get ready. We're going to do this. What do you reckon? Second?

Paris Martineau (155:20)

Don't know.

Leo Laporte (155:23)

It's a dog.

Paris Martineau (155:25)

Arguably.

Leo Laporte (155:27)

That's the funniest thing I've seen all week. Comment section. Who would do that to a dog?

Ethan Sutton (155:32)

That looks like a dog I drew.

Paris Martineau (155:33)

In grade two at Tina Smith.

Jeff Jarvis (155:35)

This is literally you. It's literally me.

Ethan Sutton (155:38)

Everyone calm down.

Leo Laporte (155:39)

It's fake. So it's like if the holster were real.

Jeff Jarvis (155:44)

Life is the comment section.

Paris Martineau (155:46)

This film was made.

Jeff Jarvis (155:47)

Was made with 100 real toxic.

Ethan Sutton (155:51)

Do you think this is happening everywhere?

Jeff Jarvis (155:56)

Madness.

Leo Laporte (155:57)

We're killing the environment. We're saving the environment. They only work when there's enough wind. $5,000 in a week. Working from home with this one simple trick I love. Renewable CO2 is behind perhaps the near lowest in Earth's history.

Jeff Jarvis (156:12)

Oh, you're right. Next thing you'll tell me the Earth is flat. Say flat ass.

Paris Martineau (156:18)

Another in a speech.

Leo Laporte (156:19)

That could be AI. No, that's AI. It's achievable. Naturally.

Jeff Jarvis (156:23)

I have a body similar to that.

Leo Laporte (156:24)

Looking good. Cushy.

G (156:25)

I made $5,000.

Leo Laporte (156:28)

That's the way you make the perfect couple.

Jeff Jarvis (156:31)

Milk before tea.

Leo Laporte (156:33)

This is a crime.

Paris Martineau (156:34)

Same as with cereals.

Marie Delores Zoyo (156:35)

Milk first.

Leo Laporte (156:36)

She only has a handful of commenters, by the way. It's not real.

Jeff Jarvis (156:40)

Oh, we're fighting over tea now?

Paris Martineau (156:42)

Could be worse.

Ethan Sutton (156:44)

How?

Leo Laporte (156:50)

She broke dancing.

Jeff Jarvis (156:52)

That's the famous Australian brick. Cancer.

Leo Laporte (156:54)

I tell you, this would be a lethal dose.

Paris Martineau (156:56)

Oh, another keyboard warrior at Gina Smith.

Leo Laporte (156:59)

Literally.

Paris Martineau (157:00)

Not you.

Jeff Jarvis (157:01)

So not.

Paris Martineau (157:01)

Not me.

Jeff Jarvis (157:02)

It's fake.

Leo Laporte (157:03)

Why is everyone hating?

Ethan Sutton (157:04)

It's fake.

Marie Delores Zoyo (157:05)

Jes.

Leo Laporte (157:06)

You suck. Booked and reported. Booked and reported.

Jeff Jarvis (157:13)

Everyone relax. It's not real democracy.

Leo Laporte (157:17)

What's next? What does this have to do with lamb?

Jeff Jarvis (157:20)

You'll get there.

Paris Martineau (157:21)

What does this. How long of an ad is this?

Jeff Jarvis (157:24)

It's one of those ones that's so special, they do one a year.

Leo Laporte (157:27)

Something that unites us.

Jeff Jarvis (157:29)

There's got to be something unites us. You know this is coming.

Leo Laporte (157:32)

Yeah, he's got a little lamb on the babby.

Paris Martineau (157:36)

Oh, that does look pretty good.

Jeff Jarvis (157:37)

It does look good, doesn't it?

Leo Laporte (157:39)

Lamb chop on the babby.

Jeff Jarvis (157:41)

As if a torch to civilization, here comes one honest man with a lamb chop. A bite. Thumbs up. Come on, everyone. Get out of the comments and into the cutlets.

Paris Martineau (157:58)

Oh, that. That actually was worth it.

Leo Laporte (158:00)

That one line did see it. I am such a ditzy Debbie.

Ethan Sutton (158:04)

I've been a big old greedy gun.

Jeff Jarvis (158:06)

Now this is real.

Paris Martineau (158:07)

At Gina Smith, it's literally you.

Jeff Jarvis (158:09)

Oh, my God.

Leo Laporte (158:10)

It's me, Gina Smith. Wait, why is everyone being so nice?

Jeff Jarvis (158:13)

I think everyone's a lot nicer in.

Paris Martineau (158:15)

Real life than we are online.

Leo Laporte (158:17)

I guess the anonymousness of. Of the Internet promotes disagreements and the platforms that we use.

Paris Martineau (158:21)

Yeah, we get it.

Leo Laporte (158:21)

Just eat some lamb. That's the best part.

Jeff Jarvis (158:26)

Is that great?

Leo Laporte (158:26)

That's the best part.

Ethan Sutton (158:28)

I hope people post nice comments about this ad.

Leo Laporte (158:32)

And he looks straight at you. Straight at you. He's still looking at me. Stop looking at me. I'm not going to post. Stop. I told you, stop. No, don't. No. Go away. I thank you for joining us for this bizarre edition of intelligent machines. Special thanks to our guests from B Dot Computer. Great fun having. Maria, Lourdes de Lourdes, Zoyo and Ethan. Oh, God, I'm never gonna get their name.

Jeff Jarvis (159:05)

It's on the rundown. It's on the rundown. It's right there. Sutton, right?

Leo Laporte (159:07)

Sutton. He likes Sutton.

Jeff Jarvis (159:09)

Sutton. Sutton.

Leo Laporte (159:09)

Sutton, Sutton, Ethan, Sutton. And Maria Delores Zoyo from B Computer. Really fun having them on. I can't wait to hear what the bee has to say about it. Thanks to all of you for watching. A special thanks of course, to Paris Martineau, weekend reporter for the information dot com. You must subscribe. You'll find her on signal if you've got a tip. Martineau01 thank you. Now you can go off and enjoy your biscuit.

Paris Martineau (159:37)

I've already consumed it. Oh, I will be enjoying my chicken thighs though.

Leo Laporte (159:41)

Did you make some more?

Paris Martineau (159:42)

They keep very well. I made some last night. Been nibbling all day. It's a delight.

Leo Laporte (159:50)

Thanks to Jeff Jarvis, Emeritus professor of Journalistic innovation at the Craig Arm at the Craig Newmark School Graduate School of Journalism at the City of University. Now at Montclair State University and SUNY.

Jeff Jarvis (160:06)

Stony Brook, we have a new president as of today who is really interesting.

Leo Laporte (160:11)

Oh good.

Jeff Jarvis (160:12)

She's now the Dean of Engineering at Princeton, but she's in the Marconi hall of Fame.

Leo Laporte (160:17)

Wow.

Jeff Jarvis (160:18)

The work she's done on WI fi and telecommunication.

Leo Laporte (160:22)

And she's now the president of suny. How interesting. We should get her on the show. Thank you, Jeff.

Jeff Jarvis (160:30)

Suck up. Yeah, I like that.

Ethan Sutton (160:31)

Yeah.

Leo Laporte (160:32)

Oh, she's great. She's great. Jeff is the author of the Web We Weave Gutenberg Parenthesis and many other fine volumes available all@jeffjarvis.com but they're real books. It's not like self published. He actually has a publisher and you get it from the publisher just has.

Jeff Jarvis (160:51)

Links to it and Good. Parenthesis is now out in paperback and I just found out that I'm going to be able to do an audiobook version of the magazine.

Leo Laporte (160:59)

Oh good. That'll be really good.

Paris Martineau (161:01)

That'll be really good because a lot.

Leo Laporte (161:02)

Of that is in your voice about your story. Yeah.

Jeff Jarvis (161:05)

Excellent. I'm happy.

Leo Laporte (161:07)

Thank you all for joining us. We do Intelligent machines Wednesday afternoon, 2pm Pacific, 5pm Eastern, 2100 UT or 2200 UTC. You can watch live if you want on YouTube, Twitch, tick tock, x.com, linkedIn, Facebook, kick. But you don't need to watch live. The live version will have all of the unexpurgated AI insights from my phone. The edited version will all be blurred out. Apparently I revealed far too much.

Jeff Jarvis (161:41)

I.

Leo Laporte (161:41)

Didn'T realize at the time. So that's why you might want to watch live. Of course. Easy to watch after the fact. We have copies of the show at our website, Twitter TV. You there is an intelligent machines channel on YouTube so you can share clips if you wish. And of course the best way to listen or watch is to get the audio or the video in your favorite podcast player. Subscribe and you'll get it automatically just in time for Thursday morning. Thank you, everybody, for being here. Thank you, Paris. Thank you, Jeff. Have a great week. We'll see you next time on Intelligent Machines. Machines. I'm not a human being.

Paris Martineau (162:19)

Not into this animal scene.