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Speaker 1: From Kaleidoscope and iHeart podcasts. This is tech stuff.

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Speaker 2: I'm as Luscian and I'm care price.

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Speaker 1: Today we get into spy cockroaches and why Europe is

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Speaker 1: getting armed with a bunch of sci fi weapons, and

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Speaker 1: a revealing conversation with the human team behind a viral.

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Speaker 3: AI artist then un chatting me, and then I put

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Speaker 3: in what formation should you play to be?

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Speaker 2: NWSL teams all of that on the Weekend Tech. It's Friday,

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Speaker 2: November seventh, US. I want to tell you about this

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Speaker 2: bird that I saw on the way to work today.

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Speaker 1: Birds can be very symbolic.

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Speaker 2: It was a brown and white pigeon.

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Speaker 1: Or feral as US ornithologists like to call it.

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Speaker 2: A pharal dove. So do you know I'm telling you

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Speaker 2: about a bird? Have no idea, no idea you could pass.

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Speaker 1: What's the test?

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Speaker 2: You just experienced? Something called bird theory. It's this relationship

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Speaker 2: test that I saw on TikTok, and a lot of

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Speaker 2: TikTokers are using it to test their partners by secretly

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Speaker 2: filming their partner's reactions when they point out a bird.

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Speaker 2: If their partner reacts enthusiastically like I did, good sign.

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Speaker 2: If not some say, bird, watch out.

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Speaker 1: What's behind this?

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Speaker 2: It's actually from this theory developed by the Gotmans, who

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Speaker 2: are prominent couples therapists, and this test is used to

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Speaker 2: evaluate how a partner responds to what the Gotmans call

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Speaker 2: a bid for connection. So just now, had we been

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Speaker 2: together and you were like, Babe.

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Speaker 1: I can't in the studio, I'm by definition forced to

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Speaker 1: pay attention to what you're telling me. In real life,

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Speaker 1: I feel like, as a bird over there, I had

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Speaker 1: just been on my phone, I won't even heard you

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Speaker 1: locked into my phone.

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Speaker 2: That's very true, and so it is also contextual, I think.

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Speaker 2: But what is really interesting to me about this is

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Speaker 2: that there is a thing that's happening on TikTok that

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Speaker 2: I think is really just an extension of like the

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Speaker 2: BuzzFeed or Cosmo quiz. Remember BuzzFeed quizzes.

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Speaker 1: Like are you astrologically compatible with your partner? Or like

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Speaker 1: do you like the same things? And that kind of thing.

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Speaker 2: Well, that's exactly what I mean. But I think that

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Speaker 2: there's this interesting thing that's happening on TikTok, which is

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Speaker 2: that like people are gamifying their relationships very publicly and

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Speaker 2: inviting viewers to judge their partner's responses as well, and

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Speaker 2: so it kind of is the first time people are

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Speaker 2: like opening up their relationships to a peanut gallery.

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Speaker 1: That's the weird part, right, Like you would think the

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Speaker 1: idea of the peanut galleries. That's different from the old

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Speaker 1: days of Cosmo and BuzzFeed quizzes.

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Speaker 2: What's amazing is how seriously people take this stuff. People

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Speaker 2: are really like in the comments being like, if your

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Speaker 2: partner doesn't pay at ten, you talking about the bird?

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Speaker 2: Dump it, believe?

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Speaker 1: How about this for a transition please? Is it a bird?

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Speaker 1: Is it a plane? No, it's a drone.

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Speaker 4: That's good.

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Speaker 1: Okay, that's good. So the general headline that I want

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Speaker 1: to share with you today is Europe is investing heavily

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Speaker 1: in defense tech with mixed results.

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Speaker 2: What are the mixed results?

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Speaker 1: Some of it's working and some of it's absolutely not.

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Speaker 1: There was a story in the Financial Times with the

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Speaker 1: headline drones startup back by Peter Teel crashed and burned

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Speaker 1: in Armed forces trials. So of course I couldn't read

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Speaker 1: it up exactly. But this all started with Vice President

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Speaker 1: jd Vance. Earlier this year. Jd Vance, in his first

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Speaker 1: major public speech, was in Munich, at something called the

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Speaker 1: Munich Security Conference, which is basically when all of the

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Speaker 1: big wigs of European defense get together and you know,

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Speaker 1: prominent Americans from government come and normally it's a kind

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Speaker 1: of backslapping, handshaking thing. This year was very different. Vance

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Speaker 1: shocked Europe and I had some friends who had that conference.

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Speaker 2: You had friends that were shocked.

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Speaker 1: Shocks say, you, guys, you're on your own. You've been

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Speaker 1: eating out of our hand for too long. You've gonna

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Speaker 1: boost your own defense spending because we're not coming to

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Speaker 1: the rescue anymore. So now Europe is stepping up her

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Speaker 1: Reuters venture capital funding of European defense tech hit one

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Speaker 1: billion dollars this year, up from around four hundred million

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Speaker 1: in twenty twenty two. More interesting than that, there was

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Speaker 1: a stat is this year, for the first time ever,

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Speaker 1: combined European spending on defense procurement, by the way, this

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Speaker 1: includes Turkey and Ukraine is expects to be more than

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Speaker 1: in the US. What Europe is spending.

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Speaker 2: More just because JD.

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Speaker 1: Vance marked then yeah, I mean I think it's that,

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Speaker 1: And obviously it's it's carroting. Well, it's two sticks. It's

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Speaker 1: an American stick and the Russian stick. There's a very

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Speaker 1: little carrot. One is saying you've got to do this,

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Speaker 1: and the other is all but saying you've got to

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Speaker 1: do this. A lot of this investment is happening in Germany,

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Speaker 1: which also obviously has a troubled history when it comes

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Speaker 1: to quite armament production and has really not done very

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Speaker 1: much since the Second World War and till now when

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Speaker 1: there is demand from the US, pressure from Russia, but

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Speaker 1: also a whole bunch of talented engineers who have worked

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Speaker 1: for decades in the auto industry which is now on

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Speaker 1: its knees. Interesting, but I do want to talk a

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Speaker 1: little bit more about this Financial Time story about Yes, that's.

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Speaker 2: What I want to hear more about is stashing and burning.

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Speaker 1: This is about a company called Stark, which rhymes with Lark.

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Speaker 1: It's a Berlin based drone company back by Peter Teel,

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Speaker 1: Sequoia Capital and the NATO Innovation Fund. Stark was founded

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Speaker 1: fifteen months ago and they've built weaponized drones that look

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Speaker 1: kind of reminiscent of ex fighters from Star Wars. The

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Speaker 1: base of the drone is shaped like an X.

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Speaker 2: And this has all been developed in the last fifteen months.

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Speaker 1: It has and it had two trials which were covered

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Speaker 1: by the Financial Times, which called them a disaster. The

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Speaker 1: drones quote failed to hit a single target during four

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Speaker 1: attempts at two separate exercises. You know, the failure, especially

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Speaker 1: when and it's failure of investments by some of these

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Speaker 1: Silicon valley overlords is always delicious, extremely delicious. But the

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Speaker 1: new idea in defense tech is build it and they

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Speaker 1: will come. Previously, it was like the government would commission

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Speaker 1: a new fighter jet, There'll be a twenty year procurement cycle,

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Speaker 1: and it would cost like, you know, five hundred billion

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Speaker 1: dollars for the government to get the new technology wanted.

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Speaker 1: Now all these startups are saying, wow, like wars here

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Speaker 1: or war's coming, Why don't we raise money from the

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Speaker 1: private market and make weapons that governments don't even think

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Speaker 1: they need yet. And so that's a huge shift, and

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Speaker 1: it's being powered, of course by AI, automation and robotics.

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Speaker 1: There's a story in the New York Times about the

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Speaker 1: same phenomenon which had a great, great quote, which was

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Speaker 1: the new business model reflects a sea change in warfare

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Speaker 1: that maybe as profound as the shift from horse cavalries

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Speaker 1: to armoured tanks and airplanes in World War One.

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Speaker 2: That's quite a distinction. I mean, I guess my question

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Speaker 2: is why do things on the cheap?

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Speaker 1: Well, I think because, I mean, the point of these

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Speaker 1: venture back companies is you're trying to do a minimal

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Speaker 1: viable product as cheaply as you can. If it works,

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Speaker 1: you raise a bunch more money and go into full production.

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Speaker 1: So most Series A companies fail. But what's interesting is

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Speaker 1: that private investors are willing to take risk to fund weapons,

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Speaker 1: most of which won't work. But the ones that do work,

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Speaker 1: or that do convince governments they work, will presumably print

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Speaker 1: fortunes for their founders and backers.

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Speaker 2: So this is quite literally the old Silicon Valley model

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Speaker 2: of moved fast and break things.

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Speaker 1: It's exactly that, exactly that, but for defense tech, which.

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Speaker 2: Is a little scarier. When it comes to defense tech.

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Speaker 2: You know, I'm sure some of this tech is incredibly

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Speaker 2: sci fi and inspired, but like, this is stuff that's

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Speaker 2: being made for war.

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Speaker 1: Yeah, I mean, look, so one of the most successful

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Speaker 1: companies in the space seems to be this company called Helsing.

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Speaker 1: They're featuring the same ft story. They hit their targets

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Speaker 1: seventeen times and successfully completed five trial runs. Their valuation

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Speaker 1: is now over four teen billion dollars.

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Speaker 2: That's crazy.

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Speaker 1: And it's not just drones. There are other companies who

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Speaker 1: are trying to create the weapons of the future. In parallel,

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Speaker 1: there's a company called euro Atlas who have quote created

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Speaker 1: a division to develop autonomous underwater vehicles. The commonitor vital

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Speaker 1: cables on the ocean floor. They are vital cables without

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Speaker 1: no Internet without without them. They've created this underwater vehicle

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Speaker 1: called gray Shark that actually looks more like a dolphin.

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Speaker 1: And to me, bionic cockroaches are the most fascinating part

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Speaker 1: of the story. I'm serious. So let me tell you

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Speaker 1: about a little company called Swarm Biotechniques.

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Speaker 2: There you go.

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Speaker 1: Swarm Biotechniques have taken cockroaches and made these little backpacks

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Speaker 1: for them. These backpacks again a little baby corroach, baby

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Speaker 1: cockroach backpack. The backpack interacts with the cockroaches nervous system

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Speaker 1: such that a human operator can use the cockroach like

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Speaker 1: a remote control cars.

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Speaker 2: So these are real cockroaches.

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Speaker 1: These are real cockroaches.

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Speaker 2: That have implants in their brain.

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Speaker 1: Implants in their brain and tiny cameras and can go

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Speaker 1: in swarms into areas where humans or other bigger robots.

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Speaker 1: This is from the Swarm biotechniques websites has been referenced

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Speaker 1: in a bunch of mainstream press. So I haven't seen

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Speaker 1: for myself the swamp that the cockroach being remote controlled.

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Speaker 1: But what a world we're living in.

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Speaker 2: So why is this happening in Europe specifically?

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Speaker 1: Yeah, well, I mentioned that the Vance thing, and one

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Speaker 1: of the co founders of Helsing, the more successful drone company,

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Speaker 1: told the New York Times quote before, no European VC

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Speaker 1: was interested in defense. Now everyone wants to invest. And

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Speaker 1: so I think you can't really understand this story without

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Speaker 1: understanding how scary it is to be next to Russia

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Speaker 1: if you live in Europe. So you know, in New

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Speaker 1: York a few months ago, everyone was looking up at

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Speaker 1: the sky and seeing all these drones. It's kind of

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Speaker 1: scary and chilling. Yeah, it's a lot more scary and

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Speaker 1: more chilling to Russia. You look up the sky and

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Speaker 1: you see stuff.

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Speaker 4: Right.

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Speaker 1: The market is fear driven, of course, So drones, presumably

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Speaker 1: Russian drones, have been spotted flying over European bases hosting

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Speaker 1: US tactical nuclear weapons that are part of Europe's nuclear

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Speaker 1: terrance strategy. The US is believed to have around one

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Speaker 1: hundred tactical nuclear bombs in Europe spread across five countries,

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Speaker 1: which is half of the US supply not something you

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Speaker 1: want to.

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Speaker 2: See, No, you definitely don't. And I know this because

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Speaker 2: I just saw the new Cafine Bigelow movie called House

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Speaker 2: of Dynamite, and it's about basically the hours leading up

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Speaker 2: to a nuclear missile heading to the United States. I'm

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Speaker 2: just thinking about it because of this. It sort of

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Speaker 2: shows the ways in which the United States isn't exactly prepared.

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Speaker 2: I actually want to I want to transition to something

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Speaker 2: that is a little bit more CARA, which is music

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Speaker 2: culture AI the synthetic creation of new musical artists.

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Speaker 1: Well, I have to tell you that one of the

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Speaker 1: main investors in Helsing, In fact, I think the lead

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Speaker 1: investor in the last round was none other than Daniel Eck,

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Speaker 1: who also found his Spotify.

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Speaker 2: So there's always but it's also gotten him in trouble,

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Speaker 2: that's right, Like many artists have left the platform because

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Speaker 2: of this, and it's also why Eck is no longer

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Speaker 2: the CEO of Spotify. But yes, I'm here to talk

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Speaker 2: about the state of music, and you know that AI

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Speaker 2: music is nothing really new, Like we talked about it

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Speaker 2: on Sleepwalkers. There was a company called Endell that was

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Speaker 2: like the first algorithm to have a record deal. And

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Speaker 2: actually in September, Spotify revealed that it moved seventy five

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Speaker 2: million spam tracks over the last year. And this has

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Speaker 2: become a really big business for people because it's cheap

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Speaker 2: to make and if a song is played for more

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Speaker 2: than thirty seconds on a streaming platform, the person uploading

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Speaker 2: the music gets a royalty. So I'm curious first and foremost, like,

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Speaker 2: what do you think about AI music?

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Speaker 1: Very good question. I don't know what I think about

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Speaker 1: AI music, but what I do think about is that

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Speaker 1: I know when people think they are interacting with AI

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Speaker 1: created media, they slightly switch off. But I'm wondering if

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Speaker 1: that if that continues, I mean.

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Speaker 2: They're turned off by the idea. Yeah, sorry, I was

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Speaker 2: trying to translate your British. Well. I want to play

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Speaker 2: you a song that is number thirty on Billboard's Adult

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Speaker 2: R and B Airplay charts, and this artist has over

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Speaker 2: one million monthly listeners on Spotify.

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Speaker 3: He didn't want me down no stairs, didn't want me

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Speaker 3: about them boys who wouldn't care.

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Speaker 2: Didn't tell me what a real man sounds like.

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Speaker 5: So a fell for that's right.

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Speaker 2: He never pulled much. Tell me what you think about it.

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Speaker 1: I would have no idea. This wasn't real, right. I

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Speaker 1: did like it though, I like especially that. Oh at

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Speaker 1: the beginning it sounded a bit like I don't want

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Speaker 1: to look for Christmas.

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Speaker 2: Well, let me tell you something. Not a lick of

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Speaker 2: it is real. However, there is some reality.

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Speaker 1: It's a human in the loop.

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Speaker 2: There is a human in the loop. The person that

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Speaker 2: you just heard is Zania Monet.

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Speaker 1: Okay, she's real. If she's not real, she.

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Speaker 2: Is fake as they come. So I'm a person who

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Speaker 2: likes to see when AI is creating an inflection point

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Speaker 2: in the culture where it's like, you know, like we

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Speaker 2: talked about with Sora, like all of a sudden, we

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Speaker 2: can't trust video anymore. Ever, Yeah, I think because this

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Speaker 2: artist Zanaia is on the Billboard charts, Yeah.

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Speaker 1: That makes it feel different. Right. This is not just

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Speaker 1: so garbage that's on Spotify and from time to time

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Speaker 1: gets weeded out by a mod or an automatic mod.

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Speaker 1: This is a charting song of an artist with a

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Speaker 1: name who doesn't exist. That's right.

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Speaker 2: And on top of that, she has a manager and

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Speaker 2: a multimillion dollar record yeah with Hollwood Media, which is

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Speaker 2: an independent record label founded by a former Interscope executive.

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Speaker 2: So when I was reading about Zania, it was actually

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Speaker 2: unclear to me what about her was a and what

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Speaker 2: wasn't like Was it the beats, was it her voice?

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Speaker 2: Was it the lyrics? So I actually called up Zenia's manager,

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Speaker 2: Ramel Murphy, and he told me that this whole project

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Speaker 2: is actually the brainchild of a lifelong friend and poet,

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Speaker 2: a thirty one year old named Talisha Nicki Jones.

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Speaker 6: Here's Ramel, so I would say Zanayah is an extension

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Speaker 6: of Talisha Jones. So that's why it feels so real,

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Speaker 6: because she put her raw motions on real life experiences

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Speaker 6: into the song. AI is just a tool to enhance

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Speaker 6: and give a different experience, not a better, not a worse,

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Speaker 6: a different experience. And we're going to be completely transparent

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Speaker 6: as we've been from the jump. She's not the vocal

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Speaker 6: beast as Zanaia is.

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Speaker 2: Ramel and Talisha don't see Talisha's lack of musical training

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Speaker 2: as any issue. She's basically using an R and B

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Speaker 2: singer songwriter avatar to elevate the real life stories and

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Speaker 2: emotions she's putting into her lyrics. And Talisha the poet

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Speaker 2: has an undeniably larger reach as Zanaia, the R and

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Speaker 2: B singer. So what was interesting about what Ramel said

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Speaker 2: is that this is art. Talisha's music is real music,

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Speaker 2: even if Talisha herself isn't musically talented in her own right.

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Speaker 2: So Talisha has been using an AI music generator called

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Speaker 2: Suno to make these songs because, as Ramel put it,

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Speaker 2: she is not a singer and she's not a musician,

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Speaker 2: She's just a poet. And actually, Talisha appeared on CBS

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Speaker 2: Mornings and showed co host Gail King how she uses

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Speaker 2: Suno to turn her poems into music.

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Speaker 3: And I just take those lyrics, just go in and

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Speaker 3: paste a simple style, slow tempo R and B.

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Speaker 1: So you're saying, I want slow tempo R and B.

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Speaker 5: She adds a few more prompts into AI music generator Suno,

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Speaker 5: like female soulful vocals, light guitar and heavy drums, and

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Speaker 5: Hilary's just creat.

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Speaker 2: I watched some videos of her on Facebook. Talicia, Yeah, Talisha,

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Speaker 2: she teaches how to leverage AI tools into financial rewards,

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Speaker 2: and I don't know. I feel like she's kind of

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Speaker 2: taking this software by the scruff of its neck and saying,

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Speaker 2: look like, I'm not going to be a superstar. I

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Speaker 2: can build a superstar, and isn't it better for me

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Speaker 2: to build a superstar who I then don't have to

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Speaker 2: respond for She's a producer.

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Speaker 1: Is an interesting combination being a good producer and a

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Speaker 1: good marketer. Right She's written Talisha's written the hype cycle

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Speaker 1: of AI to become a producer and artist, and Aaron

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Speaker 1: write good on I think.

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Speaker 2: What people have a problem with is is this music.

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Speaker 2: So there's a very well known R and B artist

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Speaker 2: named Kailani and she was quoted in a piece in

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Speaker 2: Billboard magazine saying, quote, nothing and no one on earth

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Speaker 2: will ever be able to justify AI to me. And

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Speaker 2: I do understand this. There are many R and B

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Speaker 2: artists who have worked for years and years tirelessly to

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Speaker 2: hone their craft. And Talisha is a poet, is using

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Speaker 2: soon know and is pushing out songs pretty easily using

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Speaker 2: digital tools. So Kilani makes a point in the sense

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Speaker 2: of like, can we really call Zanaya an artist in

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Speaker 2: the same way we call Kilani an artist? I think right.

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Speaker 1: I mean, look, if all music was this, I would

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Speaker 1: be super depressed. But I think because I'm attracted to

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Speaker 1: Talsia's story, I feel like I give this a pause.

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Speaker 1: But it's kind of agel debate. Right, is a musical

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Speaker 1: act more the product of the musician or the producer

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Speaker 1: in today's world?

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Speaker 2: That is the question. And actually Suno has been sued

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Speaker 2: for copyright infringement and a lot of artists are very

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Speaker 2: anti Suno that sense.

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Speaker 1: Yes, basically stealing people's voices and musical ideas and melodies

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Speaker 1: to make this possible.

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Speaker 2: Well, they're training SUNO's algorithms with pre existing material, and

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Speaker 2: a lot of record companies are part of this larger

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Speaker 2: lawsuit where they're saying that is not okay. You can't

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Speaker 2: basically facilitate someone's entire musical career on the voice and

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Speaker 2: sound of you know, thousands of artists music.

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Speaker 1: After the break a pitch to build data centers in

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Speaker 1: space and professional soccer coaches experiment with Chatgibt stay with

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Speaker 1: us and we're back, Cara, What if I told you

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Speaker 1: that Google wanted to harness the power of the sun

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Speaker 1: to power data centers.

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Speaker 2: I would quote the oft quotable Mel Robbins and say

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Speaker 2: let them.

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Speaker 1: Let them. I would say, let them. What if I

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Speaker 1: told you they would also be honessing the power of

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Speaker 1: the Sun for these data centers in space.

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Speaker 2: I would say that nothing surprises me anymore. Are they

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Speaker 2: doing this well?

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Speaker 1: They just We've talked about making plans to send trial

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Speaker 1: equipment into orbit by early twenty twenty seven. It's called

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Speaker 1: Project Suncatcher.

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Speaker 2: Brilliant name.

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Speaker 1: Basically, the idea is that tightly packed constellations of solar

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Speaker 1: powered satellites orbiting four hundred miles above Earth's surface with

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Speaker 1: AI processors could help meet rising demand for AI without

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Speaker 1: putting additional strain on our energy resources here on Earth.

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Speaker 2: And this is a good idea why.

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Speaker 1: The article in the Guardian that I read talked about

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Speaker 1: how the bet Google is making here is that it

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Speaker 1: will be so cheap to send stuff into space by

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Speaker 1: twenty twenty seven, that will actually be cheaper to do

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Speaker 1: data centers in space than on Earth. Google researchers predict

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Speaker 1: that the cost of sending stuff to space could drop

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Speaker 1: to two hundred dollars per kilogram in this decade, which

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Speaker 1: is a multiple lower than it is today.

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Speaker 2: So we'd basically be sending these to space because it's

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Speaker 2: saving money.

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Speaker 1: It's cheaper than using Earth Earth energy. It's better for

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Speaker 1: the environment because you don't need you don't need fossil fuels,

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Speaker 1: and you don't need water to cool them down because

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Speaker 1: it's cold up there.

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Speaker 2: Because it's cold up there.

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Speaker 1: There are of course some downsides as well. Launching rockets

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Speaker 1: emits loads and loads and loads of CO two, So

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Speaker 1: you have to believe that the price in CO two

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Speaker 1: for the rocket launches will be less than the price

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Speaker 1: of CO two in fossil fuels burnt to power data

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Speaker 1: centers on Earth. One astronomer gave a quote that also,

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Speaker 1: so many satellites in low orbit will be quote like

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Speaker 1: bugs on a windshield when trying to observe outer space. Really,

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Speaker 1: and so you know, you think we talked, I think

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Speaker 1: a couple of weeks ago about all this space junk

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Speaker 1: raining down from the sky, sending loads more stuff, And

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Speaker 1: you also have to step back and ask the question,

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Speaker 1: do we really really need this much AI processing power

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Speaker 1: that we have to send me a bunch of stuff

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Speaker 1: into space.

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Speaker 2: In order to do like shouldn't we do less?

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Speaker 1: Maybe? But that's unlikely. Elon Musk is also working on

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Speaker 1: data centers in space, and Nvidia AI chips are shortly

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Speaker 1: to be launched into space this month in partnership with

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Speaker 1: a startup called star Cloud to see if they actually

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Speaker 1: work in space. I mean, this is living, this is insane.

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Speaker 2: They're sending chips. They're sending Nvidia chips to space to

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Speaker 2: see if Nvidia chips will work in space.

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Speaker 1: As far as I understand, yes, I don't want to

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Speaker 1: see what happens.

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Speaker 2: I'm not interested in finding out.

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Speaker 1: Don't look up it is.

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Speaker 2: Don't look up. I have a story that just is

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Speaker 2: much more on Earth.

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Speaker 5: You know.

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Speaker 2: I'm obsessed with school phone bans. And I don't have

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Speaker 2: a child, but I think that no.

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Speaker 1: Child should use a phone in school ever.

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Speaker 2: Really, ever until they're eighteen. Kentucky had a school phone ban,

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Speaker 2: and listen to what happened. More people are taking out

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Speaker 2: books from the library.

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Speaker 1: Well.

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Speaker 2: The Jefferson County Public School District in Kentucky reported that

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Speaker 2: kids are checking out more books from the library this

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Speaker 2: year than last year. One school in the district saw

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Speaker 2: an increase of sixty seven percent more checkouts year over year.

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Speaker 2: And they're only like, what is it November. There are

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Speaker 2: only a few months into school. It has made a

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Speaker 2: huge difference, not just for library usage, but administrators are

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Speaker 2: hopeful that this trend will do wonders for reading scores,

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Speaker 2: and it's already improved at least one student's mental health

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Speaker 2: who told a local outlet quote. At first, I was

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Speaker 2: really dramatic about the policy. I thought it was going

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Speaker 2: to end my whole life. I just realized this is

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Speaker 2: a good chance for me to put my phone down

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Speaker 2: and start focusing back on school. It helps people socialize

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Speaker 2: because this year I have talked to people more than

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Speaker 2: in all the twelve years of me being in school.

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Speaker 4: Wow.

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Speaker 1: You know what is funny? You have a book club? Yes,

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Speaker 1: able to social media power use that.

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Speaker 2: That's true.

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Speaker 1: I if I'm on vacation, I can read quite quite

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Speaker 1: easily and happily. If I'm at home, I want to

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Speaker 1: read in the evening, I have shut my phone in

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Speaker 1: the other room.

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Speaker 2: You have to.

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Speaker 1: You have to have your phones anywhere near that book

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Speaker 1: is normal.

441
00:22:50,640 --> 00:22:52,800
Speaker 2: And we're in our thirties. We're talking about kids who

442
00:22:52,800 --> 00:22:54,679
Speaker 2: are twelve years old who have been in school, or

443
00:22:55,080 --> 00:22:58,000
Speaker 2: fifteen years old. It goes back to the thing like

444
00:22:58,280 --> 00:23:00,720
Speaker 2: if you give someone a phone, and you give someone

445
00:23:00,720 --> 00:23:04,919
Speaker 2: a book, which one was designed using mechanisms that they

446
00:23:05,000 --> 00:23:06,760
Speaker 2: used to create slot machines. Not a book.

447
00:23:06,800 --> 00:23:09,760
Speaker 1: Not a book, Not a book. I have something which

448
00:23:09,800 --> 00:23:14,919
Speaker 1: is the definition of not boring story, the louver Heist.

449
00:23:15,800 --> 00:23:18,120
Speaker 2: I love the lead following Yes, it's the best.

450
00:23:18,760 --> 00:23:25,200
Speaker 1: So the Crown Jewels of France were essentially poloined. Recently,

451
00:23:25,600 --> 00:23:26,080
Speaker 1: in the.

452
00:23:25,960 --> 00:23:29,400
Speaker 2: Age of US sending data centers to space, there are

453
00:23:29,520 --> 00:23:31,399
Speaker 2: still just mom and pop thieves.

454
00:23:31,600 --> 00:23:34,000
Speaker 1: Well that's the thing. So that's so. The original thought was,

455
00:23:34,000 --> 00:23:36,440
Speaker 1: oh my god, this is the most sophisticated criminal gang

456
00:23:36,480 --> 00:23:38,920
Speaker 1: in the world. It turned out to be according to

457
00:23:38,920 --> 00:23:43,440
Speaker 1: the French prostitute of the bunch of local Joshmo, which

458
00:23:43,560 --> 00:23:47,480
Speaker 1: meant that they left their DNA all over the place.

459
00:23:47,640 --> 00:23:50,840
Speaker 1: This is why it's a textuff story. So essentially, in

460
00:23:50,920 --> 00:23:53,040
Speaker 1: France there is a database like there is in the

461
00:23:53,160 --> 00:23:56,680
Speaker 1: US of DNA of people who have been arrested and

462
00:23:57,000 --> 00:24:00,000
Speaker 1: convicted of crimes, and essentially they were able to get

463
00:24:00,160 --> 00:24:05,040
Speaker 1: the DNA from various places, including the Empress Eugenniese Crown,

464
00:24:05,200 --> 00:24:09,119
Speaker 1: which was dropped on the way out, advised and cross

465
00:24:09,119 --> 00:24:12,120
Speaker 1: referenced it with the database of people who had committed

466
00:24:12,160 --> 00:24:16,600
Speaker 1: a crime in France and they got four hits being immediately.

467
00:24:16,200 --> 00:24:17,160
Speaker 2: So easy for them.

468
00:24:17,400 --> 00:24:20,159
Speaker 1: Yeah, what's really interesting here is that the reason the

469
00:24:20,240 --> 00:24:25,400
Speaker 1: DNA samples could catch allegedly the thieves was because these

470
00:24:25,440 --> 00:24:29,200
Speaker 1: people had already committed crimes and been booked about the system.

471
00:24:29,280 --> 00:24:32,360
Speaker 1: So there's four point four million people in France who

472
00:24:32,400 --> 00:24:34,960
Speaker 1: are in this DNA database and they can cross reference.

473
00:24:35,240 --> 00:24:37,200
Speaker 1: That's the same as how it works in the US

474
00:24:37,200 --> 00:24:40,560
Speaker 1: with something called CODIS. You know about COTIS, I do

475
00:24:40,880 --> 00:24:43,320
Speaker 1: the Combined DNA Index System. How do you know about it?

476
00:24:43,440 --> 00:24:47,280
Speaker 2: Because I watched CSI, right, I watch a lot of crimes.

477
00:24:47,000 --> 00:24:49,440
Speaker 1: So CODIS has a lot more hits in it, but

478
00:24:49,480 --> 00:24:51,680
Speaker 1: I think in order of magnitude more. And we actually

479
00:24:51,680 --> 00:24:55,800
Speaker 1: at Kulleioscope have another podcast called America's Crime Lab, which

480
00:24:55,800 --> 00:24:57,760
Speaker 1: I'm going to plug hard here. It is about a

481
00:24:57,800 --> 00:25:03,119
Speaker 1: startup called Othrum who were using forensic DNA techniques, so

482
00:25:03,400 --> 00:25:07,160
Speaker 1: in other words, they can extrapolate based on DNA databases

483
00:25:07,359 --> 00:25:10,200
Speaker 1: even if you haven't already been put in the database

484
00:25:10,560 --> 00:25:13,520
Speaker 1: who you may be based on your relatives and stuff.

485
00:25:14,119 --> 00:25:17,800
Speaker 1: And so they were actually very heavily involved in Golden State,

486
00:25:17,880 --> 00:25:20,359
Speaker 1: well not Golden State actually, but in the Idaho. Oh

487
00:25:20,359 --> 00:25:23,160
Speaker 1: the Idaho, but Golden State was exactly one of these

488
00:25:23,200 --> 00:25:29,240
Speaker 1: cases as well. It's controversial. In France, consumer DNA sort

489
00:25:29,280 --> 00:25:32,080
Speaker 1: of businesses are actually banned. We were talking at the

490
00:25:32,119 --> 00:25:36,160
Speaker 1: beginning of the episode about Germany's history and the armaments industry,

491
00:25:36,440 --> 00:25:40,000
Speaker 1: because in France, the reason consumer genetic companies are banned

492
00:25:40,119 --> 00:25:45,480
Speaker 1: is because of the Holocaust. You know, inferring people's genealogy

493
00:25:45,680 --> 00:25:48,840
Speaker 1: and Jewishness was a way that you know, many many

494
00:25:48,880 --> 00:25:52,440
Speaker 1: people are senter their deaths in France. So it's always

495
00:25:52,520 --> 00:25:55,040
Speaker 1: two sided, right, I mean, you want you want the

496
00:25:55,560 --> 00:25:58,240
Speaker 1: to use the DNA technology to you know, to help

497
00:25:58,320 --> 00:26:01,560
Speaker 1: figure out who to the Jewels.

498
00:26:01,320 --> 00:26:03,280
Speaker 2: But not who is the jew.

499
00:26:05,119 --> 00:26:05,239
Speaker 1: It.

500
00:26:05,280 --> 00:26:06,800
Speaker 2: I can say it. I can say.

501
00:26:06,640 --> 00:26:17,680
Speaker 4: It now for Chat and me.

502
00:26:18,320 --> 00:26:22,320
Speaker 1: I like reading about soccer as you might call it,

503
00:26:22,560 --> 00:26:25,960
Speaker 1: football as I call it on the Guardian, I pretty compulsively.

504
00:26:26,000 --> 00:26:27,680
Speaker 1: I didn't down at for a weird reason like a

505
00:26:27,720 --> 00:26:30,520
Speaker 1: Guardian dot co or UK slash football probably forty times

506
00:26:30,520 --> 00:26:33,800
Speaker 1: a day. I stumbled upon an article with a surprising

507
00:26:34,400 --> 00:26:38,800
Speaker 1: revelation about how professional soccer might be getting help from

508
00:26:38,920 --> 00:26:39,359
Speaker 1: jen Ai.

509
00:26:39,800 --> 00:26:43,520
Speaker 2: I saw this too. Yes. The Seattle rainhead coach Laura

510
00:26:43,560 --> 00:26:47,359
Speaker 2: Harvey confessed on the soccer Is podcast to using chat

511
00:26:47,400 --> 00:26:49,840
Speaker 2: GPT to help coach your team. It was in the

512
00:26:49,880 --> 00:26:50,320
Speaker 2: off season.

513
00:26:50,359 --> 00:26:52,560
Speaker 3: I was like writing things into chat GPT of like

514
00:26:52,760 --> 00:26:54,960
Speaker 3: what is Seattle Rain's identity?

515
00:26:55,200 --> 00:26:57,400
Speaker 2: And it would like spur it out and I was like, no,

516
00:26:57,400 --> 00:26:57,680
Speaker 2: I don't.

517
00:26:57,840 --> 00:26:58,919
Speaker 6: I don't know if that or not.

518
00:26:59,000 --> 00:27:00,880
Speaker 3: And then it would be I would like, what what

519
00:27:00,960 --> 00:27:03,639
Speaker 3: do you need to do to be successful in the NWSL?

520
00:27:03,680 --> 00:27:06,600
Speaker 1: Lot really broad questions so far so generic?

521
00:27:06,680 --> 00:27:09,200
Speaker 2: Yeah, totally. I mean it's what many people do with chat.

522
00:27:09,240 --> 00:27:12,000
Speaker 2: They start small, but then pretty soon you realize you're

523
00:27:12,040 --> 00:27:14,200
Speaker 2: asking a chat but whether or not you should stay

524
00:27:14,200 --> 00:27:16,360
Speaker 2: in your relationship even though your partner didn't show enough

525
00:27:16,440 --> 00:27:18,960
Speaker 2: enthusiasm when you started talking about a fake bird you saw.

526
00:27:20,840 --> 00:27:22,480
Speaker 1: So what happened next with the Seattle.

527
00:27:22,200 --> 00:27:25,520
Speaker 3: Rain and then I put in what formation should you

528
00:27:25,640 --> 00:27:29,919
Speaker 3: play to be NWSL teams? And it spurred out every

529
00:27:29,960 --> 00:27:32,560
Speaker 3: team in the league on what formation you should play?

530
00:27:32,640 --> 00:27:34,280
Speaker 3: And for two teams I'm not going to say who

531
00:27:34,280 --> 00:27:36,560
Speaker 3: they are, they'll know the two teams, it went you

532
00:27:36,600 --> 00:27:37,440
Speaker 3: should play back five.

533
00:27:37,560 --> 00:27:38,679
Speaker 4: So I did you know?

534
00:27:38,760 --> 00:27:42,000
Speaker 1: It's interesting because if all soccer teams are playing against

535
00:27:42,040 --> 00:27:44,320
Speaker 1: each other are using AI tactics, and it's like.

536
00:27:44,359 --> 00:27:47,800
Speaker 2: It's like saying all coaches have the same knowledge base,

537
00:27:47,880 --> 00:27:48,360
Speaker 2: but it's like.

538
00:27:48,960 --> 00:27:52,920
Speaker 1: On acid exactly, except Laura Harvey and the rest of

539
00:27:52,920 --> 00:27:57,000
Speaker 1: the coaching staff, like Talisia, did a deep dive into

540
00:27:57,000 --> 00:28:00,960
Speaker 1: whether or not the defensive tactic recommended by would actually work.

541
00:28:01,400 --> 00:28:04,200
Speaker 1: So again, good to get the prompts or good to

542
00:28:04,200 --> 00:28:06,199
Speaker 1: get the output, and then good to use it for

543
00:28:06,240 --> 00:28:08,320
Speaker 1: your purposes. Yep, smartly.

544
00:28:08,440 --> 00:28:09,879
Speaker 2: So was this a winning strategy?

545
00:28:10,119 --> 00:28:12,600
Speaker 1: Well, Laura didn't say which teams are they played against

546
00:28:12,720 --> 00:28:16,119
Speaker 1: using chats tactics, but Seattle Rain have played with a

547
00:28:16,160 --> 00:28:19,280
Speaker 1: back five at multiple points this season and it seems

548
00:28:19,320 --> 00:28:21,800
Speaker 1: to be paying off. In twenty twenty four, they rank

549
00:28:21,960 --> 00:28:25,399
Speaker 1: second bottom in the league, and this year they're number

550
00:28:25,440 --> 00:28:28,440
Speaker 1: five and heading into the twenty twenty five quarter finals.

551
00:28:28,080 --> 00:28:30,440
Speaker 2: A win for chat GPT. I'm going to be looking

552
00:28:30,520 --> 00:28:33,000
Speaker 2: out for that back five. I'm also going to be

553
00:28:33,000 --> 00:28:35,080
Speaker 2: looking out for submissions for chatting me. We want to

554
00:28:35,119 --> 00:28:38,800
Speaker 2: hear how you were using chatbots to win championships. Email

555
00:28:38,880 --> 00:29:04,880
Speaker 2: us at tex Stuff podcast at gmail dot com. That's

556
00:29:04,920 --> 00:29:07,440
Speaker 2: it for this week for Tech Stuff I'm Kara Price.

557
00:29:07,320 --> 00:29:09,920
Speaker 1: And I'm os Valoshin. This episode was produced by Elisa

558
00:29:09,960 --> 00:29:13,720
Speaker 1: Dennis Tyler Hill and Melissa Slaughter. It was executive produced

559
00:29:13,720 --> 00:29:17,000
Speaker 1: by me Kara Price, Julian Nutter, and Kate Osborne for

560
00:29:17,080 --> 00:29:21,640
Speaker 1: Kaleidoscope and Katria Norvel for iHeart Podcast. The engineer is

561
00:29:21,680 --> 00:29:25,240
Speaker 1: Beheth Fraser and Jack Insley makes this episode. Kyle Murdoch

562
00:29:25,280 --> 00:29:26,160
Speaker 1: wrote up be himself.

563
00:29:26,360 --> 00:29:28,920
Speaker 2: Join us next Wednesday for a deep dive into Nvidia,

564
00:29:29,280 --> 00:29:33,000
Speaker 2: the first publicly traded company to top five trillion dollars

565
00:29:33,040 --> 00:29:34,480
Speaker 2: in market value.

566
00:29:34,080 --> 00:29:36,560
Speaker 1: And please do rate, review and reach out to us

567
00:29:36,600 --> 00:29:39,400
Speaker 1: at tech Stuff Podcast at gmail dot com. We love

568
00:29:39,400 --> 00:29:39,920
Speaker 1: hearing from you