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Speaker 1: Welcome to Creature, future production of iHeartRadio. I'm your host

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Speaker 1: of Many Parasites, Katie Golden. I studied psychology and evolutionary biology,

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Speaker 1: and today on this show, the robots are taking over.

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Speaker 1: Robots are coming for these animals, from robotic dinosaurs to

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Speaker 1: terrifying robotic beads. Discover this and more as we answer

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Speaker 1: the age old question, can you really just turn sheep

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Speaker 1: robots these days? Is that what we're doing? Are we

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Speaker 1: turning birds to robots? Are you a robot? Are you

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Speaker 1: a robot? Right now? Joining me today to discuss the

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Speaker 1: singularity is friend of the show, My Poisonal Friend, director, writer,

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Speaker 1: podcast host for Small Beans and the One Upmanship podcast,

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Speaker 1: Adam Ganzer.

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Speaker 2: Welcome, Oh love love being here, always a pleasure. Thank

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Speaker 2: you for inviting me back. All of your questions are true?

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Speaker 2: Those are the questions.

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Speaker 1: Yeah, a lot of questions we need to ask. Are

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Speaker 1: you a robot?

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Speaker 3: It's unconfirmed. Every time I check the box, though, I

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Speaker 3: feel better about.

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Speaker 1: Can you do the capture? Are you good?

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Speaker 2: You can do? You can do a capture?

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Speaker 1: Okay, well that's good. If you come upon a tortoise

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Speaker 1: that's on its back, what do you.

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Speaker 2: Do eat it right, that's the right.

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Speaker 1: Yeah, sounds human to me.

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Speaker 2: Check that box for human.

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Speaker 1: Check for humans. So yeah, we're talking about actual robots.

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Speaker 1: Like it's not a metaphor just these are robots that

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Speaker 1: people have built to research animals or to do weird

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Speaker 1: things to animals. That sounded more menacing than it actually is,

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Speaker 1: But we'll see anyways, or doesn't I am I feel

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Speaker 1: like I don't know with all this like AI and stuff. Yeah,

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Speaker 1: like this this new AI sort of thing just dropped

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Speaker 1: today where it's like AI videos, It's like it's both

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Speaker 1: interesting and menacing. I don't know, Like I feel like

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Speaker 1: you would have a unique perspective on this because you

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Speaker 1: are a filmmaker as well, Like you're a director and

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Speaker 1: you understand sort of the artistry that goes into shooting

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Speaker 1: video and making film and so to have just a

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Speaker 1: robot come in and be like bab boop, here's your

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Speaker 1: movie seems I don't know, it seems like it's it's well,

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Speaker 1: what do you think, Adam.

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Speaker 3: Like full of Despair is the you're looking for? Deeply

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Speaker 3: dark and disturbing.

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Speaker 2: No, So, like I guess on some.

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Speaker 3: Level, ultimately it's good for filmmakers, especially people who are

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Speaker 3: not entrenched in the way things have been, right, Like,

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Speaker 3: the younger you are at filmmaking this, the better this

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Speaker 3: will be for you. You because ultimately, AI is still

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Speaker 3: just a tool, you know what I mean? Like like

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Speaker 3: AI is such a misnomer for what this is. It's

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Speaker 3: just an extremely sophisticated tool that can do almost all

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Speaker 3: the legwork of creating an image for you. You still

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Speaker 3: have to shape it to make it mean something, you know.

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Speaker 3: I mean, filmmaking is still the conveyance of human emotion

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Speaker 3: and meaning. AI can't do that. They can create images

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Speaker 3: that are super well resourced and you know, rendered, but

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Speaker 3: not meaningful unless you made it meaningful with the parameters.

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Speaker 3: That's the difference that I don't see AI jumping over

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Speaker 3: that chasm. That's a you know, yeah, I don't believe

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Speaker 3: that that is that they are conscious or it will

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Speaker 3: become conscious. I do not believe in that.

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Speaker 1: Yeah, I mean, robots would have to learn how to

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Speaker 1: love first, and they have not yet.

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Speaker 2: Good luck. I barely know.

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Speaker 1: No, I agree. I think that with any new technology

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Speaker 1: there's this initial like I guess because it's so novel

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Speaker 1: and exciting, people use it in sort of cheesy ways.

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Speaker 1: And then I think like later the artists like learn

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Speaker 1: how to use it in a way that is actually art, right,

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Speaker 1: like having an AI like poop out an image for

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Speaker 1: your news station that doesn't really mean it, Like that's

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Speaker 1: not art, that's vile. Well, virule can be art. But

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Speaker 1: you know what I'm saying, Like, it's.

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Speaker 2: The same misunderstanding.

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Speaker 3: It's the idea that the image is what makes it art,

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Speaker 3: and right, we've already done enough art to know that

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Speaker 3: isn't true. It's you know, like it's the same reason

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Speaker 3: why I think AI is such a misnomer for what

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Speaker 3: this is. It's the reduction of consciousness to being able

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Speaker 3: to do all these complex processes.

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Speaker 2: That's all that it is.

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Speaker 1: It's an automation of process rather than like intelligence. I

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Speaker 1: think that people when they think about art, right, like,

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Speaker 1: there's sort of this idea that being really good at

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Speaker 1: doing high fidelity stuff like making a painting that copies reality,

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Speaker 1: do it like or in film, like having film that

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Speaker 1: looks real, right, like like real life or sound design.

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Speaker 1: And there's also like kind of hyper like like realism

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Speaker 1: that becomes trophy where it's like we got to make

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Speaker 1: the film really dark so it looks realistic, right, and

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Speaker 1: the coloring, the tone and the shading and the lighting

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Speaker 1: has to look real.

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Speaker 2: And gritty, realistic.

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Speaker 3: Is I'm putting air quotes up for that because that's

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Speaker 3: you know, there's no that's no such thing.

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Speaker 1: But you need a sound. We need a sound for

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Speaker 1: air quotes.

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Speaker 3: Air quotes, Yeah, like an air dog. It's funny that

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Speaker 3: you say that, because like everything you're saying is exactly

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Speaker 3: what most of art in the twentieth century is rebelling against, right.

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Speaker 1: Exactly, like the like the postmodern yes.

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Speaker 3: Well, and also you know the modernist stuff like Blo

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Speaker 3: Picasso's work, yes, or Marcel Duchamp's work, right, yeah, or

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Speaker 3: even Impressionists like Claude Monett. Those are guys who were

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Speaker 3: rejecting the idea that that hyper fidelity to reality is

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Speaker 3: the most artistic thing, right, you.

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Speaker 1: Know, there's nothing wrong with it, Like like if you

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Speaker 1: do a hyper if you're a really good painter and

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Speaker 1: you can do a hyper realistic painting, that's a that

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Speaker 1: is a skill. That's an amazing talent, and I'm jealous

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Speaker 1: of it, which is why I'm talking it down. No, yeah,

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Speaker 1: I mean I think no, I think it's amazing and

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Speaker 1: I but I think that like when you it is

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Speaker 1: not just being able to recreate something realistically. While it

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Speaker 1: takes a tremendous amount of skill. I think that if

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Speaker 1: you're if you don't necessarily have like the technical skill

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Speaker 1: to like recreate say like a perfect human hand, you

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Speaker 1: may still have the creativity to make art, right Like

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Speaker 1: you don't having a high fidelity thing doesn't isn't necessary

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Speaker 1: for it to be art like, and you can learn

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Speaker 1: you can learn technical skills, but you can also learn creativity.

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Speaker 1: Like I don't think creativity is just something you're born with.

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Speaker 1: Some people you can develop, yes, yeah, you can train it.

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Speaker 1: You can learn creativity, yes, and so yeah, like these

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Speaker 1: these tools, right, like so called AI art, It's like, well,

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Speaker 1: so it's a tool so called it can be used

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Speaker 1: I think creatively. I haven't seen it yet. I'm gonna

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Speaker 1: be honest with you. I've not seen someone in the hit.

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Speaker 1: I have not seen AI in the hands of someone

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Speaker 1: truly using it to its full artistic potential.

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Speaker 2: But I would like to.

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Speaker 3: I so, like I'm we're looking for Marcel to shomp here, right,

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Speaker 3: Like that's that's that's the kind of figure we're looking for.

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Speaker 3: It's a person who has a brain for what it

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Speaker 3: is about AI that can be done artistically and haven't

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Speaker 3: we haven't seen. I wish I was that guy, because man,

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Speaker 3: I'd be changing the universe right now.

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Speaker 2: But I'm not. I'm not yet that guy.

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Speaker 3: Uh yeah, I mean I Ultimately a lot of the

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Speaker 3: conversation around AI still kind of feels like it's based

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Speaker 3: on movie tropes and the human need to control everything

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Speaker 3: that you know, and AI is clearly not a thing

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Speaker 3: that's gonna be controlled in the same way that a

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Speaker 3: calculator will.

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Speaker 2: You know, it's it's.

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Speaker 3: Gonna run, it's gonna be, it's gonna have consequences that

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Speaker 3: are not you can't stop them.

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Speaker 4: Right.

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Speaker 2: We're not gonna be able to fence in all the

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Speaker 2: things AI will do.

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Speaker 3: And that's scary, but it not scary like the things

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Speaker 3: we're actually afraid of, like it'll take over and replace your.

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Speaker 1: Gap, right, not not that it's it's kind of a

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Speaker 1: Jurassic Park situation where once you make a dinosaur, you

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Speaker 1: can't uncork that dinosaur bottle.

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Speaker 3: It's Pandora's Park's.

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Speaker 1: Park exactly, Pandora's Box of velociraptors. Speaking of dinosaurs and

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Speaker 1: oh AI we're talking about a robot dinosaur. This actually

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Speaker 1: has nothing to do with ai ai is so missus

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Speaker 1: I was wondering, this actually has to do with a

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Speaker 1: just a mechanical robot dinosaur used to try to answer

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Speaker 1: one of the questions of bird evolution. So one of

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Speaker 1: the questions behind bird evolution is like we know that

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Speaker 1: dinosaurs developed feathers and wings potentially before they were used

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Speaker 1: for flight, and so why why did they have wings?

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Speaker 1: Why did they have feathers? Like what purposes do they have?

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Speaker 1: We have a lot of theories, these aren't mutually exclusive theories.

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Speaker 1: So like one of them is that hollow bone structure, right,

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Speaker 1: like with with the early birds mate have had nothing

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Speaker 1: to do with flight, but with thermal regulation or being

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Speaker 1: able to like run and thermal regulate, like feathers could

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Speaker 1: have been for a warmth. There could have been a

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Speaker 1: lot of different things for sexual signaling.

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Speaker 2: And so the first thing that's the first thing to.

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Speaker 1: Look hot, Yeah, exactly exactly. You might think humans are

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Speaker 1: uniquely but a lot of animals just have a feature

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Speaker 1: just to look hot, that's right. Another theory is that

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Speaker 1: wings could have been used to flush out prey, so

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Speaker 1: used in hunting so like you, basically there's a bunch

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Speaker 1: of insects or small prey adding items hiding in a bush.

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Speaker 1: How do you get them out of that bush? Well,

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Speaker 1: if you flap your wings right and it scares them

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Speaker 1: and they start and they run out of the bush.

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Speaker 1: Then you grab them as they're fleeing. And yeah, and

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Speaker 1: we know this is an actual behavior in modern dinosaurs,

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Speaker 1: which are birds. So birds are the modern form of dinosaurs.

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Speaker 1: They're the only like survivors of sort of the dinosaur times,

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Speaker 1: and which you can tell I'm really good with the

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Speaker 1: timeline when I'm calling it the dinosaur times.

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Speaker 2: The time of the dinosaur.

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Speaker 1: I don't know my Jurassic for my Cretaceous. But anyways,

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Speaker 1: so the these birds do in fact use this flushing technique.

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Speaker 1: So anything from like the Greater Run Greater road runner

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Speaker 1: in California will do this to flesh out its prey.

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Speaker 1: Uh to the Australian willy wagtail, which just sounds made up.

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Speaker 1: Doesn't sound like a real bird, but.

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Speaker 2: It is cartoon creature.

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Speaker 1: Okay, it's like that's like, what's his name, Woody Woodpecker's

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Speaker 1: rival willy wagtail.

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Speaker 3: I didn't even know that was a thing. Now I

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Speaker 3: have a Saturday. You just gave me a Saturday finding

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Speaker 3: all that.

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Speaker 1: Seeing it woodpecker you haven't heard of.

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Speaker 2: I know what he would pecker? Did he really have

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Speaker 2: a rival that the same?

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Speaker 1: It's just that there is a real It's just that

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Speaker 1: there's a real bird called Willy wagtail, and I'm imagining

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Speaker 1: in the Greater Woody wood Pecker cinematic universe that he

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Speaker 1: has a rival named Willy Wagtail.

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Speaker 2: God, I want to see that universe. Boy, get it.

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Speaker 1: We can make it with AI. Can't wait. It'll look great.

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Speaker 1: It will be uncanny at all type in uh uh

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Speaker 1: boody woodpecker fighting williwagtail universe.

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Speaker 2: Verse universe.

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Speaker 1: See what it does? Okay? So yeah, and so yes,

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Speaker 1: we know this behavior exists in modern birds. So could

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Speaker 1: it have existed in dinosaurs? Well, a team of engineers

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Speaker 1: and biological scientists were like, this is a great excuse

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Speaker 1: to build a robot, and so in South Korea they

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Speaker 1: did just that.

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Speaker 2: Sure, why why do we need a robot for that? Still?

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Speaker 1: Because number one, it's why not number one cool? Why

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Speaker 1: are you even asking that we're building a dinosaur robot?

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Speaker 1: But number two, to see if they can recreate the

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Speaker 1: anatomy of a known dinosaur that was a bird like

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Speaker 1: dinosaur that had wings that didn't fly. And if they

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Speaker 1: could use that robot dinosaur to scare grassofs first.

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Speaker 2: Then they know they were right.

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Speaker 1: Then they'd know that they're right. It like, is this

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Speaker 1: robot scared to grasshoppers?

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Speaker 2: Check?

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Speaker 3: So, by the way I'm looking at it, it's scary

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Speaker 3: to everything. I don't know what they're proven with this.

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Speaker 2: It's insane. It looks like a harbinger of doom. Like

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Speaker 2: I think it's cute, Okay.

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Speaker 1: Got a bit of a gothic waly kind of.

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Speaker 2: It's like dark. It's got like wheels for legs.

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Speaker 1: Yeah, Adam, do you just have buckets of money to

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Speaker 1: create realistic dinosaur bird legs?

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Speaker 3: You're right, a robotic Who am I to question the

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Speaker 3: fidelity of this task. I still don't even think you've

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Speaker 3: given one legit reason to do it.

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Speaker 2: But that's fine, no, So.

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Speaker 1: So listen, the researchers created robot Jesus. This is a

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Speaker 1: hard one robot to rix robopterrickx, a robot meant to

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Speaker 1: mimic the anatomy of CODYP. Direx, which is a dinosaur

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Speaker 1: that roamed Asia over one hundred and twenty five million

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Speaker 1: years ago during the Early Cretaceous period. It was a flightless,

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Speaker 1: bipedal bird like dinosaur with feathers wings, and a fan

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Speaker 1: like tail. It was about the size of a turkey.

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Speaker 1: So there's this thing now that is a robot version

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Speaker 1: of this, but with wheels. Now, the original CODYP. Direx

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Speaker 1: did not have wheels. It had like ah, so little problem,

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Speaker 1: no little note might change the results of the experiment

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Speaker 1: peer review note why wheels. So they experimented with different

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Speaker 1: wing colorations, sort of wing movements, and if you watch

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Speaker 1: a video of it, it looks pretty cool, man, Like

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Speaker 1: the wing movements.

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Speaker 2: I don't know, sure, yes, yeah, yeah, I'd buy it.

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Speaker 3: They're also very funny, like no robot has ever yet

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Speaker 3: escaped being funny from my opinion, like from my vantage point, honestly,

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Speaker 3: like like, you know, I'm talking about real robots, not

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Speaker 3: the ones that are in movies that are you know,

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Speaker 3: they don't have to really be robots.

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Speaker 2: This one has to really be a robot and it's

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Speaker 2: still funny. Look at it.

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Speaker 3: It's I mean, you can't because it's a podcast, But man,

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Speaker 3: listen to if you're listening to the podcast, just look

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Speaker 3: at it.

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Speaker 1: It'll be in the show notes. I will link to

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Speaker 1: the video in the show notes. I do look at it.

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Speaker 1: There is a New York Times article about it. If

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Speaker 1: you look for robotic wing dinosaur New York Times, you'll

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Speaker 1: find it. But I'll also link to it in the

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Speaker 1: show notes. It's really funny, and so they did indeed

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Speaker 1: find that when the robopter ricks. Ugh, that's a word

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Speaker 1: that doesn't come out of the mouth.

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Speaker 2: Good fix it, scientists, come on, man.

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Speaker 4: It.

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Speaker 1: Yes, when the grasshoppers are faced with this robot dinosaur

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Speaker 1: and it flaps its wings, the grasshoppers get scared and

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Speaker 1: hop slash fly away. So interestingly, this was only tested

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Speaker 1: on like thirty grasshoppers. I don't know why they didn't

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Speaker 1: just make it like one hundred grasshoppers. Uh, increase those

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Speaker 1: that significance s fellas, But like I guess it's hard

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Speaker 1: to find grasshoppers to sign up for an experiment where

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Speaker 1: they get scared by a robot dinosaur. I don't know,

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Speaker 1: do it on more grasshoppers, is what I'm saying.

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Speaker 3: Yeah, literally, everything about this sounds like a scam. Like,

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Speaker 3: nothing about this it sounds like any real science was done.

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Speaker 3: It really sounds like a dude wanted grant money to

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Speaker 3: build an awesome dinosaur.

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Speaker 2: I get some bolt. None of this is real.

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Speaker 1: I think that wanting to build a cool robot is

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Speaker 1: a huge part of this. And yeah, like I think

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Speaker 1: they knew that if they're, like, we built a robot dinosaur,

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Speaker 1: they would get a ton of press attention, they would

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Speaker 1: get the funding. Sure, this is this is like, this

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Speaker 1: is a honey pot for UH funders, like like looking

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Speaker 1: at like T cell receptors in my boring ooh robot dinosaur.

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Speaker 3: Hello, Yeah, it's definitely a sexy idea for a project.

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Speaker 2: Like and I'm and I'm not impugning you at all.

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Speaker 3: It just doesn't sound very scientific to me, man, it

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Speaker 3: really doesn't.

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Speaker 1: Well, look, I don't bring up these studies to say, look,

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Speaker 1: how good and perfect to this study is. I bring

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Speaker 1: it up because it is really funny. So I think

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Speaker 1: that skepticism. I think that skepticism is healthy and all.

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Speaker 1: All I mean honestly, like, and we'll talk more about

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Speaker 1: these like because I'm I'm not like I have not

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Speaker 1: fully embraced the UH research robustness of this robot dinosaur.

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Speaker 1: I think it's very cool. Like I think it's awesome

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Speaker 1: that they don't just think it's a door. It's really cool.

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Speaker 1: I do not believe this is conclusive proof that birdling

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Speaker 1: dinosaurs use this flapping technique to flush out prey. I

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Speaker 1: think it shows that it's possible.

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Speaker 2: Sure, maybe they did.

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Speaker 3: I feel like the existence of birds doing it proves

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Speaker 3: it's possible, you know what I mean? Like, that's the

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Speaker 3: thing is like we already have enough to I don't

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Speaker 3: think we've advanced any information from this robot doing it.

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Speaker 2: It's you know, a.

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Speaker 1: Little superfluous, doesn't it.

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Speaker 3: Yes, if a bird does it and they and they

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Speaker 3: use wings and their theory is what if it was

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Speaker 3: just like birds do it? Then you build a thing

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Speaker 3: that does it kind of and tests it on like

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Speaker 3: six grasshoppers.

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Speaker 2: What have you proved?

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Speaker 1: Man? It is thirty grasshoppers to thirty fair give me.

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Speaker 1: We will discuss another. We will discuss another one where

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Speaker 1: it's like six bees that.

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Speaker 2: This is.

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Speaker 3: I am in love with this topic. Wow, you've really

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Speaker 3: done it today. Okay, this is really so.

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Speaker 1: This is But like the reason I bring up like

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Speaker 1: sample size and stuff is like whenever you read a

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Speaker 1: cool article about a study, they usually do link you

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Speaker 1: to the actual research paper and there's jump on down

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Speaker 1: to the method section because that is always wild. Like, Okay,

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Speaker 1: I I you don't necessarily have to like have be

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Speaker 1: able to understand statistical methodology or anything too complex. But

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Speaker 1: sometimes when you go to the method section in a

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Speaker 1: research paper, you will find some interesting buried bodies in

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Speaker 1: there that are funny or sometimes like the research can

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Speaker 1: be really good and it's just really funny. Like I

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Speaker 1: once read a paper where like the in the this

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Speaker 1: was not mentioned right like in like the article about it,

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Speaker 1: but it was like they did. They just created like

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Speaker 1: a bunch of different images of like human and primate butts,

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Speaker 1: and that's just it's wonderful.

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Speaker 2: It's wonderful what people that's hel for all we can

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Speaker 2: all use.

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Speaker 1: And like, So basically what I'm saying is, I would

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Speaker 1: I think that there's interesting applications of this robot. I

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Speaker 1: certainly prefer this to like using robots for like police dogs,

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Speaker 1: you know what I mean. Oh yeah, I think this

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Speaker 1: is much cooler, much more pro social than using robots

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Speaker 1: to like spy on us.

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Speaker 3: I would love to see all the packages that are

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Speaker 3: being delivered by robots done by this thing. You know

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Speaker 3: its packages here, you know like here it is.

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Speaker 2: That's pretty great.

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Speaker 1: I have delivered your la CROs.

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Speaker 2: That it bows to you, It takes off, that's all

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Speaker 2: you want.

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Speaker 1: Away away, rolls away on it slowly. Yeah, so you

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Speaker 1: know phase one of the conductorists. I would say, it's

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Speaker 1: not there yet.

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Speaker 2: It's not there yet. Question.

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Speaker 1: But I think with more funding we could make a

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Speaker 1: real uh interesting thing here. Let's keep let's keep spending

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Speaker 1: money on it.

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Speaker 3: This is not a finished product. Who knows the limitations

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Speaker 3: for this? The sky's the limit? How far can these

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Speaker 3: wings store?

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Speaker 2: We all agree.

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Speaker 3: I just want to add, based on a light dusting

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Speaker 3: of googling, I do think this robot operated on AI.

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Speaker 2: Oh really interesting?

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Speaker 3: Yes, it's I mean I can't quite get to the article,

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Speaker 3: but like the headline here, robot robotix, an AI based

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Speaker 3: system is demonstrating. Yeah, me too, but maybe that's maybe

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Speaker 3: that justifies what this robots doing, right.

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Speaker 1: I think it may be operate like it might not

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Speaker 1: so much be AI but on wing physics modeling that

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Speaker 1: is used to me, which is an AI. But I

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Speaker 1: mean what is AI?

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Speaker 3: You know we could spend another ten on that easy.

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Speaker 3: We can spend all day on it, uh and have

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Speaker 3: contributed literally nothing to the conversation.

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Speaker 1: Right now. I think I think it. I think I

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Speaker 1: think what it used was like models of bird wing

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Speaker 1: flapping physics slash behavior to guide the bird wing motion. Sure,

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Speaker 1: but sure, I don't I don't know if I would

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Speaker 1: call that AI.

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Speaker 2: I don't know.

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Speaker 3: I mean, it's definitely not some dude with a crank

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Speaker 3: on the backs doing this crank in the crank of

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Speaker 3: the winds flap.

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Speaker 2: Yeah, I get it. It's got to be a little bit.

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Speaker 5: Uh.

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Speaker 1: Yeah, I think that like using sort of real The

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Speaker 1: thing that's exciting about this to me is that recreating

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Speaker 1: an extinct animal and then testing some of its behaviors

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Speaker 1: in real world situations, Uh, is really cool. That's cool.

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Speaker 1: I think that is that could teach us things about

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Speaker 1: extinct animals. Uh. And it could also it's also like

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Speaker 1: even animals that are not extinct, but figuring out out

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Speaker 1: how to create sort of like robot versions of animals

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Speaker 1: to do sort of like certain tests on can could

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Speaker 1: be really interesting.

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Speaker 2: Now we're going to talk talk about some.

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Speaker 1: More of those. First, we're going to take a quick

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Speaker 1: break while I ask Adam some questions that only the

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Speaker 1: real human atom would know.

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Speaker 3: M don't get Yeah, look out, turtles, I'm hungry.

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Speaker 1: So, Adam, what sounds more refreshing to you? A glass

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Speaker 1: of water or a glass of motor oil for robots?

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Speaker 2: I know the correct answer. Yeah, it's water, right.

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Speaker 1: Yes, human human.

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Speaker 2: Checkbot check them.

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Speaker 1: So we're going to talk about robot bees. This study

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Speaker 1: is so funny, and look, I I love it and

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Speaker 1: I think it's very When I make critiques of this study,

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Speaker 1: I'm not saying the researchers are bad or that we

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Speaker 1: should dismiss it, but I even they acknowledge that this

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Speaker 1: is like kind of a prototype, and so keep that

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Speaker 1: in keep that in mind.

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Speaker 2: I think all robot animals can be called a prototype

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Speaker 2: at this point.

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Speaker 1: Have you seen the Have you seen that that Spy

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Speaker 1: in the Wild Nature show? Like it's okay, so it's

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Speaker 1: it's incredible. We're not really talking about that kind of

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Speaker 1: robot today, but like it's where they created sort of

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Speaker 1: realistic ish looking robots to put amongst the animals, and

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Speaker 1: then they put a camera in the robot to like

439
00:24:57,240 --> 00:25:00,119
Speaker 1: capture the animals and the here's what's funny about that

440
00:25:00,200 --> 00:25:04,200
Speaker 1: show is that the premise is that, look, we can

441
00:25:04,240 --> 00:25:07,480
Speaker 1: get a camera inside these robots and observe behavior that

442
00:25:07,640 --> 00:25:10,800
Speaker 1: we wouldn't be able to observe otherwise because you're infiltrating, right,

443
00:25:10,880 --> 00:25:14,760
Speaker 1: Like you have a robot Langer or a robot wolf

444
00:25:14,880 --> 00:25:17,760
Speaker 1: or whatever, or a robot fish, and so we can

445
00:25:17,840 --> 00:25:20,439
Speaker 1: observe these behaviors that otherwise the animals would be too

446
00:25:20,440 --> 00:25:22,439
Speaker 1: shy to do in front of the human And I

447
00:25:22,520 --> 00:25:25,440
Speaker 1: swear like eighty percent to ninety percent of the footage

448
00:25:25,440 --> 00:25:29,560
Speaker 1: in that show is by another camera, so like a

449
00:25:29,640 --> 00:25:33,760
Speaker 1: camera taking video of like the robot camera and the

450
00:25:33,800 --> 00:25:36,040
Speaker 1: animals interacting with the robots. It's like, is it really

451
00:25:36,040 --> 00:25:39,439
Speaker 1: about the camera? I mean there's there's some footage from

452
00:25:39,560 --> 00:25:42,719
Speaker 1: like the robot. Yes, they do include that. It's usually

453
00:25:42,760 --> 00:25:44,280
Speaker 1: not as good as the human.

454
00:25:44,119 --> 00:25:47,560
Speaker 3: Pot well, right, because you don't want to be a

455
00:25:47,600 --> 00:25:49,600
Speaker 3: pack animal in the pack.

456
00:25:49,760 --> 00:25:51,080
Speaker 2: That's not fun to watch.

457
00:25:51,280 --> 00:25:54,560
Speaker 3: You want to see what the pack does from the outside, which, yes,

458
00:25:54,720 --> 00:25:55,640
Speaker 3: that's just a pure.

459
00:25:55,440 --> 00:25:58,720
Speaker 4: Filmmaking thing, right, Yeah.

460
00:25:58,320 --> 00:26:00,040
Speaker 3: I gotta say, just I want to I think I

461
00:26:00,040 --> 00:26:03,080
Speaker 3: want to plant my flag on this point, like just

462
00:26:03,160 --> 00:26:06,280
Speaker 3: it's a philosophical thing. I am in favor of scientists

463
00:26:06,320 --> 00:26:07,440
Speaker 3: doing awesome things.

464
00:26:07,200 --> 00:26:08,840
Speaker 2: For its own sake. Yeah.

465
00:26:08,840 --> 00:26:11,600
Speaker 3: I mean, like like I'm not sure how much scientific

466
00:26:11,640 --> 00:26:14,240
Speaker 3: value there was on landing on the Moon, but hey man,

467
00:26:14,359 --> 00:26:15,040
Speaker 3: we did it, you know.

468
00:26:15,119 --> 00:26:18,160
Speaker 1: I mean that's cool, right, I think, yes, I think

469
00:26:18,200 --> 00:26:22,160
Speaker 1: there's a certain amount of utility in cool science things. Yeah,

470
00:26:22,359 --> 00:26:26,360
Speaker 1: inspire people, And it's almost it's like art in a way, right,

471
00:26:26,400 --> 00:26:29,280
Speaker 1: where it's like, what's the point of doing art? Like

472
00:26:29,320 --> 00:26:33,800
Speaker 1: there's no necessarily purely practical thing. It's just enriching. And

473
00:26:33,880 --> 00:26:37,439
Speaker 1: so sometimes just like why do we need to figure

474
00:26:37,480 --> 00:26:40,960
Speaker 1: out be communication using a robot? We don't really need to,

475
00:26:41,400 --> 00:26:42,159
Speaker 1: but it's cool.

476
00:26:42,560 --> 00:26:45,119
Speaker 2: Yeah, but look at that, you know.

477
00:26:45,240 --> 00:26:49,520
Speaker 3: Yeah, so yeah.

478
00:26:48,119 --> 00:26:50,240
Speaker 1: So yeah, we're talking about we're talking about robot bees.

479
00:26:51,320 --> 00:26:55,480
Speaker 1: There is some research that uses robot bees to infiltrate

480
00:26:55,560 --> 00:26:59,960
Speaker 1: a hive to learn the art of be dance. Now, Adam,

481
00:27:00,160 --> 00:27:03,160
Speaker 1: do you know sort of the general just about bee dancing.

482
00:27:04,040 --> 00:27:07,480
Speaker 3: Yeah, as I understand it, it is part of their

483
00:27:07,520 --> 00:27:11,439
Speaker 3: bee mating ritual, Like they go to bee clubs, you know,

484
00:27:11,680 --> 00:27:14,919
Speaker 3: after they at hard b nine to five jobs and

485
00:27:15,080 --> 00:27:15,959
Speaker 3: cut loose a little bit?

486
00:27:16,240 --> 00:27:18,680
Speaker 2: Am I right about this? No?

487
00:27:18,680 --> 00:27:21,600
Speaker 1: No, no, sadly no, I mean you would be right,

488
00:27:21,800 --> 00:27:23,520
Speaker 1: that kind of guess would be right for like so

489
00:27:23,600 --> 00:27:25,840
Speaker 1: many animals when you're like, why does this animal dance

490
00:27:25,880 --> 00:27:30,480
Speaker 1: but not bees? Bees dance mainly to locate food. So

491
00:27:30,720 --> 00:27:33,600
Speaker 1: a bee will So most of these are sisters, right,

492
00:27:33,640 --> 00:27:36,120
Speaker 1: These are bees and a hive, and they're mostly worker bees,

493
00:27:36,160 --> 00:27:39,720
Speaker 1: and so they're mostly sisters, and so they will wiggle

494
00:27:40,040 --> 00:27:46,600
Speaker 1: they're abdomen to indicate where a food source is, basically

495
00:27:46,600 --> 00:27:48,960
Speaker 1: where there are some flowers or nectar that can be found,

496
00:27:49,520 --> 00:27:51,840
Speaker 1: and the other bees watch them and then they go

497
00:27:52,080 --> 00:27:54,600
Speaker 1: and they find this food source. So this is like

498
00:27:54,640 --> 00:27:57,040
Speaker 1: an established thing. This is not what they're researching, but

499
00:27:57,119 --> 00:28:01,679
Speaker 1: that bees use the language of dance to communicate where

500
00:28:02,240 --> 00:28:03,000
Speaker 1: uh there.

501
00:28:02,880 --> 00:28:05,240
Speaker 2: Is food, as do I?

502
00:28:07,080 --> 00:28:10,560
Speaker 1: Just like you dirate in the direction of the McDonald.

503
00:28:10,080 --> 00:28:13,600
Speaker 3: Yes, this way you can't see it, but it was

504
00:28:13,760 --> 00:28:14,760
Speaker 3: glorious what I did.

505
00:28:15,280 --> 00:28:18,320
Speaker 1: Yeah, it's it's the they Basically, these bees are like

506
00:28:18,359 --> 00:28:21,320
Speaker 1: the sign spinners. I have not seen a spot sign

507
00:28:21,400 --> 00:28:24,080
Speaker 1: spinner in a hot minute. Is that not a job anymore?

508
00:28:24,800 --> 00:28:27,000
Speaker 3: I think they're gone. I mean I think there's no

509
00:28:27,119 --> 00:28:30,800
Speaker 3: stores mans COVID destroyed stores.

510
00:28:30,880 --> 00:28:32,800
Speaker 2: What do I need to Yeah?

511
00:28:33,160 --> 00:28:36,240
Speaker 1: Yeah, that's why, Like people are asking demeaning things of

512
00:28:36,240 --> 00:28:38,600
Speaker 1: their Amazon delivery people like, could you do a cool

513
00:28:38,680 --> 00:28:42,800
Speaker 1: dance for me? I have to pee in a water bottle.

514
00:28:42,840 --> 00:28:47,280
Speaker 1: But okay, so I already did one for Jeff this morning,

515
00:28:50,160 --> 00:28:56,680
Speaker 1: so yes, so uh, essentially yes, bees waggle their bodies

516
00:28:56,760 --> 00:28:59,800
Speaker 1: in a dance to communicate. So there is of course

517
00:28:59,800 --> 00:29:03,640
Speaker 1: in in decoding this language of dance. But how do

518
00:29:03,720 --> 00:29:08,720
Speaker 1: you decode B dance and have a B co conspirator

519
00:29:08,760 --> 00:29:12,440
Speaker 1: who does just certain types of dances and sort of

520
00:29:12,520 --> 00:29:15,200
Speaker 1: a in an experimental setting. Well, you can't just like,

521
00:29:15,560 --> 00:29:19,640
Speaker 1: you know, kind of convince a bee to do your research.

522
00:29:19,720 --> 00:29:25,160
Speaker 1: So you need a robot b a highly sophisticated, extremely detailed,

523
00:29:25,520 --> 00:29:32,360
Speaker 1: cleverly disguised robot. So adam, here it is.

524
00:29:33,080 --> 00:29:36,360
Speaker 3: Oh my god, I can't even wait. This is a

525
00:29:36,440 --> 00:29:40,840
Speaker 3: thrilling moment. This is whatever this turns out to be,

526
00:29:41,560 --> 00:29:43,520
Speaker 3: where it's taking us is going to be awful.

527
00:29:44,280 --> 00:29:46,959
Speaker 1: You know, I sent it to you in the in

528
00:29:47,000 --> 00:29:47,440
Speaker 1: the chat.

529
00:29:47,800 --> 00:29:51,120
Speaker 2: Oh my god, okay.

530
00:29:52,120 --> 00:29:59,120
Speaker 1: What yeah, So it's not really you know, it's a prototype.

531
00:29:59,160 --> 00:30:01,520
Speaker 1: It is a plas stick like kind of looks like

532
00:30:01,560 --> 00:30:06,880
Speaker 1: a plastic pill with like a piece of square plastic

533
00:30:06,920 --> 00:30:10,640
Speaker 1: on it, which is like the wing attachment and then

534
00:30:10,680 --> 00:30:14,680
Speaker 1: a huge metal rod which is like a mechanical shaft

535
00:30:14,920 --> 00:30:20,239
Speaker 1: that is used to like control the bee. It's not

536
00:30:20,280 --> 00:30:21,120
Speaker 1: really fooling me.

537
00:30:21,480 --> 00:30:25,720
Speaker 3: No, it doesn't do it for me. I'm surprised any

538
00:30:25,760 --> 00:30:26,920
Speaker 3: be's taking it seriously.

539
00:30:27,680 --> 00:30:31,680
Speaker 1: Yeah, it's just like, I mean, it's the equivalent of like,

540
00:30:32,280 --> 00:30:37,040
Speaker 1: I don't know, if you're a robot, sort of painting

541
00:30:37,160 --> 00:30:41,520
Speaker 1: your metal chassis like sort of a beige color and

542
00:30:41,560 --> 00:30:45,800
Speaker 1: then shuffling in to a supermarket and being like, hello,

543
00:30:45,960 --> 00:30:49,840
Speaker 1: I am one of you, and you're just angular.

544
00:30:49,720 --> 00:30:51,640
Speaker 2: And do you have cupons being.

545
00:30:52,520 --> 00:30:57,280
Speaker 1: Covered in wires? I have heard that you trade coupons

546
00:30:57,320 --> 00:31:02,400
Speaker 1: for sustenance. Yeah, so this b maybe not the most

547
00:31:02,440 --> 00:31:06,800
Speaker 1: convincing thing, but yes, this is uh the handiwork of

548
00:31:08,280 --> 00:31:11,600
Speaker 1: robot us his named Tim Tim Langraf at the University

549
00:31:11,640 --> 00:31:18,160
Speaker 1: of Berlin and his team. Before you mock, remember bees

550
00:31:18,200 --> 00:31:22,480
Speaker 1: are dumb. Maybe they'll just sure enough be fooled by

551
00:31:22,520 --> 00:31:27,800
Speaker 1: this chull little fake bee. So yeah, what they did

552
00:31:27,960 --> 00:31:31,640
Speaker 1: was they they did actually kind of an interesting like

553
00:31:32,360 --> 00:31:36,880
Speaker 1: I mean, some would potentially call this AI incorrectly in

554
00:31:36,920 --> 00:31:40,400
Speaker 1: my opinion, it is where they use like a a

555
00:31:40,560 --> 00:31:47,040
Speaker 1: model uh to uh basically like modeled on actual B

556
00:31:47,240 --> 00:31:50,840
Speaker 1: dances and then programmed the robot B with these like

557
00:31:51,160 --> 00:31:55,520
Speaker 1: B dances that were done with sort of a you know,

558
00:31:55,840 --> 00:32:01,560
Speaker 1: like a sort of this this more or less complex

559
00:32:02,680 --> 00:32:07,320
Speaker 1: like learning model, and then it does a dance and

560
00:32:07,360 --> 00:32:11,400
Speaker 1: then sometimes the bees pay attention to it, sometimes they don't.

561
00:32:12,120 --> 00:32:16,200
Speaker 1: About six bees were the ones that ended up paying

562
00:32:16,200 --> 00:32:18,200
Speaker 1: attention to this bee dancing.

563
00:32:18,080 --> 00:32:19,040
Speaker 2: And they were drunk.

564
00:32:21,320 --> 00:32:22,000
Speaker 1: There's this guy.

565
00:32:22,200 --> 00:32:25,960
Speaker 2: Oh it's cool.

566
00:32:26,120 --> 00:32:30,400
Speaker 1: I think I think she's one of us girls. And

567
00:32:30,560 --> 00:32:34,320
Speaker 1: so they then would like if a bee was seen

568
00:32:34,400 --> 00:32:37,400
Speaker 1: to be like paying attention to the robot B and

569
00:32:37,440 --> 00:32:40,520
Speaker 1: like tracking the bee dance. When it flew off, it

570
00:32:40,600 --> 00:32:45,600
Speaker 1: was snatched and a radio antenna was attached.

571
00:32:45,080 --> 00:32:47,520
Speaker 2: To Oh my god, just a.

572
00:32:47,440 --> 00:32:52,280
Speaker 1: Normal day for this bee, Like, right, is that a robot?

573
00:32:52,400 --> 00:32:54,280
Speaker 1: I'm getting out of here. It's like, oh my god,

574
00:32:55,120 --> 00:32:57,600
Speaker 1: get a big antenna on my head now normal times.

575
00:32:57,840 --> 00:33:00,360
Speaker 3: This is the worst roof ever for a bee, you

576
00:33:00,360 --> 00:33:02,640
Speaker 3: know what I mean. They get into this dance, they're like, okay, cool,

577
00:33:02,720 --> 00:33:03,240
Speaker 3: let's go back.

578
00:33:03,160 --> 00:33:04,280
Speaker 2: To your place. Nope.

579
00:33:04,520 --> 00:33:07,560
Speaker 1: Yeah, it's like, oh, you know, take any show where

580
00:33:07,600 --> 00:33:10,760
Speaker 1: like people are replaced by robots. But it's just like

581
00:33:10,800 --> 00:33:13,640
Speaker 1: a huge antenna on your head. It's like, uh, something

582
00:33:13,800 --> 00:33:16,560
Speaker 1: different about you, Susan, I don't know what you are

583
00:33:16,680 --> 00:33:17,480
Speaker 1: talking about.

584
00:33:17,720 --> 00:33:19,760
Speaker 2: No, I remain equivalent to before.

585
00:33:21,040 --> 00:33:23,360
Speaker 1: No, but these bees, these bees aren't turned into robots.

586
00:33:23,400 --> 00:33:26,400
Speaker 1: They're just uh, they're just tracked with these radio antenna.

587
00:33:26,840 --> 00:33:30,440
Speaker 1: And then essentially the idea is that this robot bee

588
00:33:30,480 --> 00:33:33,640
Speaker 1: is trying to communicate to them a new source of food.

589
00:33:34,160 --> 00:33:37,840
Speaker 1: So if they go and they find this new location

590
00:33:38,000 --> 00:33:40,959
Speaker 1: where they previously would not expect to find food, but

591
00:33:41,000 --> 00:33:43,560
Speaker 1: then you know they go after watching this bee dance,

592
00:33:43,640 --> 00:33:46,040
Speaker 1: it would be proof of concept that you can teach

593
00:33:46,080 --> 00:33:49,320
Speaker 1: bees where to find food with a robotic bee dance,

594
00:33:50,000 --> 00:33:53,920
Speaker 1: and you know they seem to find it and maybe

595
00:33:54,000 --> 00:34:00,160
Speaker 1: six bees. So yeah, here's here's the issue.

596
00:34:00,760 --> 00:34:01,840
Speaker 2: Here's the issue.

597
00:34:02,320 --> 00:34:02,480
Speaker 1: Bro.

598
00:34:03,240 --> 00:34:06,239
Speaker 3: If I've seen nothing but issues with this this So

599
00:34:07,040 --> 00:34:08,799
Speaker 3: you have to look at this thing a long time

600
00:34:08,840 --> 00:34:10,799
Speaker 3: to think it looks like a bee, Like, it takes

601
00:34:10,840 --> 00:34:12,560
Speaker 3: you a really long time to figure out is it

602
00:34:12,560 --> 00:34:13,040
Speaker 3: a bee?

603
00:34:13,080 --> 00:34:13,960
Speaker 2: How is it a bee?

604
00:34:14,360 --> 00:34:16,920
Speaker 1: You imagine if your brain was the size of a

605
00:34:16,960 --> 00:34:20,680
Speaker 1: bee's brain though, right, Like, I guess how that would feel.

606
00:34:20,840 --> 00:34:22,200
Speaker 2: I guess I don't know.

607
00:34:23,680 --> 00:34:28,560
Speaker 1: So yeah, no, I mean. One of the issues, of

608
00:34:28,560 --> 00:34:31,280
Speaker 1: course is the very small sample size of six bees.

609
00:34:31,600 --> 00:34:34,200
Speaker 1: Of course, also it doesn't seem like they were able

610
00:34:34,280 --> 00:34:38,680
Speaker 1: to compare that behavior to a really large like control group,

611
00:34:38,719 --> 00:34:41,279
Speaker 1: like they did try to have a control group, but

612
00:34:41,640 --> 00:34:44,319
Speaker 1: that also seemed like to be only a couple of

613
00:34:44,360 --> 00:34:47,359
Speaker 1: bees or a handful of bees, which I feel like

614
00:34:47,520 --> 00:34:52,279
Speaker 1: robust research demands more than a handful of bees. You

615
00:34:52,320 --> 00:34:53,080
Speaker 1: need more bees.

616
00:34:53,360 --> 00:34:57,120
Speaker 2: Yeah, more bees.

617
00:34:58,000 --> 00:35:02,360
Speaker 1: As someone who's not a robotic system or sort of

618
00:35:02,360 --> 00:35:07,560
Speaker 1: a professional in any sense in any field, more bees,

619
00:35:07,800 --> 00:35:11,440
Speaker 1: I would say it's at least eighty percent more bees.

620
00:35:11,760 --> 00:35:15,919
Speaker 1: You're gonna need more bees. Repeat the study, get more

621
00:35:16,000 --> 00:35:20,000
Speaker 1: funding for your cool robot. B make it better because

622
00:35:20,160 --> 00:35:23,719
Speaker 1: and more guys like more more of a bee.

623
00:35:23,440 --> 00:35:25,560
Speaker 2: Make it more of a thank you.

624
00:35:25,800 --> 00:35:29,200
Speaker 1: It's it's not giving be to me now. They they

625
00:35:29,800 --> 00:35:32,920
Speaker 1: they admit to this in their in their paper that like,

626
00:35:33,360 --> 00:35:36,600
Speaker 1: this is a prototype. We think that with higher fidelity,

627
00:35:37,000 --> 00:35:41,439
Speaker 1: more bees might pay attention. So you think fun, Yeah,

628
00:35:41,640 --> 00:35:46,399
Speaker 1: fund this research, get the bee, the robot, be more realistic.

629
00:35:46,760 --> 00:35:50,960
Speaker 1: I want more passion, more movement or energy from this bee,

630
00:35:51,280 --> 00:35:54,839
Speaker 1: and do it on more bees, Like I'm gonna need this.

631
00:35:55,080 --> 00:35:57,839
Speaker 1: I'm gonna need at least a control group of like

632
00:35:58,120 --> 00:36:01,960
Speaker 1: at least fifty bees, test group of at least fifty bees.

633
00:36:04,320 --> 00:36:07,000
Speaker 2: Yeah, yeah, those are minimumsbes, that's that minimum.

634
00:36:07,000 --> 00:36:10,520
Speaker 3: That's minimum in my writer to care about this, right,

635
00:36:10,680 --> 00:36:13,399
Speaker 3: This bee feels like that Steve Bishemi meme where he's

636
00:36:13,400 --> 00:36:15,200
Speaker 3: like dressed like a child at a high school.

637
00:36:15,239 --> 00:36:18,799
Speaker 2: He's like, hello, fellow kids, that's a fellow bees. That's

638
00:36:18,840 --> 00:36:22,120
Speaker 2: what this is. Yes, uh yeah, you ever watched air Bud.

639
00:36:23,880 --> 00:36:26,360
Speaker 1: I know the concept of it that it's a dog

640
00:36:26,440 --> 00:36:30,920
Speaker 1: who joins a basketball team because the rules do not

641
00:36:31,360 --> 00:36:35,800
Speaker 1: apply sipically say, the rules don't specifically say you can't

642
00:36:35,840 --> 00:36:37,200
Speaker 1: be a dog in basketball.

643
00:36:37,360 --> 00:36:40,040
Speaker 2: That's right, they don't. That's correct, they don't.

644
00:36:40,120 --> 00:36:43,600
Speaker 3: So what I my understanding about air Bud is Airbud

645
00:36:43,719 --> 00:36:47,280
Speaker 3: is what you might call conglomeration of fifteen different dogs

646
00:36:47,360 --> 00:36:49,719
Speaker 3: or like twenty different dogs, all of whom are good

647
00:36:49,760 --> 00:36:52,560
Speaker 3: at one thing. Right, This dog's good at sitting. This

648
00:36:52,640 --> 00:36:53,840
Speaker 3: dog is good at high fiving.

649
00:36:54,080 --> 00:36:56,000
Speaker 1: In terms of the film you're talking about the film,

650
00:36:56,320 --> 00:36:58,520
Speaker 1: the movie is not about a hyper dog.

651
00:36:58,640 --> 00:36:59,640
Speaker 2: That's not many dogs.

652
00:36:59,640 --> 00:37:00,600
Speaker 1: Although that would be.

653
00:37:00,520 --> 00:37:02,040
Speaker 2: Cool, that would be better than what they did.

654
00:37:02,080 --> 00:37:04,719
Speaker 1: I get notice of a Clifford where it's like one

655
00:37:05,160 --> 00:37:06,719
Speaker 1: big dog biological dogs.

656
00:37:06,800 --> 00:37:08,879
Speaker 2: Let's take them all together, twenty.

657
00:37:08,640 --> 00:37:11,799
Speaker 1: Dogs that like stand on each other and sort of

658
00:37:12,480 --> 00:37:14,879
Speaker 1: form a giant dog, and then they can split off

659
00:37:14,880 --> 00:37:16,040
Speaker 1: and do their own dogs like.

660
00:37:16,000 --> 00:37:18,520
Speaker 2: A school of dogs. Yeah, totally cool of dogs.

661
00:37:18,640 --> 00:37:22,040
Speaker 1: Yeah, a school of golden true came out of just

662
00:37:22,080 --> 00:37:25,279
Speaker 1: like lying down and having a stampede of dogs run

663
00:37:25,320 --> 00:37:26,600
Speaker 1: over you. That would be cute.

664
00:37:26,640 --> 00:37:28,480
Speaker 2: And anyway, point being.

665
00:37:28,280 --> 00:37:31,279
Speaker 3: I think that this bee experiment is kind of the

666
00:37:31,320 --> 00:37:34,560
Speaker 3: air Bud of experiments in that they just kind of

667
00:37:34,600 --> 00:37:37,040
Speaker 3: need to make a bee that does more stuff like

668
00:37:37,080 --> 00:37:39,719
Speaker 3: the one the one be does isn't very good.

669
00:37:40,440 --> 00:37:41,040
Speaker 2: You know, like.

670
00:37:41,040 --> 00:37:44,160
Speaker 3: Maybe it would work better if they had a bee

671
00:37:44,160 --> 00:37:46,839
Speaker 3: that also was good at you know, making honey or whatever.

672
00:37:46,880 --> 00:37:49,680
Speaker 2: Maybe they would trust the bee more if it did

673
00:37:49,760 --> 00:37:51,400
Speaker 2: more stuff. That's the point.

674
00:37:51,719 --> 00:37:53,400
Speaker 1: Tell's good bee joke, right.

675
00:37:53,520 --> 00:37:56,560
Speaker 2: It's just you know, a social bee, you know, like

676
00:37:56,680 --> 00:37:57,560
Speaker 2: just better at stuff.

677
00:37:57,640 --> 00:38:01,480
Speaker 1: Guess what I'm cooking for dinner tonight, girls, paul In.

678
00:38:05,600 --> 00:38:06,480
Speaker 2: With one appendage.

679
00:38:06,680 --> 00:38:14,759
Speaker 1: Yeah yeah, maybe I think honestly, like I would be

680
00:38:14,800 --> 00:38:18,439
Speaker 1: satisfied with just more bees being studied, Like six six

681
00:38:18,520 --> 00:38:21,479
Speaker 1: bees doesn't do it for me with a small control group,

682
00:38:21,520 --> 00:38:25,720
Speaker 1: because like, could the bees not have found the food

683
00:38:27,239 --> 00:38:31,600
Speaker 1: by chance? Right, like or by some other means of

684
00:38:31,640 --> 00:38:34,360
Speaker 1: finding this food. I think that it's a very like

685
00:38:34,560 --> 00:38:38,080
Speaker 1: I think it's a good start, Like I think it's

686
00:38:38,120 --> 00:38:43,120
Speaker 1: a really strong, like first proof of concept. And what

687
00:38:43,160 --> 00:38:46,120
Speaker 1: I'm saying is this is a great proof of concept.

688
00:38:46,200 --> 00:38:48,959
Speaker 1: Give me more. I want more of this robot bee.

689
00:38:49,000 --> 00:38:51,680
Speaker 1: I want to see it, you know, go like I

690
00:38:51,719 --> 00:38:55,400
Speaker 1: want DARPA to fund this, like make up some war purpose,

691
00:38:55,480 --> 00:38:58,120
Speaker 1: say like this bee can be used to kill people.

692
00:38:58,200 --> 00:39:01,560
Speaker 3: Of that's the thing about robot bees, man, you just

693
00:39:01,680 --> 00:39:04,640
Speaker 3: you just pointed out that's where it inevitably ends, is

694
00:39:04,640 --> 00:39:06,160
Speaker 3: that you know, use them for.

695
00:39:06,120 --> 00:39:10,239
Speaker 1: Attacks, right, But then you know this is okay, So

696
00:39:10,520 --> 00:39:14,399
Speaker 1: we all know that the military will fun thing that sure,

697
00:39:14,640 --> 00:39:17,759
Speaker 1: like and DARPA and stuff. The coolest robots are the

698
00:39:17,800 --> 00:39:20,320
Speaker 1: ones where it's like, could this be used to hurt people?

699
00:39:20,760 --> 00:39:23,080
Speaker 1: And so what what I would like to see is

700
00:39:23,120 --> 00:39:26,680
Speaker 1: like the cool studies that are like harmless about like, hey,

701
00:39:26,719 --> 00:39:29,200
Speaker 1: we want to teach a robot be how to dance

702
00:39:29,239 --> 00:39:31,920
Speaker 1: and communicate to other bees, because that is really cool.

703
00:39:33,560 --> 00:39:37,560
Speaker 1: But just tell DARPA that this bee will be deadly.

704
00:39:38,440 --> 00:39:41,360
Speaker 1: Don't actually make it deadly, just be like we're working

705
00:39:41,360 --> 00:39:44,800
Speaker 1: on it. First, we've got to understand, we've got to basically,

706
00:39:44,840 --> 00:39:48,840
Speaker 1: we will teach these bees war and through the robot bees.

707
00:39:49,080 --> 00:39:52,239
Speaker 1: The robot bee will teach them war through dance. You

708
00:39:52,400 --> 00:39:54,280
Speaker 1: know that won't happen. But dark.

709
00:39:56,280 --> 00:39:59,320
Speaker 2: No, exactly tell them just make.

710
00:39:59,200 --> 00:40:01,880
Speaker 1: Stuff up about bees like be like bees could be

711
00:40:01,920 --> 00:40:06,359
Speaker 1: trained to be military soldiers. They won't check. They're not

712
00:40:06,480 --> 00:40:07,240
Speaker 1: checking that stuff.

713
00:40:07,280 --> 00:40:08,879
Speaker 3: The only thing they know is that you can get

714
00:40:08,920 --> 00:40:10,800
Speaker 3: six bees to do literally anything.

715
00:40:10,880 --> 00:40:12,600
Speaker 2: So that's not a plan. It's nothing.

716
00:40:13,120 --> 00:40:17,200
Speaker 1: What I mean, well, it's not nothing. It's six bees.

717
00:40:17,280 --> 00:40:21,240
Speaker 3: Though, Look, I can get six bees hold any opinion

718
00:40:21,280 --> 00:40:23,160
Speaker 3: on like eight chan man, you know what I mean,

719
00:40:23,239 --> 00:40:25,200
Speaker 3: Like I could, I could get six bees to do

720
00:40:25,760 --> 00:40:29,600
Speaker 3: literally anything. You know, Like that's not a conclusive amount

721
00:40:29,640 --> 00:40:30,680
Speaker 3: of bees for anything.

722
00:40:31,840 --> 00:40:32,560
Speaker 2: You know what I mean.

723
00:40:32,760 --> 00:40:37,320
Speaker 1: It's not a bee core. It's more like a bee Supreme.

724
00:40:36,960 --> 00:40:40,200
Speaker 2: Court exactly, thank you, exactly.

725
00:40:40,000 --> 00:40:42,960
Speaker 1: How many bees. We should increase the number of bees

726
00:40:43,000 --> 00:40:44,839
Speaker 1: on the Supreme Court to.

727
00:40:44,760 --> 00:40:48,200
Speaker 2: At least thirteen thirteen Baker's doesn't.

728
00:40:47,920 --> 00:40:51,160
Speaker 1: The bees a Baker's doesn't plus.

729
00:40:50,880 --> 00:40:53,760
Speaker 2: One to break the tie break the b tie.

730
00:40:53,920 --> 00:41:00,319
Speaker 1: More bees, more bees, please? All right? So armed share

731
00:41:00,640 --> 00:41:04,560
Speaker 1: armchair robotic b experts here way.

732
00:41:04,480 --> 00:41:06,560
Speaker 2: They could have asked. They could have asked us.

733
00:41:06,719 --> 00:41:08,399
Speaker 1: They could have asked, they could have asked.

734
00:41:08,440 --> 00:41:09,960
Speaker 2: We wouldn't have had to We wouldn't have had to

735
00:41:10,000 --> 00:41:11,600
Speaker 2: do this if they'd asked, you know.

736
00:41:12,239 --> 00:41:14,960
Speaker 1: So I if it were me funding this study, I

737
00:41:15,000 --> 00:41:19,200
Speaker 1: would say I'd fund it. Just make it more bees.

738
00:41:21,520 --> 00:41:25,080
Speaker 2: I love how many times you've said it now, it's exactly.

739
00:41:25,480 --> 00:41:27,680
Speaker 3: That was the last one, exactly the amount of times

740
00:41:27,680 --> 00:41:31,080
Speaker 3: that was needed for them to get it right.

741
00:41:31,480 --> 00:41:33,120
Speaker 1: I don't know how many times I have to say it.

742
00:41:33,160 --> 00:41:37,359
Speaker 1: I won't say it again. So we're gonna we're going

743
00:41:37,440 --> 00:41:40,480
Speaker 1: to take a quick break. When we get back, we're

744
00:41:40,480 --> 00:41:45,879
Speaker 1: going to talk about the frightening dystopia of using robots

745
00:41:46,040 --> 00:41:50,719
Speaker 1: to do sheep dogs work, and then also play a little.

746
00:41:50,440 --> 00:41:53,799
Speaker 2: Game, right of games.

747
00:41:53,280 --> 00:41:56,320
Speaker 1: Adam, robots are coming for the jobs of hard working

748
00:41:56,360 --> 00:41:57,440
Speaker 1: American dogs.

749
00:41:57,680 --> 00:42:00,640
Speaker 2: Yeah, I mean you had to know it. Dog should

750
00:42:00,640 --> 00:42:01,279
Speaker 2: have seen that coming.

751
00:42:02,480 --> 00:42:06,040
Speaker 1: Doug should have seen that coming, just like Airbud, like.

752
00:42:05,960 --> 00:42:09,480
Speaker 2: One of the dogs from Air Budget, not all of them,

753
00:42:09,520 --> 00:42:10,200
Speaker 2: just one of them did.

754
00:42:10,719 --> 00:42:13,279
Speaker 1: Just like how they're trying to replace air Bud with AI.

755
00:42:13,680 --> 00:42:15,520
Speaker 1: One of the one of the AI. A lot of

756
00:42:15,560 --> 00:42:18,080
Speaker 1: the AI videos actually that I saw were like animals

757
00:42:18,120 --> 00:42:21,400
Speaker 1: doing stuff. There's one with like Golden Retriever puppies playing

758
00:42:21,440 --> 00:42:24,000
Speaker 1: in the snow, and like part of me is like

759
00:42:24,200 --> 00:42:26,760
Speaker 1: this is cute. And then there's like there are things

760
00:42:26,760 --> 00:42:28,880
Speaker 1: that are still kind of off in the videos, like

761
00:42:29,000 --> 00:42:32,239
Speaker 1: maybe a puppy's leg will appear out of nowhere, or

762
00:42:32,360 --> 00:42:35,839
Speaker 1: like a puppy will sort of like just ooze out

763
00:42:35,840 --> 00:42:38,640
Speaker 1: of another puppy, and so but it's very it's like

764
00:42:38,800 --> 00:42:42,040
Speaker 1: subtle enough that my brain isn't fast enough to catch

765
00:42:42,120 --> 00:42:44,520
Speaker 1: up to like what exactly is the problem. It's just

766
00:42:44,560 --> 00:42:48,200
Speaker 1: like I feel icky. These puppies are making me feel

767
00:42:48,280 --> 00:42:51,319
Speaker 1: bad inside. Usually they make me feel warm, but they're

768
00:42:51,360 --> 00:42:55,240
Speaker 1: making me feel kind of bad. YEA the key inside

769
00:42:55,280 --> 00:42:58,560
Speaker 1: And then you you play the footage, right, you replay

770
00:42:58,600 --> 00:43:00,760
Speaker 1: the footage and you realize, like one of the puppies

771
00:43:00,800 --> 00:43:03,800
Speaker 1: has like a sixth leg that just kind of poinked

772
00:43:03,800 --> 00:43:05,560
Speaker 1: out of its chest.

773
00:43:05,680 --> 00:43:08,000
Speaker 2: It was a problem. I'm worried.

774
00:43:08,040 --> 00:43:09,919
Speaker 3: I'm worried that one day AI is going to get

775
00:43:10,000 --> 00:43:13,480
Speaker 3: good at this so that that doesn't happen. Anymore, and

776
00:43:13,520 --> 00:43:16,239
Speaker 3: then you'll like the cumulative effect of it will be

777
00:43:16,400 --> 00:43:20,120
Speaker 3: that we just stop enjoining dog videos like that right

778
00:43:20,200 --> 00:43:22,879
Speaker 3: where it's like because I kind of feel like part

779
00:43:22,920 --> 00:43:25,320
Speaker 3: of the appeal of a dog video is seeing knowing

780
00:43:25,320 --> 00:43:28,160
Speaker 3: a dog did that, you know what I mean, if

781
00:43:28,440 --> 00:43:32,040
Speaker 3: AI did it, it's like, well, nor do I care

782
00:43:32,080 --> 00:43:33,160
Speaker 3: about that, you know.

783
00:43:33,760 --> 00:43:36,280
Speaker 1: Right, Like if it's like if my dog farts, that's

784
00:43:36,400 --> 00:43:39,880
Speaker 1: hilarious and cute. If it's like a robot going like

785
00:43:40,400 --> 00:43:44,040
Speaker 1: I am about to fart, fart, nice, it's not fun. Really,

786
00:43:44,080 --> 00:43:47,960
Speaker 1: it's not cute, not really, it's just disgusting. Yeah, No,

787
00:43:48,040 --> 00:43:50,719
Speaker 1: I mean I think also there's a possibility that it

788
00:43:50,840 --> 00:43:54,759
Speaker 1: goes into the snack food sort of root, which is

789
00:43:55,400 --> 00:44:01,680
Speaker 1: hyper like hyper optimizing AI puppies to be as like

790
00:44:02,400 --> 00:44:06,640
Speaker 1: lab tested cute as possible, to sort of strike the

791
00:44:06,719 --> 00:44:09,080
Speaker 1: cuteness thing sort of like you know, like how like

792
00:44:09,120 --> 00:44:12,200
Speaker 1: snack food like cheetahs and stuff, they like test them

793
00:44:12,200 --> 00:44:15,360
Speaker 1: over and over until they reach like a maximum flavor

794
00:44:15,400 --> 00:44:17,680
Speaker 1: point and like melt in your mouth, so like people

795
00:44:17,800 --> 00:44:19,759
Speaker 1: just eat it and they don't even know they're eating it.

796
00:44:19,640 --> 00:44:20,799
Speaker 2: It just happens.

797
00:44:21,120 --> 00:44:23,920
Speaker 1: I think there might be the same thing with AI

798
00:44:24,080 --> 00:44:26,799
Speaker 1: generated puppies where they are optimized to be as cute

799
00:44:26,840 --> 00:44:28,880
Speaker 1: as possible, and then when you look at a real puppy,

800
00:44:28,880 --> 00:44:30,000
Speaker 1: you're like trash.

801
00:44:30,120 --> 00:44:31,000
Speaker 2: You're a trash dog.

802
00:44:31,280 --> 00:44:33,840
Speaker 1: That's trash. Give me the AI puppies.

803
00:44:34,160 --> 00:44:36,400
Speaker 2: I hope that's not true. I hope.

804
00:44:36,760 --> 00:44:39,360
Speaker 3: I think I'm probably wrong, but I kind of hope

805
00:44:39,400 --> 00:44:42,880
Speaker 3: that the effect of that will be sort of like

806
00:44:42,920 --> 00:44:44,720
Speaker 3: that movie Alita Battle Angel.

807
00:44:45,160 --> 00:44:47,680
Speaker 2: You've seen that movie where like they tried.

808
00:44:47,680 --> 00:44:50,239
Speaker 1: That's the one with the like brat Styll.

809
00:44:51,760 --> 00:44:52,160
Speaker 2: Robot.

810
00:44:52,440 --> 00:44:56,040
Speaker 3: I kind of feel like, ultimately we end up sort

811
00:44:56,040 --> 00:44:59,400
Speaker 3: of making weird monster things when we try to maximize cuteness,

812
00:45:00,280 --> 00:45:01,439
Speaker 3: you know what I mean. Like I might be wrong

813
00:45:01,440 --> 00:45:03,680
Speaker 3: about that, but I kind I kind of feel like

814
00:45:03,880 --> 00:45:05,960
Speaker 3: I'm not though, you know what I mean, Like where

815
00:45:06,160 --> 00:45:09,160
Speaker 3: it is actually off putting when you get closer to

816
00:45:09,200 --> 00:45:10,000
Speaker 3: a consensus.

817
00:45:10,200 --> 00:45:10,600
Speaker 2: I don't know.

818
00:45:11,920 --> 00:45:15,240
Speaker 1: There are specific things in human psychology that give me hope,

819
00:45:15,400 --> 00:45:19,000
Speaker 1: like the fact that there's this phenomenon where people when

820
00:45:19,000 --> 00:45:22,280
Speaker 1: they watch like sports, they don't really like to watch

821
00:45:23,480 --> 00:45:25,680
Speaker 1: like they like to watch it live when other people

822
00:45:25,680 --> 00:45:29,960
Speaker 1: are watching it literally has no like and even before

823
00:45:30,000 --> 00:45:32,200
Speaker 1: like mass social media, this was the case it's like

824
00:45:32,600 --> 00:45:34,759
Speaker 1: it does not have an effect, right, like if you

825
00:45:34,840 --> 00:45:37,959
Speaker 1: watch it live versus you tape it and then watch

826
00:45:38,000 --> 00:45:41,160
Speaker 1: it later, Like there's actually an effect in terms of

827
00:45:41,520 --> 00:45:44,000
Speaker 1: how you feel about it, even though it's the same thing.

828
00:45:44,000 --> 00:45:47,680
Speaker 1: It's the same experience, but people really like feeling like

829
00:45:47,719 --> 00:45:51,600
Speaker 1: they're watching it with the rest of the world, like

830
00:45:51,640 --> 00:45:53,880
Speaker 1: there is a there there is a feeling of like

831
00:45:54,000 --> 00:45:59,120
Speaker 1: I want the puppy that I'm looking into its deep

832
00:45:59,160 --> 00:46:03,440
Speaker 1: brown eyes to actually be a feeling, thinking creature. I

833
00:46:03,440 --> 00:46:06,360
Speaker 1: think this is why, like robotic pets never took off. Remember,

834
00:46:06,480 --> 00:46:09,319
Speaker 1: like there was a whole thing about like robot puppies

835
00:46:09,400 --> 00:46:13,799
Speaker 1: and robotic pets, and they never really that did not

836
00:46:13,880 --> 00:46:16,120
Speaker 1: become a big thing because I think people one of

837
00:46:16,120 --> 00:46:18,000
Speaker 1: the things people like about pets is that this is

838
00:46:18,040 --> 00:46:21,319
Speaker 1: a sentient creature. It's not just a soft, mindless like

839
00:46:21,480 --> 00:46:25,280
Speaker 1: lump of warm fur that I can pet. It actually

840
00:46:25,320 --> 00:46:29,160
Speaker 1: has a brain and when I pet it, it is happy.

841
00:46:29,280 --> 00:46:31,360
Speaker 1: When I give it a treat, it is happy. You

842
00:46:31,400 --> 00:46:33,640
Speaker 1: can't get the same thing out of a robot or

843
00:46:33,680 --> 00:46:36,640
Speaker 1: an AI, and so I think people really need that.

844
00:46:37,480 --> 00:46:40,480
Speaker 1: So I don't think real puppies are going anywhere.

845
00:46:40,600 --> 00:46:43,680
Speaker 2: We're not getting rid of puppies yet. Puppies are keeping

846
00:46:43,719 --> 00:46:46,479
Speaker 2: their job and we're keeping them safe. That's a safe

847
00:46:46,560 --> 00:46:47,640
Speaker 2: stance for the podcast.

848
00:46:49,000 --> 00:46:51,640
Speaker 1: Yeah it's a hot take, but we might get rid

849
00:46:51,680 --> 00:46:59,640
Speaker 1: of herding dogs. Well probably not, but well no, I

850
00:46:59,640 --> 00:47:02,359
Speaker 1: don't think so. I'm I'm kind of joking. I think

851
00:47:02,400 --> 00:47:04,960
Speaker 1: that a lot of dog breeds just we will keep

852
00:47:05,000 --> 00:47:07,759
Speaker 1: because I think that we like in terms of like

853
00:47:08,040 --> 00:47:10,480
Speaker 1: sheep herding, even though that might not be used in

854
00:47:10,520 --> 00:47:13,879
Speaker 1: a commercial since anymore. I think that there will still

855
00:47:13,920 --> 00:47:16,840
Speaker 1: be like basically done as like a hobby.

856
00:47:16,560 --> 00:47:20,200
Speaker 2: Or keep the traditional babe babe.

857
00:47:20,160 --> 00:47:24,279
Speaker 1: Like like the movie Babe exactly like there will there

858
00:47:24,320 --> 00:47:27,680
Speaker 1: will always be a babe. Yes, there will always be

859
00:47:27,719 --> 00:47:29,200
Speaker 1: a pig proven.

860
00:47:29,160 --> 00:47:32,200
Speaker 2: His worth, so uh, thank god for that.

861
00:47:33,719 --> 00:47:39,719
Speaker 1: But yeah, researchers are looking into like using robots to

862
00:47:39,920 --> 00:47:45,000
Speaker 1: herd animals sometimes in farm settings, like those horrifying robot

863
00:47:45,120 --> 00:47:50,799
Speaker 1: dogs being used to herd animals, which is you know, man,

864
00:47:50,840 --> 00:47:52,399
Speaker 1: what a metaphor? Am I right?

865
00:47:52,680 --> 00:47:55,319
Speaker 3: I mean it's funny because I'm thinking about this right

866
00:47:55,360 --> 00:47:57,879
Speaker 3: now as you're saying it and realize it. I mean,

867
00:47:57,960 --> 00:48:03,600
Speaker 3: dogs are essentially we've essentially trained dogs to do this anyway,

868
00:48:03,760 --> 00:48:06,400
Speaker 3: so it's like hard for me to get super up

869
00:48:06,400 --> 00:48:06,960
Speaker 3: in arms for.

870
00:48:06,920 --> 00:48:09,120
Speaker 2: The dog's job. But I don't know, I don't know,

871
00:48:09,160 --> 00:48:10,919
Speaker 2: you know what I mean, because like I don't.

872
00:48:11,560 --> 00:48:13,920
Speaker 3: I mean, this may be my ignorance, but dogs weren't

873
00:48:14,000 --> 00:48:16,279
Speaker 3: hurting sheep before human beings trained them to do it?

874
00:48:16,280 --> 00:48:16,840
Speaker 2: Were they.

875
00:48:18,320 --> 00:48:21,640
Speaker 1: No it? Actually they had to be trained out of

876
00:48:21,760 --> 00:48:25,000
Speaker 1: actually like attacking this right of course, it is a

877
00:48:25,120 --> 00:48:28,680
Speaker 1: predatory behavior that's been pulled back, so they don't actually

878
00:48:28,719 --> 00:48:30,799
Speaker 1: attack the sheep, but they go after them, and then

879
00:48:30,840 --> 00:48:33,600
Speaker 1: they pay attention to the human. I mean, I think

880
00:48:33,640 --> 00:48:37,920
Speaker 1: that like in terms of dog and human happiness, I

881
00:48:37,920 --> 00:48:42,120
Speaker 1: think working together with dogs can be deeply enriching and

882
00:48:42,200 --> 00:48:44,880
Speaker 1: something like hurting as long as it's in a farm

883
00:48:44,920 --> 00:48:47,160
Speaker 1: that is well run and stuff, I think that can

884
00:48:47,200 --> 00:48:49,520
Speaker 1: be you know, enjoyable for both the human and dog.

885
00:48:51,200 --> 00:48:53,960
Speaker 1: But you know, there are certain situations where it might

886
00:48:53,960 --> 00:48:56,239
Speaker 1: be dangerous for a dog, and I would not have

887
00:48:56,280 --> 00:48:59,680
Speaker 1: a problem with a robot being sent in and like

888
00:48:59,680 --> 00:49:02,640
Speaker 1: I said, situation where like like especially with say like

889
00:49:02,719 --> 00:49:04,480
Speaker 1: maybe like a bomb stiff.

890
00:49:04,400 --> 00:49:06,040
Speaker 2: Dog or something that.

891
00:49:08,160 --> 00:49:12,640
Speaker 1: Instead of dog, right exactly, yeah, exactly, And so like

892
00:49:13,520 --> 00:49:15,759
Speaker 1: I don't, I don't like the use of dogs in

893
00:49:15,920 --> 00:49:20,000
Speaker 1: sort of combat situation. So, uh, but like, I also

894
00:49:20,000 --> 00:49:28,200
Speaker 1: don't like combat situations in hopes like keeping puppies. I

895
00:49:28,239 --> 00:49:30,960
Speaker 1: don't want to go to war with Can we that's

896
00:49:31,040 --> 00:49:31,440
Speaker 1: just my.

897
00:49:33,320 --> 00:49:34,440
Speaker 2: Can we just stop it?

898
00:49:34,600 --> 00:49:37,560
Speaker 1: I mean, y'all aren't ready to hear this, but can

899
00:49:37,640 --> 00:49:39,080
Speaker 1: we stop?

900
00:49:41,480 --> 00:49:41,960
Speaker 5: Uh?

901
00:49:42,360 --> 00:49:45,400
Speaker 1: So, Yeah, there's also something called bioherding, where you're like

902
00:49:45,480 --> 00:49:48,680
Speaker 1: actually hurting animals that are wild animals not in farms

903
00:49:49,520 --> 00:49:52,920
Speaker 1: to like maintain sort of like either the safety of

904
00:49:52,960 --> 00:49:56,040
Speaker 1: the animals or like safely kind of try to separate

905
00:49:56,200 --> 00:50:00,279
Speaker 1: human activity and animal activity. Like say, like you maybe

906
00:50:00,360 --> 00:50:04,359
Speaker 1: use a drone to herd birds away from like a

907
00:50:04,400 --> 00:50:09,360
Speaker 1: wind farm or airports, right to prevent them from getting

908
00:50:09,400 --> 00:50:12,360
Speaker 1: sucked into a wind turbine or colliding with a plane.

909
00:50:13,200 --> 00:50:16,319
Speaker 1: So like having a drone doing this might actually be

910
00:50:16,920 --> 00:50:20,520
Speaker 1: like useful because hey, you know, you're telling the bird,

911
00:50:20,600 --> 00:50:23,719
Speaker 1: like get away from this airport. This airplane wants to eat.

912
00:50:23,800 --> 00:50:26,720
Speaker 2: True. Sure, I see that that makes sense.

913
00:50:28,160 --> 00:50:32,640
Speaker 1: There's also been research where they've used drones essentially to

914
00:50:32,840 --> 00:50:37,120
Speaker 1: herd birds, but it was a robotic falcon, so a

915
00:50:37,400 --> 00:50:41,320
Speaker 1: sounds awesome. An aerial a drone shaped like a falcon,

916
00:50:41,800 --> 00:50:44,279
Speaker 1: which I know there's I know there's this internet joke

917
00:50:44,480 --> 00:50:47,319
Speaker 1: of like the bird birds aren't real, kind of like

918
00:50:47,880 --> 00:50:50,520
Speaker 1: where you know it's the whole thing, but like, you know,

919
00:50:50,800 --> 00:50:53,480
Speaker 1: we know birds are real, but this one specifically is not.

920
00:50:54,280 --> 00:50:58,719
Speaker 1: It's it's a robotic falcon. And research is at the

921
00:50:58,880 --> 00:51:03,960
Speaker 1: University of Grown in Gin. I don't that's not a

922
00:51:04,000 --> 00:51:06,560
Speaker 1: word I can say good and the University of London

923
00:51:06,960 --> 00:51:11,440
Speaker 1: found that they could use this robot falcon to trigger

924
00:51:11,480 --> 00:51:13,120
Speaker 1: a fear response in pigeons.

925
00:51:13,239 --> 00:51:15,480
Speaker 2: Of course stop, I.

926
00:51:15,440 --> 00:51:19,520
Speaker 1: Mean like, obviously you're going to scare these but this

927
00:51:19,600 --> 00:51:20,480
Speaker 1: is actually.

928
00:51:20,239 --> 00:51:23,120
Speaker 2: Another thing that didn't need to be proved, did We

929
00:51:23,280 --> 00:51:24,040
Speaker 2: didn't need to.

930
00:51:24,160 --> 00:51:27,040
Speaker 1: Prove that to be fair. To be fair, they're looking

931
00:51:27,080 --> 00:51:30,200
Speaker 1: at something deeper than this. This is actually I like this.

932
00:51:30,200 --> 00:51:33,920
Speaker 1: This is I think my in terms of methodology, I

933
00:51:33,920 --> 00:51:37,240
Speaker 1: think uh one of my uh, I like this study.

934
00:51:37,280 --> 00:51:39,880
Speaker 1: I think it's other than scaring being mean to pigeons,

935
00:51:39,880 --> 00:51:43,560
Speaker 1: which I don't like, Uh, it is it seems pretty interesting.

936
00:51:43,600 --> 00:51:45,840
Speaker 1: So they found that when they would scare the pigeons

937
00:51:45,840 --> 00:51:49,160
Speaker 1: with the falcon, what they were specifically looking at was

938
00:51:49,239 --> 00:51:51,880
Speaker 1: flocking behavior in these pigeons to see if they would

939
00:51:51,920 --> 00:51:55,080
Speaker 1: like there's this idea of selfish flocking where if like

940
00:51:55,480 --> 00:51:58,719
Speaker 1: you're the safest position in a flock would be like

941
00:51:58,800 --> 00:52:03,600
Speaker 1: in the middle, right, like protected, protect yourself, And so

942
00:52:03,680 --> 00:52:06,640
Speaker 1: the idea would be like maybe there's some like flocks

943
00:52:06,640 --> 00:52:09,880
Speaker 1: of birds where there are permutations of these birds because

944
00:52:10,560 --> 00:52:13,000
Speaker 1: individuals keep trying to get in the middle, right, and

945
00:52:13,040 --> 00:52:15,279
Speaker 1: so it's like a sort of this effect of like

946
00:52:15,360 --> 00:52:18,000
Speaker 1: everyone's trying to shove their way into the middle. But

947
00:52:18,080 --> 00:52:20,960
Speaker 1: they looked at this in these homing pigeons and they

948
00:52:20,960 --> 00:52:23,560
Speaker 1: did not find this. So they found that the pigeons

949
00:52:23,840 --> 00:52:26,480
Speaker 1: of flocking behavior was you know, they were not trying

950
00:52:26,480 --> 00:52:29,680
Speaker 1: to vuy for this center position that they were. They

951
00:52:29,719 --> 00:52:32,800
Speaker 1: did in fact run away from or not run away,

952
00:52:32,840 --> 00:52:37,480
Speaker 1: fly away from the falcon bot, but that they were

953
00:52:37,520 --> 00:52:40,480
Speaker 1: not trying to like shove their way into the middle.

954
00:52:40,520 --> 00:52:42,600
Speaker 1: They seemed to be just kind of like following each

955
00:52:42,640 --> 00:52:46,040
Speaker 1: other and going getting trying to get away from the falcons. So,

956
00:52:46,520 --> 00:52:51,000
Speaker 1: you know, the homing pigeons are nice. I guess it's

957
00:52:51,000 --> 00:52:53,680
Speaker 1: not really about being nice. It's about like probably this

958
00:52:53,880 --> 00:52:57,680
Speaker 1: system of not trying to scoot your way into the

959
00:52:57,680 --> 00:53:00,840
Speaker 1: middle actually create some more stable safe for a situation

960
00:53:00,960 --> 00:53:01,840
Speaker 1: for all the pigeons.

961
00:53:02,520 --> 00:53:04,200
Speaker 3: But still I kind of wish they'd do that on

962
00:53:04,280 --> 00:53:08,040
Speaker 3: airlines so we didn't have all this you know, economy

963
00:53:08,080 --> 00:53:11,000
Speaker 3: plus and first class and like all this class warfare.

964
00:53:11,400 --> 00:53:13,800
Speaker 3: Just get a robot falcon the scare us into our seats,

965
00:53:13,840 --> 00:53:16,280
Speaker 3: you know, the way better.

966
00:53:16,360 --> 00:53:20,200
Speaker 1: Planes are not so the the like seating on planes

967
00:53:20,239 --> 00:53:23,920
Speaker 1: are usually done to try to incentivize people to try

968
00:53:24,440 --> 00:53:28,160
Speaker 1: more expensive tickets, because if you want the most efficient seating,

969
00:53:28,200 --> 00:53:30,640
Speaker 1: you would see like from back to front. Obviously you

970
00:53:30,640 --> 00:53:34,319
Speaker 1: don't see it from from the book back, but you know,

971
00:53:34,320 --> 00:53:36,879
Speaker 1: they got it. They gotta like have you be sort

972
00:53:36,920 --> 00:53:40,440
Speaker 1: of like walk of shame past everyone in their cozy

973
00:53:40,520 --> 00:53:45,080
Speaker 1: business class as you you know, it's like seats getting small,

974
00:53:45,400 --> 00:53:45,759
Speaker 1: it is.

975
00:53:46,520 --> 00:53:48,400
Speaker 3: I could do an hour on that, Katie. I just

976
00:53:48,400 --> 00:53:51,640
Speaker 3: went on four flights last week and it was a healthscape.

977
00:53:51,920 --> 00:53:54,080
Speaker 3: It was a healthscape anyway. But we're here to talk

978
00:53:54,120 --> 00:53:55,279
Speaker 3: about robot falcons, as I.

979
00:53:55,360 --> 00:54:00,680
Speaker 1: Understand it to airlines, am I, I adam, maybe you

980
00:54:00,760 --> 00:54:03,080
Speaker 1: maybe have you seen this? Have you heard about this?

981
00:54:03,280 --> 00:54:07,759
Speaker 1: I find flying on an arrowplane sometimes.

982
00:54:07,280 --> 00:54:09,920
Speaker 2: Uncomfortable and mean, yeah a little bit.

983
00:54:10,960 --> 00:54:13,799
Speaker 1: Uh yeah, so no, I mean that's it the they

984
00:54:14,040 --> 00:54:19,799
Speaker 1: scared these birds with the robot falcon and the birds, uh,

985
00:54:20,400 --> 00:54:23,680
Speaker 1: these homing pigeons were like, hey, we're scared, but we're

986
00:54:23,680 --> 00:54:25,960
Speaker 1: going to keep her cool and work as a unit

987
00:54:27,200 --> 00:54:30,600
Speaker 1: with you. I'm sharing with you an image of like

988
00:54:30,680 --> 00:54:34,560
Speaker 1: these these robots used in these situations, which just seems

989
00:54:35,960 --> 00:54:38,920
Speaker 1: kind of distoed. It looks like a cyber does kind

990
00:54:39,000 --> 00:54:40,960
Speaker 1: of thing for these animals.

991
00:54:41,400 --> 00:54:44,480
Speaker 2: Well, yeah, this is wild.

992
00:54:45,239 --> 00:54:51,000
Speaker 3: These things look insane, like they're completely Orwellian, right, like

993
00:54:51,719 --> 00:54:56,040
Speaker 3: it is right, Yeah, I mean I know that animals

994
00:54:56,080 --> 00:54:59,359
Speaker 3: don't rely on site in the same way that we do,

995
00:54:59,480 --> 00:55:03,640
Speaker 3: or not all of the them do. Yeah, it just

996
00:55:03,680 --> 00:55:08,160
Speaker 3: seems tough to me to draw the kinds of conclusions about, say,

997
00:55:08,239 --> 00:55:12,000
Speaker 3: pigeons from these machines, because these machines are like, what

998
00:55:12,040 --> 00:55:13,799
Speaker 3: would not be scared looking at that thing?

999
00:55:15,320 --> 00:55:17,680
Speaker 1: It's not. But the thing about that study, right is,

1000
00:55:17,719 --> 00:55:19,520
Speaker 1: it's not about whether they're.

1001
00:55:19,719 --> 00:55:22,200
Speaker 2: Scared, how do they respond to It's about.

1002
00:55:22,600 --> 00:55:25,000
Speaker 1: How do they respond to fears. So whether they think

1003
00:55:25,080 --> 00:55:28,200
Speaker 1: like it's a falcon chasing us, or this is a

1004
00:55:28,200 --> 00:55:34,640
Speaker 1: demon from the depths of pigeon health, right making a

1005
00:55:34,920 --> 00:55:38,560
Speaker 1: terrifying noise. We got to get out of here. No,

1006
00:55:38,640 --> 00:55:40,719
Speaker 1: I think that that's why for that study, Like I

1007
00:55:41,120 --> 00:55:44,560
Speaker 1: actually think that's succeptable. It's relatively strong study. Yeah, yeah,

1008
00:55:44,600 --> 00:55:47,480
Speaker 1: because it's like it's looking you know at sort of this,

1009
00:55:47,719 --> 00:55:51,879
Speaker 1: uh the behavior of the birds specific and it's kind

1010
00:55:51,880 --> 00:55:56,120
Speaker 1: of like you know, they're comparing it to like non

1011
00:55:56,440 --> 00:55:58,359
Speaker 1: frightened flocking and.

1012
00:55:58,280 --> 00:56:02,080
Speaker 3: That's fine, and look again I'm not a pigeon scientist.

1013
00:56:03,239 --> 00:56:06,880
Speaker 3: That's not my area of expertise. But I still feel like,

1014
00:56:07,560 --> 00:56:10,600
Speaker 3: what the kind of fear that the pigeons are experiencing

1015
00:56:10,680 --> 00:56:13,120
Speaker 3: or type seems like it might have bearing on how

1016
00:56:13,120 --> 00:56:16,279
Speaker 3: they respond or maybe I'm entirely cold, yes, you know.

1017
00:56:16,520 --> 00:56:18,400
Speaker 1: No, no, no, that's I think that's a that is

1018
00:56:18,440 --> 00:56:20,960
Speaker 1: a fair critique of the study because like it could

1019
00:56:20,960 --> 00:56:24,400
Speaker 1: be that this is such a novel situation for you

1020
00:56:25,040 --> 00:56:27,640
Speaker 1: that if it was an actual right, like if it's

1021
00:56:27,640 --> 00:56:31,560
Speaker 1: an actual falcon, they might have like a specific response.

1022
00:56:31,600 --> 00:56:34,440
Speaker 1: But given that this is a completely unknown thing, like

1023
00:56:34,600 --> 00:56:37,880
Speaker 1: maybe their behavior is different because this is like I

1024
00:56:37,920 --> 00:56:40,640
Speaker 1: have never heard or seen this before, and we are

1025
00:56:40,719 --> 00:56:44,279
Speaker 1: just doing something to get away from it because they

1026
00:56:44,360 --> 00:56:46,200
Speaker 1: because like there is it is true that for a

1027
00:56:46,280 --> 00:56:52,040
Speaker 1: lot of animals, like they develop different strategies for predator

1028
00:56:52,080 --> 00:56:55,480
Speaker 1: of aasion depending on the predators. Like animals will have

1029
00:56:55,560 --> 00:56:59,320
Speaker 1: like specific alarm calls for say like a flying predator

1030
00:56:59,400 --> 00:57:03,759
Speaker 1: or own predator or snake or something, And so it's

1031
00:57:03,840 --> 00:57:07,840
Speaker 1: possible that their evasion strategies differ depending on the predator,

1032
00:57:08,320 --> 00:57:11,279
Speaker 1: and there could be some effect of like, this is

1033
00:57:11,320 --> 00:57:15,840
Speaker 1: a robot and that is eliciting a specific response. But

1034
00:57:16,480 --> 00:57:19,640
Speaker 1: even if this is like specific to robot fear, it's

1035
00:57:19,640 --> 00:57:23,280
Speaker 1: still interesting that they have this that they don't seem

1036
00:57:23,320 --> 00:57:28,160
Speaker 1: to like be basically the equivalent of shoving someone aside

1037
00:57:28,200 --> 00:57:30,440
Speaker 1: as you try to escape.

1038
00:57:30,880 --> 00:57:34,200
Speaker 3: That's yeah, yeah, I mean, you know again, I I

1039
00:57:34,520 --> 00:57:37,000
Speaker 3: barb it for me to speak to the mind of pigeons, but.

1040
00:57:38,480 --> 00:57:41,160
Speaker 2: These things are horrifying. Yeah, these things are horrifying.

1041
00:57:42,160 --> 00:57:46,160
Speaker 3: Also, I don't know why every robot that walks on

1042
00:57:46,240 --> 00:57:48,680
Speaker 3: the on the land looks like an animal its head's

1043
00:57:48,720 --> 00:57:50,439
Speaker 3: been cut off. That's like a.

1044
00:57:50,360 --> 00:57:52,840
Speaker 2: Thing because it doesn't need to hell, it doesn't need

1045
00:57:52,840 --> 00:57:53,160
Speaker 2: to know.

1046
00:57:53,480 --> 00:57:56,800
Speaker 3: But it just enhances the feeling that we that you

1047
00:57:56,880 --> 00:57:59,760
Speaker 3: get as a human of like that thing is is

1048
00:58:00,160 --> 00:58:03,640
Speaker 3: uh is unnatural, you know what I mean, It's like, yeah,

1049
00:58:03,760 --> 00:58:04,240
Speaker 3: I don't.

1050
00:58:04,040 --> 00:58:07,360
Speaker 1: Know you're I think you're looking at that like darkness

1051
00:58:08,320 --> 00:58:11,080
Speaker 1: at the robot do I mean? I don't. I feel

1052
00:58:11,120 --> 00:58:14,440
Speaker 1: like because they've sometimes pitched it in terms of like

1053
00:58:14,480 --> 00:58:18,840
Speaker 1: these like security security situations and stuff, I think part

1054
00:58:18,880 --> 00:58:21,720
Speaker 1: of the like appeal of it is it's a little

1055
00:58:21,760 --> 00:58:27,200
Speaker 1: bit like one of these things like like shows up

1056
00:58:27,240 --> 00:58:30,160
Speaker 1: at your door and starts blaring at sirens at you.

1057
00:58:30,240 --> 00:58:34,960
Speaker 1: Like I'm I just like immediately fall over out of terror.

1058
00:58:35,640 --> 00:58:37,480
Speaker 3: It hurts me to where it wants me to go.

1059
00:58:37,760 --> 00:58:40,720
Speaker 3: I'm saying, all right, the door, I'll open it.

1060
00:58:41,600 --> 00:58:44,200
Speaker 1: Yeah. Like if a human officer like comes right and

1061
00:58:44,240 --> 00:58:47,080
Speaker 1: it's like I need to search your car, I'm like,

1062
00:58:47,120 --> 00:58:49,640
Speaker 1: what you got probable cause? Certain? No, I'm not.

1063
00:58:49,760 --> 00:58:52,320
Speaker 2: I'm I'm a i'm let's your warrant.

1064
00:58:52,520 --> 00:58:55,240
Speaker 1: But still I'm more I'm more likely to be like, hey,

1065
00:58:55,440 --> 00:58:57,040
Speaker 1: you know, let's see your warrant. But if one of

1066
00:58:57,040 --> 00:58:59,360
Speaker 1: these robot dogs comes up to me and is like,

1067
00:58:59,520 --> 00:59:02,480
Speaker 1: realink your car, I'm like, okay, I'm done.

1068
00:59:02,760 --> 00:59:05,680
Speaker 3: Yeah you're not the PI in that noir tale when

1069
00:59:05,680 --> 00:59:06,680
Speaker 3: that dog shows up.

1070
00:59:06,960 --> 00:59:10,240
Speaker 2: Now you know what took you so long? Rogers, I've

1071
00:59:10,280 --> 00:59:10,960
Speaker 2: been waiting for you.

1072
00:59:11,120 --> 00:59:14,040
Speaker 1: Yeah you don't you don't. You don't have ahead, and

1073
00:59:14,160 --> 00:59:17,520
Speaker 1: you move and a way that I do not like.

1074
00:59:17,960 --> 00:59:22,320
Speaker 3: So whatever you need you Yeah, those I mean, I've

1075
00:59:22,440 --> 00:59:26,040
Speaker 3: enjoyed the the advent of robot dancing videos and stuff

1076
00:59:26,080 --> 00:59:30,800
Speaker 3: that have come out in the last few years, but like, well,

1077
00:59:30,800 --> 00:59:31,360
Speaker 3: that's the thing.

1078
00:59:31,560 --> 00:59:33,320
Speaker 2: They're always menacing because.

1079
00:59:33,080 --> 00:59:37,040
Speaker 3: The robots are always designed in a way that removes us,

1080
00:59:37,200 --> 00:59:40,120
Speaker 3: like gives you that it puts the uncanny feeling front

1081
00:59:40,160 --> 00:59:40,560
Speaker 3: and center.

1082
00:59:41,040 --> 00:59:44,040
Speaker 2: I don't know why they insist on doing that, you know.

1083
00:59:44,600 --> 00:59:46,880
Speaker 1: Because I don't Yeah, I don't know. It's like, I

1084
00:59:46,920 --> 00:59:50,560
Speaker 1: don't know, I've never seen a real life robot that

1085
00:59:50,680 --> 00:59:57,280
Speaker 1: I would describe as not that way, especially like the

1086
00:59:57,320 --> 01:00:00,840
Speaker 1: only time there's been a cute robot, in my opinion,

1087
01:00:01,400 --> 01:00:03,560
Speaker 1: like actually cute, adorable robot.

1088
01:00:03,280 --> 01:00:05,960
Speaker 2: Is Wall, of course, in the toys.

1089
01:00:06,000 --> 01:00:07,480
Speaker 1: And that's because that robot.

1090
01:00:07,280 --> 01:00:10,120
Speaker 3: No just general like toy robots in generally there's lots

1091
01:00:10,160 --> 01:00:10,560
Speaker 3: of cute one.

1092
01:00:10,680 --> 01:00:13,600
Speaker 1: I guess maybe there's some some cute ones, but the

1093
01:00:14,000 --> 01:00:16,320
Speaker 1: most truly cute one is that one, because that was

1094
01:00:16,400 --> 01:00:22,280
Speaker 1: animated by handshake humans sure to make it cute and expressive,

1095
01:00:22,520 --> 01:00:26,000
Speaker 1: and so every time Wally makes an adorable expression right there,

1096
01:00:26,040 --> 01:00:29,680
Speaker 1: was a human being making sure that that robot looked

1097
01:00:29,760 --> 01:00:31,360
Speaker 1: cute and expressive.

1098
01:00:30,920 --> 01:00:32,960
Speaker 2: Right, right, So you know it's tough, man.

1099
01:00:33,520 --> 01:00:37,000
Speaker 3: Uh yeah, you can't really get past the there's a

1100
01:00:37,040 --> 01:00:38,920
Speaker 3: certain point where the robot has to kind of stand

1101
01:00:38,960 --> 01:00:43,200
Speaker 3: on its own as being appealing, and it just really

1102
01:00:43,200 --> 01:00:47,040
Speaker 3: doesn't feel like it works for that for that quality.

1103
01:00:47,160 --> 01:00:48,880
Speaker 3: I don't know how important that quality is if this

1104
01:00:48,960 --> 01:00:51,840
Speaker 3: thing's working behind the scenes, Like, for instance, if this dog,

1105
01:00:52,080 --> 01:00:54,920
Speaker 3: this robot dog that does the sheep hurting doesn't have

1106
01:00:55,040 --> 01:00:58,640
Speaker 3: to be watched by human beings, like this becomes an

1107
01:00:58,640 --> 01:01:01,320
Speaker 3: automated task where human being now can go do something else.

1108
01:01:02,120 --> 01:01:04,120
Speaker 3: That's that's kind of amazing, you know, because I like

1109
01:01:04,280 --> 01:01:06,400
Speaker 3: I you know, I imagine the sheep aren't going to their

1110
01:01:06,480 --> 01:01:09,480
Speaker 3: sheep therapists or whatever about this thing, right, They're probably fine,

1111
01:01:10,040 --> 01:01:12,480
Speaker 3: it's okay, But if you have to sit there and

1112
01:01:12,480 --> 01:01:15,120
Speaker 3: watch it, nah, man, give me that dog any old day.

1113
01:01:16,960 --> 01:01:20,200
Speaker 1: Yeah. No, I don't know. I I truly don't know

1114
01:01:20,200 --> 01:01:23,400
Speaker 1: what to think when it comes to these things. I

1115
01:01:23,440 --> 01:01:26,280
Speaker 1: think it's just like any tools, right, like like we're

1116
01:01:26,280 --> 01:01:30,280
Speaker 1: talking about with AI, like, it really entirely depends on

1117
01:01:30,320 --> 01:01:35,160
Speaker 1: the application. I think that things like farming and are

1118
01:01:35,600 --> 01:01:40,120
Speaker 1: kind of clash with animal human civilization and animals. There's

1119
01:01:40,160 --> 01:01:43,440
Speaker 1: a lot of things that are currently wrong with them totally.

1120
01:01:43,480 --> 01:01:46,600
Speaker 1: So like if robots could help with that, right, Like,

1121
01:01:46,640 --> 01:01:51,200
Speaker 1: if robots somehow could make farming like more humane, I'd

1122
01:01:51,200 --> 01:01:53,520
Speaker 1: be all for it. Like I've heard of these things

1123
01:01:53,520 --> 01:01:57,280
Speaker 1: that are these like automated milkers where it's like cows

1124
01:01:57,320 --> 01:02:00,400
Speaker 1: will get there's just like a robot milker where a

1125
01:02:00,480 --> 01:02:03,680
Speaker 1: cow decides like, hey, my utters are feeling kind of

1126
01:02:03,880 --> 01:02:06,120
Speaker 1: not great. I'm going to go into this automated milker,

1127
01:02:06,200 --> 01:02:09,160
Speaker 1: get milked and then wander off. That's great because then

1128
01:02:09,320 --> 01:02:12,400
Speaker 1: you give the cow more autonomy you know you have.

1129
01:02:12,920 --> 01:02:18,400
Speaker 1: It makes dairy farming slightly less horrifying. So in that case, yeah,

1130
01:02:19,000 --> 01:02:23,120
Speaker 1: team robot. But if robots are used to like increase

1131
01:02:23,400 --> 01:02:28,360
Speaker 1: like the sort of inhuman conditions of animals, right, people, then.

1132
01:02:28,440 --> 01:02:31,000
Speaker 3: I'm getting maximum milk right, Like, that's the kind of

1133
01:02:31,040 --> 01:02:31,760
Speaker 3: thing you're worried about.

1134
01:02:32,000 --> 01:02:35,360
Speaker 1: You know, we have found if we smash the cows

1135
01:02:35,360 --> 01:02:38,439
Speaker 1: with a hammer, it increases milk production by twenty point

1136
01:02:38,520 --> 01:02:39,640
Speaker 1: seven hammer.

1137
01:02:42,200 --> 01:02:43,840
Speaker 2: It's like, no, no, no, I don't want that.

1138
01:02:44,000 --> 01:02:46,760
Speaker 1: Insulting the cows makes the milk sweeter and more rich

1139
01:02:46,800 --> 01:02:47,360
Speaker 1: in protein.

1140
01:02:47,880 --> 01:02:51,840
Speaker 2: Have an insulted insult cow lata, it's excellent.

1141
01:02:54,680 --> 01:02:58,200
Speaker 1: Because the best milk comes from sad and depressed.

1142
01:02:57,800 --> 01:03:02,600
Speaker 2: Well in fact industry secret. Uh yeah.

1143
01:03:02,120 --> 01:03:05,680
Speaker 3: I wonder if at some point, when we're used to

1144
01:03:05,720 --> 01:03:09,680
Speaker 3: the idea that robots, we're going to encounter robots every day,

1145
01:03:10,320 --> 01:03:11,640
Speaker 3: you know what I mean, because we're not there yet,

1146
01:03:11,680 --> 01:03:13,600
Speaker 3: but like I feel like in our lifetime we will

1147
01:03:13,640 --> 01:03:16,960
Speaker 3: be like when we get to that point, are there,

1148
01:03:17,080 --> 01:03:19,200
Speaker 3: is there going to be a movement to make them

1149
01:03:19,240 --> 01:03:21,400
Speaker 3: feel more like accessible?

1150
01:03:21,640 --> 01:03:26,080
Speaker 1: Absolutely? Absolutely, I think so. I think I think that

1151
01:03:26,160 --> 01:03:29,240
Speaker 1: we see this with sort of like all kind of

1152
01:03:29,280 --> 01:03:34,320
Speaker 1: product design. It's like really optimized to make us want

1153
01:03:34,360 --> 01:03:38,960
Speaker 1: to you know, because like like robots are going to

1154
01:03:39,000 --> 01:03:42,240
Speaker 1: become kind of consumers at some point, and so there's

1155
01:03:42,280 --> 01:03:46,120
Speaker 1: going maybe the robot that like again is like you know,

1156
01:03:46,840 --> 01:03:50,440
Speaker 1: hurting all the robotic cows, and the robotic cows themselves,

1157
01:03:50,520 --> 01:03:53,120
Speaker 1: like they're all probably gonna look like garbage. But like

1158
01:03:53,280 --> 01:03:57,440
Speaker 1: you know, the robots that are you know, basically there

1159
01:03:57,560 --> 01:04:00,439
Speaker 1: that are visible to humans and doing stuff, We're gonna

1160
01:04:00,440 --> 01:04:02,160
Speaker 1: try to make him cute as hell so that you

1161
01:04:02,200 --> 01:04:05,160
Speaker 1: can tolerate, like are an upset by then? Yeah, Like

1162
01:04:05,320 --> 01:04:07,760
Speaker 1: it's hard to get mad at a robot that took

1163
01:04:07,800 --> 01:04:10,120
Speaker 1: your job when it looks like Wally and it does

1164
01:04:10,160 --> 01:04:11,400
Speaker 1: a little like top half damn.

1165
01:04:11,520 --> 01:04:13,800
Speaker 2: Yeah, and it's really good at it. It's like, well, shucks,

1166
01:04:15,400 --> 01:04:19,520
Speaker 2: well shucks on that. Uh got Do you want to

1167
01:04:19,520 --> 01:04:21,160
Speaker 2: play a little I love a game. Let's do it?

1168
01:04:22,760 --> 01:04:24,960
Speaker 1: Play the game gets to Squawk in the Mystery Animal

1169
01:04:25,000 --> 01:04:27,560
Speaker 1: Sound game. Every week I play a mystery animal sound

1170
01:04:27,560 --> 01:04:29,560
Speaker 1: and you the listener, and you the guests, try to

1171
01:04:29,560 --> 01:04:31,760
Speaker 1: guess who was making that sound. It can be any

1172
01:04:31,800 --> 01:04:33,000
Speaker 1: animal in the world.

1173
01:04:33,520 --> 01:04:33,800
Speaker 5: Uh.

1174
01:04:33,880 --> 01:04:37,200
Speaker 1: Last week's mystery animal sound hint was this This monk

1175
01:04:37,280 --> 01:04:40,000
Speaker 1: is ignoring his vows of celibacy and is on the

1176
01:04:40,080 --> 01:04:41,600
Speaker 1: market for some kinky kanky.

1177
01:04:51,040 --> 01:04:52,360
Speaker 2: All right, Adam, what.

1178
01:04:52,360 --> 01:04:53,240
Speaker 1: Do you think about that?

1179
01:04:53,320 --> 01:04:57,720
Speaker 3: This monk is violating his vow of celibacy and it's

1180
01:04:57,760 --> 01:04:59,240
Speaker 3: down for some hanky panky.

1181
01:05:00,600 --> 01:05:02,280
Speaker 2: That is a mating call.

1182
01:05:03,680 --> 01:05:12,200
Speaker 4: Of a uh a, uh, I'm some kind of uh

1183
01:05:12,680 --> 01:05:14,680
Speaker 4: catholic named monkey.

1184
01:05:15,800 --> 01:05:20,960
Speaker 1: That's that's that is that is close. It's close, but

1185
01:05:21,200 --> 01:05:24,600
Speaker 1: it's not a monkey. Uh. It is a capuchin bar

1186
01:05:24,960 --> 01:05:30,920
Speaker 1: so uh yes, so you got the you got the like, well, actually,

1187
01:05:30,960 --> 01:05:32,560
Speaker 1: I don't know our capuchin monk's Catholic.

1188
01:05:33,680 --> 01:05:35,600
Speaker 2: This is also the name of a monkey, is it not?

1189
01:05:37,280 --> 01:05:39,240
Speaker 1: It is? It is also the name of a monkey,

1190
01:05:39,360 --> 01:05:43,120
Speaker 1: but this one is the bird version of that. So

1191
01:05:43,600 --> 01:05:47,760
Speaker 1: congratulations to Lara W who said that she's heard one

1192
01:05:47,760 --> 01:05:49,760
Speaker 1: of these at the San Diego Zoo, which is a

1193
01:05:49,760 --> 01:05:53,920
Speaker 1: great zoo. So capuchin birds have that name because they

1194
01:05:53,920 --> 01:05:58,760
Speaker 1: look like capuchin monks. It's actually the same monkeys. The

1195
01:05:58,840 --> 01:06:01,440
Speaker 1: little like it's like they look like they have a

1196
01:06:01,440 --> 01:06:03,680
Speaker 1: little brown robe, but they have a bald Yeah.

1197
01:06:03,880 --> 01:06:04,040
Speaker 4: They do.

1198
01:06:05,600 --> 01:06:06,480
Speaker 1: Adam, are you looking at it?

1199
01:06:06,600 --> 01:06:09,120
Speaker 2: I'm super looking at one. They are incredible looking.

1200
01:06:09,360 --> 01:06:13,360
Speaker 1: They're weird. They're you know, it's interesting because as much

1201
01:06:13,400 --> 01:06:18,080
Speaker 1: as I adore vultures and buzzards and like condors, they

1202
01:06:18,080 --> 01:06:20,640
Speaker 1: are kind of ugly because bald head. And I gotta

1203
01:06:20,640 --> 01:06:22,960
Speaker 1: admit it, but I love them. I love them still,

1204
01:06:23,400 --> 01:06:25,960
Speaker 1: these guys, even though they have a bald head. I

1205
01:06:26,000 --> 01:06:30,080
Speaker 1: would describe it's super cute. They're really cute because there's

1206
01:06:30,080 --> 01:06:32,919
Speaker 1: something about like they've got Yes, they're bald, but they're

1207
01:06:33,000 --> 01:06:37,000
Speaker 1: smooth and then they have this enormous cut. It like

1208
01:06:37,200 --> 01:06:41,200
Speaker 1: gives me it's like it's like a little it's the

1209
01:06:41,240 --> 01:06:44,640
Speaker 1: cuteness of like a little old man wearing an extremely

1210
01:06:44,760 --> 01:06:46,320
Speaker 1: fluffy winter coat where you.

1211
01:06:46,280 --> 01:06:53,000
Speaker 3: Just you know, north face winter coat. It does look

1212
01:06:53,040 --> 01:06:55,920
Speaker 3: like a small bird wrapped up so warm and cozy.

1213
01:06:56,080 --> 01:06:56,800
Speaker 3: It really does.

1214
01:06:57,360 --> 01:07:01,680
Speaker 1: Yeah, it's a little bird bird Brito. They're adorable. And

1215
01:07:02,640 --> 01:07:04,480
Speaker 1: why do you think it's making this call?

1216
01:07:04,640 --> 01:07:06,200
Speaker 2: It's got to be a mating call. I mean, birds

1217
01:07:06,200 --> 01:07:07,680
Speaker 2: are the best for mating calls.

1218
01:07:08,800 --> 01:07:09,400
Speaker 1: Absolutely.

1219
01:07:09,920 --> 01:07:11,640
Speaker 2: Yeah, that one was funny too.

1220
01:07:11,720 --> 01:07:16,120
Speaker 1: Yes, this is this is a sexy dial up internet

1221
01:07:16,160 --> 01:07:20,040
Speaker 1: tone that it is using to try to attract a mate.

1222
01:07:20,800 --> 01:07:24,040
Speaker 1: They are found in the Amazon. So the Amazon the

1223
01:07:24,080 --> 01:07:26,200
Speaker 1: best place in the world to find the weird, really

1224
01:07:26,520 --> 01:07:31,000
Speaker 1: wackiest animals. Such a hot spot of culture, of animal diversity.

1225
01:07:31,600 --> 01:07:32,160
Speaker 2: You gotta get it.

1226
01:07:32,160 --> 01:07:34,600
Speaker 3: I gotta get out there once in my life, you know,

1227
01:07:34,800 --> 01:07:37,120
Speaker 3: I got to get out to the Yeah Amazon, honestly,

1228
01:07:37,760 --> 01:07:41,160
Speaker 3: you know, try, you know, see what's out there?

1229
01:07:41,280 --> 01:07:46,920
Speaker 2: Give it. Yeah, get out there, get in there, get it.

1230
01:07:47,240 --> 01:07:48,440
Speaker 1: What are you doing you're still doing?

1231
01:07:48,440 --> 01:07:50,960
Speaker 2: What are you still doing here in that Amazon? Check

1232
01:07:51,000 --> 01:07:52,680
Speaker 2: it out? Gotta do it.

1233
01:07:54,360 --> 01:07:57,960
Speaker 1: Onto this week. This animal sound the hint is this

1234
01:07:58,400 --> 01:08:03,040
Speaker 1: sure sounds fierce, but am sure sounds fierce, but the

1235
01:08:03,040 --> 01:08:05,560
Speaker 1: most you have to fear from him with his chomdia.

1236
01:08:07,360 --> 01:08:26,280
Speaker 5: Yeah, so what.

1237
01:08:26,280 --> 01:08:26,680
Speaker 2: Do you think?

1238
01:08:26,720 --> 01:08:30,920
Speaker 3: That's pretty epic that those events were pretty epic.

1239
01:08:32,160 --> 01:08:36,680
Speaker 1: That was just my that was my tummy. It's lunchtime

1240
01:08:36,720 --> 01:08:40,920
Speaker 1: for me, almost robot talk dinner, dinner time for me.

1241
01:08:42,280 --> 01:08:47,000
Speaker 2: Hm hmm. That's great. I got any guests, oh Am,

1242
01:08:47,000 --> 01:08:49,560
Speaker 2: I supposed to guess? Okay, great, Okay.

1243
01:08:49,560 --> 01:08:50,120
Speaker 5: Go for it.

1244
01:08:50,840 --> 01:08:56,200
Speaker 3: Boy, that's that sounds like sometimes like I'm gonna guess

1245
01:08:56,200 --> 01:08:59,320
Speaker 3: it's some kind of bore it like it's the sex

1246
01:08:59,400 --> 01:09:00,519
Speaker 3: sounds of something of four.

1247
01:09:02,600 --> 01:09:05,280
Speaker 1: That sounds great to me too, But I actually know

1248
01:09:05,360 --> 01:09:07,880
Speaker 1: the answer. I'm not gonna tell you. I'm tell next.

1249
01:09:08,720 --> 01:09:12,720
Speaker 1: I gotta know you'll you'll have to find out on

1250
01:09:12,800 --> 01:09:15,880
Speaker 1: next week's episode of Creature Feature. You think you know

1251
01:09:16,120 --> 01:09:22,439
Speaker 1: who is making that? Just wonderful? Beautiful sense suel sound

1252
01:09:24,200 --> 01:09:26,880
Speaker 1: sense sual sound right to me at Creature Feature Pod

1253
01:09:26,960 --> 01:09:29,360
Speaker 1: at gmail dot com. You can write to me also

1254
01:09:29,400 --> 01:09:33,639
Speaker 1: your questions, pictures of your pet, random bird, you saw

1255
01:09:33,880 --> 01:09:35,679
Speaker 1: a bug you found you thought was cool?

1256
01:09:35,760 --> 01:09:36,519
Speaker 2: Yeah? Whatever?

1257
01:09:37,280 --> 01:09:41,360
Speaker 1: Uh yeah, books are great, Adam, Thank you so much

1258
01:09:41,400 --> 01:09:44,680
Speaker 1: for joining me today pleasure, Where can people? Where can

1259
01:09:44,720 --> 01:09:47,720
Speaker 1: people find you? And the content that you make with

1260
01:09:47,840 --> 01:09:48,800
Speaker 1: your own brain and.

1261
01:09:48,720 --> 01:09:52,559
Speaker 3: Not something definitely not AI content because I don't know

1262
01:09:52,560 --> 01:09:54,720
Speaker 3: how to use it well enough to get UH.

1263
01:09:55,360 --> 01:09:58,160
Speaker 2: You can find all the thousands of podcasts.

1264
01:09:57,720 --> 01:10:02,839
Speaker 3: I do on uh a patreon called smallbeans patreon dot com.

1265
01:10:02,640 --> 01:10:06,200
Speaker 2: Gosh, small beans. They're our podcasts about video games. One

1266
01:10:06,240 --> 01:10:07,479
Speaker 2: op spinship for me.

1267
01:10:07,439 --> 01:10:11,400
Speaker 3: On iHeart Lives. Also some others that I do about movies.

1268
01:10:12,600 --> 01:10:15,160
Speaker 3: I have a new movie podcast.

1269
01:10:14,720 --> 01:10:17,559
Speaker 2: Launching a Magimie fish and thinking April.

1270
01:10:18,439 --> 01:10:20,200
Speaker 3: I'm not sure exactly when, but it will be on

1271
01:10:20,240 --> 01:10:23,400
Speaker 3: her YouTube channel called lynch Pins about David Lynch.

1272
01:10:25,200 --> 01:10:27,479
Speaker 2: It's good. It's a video podcast, so that's.

1273
01:10:27,439 --> 01:10:35,040
Speaker 1: Kind and uh isn't It's.

1274
01:10:33,000 --> 01:10:33,680
Speaker 2: Yes, it is.

1275
01:10:33,720 --> 01:10:36,360
Speaker 3: But you know the advantage of it just being a

1276
01:10:36,439 --> 01:10:39,160
Speaker 3: video podcast is you could have it on your YouTube

1277
01:10:39,600 --> 01:10:41,160
Speaker 3: at work and not look at me.

1278
01:10:41,479 --> 01:10:43,360
Speaker 2: You don't have to look at me, you know. Okay,

1279
01:10:44,240 --> 01:10:46,120
Speaker 2: So that's that's an advance you'll see.

1280
01:10:46,360 --> 01:10:46,479
Speaker 5: UH.

1281
01:10:46,800 --> 01:10:51,200
Speaker 2: So there's that podcast. It's like visual video.

1282
01:10:53,040 --> 01:10:56,680
Speaker 3: And lastly, I have a feature film coming out that

1283
01:10:56,720 --> 01:11:02,439
Speaker 3: I produced, documentary about the sea showing industry called Santaba.

1284
01:11:02,680 --> 01:11:03,760
Speaker 4: Yeah.

1285
01:11:03,840 --> 01:11:06,040
Speaker 3: I believe that will be coming out this year. Keep

1286
01:11:06,040 --> 01:11:08,120
Speaker 3: your eyes filled for it. We're really proud of it.

1287
01:11:08,120 --> 01:11:12,000
Speaker 2: It's come along long way, looks great. We think we don't.

1288
01:11:12,120 --> 01:11:14,719
Speaker 2: We're trying to sell it, and we feel fairly confident

1289
01:11:14,760 --> 01:11:16,200
Speaker 2: that I hope we'll see it.

1290
01:11:16,840 --> 01:11:21,599
Speaker 3: The sealling is yes of the island of Santaba, which

1291
01:11:21,640 --> 01:11:27,400
Speaker 3: is in Florida and was entirely destroyed by HURRICANEUMLIVI.

1292
01:11:26,080 --> 01:11:31,880
Speaker 1: About Oh my god, well that's really interesting. Oh listen, seashells.

1293
01:11:32,120 --> 01:11:36,759
Speaker 1: They're just the clothes of animals, and we love animals.

1294
01:11:36,760 --> 01:11:37,639
Speaker 2: They're just grade close.

1295
01:11:37,840 --> 01:11:41,240
Speaker 3: It's just grape clos that's all it is.

1296
01:11:41,479 --> 01:11:44,560
Speaker 1: It's just the bones that animals wear on the outside.

1297
01:11:45,720 --> 01:11:51,280
Speaker 1: Bones check check show, check out all those things. Highly recommend.

1298
01:11:51,400 --> 01:11:54,160
Speaker 1: I'm excited for this lunch visual.

1299
01:11:55,320 --> 01:11:58,800
Speaker 2: I like how you've grabbed onto that. I'm so happy.

1300
01:11:59,600 --> 01:12:01,840
Speaker 1: I I like David Lynch though you know, I like

1301
01:12:01,880 --> 01:12:04,920
Speaker 1: I like stuff he does, but he's very weird, so

1302
01:12:05,080 --> 01:12:07,720
Speaker 1: I'm yaiud about that. I also I like I like you,

1303
01:12:07,760 --> 01:12:10,679
Speaker 1: and I like Baba by Fish. So what's not what's.

1304
01:12:10,520 --> 01:12:12,920
Speaker 3: Not the point of that podcast is if you've ever

1305
01:12:13,040 --> 01:12:15,840
Speaker 3: wanted to get into David Lanch this is a good

1306
01:12:16,000 --> 01:12:18,599
Speaker 3: entry point for you to like appreciate what he does.

1307
01:12:18,960 --> 01:12:19,639
Speaker 2: That's the idea.

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01:12:20,320 --> 01:12:24,360
Speaker 1: Great, that's fantastic. All right, Well, thank you guys so

1309
01:12:24,439 --> 01:12:27,120
Speaker 1: much for listening. If you leave a rating or review,

1310
01:12:27,280 --> 01:12:30,559
Speaker 1: I am so grateful. I read all the reviews and

1311
01:12:30,640 --> 01:12:33,599
Speaker 1: all the ratings. They truly do help. They make a difference.

1312
01:12:34,160 --> 01:12:39,120
Speaker 1: It tells the algorithm that this podcast is valuable. And uh,

1313
01:12:39,320 --> 01:12:42,480
Speaker 1: you know that's going to be important when everything is algorithms,

1314
01:12:42,520 --> 01:12:46,160
Speaker 1: because it's all robots, just robots talking to robots.

1315
01:12:47,160 --> 01:12:47,320
Speaker 5: Uh.

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01:12:48,000 --> 01:12:50,120
Speaker 1: And thank you to the Space Classics for their super

1317
01:12:50,160 --> 01:12:53,080
Speaker 1: awesome song. Ex Aluminum Creature Feature is a production of

1318
01:12:53,080 --> 01:12:55,240
Speaker 1: iHeartRadio for our podcasts like the one you just heard.

1319
01:12:55,320 --> 01:12:58,920
Speaker 1: Visit I Heart Radio app Apple podcast or where you

1320
01:12:59,040 --> 01:13:01,960
Speaker 1: listen to your favorite job. I don't judge you. I'm

1321
01:13:02,000 --> 01:13:04,280
Speaker 1: not your mother. You've gotta live your life to beat

1322
01:13:04,360 --> 01:13:07,000
Speaker 1: up your own drum. See you next Wednesday.