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Speaker 1: Why are some animals so much smarter than others? And

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Speaker 1: in particular, what is it that makes humans so incredibly smart? Now?

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Speaker 1: I love animals as much as anyone else, and yes,

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Speaker 1: other animals do do things like use tools and communicate

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Speaker 1: with each other, But humans are the only species we

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Speaker 1: know of that uncomplicated research programs to try to understand

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Speaker 1: at a deep level why some animal species are more

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Speaker 1: intelligent than others and how would these differences come about.

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Speaker 1: We humans ask questions like why is it that crows

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Speaker 1: have learned how to fashion sticks into tools that they

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Speaker 1: can use to grab tasty bugs out of the nooks

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Speaker 1: and crannies of trees, But I can't get my dumb

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Speaker 1: chickens to stop eating the paints on the side of

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Speaker 1: the chicken coop? And what kind of conditions would you

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Speaker 1: need to have on another planet in order to produce

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Speaker 1: highly intelligent aliens? These kinds of questions keep me up

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Speaker 1: and also seem to be keeping many of you up

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Speaker 1: at night as well. Today, we're going to tackle three

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Speaker 1: wonderful listener questions that we've received related to intelligence. Welcome

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Speaker 1: to Daniel and Kelly's Extraordinary Universe.

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Speaker 2: I'm Daniel, I'm a particle physicist, and I hope that

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Speaker 2: humans are not the smartest folks in the galaxy.

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Speaker 1: Hello, I'm Kelly Wiener Smith. I study parasites and space,

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Speaker 1: and yeah, I guess I hope we're not the smartest.

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Speaker 1: As long as they're nice and smart, that would be great.

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Speaker 2: I don't even really need to put that caveat on it,

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Speaker 2: Like if they're mean and smart, like, I'll take a

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Speaker 2: little bit of meanness for some secrets of the universe.

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Speaker 1: What if they think we're delicious and they're smart, I

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Speaker 1: think that's a bad combo.

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Speaker 2: I think I don't think I'm certainly very tasty, so

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Speaker 2: I'm not worried that I'd be going to be like

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Speaker 2: high on the list of humans to be consumed.

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Speaker 1: What would make you less tasty than other humans?

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Speaker 2: This is getting personal now?

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Speaker 1: Is us this sprinkled with stinky sauce or something.

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Speaker 2: I actually kind of am marbled through with the ribbons

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Speaker 2: of fats, So maybe I would be quite tasty. I

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Speaker 2: don't know. I try to stay fit.

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Speaker 1: You know, Marbling is a delicacy.

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Speaker 2: I think we just set a record for how quickly

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Speaker 2: we started talking about aliens and consumption of human flesh.

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Speaker 1: Glad to see if by the end of the episode

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Speaker 1: we hit poop and then we'll have the trifecta. But

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Speaker 1: we'll probably get there, all right.

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Speaker 2: So my question for you today, Kelly, is do you

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Speaker 2: think that we will ever crack the barrier to communicating

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Speaker 2: with our fellow intelligent earthlings, Like somehow as AI or

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Speaker 2: future linguistics can help us understand the chirpings of prairie

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Speaker 2: dogs and the whistling of whales.

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Speaker 1: Wow, all right, So I've recently read this really great

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Speaker 1: book called Do Aliens Speak Physics?

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Speaker 3: Ed?

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Speaker 2: That is a great book.

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Speaker 1: It's a fantastic book, and it kind of convinced me

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Speaker 1: that unless you can have two way communication, it's really

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Speaker 1: hard to get on the same page regarding language. You can,

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Speaker 1: like work with dolphins to try to figure stuff out.

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Speaker 1: Maybe we could get some very preliminary like you know,

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Speaker 1: I say hello to you, you say hello to me.

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Speaker 1: We're on the same page there, But like I guess

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Speaker 1: I'm not super optimistic. What kind of communication do you need? Like, so,

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Speaker 1: my dog is really cute but about as smart as

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Speaker 1: a ton of bricks, and so I can't communicate too

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Speaker 1: many complicated ideas to her. But you know, there are

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Speaker 1: some border colligues that are brilliant. So how much communication

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Speaker 1: do you feel like you need?

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Speaker 2: I just kind of want to know what's going on

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Speaker 2: in there. Like the whales are definitely talking to each other.

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Speaker 2: What are they saying? The honeybees are wiggling to each other.

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Speaker 2: This definitely in for me. I just want to translate

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Speaker 2: that information somehow, to know what they think is interesting

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Speaker 2: to talk about. And like you, I know that my

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Speaker 2: dog understands a lot of stuff, Like he picks up

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Speaker 2: on pretty subtle patterns, so he knows when he's getting

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Speaker 2: a walk. You know, shoes on plus the leash means

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Speaker 2: a walk, shoes on plus the backpack I means Daniel's

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Speaker 2: going to work this kind of stuff. And he notices

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Speaker 2: patterns through the day, like he's definitely paying attention to

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Speaker 2: patterns and understanding stuff. But I wish we could talk.

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Speaker 2: I wish we could commun in the game more. I

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Speaker 2: wish I could tell him like, Hey, we're getting in

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Speaker 2: the car and we're going to this thing, or we're

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Speaker 2: not going to the vet. You know this information I

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Speaker 2: wish we could share with him, Like my dog when

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Speaker 2: we leave, he has no idea are we going on

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Speaker 2: a three week trip? Are we going to be back

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Speaker 2: in an hour? He's cool, and if I could talk

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Speaker 2: to him, I could share that with him.

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Speaker 1: So, first of all, I think it's very nice that

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Speaker 1: you're worried about how your dog's going to feel about

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Speaker 1: how long you're gone. I think that's very sweet. So

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Speaker 1: bees do this waggle dance, and you can tell by

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Speaker 1: the way that they're dancing, sort of like the direction

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Speaker 1: and the distance that they're telling their nest mats. We're

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Speaker 1: the great source of pollen or nectar is and so

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Speaker 1: we can decipher that. But you know, I don't think

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Speaker 1: that I could like dress up in a bee suit

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Speaker 1: and shake my butt the right way and tell them like, hey,

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Speaker 1: I just planted some flowers in the southern corner of

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Speaker 1: the property. I mean, I think we can decode a

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Speaker 1: lot of what animals say, but we have not yet

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Speaker 1: figured out how to communicate back with them.

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Speaker 2: Yeah. Well, part of that depends on what's going on

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Speaker 2: inside those animals' minds and what intelligence really means and

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Speaker 2: where it comes from. So let's dig into the episode today.

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Speaker 2: I'm dying here what you had to tell us.

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Speaker 1: I'm super excited about today's episode. But now I've got

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Speaker 1: myself wondering, like, could you make a little electronic bee

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Speaker 1: that was like a bee size and like do the

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Speaker 1: waggle dance and maybe get the bees to go to

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Speaker 1: like your source of nectar. Anyway, surely there's got to

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Speaker 1: be funding for that. That sounds awesome. I find questions

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Speaker 1: about animal intelligence to be absolutely fascinating, and this is

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Speaker 1: actually a topic that we get a lot of questions

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Speaker 1: from y'all about, And if you want to send us

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Speaker 1: your questions, you can send us questions at questions at

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Speaker 1: danieland Kelly dot org. So today we're going to tackle

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Speaker 1: three of the questions that we've gotten from listeners about

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Speaker 1: animal intelligence and they sort of build on one another,

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Speaker 1: and so we're gonna, you know, do a bit of

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Speaker 1: an overview on animal intelligence today.

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Speaker 2: This is super cool. Second new kind of episode is

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Speaker 2: like a listener questions themes episode, almost like these listeners

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Speaker 2: are working together to send us questions that click together

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Speaker 2: so nicely.

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Speaker 1: That's right, Yeah, and these three click together very nicely.

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Speaker 1: You know, we could have done something similar with our

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Speaker 1: consciousness episode, but I think we got twenty questions about

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Speaker 1: consciousness before we were like, all right, we get it,

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Speaker 1: We're going to get an expert. Hopefully we answered everyone's

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Speaker 1: questions with that one. But today we are going to

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Speaker 1: start with a question from Tom. So let's go ahead

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Speaker 1: and hear Tom's question.

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Speaker 4: I'm reasonably confident that humans are the only Earth species

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Speaker 4: that has ever built rockets and such. So why is

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Speaker 4: this level of intelligence so rare? Life on Earth is

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Speaker 4: incredibly diverse and the Earth has been teeming with it

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Speaker 4: for billions of years? How does the species even develop

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Speaker 4: rocket level intelligence? Is intelligence a biologically selectable property that

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Speaker 4: improves the chances for a population to survive? Or is

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Speaker 4: rocket level intelligence just dumb luck? After all, the fact

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Speaker 4: that rocket level intelligence emerged on Earth has proved to

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Speaker 4: be quite unlucky for many coexisting species and may yet

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Speaker 4: still be unlucky for humans. Thanks.

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Speaker 1: Wow. Okay, so that is a great question, but it's

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Speaker 1: a very specific question. So why is a rocket level

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Speaker 1: intelligence so rare? So we have an end of one.

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Speaker 1: We know of one species that is able to build rockets,

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Speaker 1: and with such a small sample size it can be

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Speaker 1: really hard to know for sure. A bit of a

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Speaker 1: spoiler here, we don't know. In general, science does not

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Speaker 1: know exactly what it was that put humans on the

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Speaker 1: trajectory that we went on to be able to build

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Speaker 1: rockets and all the other amazing things that we can do.

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Speaker 1: We will talk about some of the hypotheses today that

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Speaker 1: we think were important, but first let's back up and

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Speaker 1: ask the question why can't all species build rockets? Why

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Speaker 1: is this kind of intelligence so rare?

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Speaker 2: I mean, ants and termites can build impressive stuff, right,

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Speaker 2: they definitely work together, and we see other species doing

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Speaker 2: cool stuff. So why aren't they building rockets and getting

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Speaker 2: off planet? What does that really require?

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Speaker 1: So again, the rocket thing is very specific, and part

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Speaker 1: of it is, you know, ants just might not have

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Speaker 1: the right appendages to build a rocket. You know, maybe

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Speaker 1: they've got rocket plans and if they just had hands,

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Speaker 1: they'd be building rockets, but probably not.

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Speaker 2: That's the kind of thing we could learn if we

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Speaker 2: learned to speak ant and be you know, maybe they're

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Speaker 2: desperate to fly into space and frustrated about their limitations.

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Speaker 1: We should be bringing them with us just you know,

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Speaker 1: they might.

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Speaker 2: Appreciate that, or we should just have them on the

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Speaker 2: podcast and ask them directly.

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Speaker 1: Maybe in five or ten years. So if you ask yourself,

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Speaker 1: why can't all species build rockets. You're essentially asking, well,

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Speaker 1: why aren't all animals intelligent? What holds you back? And

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Speaker 1: we think the answer has to do with how much

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Speaker 1: energy it takes to be super smart. So our adult

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Speaker 1: brains weigh about three pounds, so for a person who

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Speaker 1: weighs one hundred and eighty pounds, that's about two percent

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Speaker 1: of your body weight. But by measuring oxygen consumption rates

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Speaker 1: in different parts of our body, we estimate that our

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Speaker 1: brain uses about twenty percent of all of the energy

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Speaker 1: that we burn every single day. So this very small

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Speaker 1: organ is using an outsized amount of energy. So brains

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Speaker 1: are just energetically expensive. So if you are a species

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Speaker 1: that doesn't need a super big, smart brain, then there's

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Speaker 1: probably not great selection pressure for you to be expending

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Speaker 1: a bunch of energy on an energetically expensive organ that

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Speaker 1: you are not using.

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Speaker 2: So you're saying that this thing is expensive, right, that

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Speaker 2: it costs us something, and then so if it doesn't

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Speaker 2: pay for itself, it's definitely not going to survive because

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Speaker 2: I've hung around and used up a bunch of energy

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Speaker 2: and didn't provide tangible benefits. Then folks with big brains

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Speaker 2: would die more often during famines and cold winters and

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Speaker 2: stuff like that. That's the idea.

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Speaker 1: Yeah, so this is more of a species level argument.

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Speaker 1: But yeah, so species with big brains that didn't need

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Speaker 1: them would have to spend more time foraging or get better,

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Speaker 1: higher quality food because of their big brains. And if

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Speaker 1: that's not happening, it's not worth having.

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Speaker 2: I guess you could also imagine a population, right of

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Speaker 2: a single species with smaller brains and bigger brains, And

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Speaker 2: if the bigger brains aren't providing a benefit and they

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Speaker 2: are providing a cost, then those big brain folks among

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Speaker 2: us are not going to survive. There is a population

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Speaker 2: level argument there also, right.

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Speaker 1: Yeah, there's a population level argument. There's often more variables

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Speaker 1: at play when you start thinking about a specific species. So,

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Speaker 1: for example, when human brains, if they were to get

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Speaker 1: too big, you would start to have trouble getting those

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Speaker 1: giant heads through the birth canal. And so there's limits

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Speaker 1: on how big brains can be. And in general, there's

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Speaker 1: not a ton of variability between the sizes of brains

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Speaker 1: of adults. And now we can get into this whole

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Speaker 1: different debate about is it really about how big the

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Speaker 1: brain is? So like blue whales have much bigger brains

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Speaker 1: than we do, but they haven't created rockets to get

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Speaker 1: them out of space. Maybe that's because it's just too

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Speaker 1: expensive to send a giant blue whale to space. But yeah,

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Speaker 1: so trying to figure out exactly what feature of the

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Speaker 1: brain we should measure is difficult. But we know that

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Speaker 1: brains are expensive energetically, right, And.

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Speaker 2: I was actually reading a study about the size of

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Speaker 2: the human brain that suggested that if you packed in

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Speaker 2: the neurons more densely, you would add more noise. So

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Speaker 2: making the neurons smaller and denser wouldn't actually improve your

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Speaker 2: cognition because you'd be ending up adding noise. And so like,

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Speaker 2: human brains are sort of at this sweet spot for

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Speaker 2: how much you can squeeze them down to get cognition.

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Speaker 1: I think maybe one of these days we should have

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Speaker 1: a whole separate episode on exactly how you should measure

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Speaker 1: brains to correlate it with intelligence, because I read a

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Speaker 1: paper that was arguing that actually, the reason we're so

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Speaker 1: great is because we have denser neurons, and that it's

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Speaker 1: not brain size, it's neuron density that really unlocks intelligence.

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Speaker 1: I think in general, the field has not figured out

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Speaker 1: exactly what we need to be measuring.

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Speaker 2: That's such a nice way of saying that these folks

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Speaker 2: don't know what they're doing. Well, you gotta start is immature, Yes, well.

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Speaker 1: Yes, brains are complicated man, Yes they are.

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Speaker 2: No, you're totally right in great respect to folks who

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Speaker 2: are studying the brain, because obviously it's important. And just

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Speaker 2: because we don't understand everything doesn't mean that early studies

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Speaker 2: are not valuable. They're the ones that lay the foundation

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Speaker 2: for future progress. Yes, absolutely, and so we shouldn't mock them.

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Speaker 2: We're grateful for their pioneering work.

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Speaker 1: Yes, I studied a brain infecting parasite of fish and

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Speaker 1: I started getting into neuroanatomy and neurochemistry and I was like, no,

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Speaker 1: I'm sticking with parasitology. Brains are complicated, so anyway, but

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Speaker 1: here we're jumping in anyway.

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Speaker 2: I think also Tom's question has this fascinating flavor to it,

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Speaker 2: which is like, you can make the evolutionary argument, and

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Speaker 2: I think you're going to get into this later for

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Speaker 2: why intelligence might be beneficial, right, it might even overcome

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Speaker 2: the expense of the brain. But it's hard to understand

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Speaker 2: why evolution would give us a brain that's capable of

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Speaker 2: doing things that you didn't need to survive on the savannah, Right,

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Speaker 2: You didn't need to be able to think about eleven

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Speaker 2: dimensional mathematics and build rockets and all sorts of other

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Speaker 2: easy stuff in order to survive evolutionarily. So why is

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Speaker 2: it that evolution provided this tool which has this crazy

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Speaker 2: capability clearly outstripping our needs.

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Speaker 1: Yeah, well, you could ask a similar question like why

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Speaker 1: do peacocks have such big, showy tales? And I think

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Speaker 1: that you know, natural selection doesn't just select for things

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Speaker 1: that help us run faster to catch our food or

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Speaker 1: help us garden more efficiently. It also selects for traits

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Speaker 1: that make us more attractive mates in one way or another.

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Speaker 1: So sometimes it makes you know, you bigger and more

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Speaker 1: muscly if that's what the females want. But you know,

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Speaker 1: there's also variability in what females of a species want,

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Speaker 1: And some of us want super smart partners, our partners

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Speaker 1: with musical abilities. And I think humans are kind of

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Speaker 1: weird in this respect, that we really are interested in

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Speaker 1: intellectual characteristics in addition to just physical characteristics. And I

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Speaker 1: don't know exactly how that played into our evolution and

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Speaker 1: made us unique. But you know, if you can do

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Speaker 1: nine dimensional math, there's got to be a group of

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Speaker 1: people who are into that.

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Speaker 2: Since both of us are married to nerds, I think

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Speaker 2: we may be an unrepresentative subset of humanity. But I

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Speaker 2: totally agree with you. Big brains are hot.

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Speaker 1: Yes, it's super hot, way more hot than muscles and

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Speaker 1: being in good shape, I think. But could be biased. Okay,

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Speaker 1: So the question was essentially about how do we get

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Speaker 1: humans in particular, and the answer is, we don't know

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Speaker 1: about how you get humans in particular, but we can

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Speaker 1: get some hints by looking at other kinds of animals

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Speaker 1: and trying to figure out what things selected for intelligence

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Speaker 1: in other animals, and that might give us some insights

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Speaker 1: into like maybe you know, we maxed out a particular

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Speaker 1: you know, trait, or there was a particularly strong selective

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Speaker 1: pressure for us. So we're going to start this conversation

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Speaker 1: by talking about how we define intelligence outside of the

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Speaker 1: ability to build rockets, and then some difficulties with measuring it,

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Speaker 1: and towards the end of the episode, when we're answering

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Speaker 1: other questions from other listeners, we'll talk about some of

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Speaker 1: the leading hypotheses. So we're going to get to.

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Speaker 2: Them, all right. I love when we start a hard

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Speaker 2: conversation with let's define what we're talking about, because it

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Speaker 2: makes me feel all philosophical and nerdy. But also it's

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Speaker 2: really important. If somebody doesn't do that, then I feel

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Speaker 2: like they're going to be sloppy with their arguments because

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Speaker 2: they're not being precise with their words.

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Speaker 1: Well, and I should say that while I was researching

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Speaker 1: this topic, I came across a lot of different definitions,

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Speaker 1: and I decided to try to go with something a

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Speaker 1: little bit more general that feels pretty good to all

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Speaker 1: of us. So here's what I came up with. It's

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Speaker 1: just the ability to solve lots of different kinds of problems,

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Speaker 1: because that's what they all seemed to narrow down to.

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Speaker 1: But what do you think how would you define intelligence?

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Speaker 2: I definitely think the ability to solve problems is a

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Speaker 2: component of it. It feels to me like also there

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Speaker 2: should be a learning aspect, right, discovering patterns generalizing from them,

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Speaker 2: you know, in machine learning, that's a big factor. We

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Speaker 2: want to develop networks that are not just capable of

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Speaker 2: solving the problems we've taught them, but solving a general

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Speaker 2: class of problems, including examples they haven't seen before, bringing

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Speaker 2: together ideas, coming up with bigger picture solutions. So something

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Speaker 2: about generalizing from the examples that you've learned feels to

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Speaker 2: me like an important part of intelligence.

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Speaker 1: Yeah, and I think that having the ability to solve

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Speaker 1: lots of different kinds of problems does, to some extent

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Speaker 1: imply the ability to learn, Okay, yeah, or at least

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Speaker 1: to experiment with your environment to try new things, which

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Speaker 1: I think is like learning. So I think we're pretty

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Speaker 1: much on the same page. What do you think.

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Speaker 2: I think that's a good working definition. Okay, So then

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Speaker 2: how do we measure this? Are we like giving tests

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Speaker 2: to cats and dogs to see can you do eleven

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Speaker 2: dimensional string theory?

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Speaker 1: We're not asking them about eleven dimensional string theory. We

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Speaker 1: will talk about some experiments that we do, but first

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Speaker 1: I wanted to tell a couple of stories about how

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Speaker 1: we've thought we were measuring something but it turns out

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Speaker 1: we're measuring something different. Just to highlight how difficult it is.

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Speaker 2: Here comes wet Blanket Kelly.

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Speaker 1: Come on, of course, we were going to get to

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Speaker 1: wet Blanka Kelly. Okay, So clever Hans is horse in

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Speaker 1: Germany at the end of the nineteenth century, and this

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Speaker 1: horse became super famous because you could ask him questions

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Speaker 1: about addition, subtraction, multiplication, division, including questions that involved fractions wow,

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Speaker 1: and he would stomp out answers, so he would like

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Speaker 1: stomp and then when he was done answering the question,

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Speaker 1: so for the right numbers, like if the answer was five,

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Speaker 1: he'd stop five times and then he'd sort of do

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Speaker 1: like a little circle thing with his hoof to be

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Speaker 1: like okay, I'm done.

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Speaker 2: Wow.

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Speaker 1: And then you could also ask Clever Hans questions like

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Speaker 1: what is that woman holding in her hand? And using

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Speaker 1: an alphabet that was on a board, each letter was

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Speaker 1: separated into columns and rows, he could clamp out what

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Speaker 1: letters he wanted to say, you know, umbrella or hot

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Speaker 1: dog or something like that.

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Speaker 2: That implies some ability to read, right, yeah.

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Speaker 1: Yeah, yeah right. Some experts came in and they were like,

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Speaker 1: oh my gosh, we totally agree because we can do

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Speaker 1: the trick with Clever Hans too. We can ask clever

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Speaker 1: Hans the same question, it's not just his owner, and

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Speaker 1: he can answer it. So they decided that Clever Hans

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Speaker 1: had the intelligence that was equivalent to about a thirteen

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Speaker 1: year old child. But then another set of experimenters came

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Speaker 1: in and they started walling clever Hans off from the

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Speaker 1: room where the questioner was asking.

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Speaker 2: Oh, yeah, that's right.

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Speaker 1: It turns out that clever Hans could only answer questions

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Speaker 1: correctly if the experimenter knew the answer ahead of time,

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Speaker 1: and if Hans could see the experimenter.

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Speaker 2: Oh, man, this is just like those experiments with the

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Speaker 2: wiki boards, right, uh right.

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Speaker 1: Like something else is sort of happening behind the scenes

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Speaker 1: that's unintended. So it turned out clever Hans isn't smart

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Speaker 1: in terms of the ability to do like division, but

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Speaker 1: clever Hans was very smart at reading human body language.

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Speaker 1: So even if you put a new person in that

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Speaker 1: clever Hans had never encountered before, Clever Hans could tell

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Speaker 1: that they were like holding their breath until he got

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Speaker 1: to like the right number of stomps, and that then

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Speaker 1: something about their body would change and then he'd know, okay,

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Speaker 1: that was it. And so clever Hans was actually just

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Speaker 1: queuing it on body language.

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Speaker 2: Wow, So clever Hans could really read human emotion and

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Speaker 2: like understand what humans wanted him to do and then

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Speaker 2: would do it.

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Speaker 1: Yes, right. But this points out one of the first

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Speaker 1: difficult things about studying animals is that sometimes it can

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Speaker 1: be really hard to figure out what you're measuring or

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Speaker 1: what it is that they're actually learning, and it's not

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Speaker 1: always what you think that they're learning. So you have

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Speaker 1: to be very careful about how you design experiments, and

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Speaker 1: you need to keep in mind that animals sense the

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Speaker 1: world in different ways. So you know, some animals echo locate,

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Speaker 1: some can see polarized light, some explore their universe using electricity,

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Speaker 1: and so you need to make sure that you're asking

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Speaker 1: questions that make sense with their sensory systems.

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Speaker 2: And not just for animals, right, like humans. Also, it's

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Speaker 2: very hard to measure intelligence among humans. A lot of

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Speaker 2: these tests have cultural biases in them.

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Speaker 1: For example, yep, yeah, absolutely, this is just really hard stuff.

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Speaker 1: And then how do you compare you know, intelligence and

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Speaker 1: a pigeon where maybe the amazing thing is their ability

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Speaker 1: to like orient and travel great distances in their great

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Speaker 1: spatial memory. And then compare that to you know, like

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Speaker 1: a really smart crow that can whittle ass stick so

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Speaker 1: that it can get at bugs in a tree, or like,

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Speaker 1: how do you compare intelligence between those two different organisms.

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Speaker 2: Right, it seems like it's obviously not a single number.

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Speaker 2: It's a multi dimensional right, And anytime you try to

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Speaker 2: boil something complex down to a single number, and you're

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Speaker 2: going to lose a lot of nuance.

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Speaker 1: Yes you are. And I want to tell one more

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Speaker 1: quick story about when we thought that we had taught

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Speaker 1: animals something and we had taught them something else. So

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Speaker 1: there's this thing called perceptual categories, where essentially you're trying

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Speaker 1: to figure out if animals can categorize things in ways

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Speaker 1: similar to how humans can. So people showed pigeons a

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Speaker 1: bunch of different pictures of things, and they trained the

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Speaker 1: pigeons to peck a key and get a reward whenever

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Speaker 1: a person was in the image, and so they actually

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Speaker 1: got pretty good at this. Over time. They would pay

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Speaker 1: the images, even if it was an image they had

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Speaker 1: never seen before, if there was a person in it.

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Speaker 1: And eventually they were able to get these pigeons to

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Speaker 1: pick out pictures with bodies of water, pictures with trees,

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Speaker 1: pictures with fish. So it turns out they were actually

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Speaker 1: really good at coming up with these categories if you

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Speaker 1: trained them. And then we did it with capuchin monkeys

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Speaker 1: and we trained them on a bunch of different photos

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Speaker 1: of people, and they were doing pretty well. But the

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Speaker 1: experimenters did something really clever. They looked at the mistakes

437
00:21:27,040 --> 00:21:29,560
Speaker 1: the monkeys were making and tried to see if there

438
00:21:29,600 --> 00:21:33,199
Speaker 1: was anything informative in those mistakes. And one mistake the

439
00:21:33,200 --> 00:21:36,639
Speaker 1: monkeys made was they did not count as a picture

440
00:21:36,680 --> 00:21:40,600
Speaker 1: of a human anything that was torso and higher. So

441
00:21:40,720 --> 00:21:42,439
Speaker 1: like you know, you think of your school pictures from

442
00:21:42,440 --> 00:21:44,280
Speaker 1: when you were a kid, and it's like your torso

443
00:21:44,320 --> 00:21:47,399
Speaker 1: and higher. The monkeys did not think that was a person.

444
00:21:49,520 --> 00:21:51,280
Speaker 2: They're counting legs only or what.

445
00:21:51,760 --> 00:21:53,760
Speaker 1: They didn't know exactly what they were counting, but they

446
00:21:53,800 --> 00:21:56,880
Speaker 1: hadn't generalized a person. There was like a very particular

447
00:21:57,240 --> 00:22:00,320
Speaker 1: all has to be there, So they hadn't learned exactly

448
00:22:00,400 --> 00:22:04,080
Speaker 1: the category we had in mind. And they counted as

449
00:22:04,119 --> 00:22:07,320
Speaker 1: a person an image of a jackal that was carrying

450
00:22:07,359 --> 00:22:11,480
Speaker 1: a dead flamingo. And the reason they ended up thinking

451
00:22:11,560 --> 00:22:13,679
Speaker 1: that the monkeys were categorizing that as a person is

452
00:22:13,680 --> 00:22:17,119
Speaker 1: because that image had read in it, and the only

453
00:22:17,280 --> 00:22:20,760
Speaker 1: other images in the training set that had read were

454
00:22:20,800 --> 00:22:23,560
Speaker 1: three of the pictures of people, and so maybe they

455
00:22:23,600 --> 00:22:25,800
Speaker 1: hadn't actually learned what humans were, but they had learned

456
00:22:25,840 --> 00:22:27,639
Speaker 1: something about the image set, and we have problems with

457
00:22:27,680 --> 00:22:28,960
Speaker 1: this with AI training sets.

458
00:22:29,000 --> 00:22:32,160
Speaker 2: Also, Yeah, there's a famous example of trying to teach

459
00:22:32,160 --> 00:22:34,920
Speaker 2: an AI to tell the difference between wolves and dogs,

460
00:22:35,640 --> 00:22:37,520
Speaker 2: and it was doing really, really well, and then they

461
00:22:37,560 --> 00:22:40,640
Speaker 2: discovered that the pictures of wolves have snow in the background,

462
00:22:40,640 --> 00:22:43,159
Speaker 2: pictures of dogs don't. As it was like learning to

463
00:22:43,200 --> 00:22:45,359
Speaker 2: tell the difference between grass and snow, which is not

464
00:22:45,400 --> 00:22:46,240
Speaker 2: that hard.

465
00:22:46,240 --> 00:22:48,960
Speaker 1: Right, right, And this was an experiment that was designed

466
00:22:48,960 --> 00:22:51,240
Speaker 1: by humans who apparently had tried to think this stuff through.

467
00:22:51,280 --> 00:22:53,639
Speaker 1: It's just really hard. So now that we have a

468
00:22:53,720 --> 00:22:56,280
Speaker 1: sense of how difficult it is to ask these kinds

469
00:22:56,280 --> 00:22:59,400
Speaker 1: of questions, let's take a break and when we come back,

470
00:22:59,440 --> 00:23:01,480
Speaker 1: we'll move on to the next question, which will bring

471
00:23:01,520 --> 00:23:05,000
Speaker 1: us to some of the hypotheses for what selects for intelligence.

472
00:23:22,200 --> 00:23:24,960
Speaker 2: Okay, we're back, and we are tackling some very difficult

473
00:23:24,960 --> 00:23:28,000
Speaker 2: but fascinating questions from listeners today about the nature of

474
00:23:28,080 --> 00:23:32,159
Speaker 2: intelligence and YBS have not yet gone off planet, and

475
00:23:32,200 --> 00:23:34,639
Speaker 2: we started with a really fun question from Tom about

476
00:23:34,680 --> 00:23:38,320
Speaker 2: how you end up with rocket level intelligence, and Kelly

477
00:23:38,320 --> 00:23:40,160
Speaker 2: shed some light on how hard it is to even

478
00:23:40,200 --> 00:23:43,800
Speaker 2: define and measure intelligence and through a wet blanket. I'm

479
00:23:43,800 --> 00:23:48,520
Speaker 2: basically the whole history of that field my day, and

480
00:23:48,640 --> 00:23:50,920
Speaker 2: so slightly unusually, we're not going to give a complete

481
00:23:50,920 --> 00:23:53,399
Speaker 2: and definite answer to Tom's question right now. We're going

482
00:23:53,480 --> 00:23:56,200
Speaker 2: to keep digging into this by answering more questions from

483
00:23:56,240 --> 00:23:58,360
Speaker 2: listeners and then hope at the end to come back

484
00:23:58,359 --> 00:24:01,520
Speaker 2: and give you our best answer to all of your queries.

485
00:24:01,880 --> 00:24:04,600
Speaker 1: All right, So let's go ahead then and jump into

486
00:24:04,680 --> 00:24:05,960
Speaker 1: question two from.

487
00:24:05,800 --> 00:24:09,080
Speaker 3: Mark, Hi, Daniel, and Kelly. My question has to do

488
00:24:09,119 --> 00:24:13,360
Speaker 3: with evolution and predation. Do you think it's possible for

489
00:24:13,720 --> 00:24:18,480
Speaker 3: complex intelligent life to evolve without predation? Or do we

490
00:24:18,520 --> 00:24:22,200
Speaker 3: think that predation is necessary for beings with human level

491
00:24:22,240 --> 00:24:27,320
Speaker 3: intelligence or higher intelligence to exist? If there are aliens

492
00:24:27,359 --> 00:24:32,360
Speaker 3: capable of interstellar travel that have never known predation, I imagine

493
00:24:32,280 --> 00:24:35,960
Speaker 3: they would find Earth to be a nightmare with billions

494
00:24:36,000 --> 00:24:40,880
Speaker 3: of organisms being murdered and consumed by other organisms every day.

495
00:24:41,760 --> 00:24:43,400
Speaker 3: Interested to know your thoughts.

496
00:24:43,400 --> 00:24:47,280
Speaker 1: Thanks, okay, wow, all right? So when I first listened

497
00:24:47,320 --> 00:24:50,520
Speaker 1: to this question, I was confused because I hadn't heard

498
00:24:50,760 --> 00:24:55,320
Speaker 1: this hypothesis that predation is important for producing intelligent life.

499
00:24:55,720 --> 00:24:57,520
Speaker 1: Had you heard this idea before Daniel.

500
00:24:57,760 --> 00:25:00,280
Speaker 2: I hadn't thought about in terms of predation. I thought

501
00:25:00,280 --> 00:25:04,080
Speaker 2: about in terms of like society and culture, that maybe

502
00:25:04,119 --> 00:25:08,040
Speaker 2: an important component of intelligence is having a complex interaction

503
00:25:08,119 --> 00:25:10,840
Speaker 2: between the individuals in the species more than just like

504
00:25:10,920 --> 00:25:13,399
Speaker 2: solitary folks. So I'm not sure how that connects to

505
00:25:13,480 --> 00:25:17,520
Speaker 2: predation or being preyed on, or preying on people, like

506
00:25:17,640 --> 00:25:20,679
Speaker 2: how taking down a mammoth requires like ten to fifteen people.

507
00:25:21,320 --> 00:25:22,480
Speaker 2: Is that the same sort of thing.

508
00:25:22,680 --> 00:25:25,680
Speaker 1: So that's the social intelligence hypothesis, which we're going to

509
00:25:25,720 --> 00:25:29,359
Speaker 1: get to next. But so the idea behind this predation

510
00:25:29,520 --> 00:25:32,600
Speaker 1: hypothesis is that you end up with an arms race

511
00:25:32,680 --> 00:25:35,080
Speaker 1: between predators in prey. So what happens here, So you've

512
00:25:35,119 --> 00:25:37,800
Speaker 1: got a predator that needs to be able to see

513
00:25:37,840 --> 00:25:39,800
Speaker 1: the prey so that it can find it in a

514
00:25:39,840 --> 00:25:41,600
Speaker 1: tree or something. You know, if it's hiding in a tree,

515
00:25:41,640 --> 00:25:43,879
Speaker 1: it kind of blends in, and so that selects for

516
00:25:43,960 --> 00:25:47,639
Speaker 1: them to have better vision or a better memory for

517
00:25:47,840 --> 00:25:51,320
Speaker 1: the kinds of behaviors that their prey typically engages in. So,

518
00:25:51,359 --> 00:25:53,879
Speaker 1: for example, if a certain kind of prey that's extra

519
00:25:54,000 --> 00:25:57,720
Speaker 1: super delicious is only active at dusk and dawn, then

520
00:25:57,760 --> 00:26:00,640
Speaker 1: maybe you focus your predation efforts at dusk and dawn,

521
00:26:00,720 --> 00:26:03,240
Speaker 1: because you've remembered that that's when the prey that's super

522
00:26:03,280 --> 00:26:06,760
Speaker 1: tasty is active. And on the other hand, the prey

523
00:26:07,080 --> 00:26:11,280
Speaker 1: might remember that when I smell cat urine, that means

524
00:26:11,320 --> 00:26:13,159
Speaker 1: that there's a cat in the area, and so anywhere

525
00:26:13,200 --> 00:26:15,119
Speaker 1: I smell cat urine, I'm just not going to hang

526
00:26:15,160 --> 00:26:18,640
Speaker 1: out anymore. And so you end up with this competition

527
00:26:18,720 --> 00:26:21,280
Speaker 1: for sensory systems and the ability to sort of like

528
00:26:21,359 --> 00:26:24,840
Speaker 1: think through risks and benefits that over time results in

529
00:26:24,880 --> 00:26:26,160
Speaker 1: smarter and smarter brains.

530
00:26:26,520 --> 00:26:30,160
Speaker 2: So is the argument just that smarter predators are more

531
00:26:30,200 --> 00:26:33,720
Speaker 2: successful and smarter prey and more successful that it's one

532
00:26:33,800 --> 00:26:37,440
Speaker 2: element of success in this sort of predator versus prey environment.

533
00:26:37,640 --> 00:26:38,480
Speaker 1: Yep, exactly.

534
00:26:38,720 --> 00:26:41,040
Speaker 2: But there's lots of predator prey situations and a lot

535
00:26:41,080 --> 00:26:42,920
Speaker 2: of times one or both of them are kind of

536
00:26:42,960 --> 00:26:46,719
Speaker 2: dumb deer not that smart, for example, though they've been

537
00:26:46,840 --> 00:26:50,080
Speaker 2: highly selected for because people like wolves been gobbling up

538
00:26:50,080 --> 00:26:53,480
Speaker 2: deer for many years. Is this just one possible way

539
00:26:53,520 --> 00:26:56,080
Speaker 2: evolution can go or is the argument that prey and

540
00:26:56,119 --> 00:26:58,119
Speaker 2: predation always leads to intelligence.

541
00:26:58,400 --> 00:27:01,840
Speaker 1: Yeah. So, first I'll note that it wouldn't surprise me

542
00:27:01,880 --> 00:27:03,960
Speaker 1: to find out that deer have gotten more stupid over

543
00:27:04,040 --> 00:27:07,800
Speaker 1: time because there's not tons of hunters out there and

544
00:27:07,840 --> 00:27:10,879
Speaker 1: we've killed most of their predators on this continent, so

545
00:27:11,280 --> 00:27:14,120
Speaker 1: they are perhaps a little bit relieved from the pressure

546
00:27:14,119 --> 00:27:17,399
Speaker 1: of this arms race, and maybe that explains their intelligence.

547
00:27:17,520 --> 00:27:19,480
Speaker 1: But yeah, so I think to me, that's one of

548
00:27:19,520 --> 00:27:22,760
Speaker 1: the issues with this hypothesis is that it's not as

549
00:27:22,760 --> 00:27:26,080
Speaker 1: far as I can tell, really clearer about which sets

550
00:27:26,080 --> 00:27:28,679
Speaker 1: of predators in prey or which kinds of predators should

551
00:27:28,720 --> 00:27:31,439
Speaker 1: end up, you know, maxing out being super smart and

552
00:27:31,520 --> 00:27:33,600
Speaker 1: which prey should end up being super smart. But it's

553
00:27:33,640 --> 00:27:36,040
Speaker 1: sort of more of a like, well, this is why

554
00:27:36,400 --> 00:27:39,120
Speaker 1: you end up with complex thought at all, and then

555
00:27:39,240 --> 00:27:41,960
Speaker 1: it just sort of depends on how much complex thought

556
00:27:41,960 --> 00:27:44,200
Speaker 1: you need, depending on what kinds of races you find

557
00:27:44,200 --> 00:27:46,879
Speaker 1: yourself in and with what kinds of organisms.

558
00:27:47,000 --> 00:27:49,239
Speaker 2: Because it's not hard to think of other examples, you know,

559
00:27:49,320 --> 00:27:53,720
Speaker 2: like spiders and flies, right, flies are very good evading spiders,

560
00:27:53,720 --> 00:27:56,040
Speaker 2: but they're not using intelligence. They're just like have a

561
00:27:56,240 --> 00:27:59,119
Speaker 2: crazy hair trigger and you can jump out of the

562
00:27:59,160 --> 00:28:00,960
Speaker 2: way in a bil I.

563
00:28:00,880 --> 00:28:03,320
Speaker 1: Mean that's pretty impressive as well, Like you know, those

564
00:28:03,400 --> 00:28:06,280
Speaker 1: crazy hair triggers could be because you know, you've had

565
00:28:06,320 --> 00:28:10,439
Speaker 1: selection for hairs that are particularly sensitive to you know,

566
00:28:10,720 --> 00:28:14,120
Speaker 1: spider webs, or maybe their visual systems are particularly good

567
00:28:14,160 --> 00:28:17,359
Speaker 1: at seeing the way light reflects off of a spider

568
00:28:17,359 --> 00:28:19,440
Speaker 1: web with some dew on it. But yes, I don't

569
00:28:19,440 --> 00:28:23,320
Speaker 1: think of flies as being particularly brilliant, although they outsmart

570
00:28:23,320 --> 00:28:25,600
Speaker 1: me when I try to smack them, so maybe they're

571
00:28:25,600 --> 00:28:26,720
Speaker 1: smarter than I give them credit for.

572
00:28:27,240 --> 00:28:30,720
Speaker 2: They're definitely amazing biological engineering. I don't know if it

573
00:28:30,760 --> 00:28:33,800
Speaker 2: counts as intelligence, but now we're back into that morass

574
00:28:33,840 --> 00:28:36,000
Speaker 2: of what is smart anyway. But I wouldn't put flies

575
00:28:36,080 --> 00:28:37,040
Speaker 2: near the top of the list.

576
00:28:37,840 --> 00:28:39,960
Speaker 1: I haven't read a lot of papers where flies were

577
00:28:39,960 --> 00:28:43,360
Speaker 1: like solving complex problems. Although I do think that they

578
00:28:43,440 --> 00:28:45,680
Speaker 1: keep track of their social structures because they fight with

579
00:28:45,760 --> 00:28:47,600
Speaker 1: each other, and I think who they fight with depends

580
00:28:47,600 --> 00:28:49,240
Speaker 1: on you know, who they fought with in the past.

581
00:28:49,320 --> 00:28:50,920
Speaker 1: So maybe there's a lot going on there that we

582
00:28:50,960 --> 00:28:51,440
Speaker 1: can't see.

583
00:28:51,520 --> 00:28:53,680
Speaker 2: All right, But you must have some fun examples of

584
00:28:53,680 --> 00:28:56,440
Speaker 2: predator and prey who have developed some clever intelligence.

585
00:28:56,600 --> 00:29:00,400
Speaker 1: I am particularly excited about chimps and New Caledonian crows

586
00:29:00,440 --> 00:29:03,800
Speaker 1: who have managed to like pull stems off of trees

587
00:29:03,840 --> 00:29:06,959
Speaker 1: and in some cases even like remove leaves and stuff

588
00:29:07,000 --> 00:29:11,120
Speaker 1: to make particularly good tools for going inside of like trees,

589
00:29:11,160 --> 00:29:13,000
Speaker 1: so that you can get bugs that are inside.

590
00:29:13,240 --> 00:29:14,920
Speaker 2: That's tool use. That's clever. Yeah.

591
00:29:15,000 --> 00:29:15,200
Speaker 3: Yeah.

592
00:29:15,240 --> 00:29:17,640
Speaker 1: And you'll find stories about crows who will sometimes like

593
00:29:17,680 --> 00:29:20,600
Speaker 1: if they have a metal implement, they'll bend it so

594
00:29:20,600 --> 00:29:22,960
Speaker 1: that they can sort of hook something out. There have

595
00:29:22,960 --> 00:29:25,840
Speaker 1: been stories of crows that are able to like drop

596
00:29:26,000 --> 00:29:29,400
Speaker 1: rocks into containers of water so that the water can

597
00:29:29,440 --> 00:29:31,040
Speaker 1: come up high enough so that they can drink it.

598
00:29:31,200 --> 00:29:34,080
Speaker 1: Oh wow, Like they do some pretty incredible things. Thinking

599
00:29:34,120 --> 00:29:36,880
Speaker 1: about prey, there are octopus that do things like use

600
00:29:37,000 --> 00:29:40,239
Speaker 1: halves of coconut shells and instead of using them like

601
00:29:40,280 --> 00:29:46,000
Speaker 1: in Monty Python to make the sound of a horse galloping. No,

602
00:29:46,280 --> 00:29:50,080
Speaker 1: I know, I don't understand, they pick up the coconuts shells,

603
00:29:50,480 --> 00:29:52,840
Speaker 1: clean them out, get inside of them, and then carry

604
00:29:52,840 --> 00:29:54,480
Speaker 1: them with them from place to place so that they've

605
00:29:54,480 --> 00:29:56,480
Speaker 1: got a shelter to hide from anything that might want

606
00:29:56,480 --> 00:29:59,120
Speaker 1: to harass or try to eat them. Okay, so there

607
00:29:59,160 --> 00:30:02,560
Speaker 1: are some amazing examples of animals that show a lot

608
00:30:02,560 --> 00:30:05,680
Speaker 1: of intelligence to either avoid being eaten or get their food.

609
00:30:06,080 --> 00:30:08,000
Speaker 2: Those are both definitely smarter than flies.

610
00:30:08,360 --> 00:30:11,720
Speaker 1: Well, but again, you know you're biased by what sounds

611
00:30:11,760 --> 00:30:14,600
Speaker 1: important to you and what you can understand. Well, but yeah,

612
00:30:14,640 --> 00:30:17,240
Speaker 1: I'll give you that. I hate flies. Diptorids are the worst.

613
00:30:19,160 --> 00:30:20,719
Speaker 1: My daughter knows I hate diptorans.

614
00:30:20,840 --> 00:30:21,920
Speaker 2: What are diptorids?

615
00:30:22,080 --> 00:30:23,680
Speaker 1: They're like flies? And mosquitoes.

616
00:30:24,200 --> 00:30:27,200
Speaker 2: Oh god, mosquitos absolutely the worst. If I could delete

617
00:30:27,200 --> 00:30:30,320
Speaker 2: mosquitos from the universe, I totally would do it. I

618
00:30:30,400 --> 00:30:33,240
Speaker 2: mostly love living creatures, and I appreciate that everything contributes

619
00:30:33,280 --> 00:30:36,520
Speaker 2: to the web of life. But mosquitoes, man, just delete those.

620
00:30:36,680 --> 00:30:39,040
Speaker 1: I know you are telling us in another episode recently

621
00:30:39,040 --> 00:30:41,240
Speaker 1: that you guys got some Chinese mosquitos that are now

622
00:30:41,320 --> 00:30:43,440
Speaker 1: all over the place and that you really hate them,

623
00:30:43,480 --> 00:30:44,600
Speaker 1: and I feel for you.

624
00:30:45,160 --> 00:30:47,200
Speaker 2: Nothing against the Chinese, nothing, no, no, of.

625
00:30:47,080 --> 00:30:49,120
Speaker 1: Course, of course, yep, nope. Didn't mean to apply that.

626
00:30:49,640 --> 00:30:53,120
Speaker 1: But so another part of Mark's question was would aliens

627
00:30:53,200 --> 00:30:56,720
Speaker 1: find our planet appalling because of all of the killings. So,

628
00:30:56,880 --> 00:30:59,880
Speaker 1: if predation is important for intelligence, and predation is like

629
00:31:00,080 --> 00:31:03,080
Speaker 1: all over the place, could you end up with an

630
00:31:03,080 --> 00:31:06,959
Speaker 1: intelligent alien species where there wasn't any predation? Is there

631
00:31:07,040 --> 00:31:11,560
Speaker 1: some other selection pressure that produces intelligent aliens and what

632
00:31:11,560 --> 00:31:14,680
Speaker 1: would aliens think of all of the killing? And you know,

633
00:31:14,920 --> 00:31:17,000
Speaker 1: I don't know, it might be appalling. There's an interesting

634
00:31:17,000 --> 00:31:21,000
Speaker 1: book called The Sparrow by Mary Doria Russell, and I

635
00:31:21,040 --> 00:31:24,280
Speaker 1: do not recommend this book to children. Maybe read the

636
00:31:24,280 --> 00:31:27,440
Speaker 1: Wikipedia summary before you read it. It covers some intense topics,

637
00:31:27,760 --> 00:31:30,560
Speaker 1: but it's an interesting look at how humans feel when

638
00:31:30,560 --> 00:31:32,960
Speaker 1: they go to an alien civilization and see how they

639
00:31:33,000 --> 00:31:36,800
Speaker 1: deal with their predator prey relationships. But would you tell

640
00:31:36,880 --> 00:31:39,120
Speaker 1: us about Eric Kirschenbaum's book.

641
00:31:39,400 --> 00:31:42,120
Speaker 2: Yeah. He's a biologist at Cambridge and he wrote this

642
00:31:42,160 --> 00:31:45,960
Speaker 2: book called The Zoologist Guide to the Galaxy. What Animals

643
00:31:46,000 --> 00:31:49,920
Speaker 2: on Earth reveal about aliens and ourselves. And I'll be honest,

644
00:31:49,960 --> 00:31:51,760
Speaker 2: when I first saw this book, I thought, oh no,

645
00:31:52,120 --> 00:31:54,640
Speaker 2: he scooped me because I was at the same time

646
00:31:54,720 --> 00:31:57,000
Speaker 2: working on a book about what we can learn about

647
00:31:57,000 --> 00:31:59,360
Speaker 2: aliens from examples here on Earth. But I was more

648
00:31:59,400 --> 00:32:01,880
Speaker 2: focused on the side of it. And his book is

649
00:32:01,920 --> 00:32:05,800
Speaker 2: really fascinating. It's essentially looking at examples of evolution on

650
00:32:05,840 --> 00:32:08,480
Speaker 2: Earth and trying to draw conclusions about what might be

651
00:32:08,600 --> 00:32:12,160
Speaker 2: universal and what might be local and unique to Earth. It's,

652
00:32:12,200 --> 00:32:14,640
Speaker 2: of course impossible to know for sure, but you know,

653
00:32:14,640 --> 00:32:17,000
Speaker 2: when you see things pop up over and over again,

654
00:32:17,440 --> 00:32:19,960
Speaker 2: you can argue that they might be more common. And

655
00:32:20,000 --> 00:32:22,360
Speaker 2: when you see something that only happened once on Earth,

656
00:32:22,640 --> 00:32:25,080
Speaker 2: you can argue that maybe it's rare and therefore might

657
00:32:25,160 --> 00:32:28,960
Speaker 2: not be everywhere in the universe. And something he talked

658
00:32:28,960 --> 00:32:31,000
Speaker 2: a lot about was predation.

659
00:32:31,000 --> 00:32:33,280
Speaker 1: And you provided me with a quote where he even

660
00:32:33,320 --> 00:32:37,120
Speaker 1: says no ecosystem can exist for long without someone trying

661
00:32:37,160 --> 00:32:38,880
Speaker 1: to take a bite out of somebody else.

662
00:32:39,640 --> 00:32:44,520
Speaker 2: Yeah, exactly. He thinks it's essentially inevitable because you'll develop mobility,

663
00:32:44,920 --> 00:32:47,280
Speaker 2: and then you'll start to consume, and then you'll start

664
00:32:47,280 --> 00:32:49,800
Speaker 2: to consume other folks, and then other folks will try

665
00:32:49,800 --> 00:32:53,120
Speaker 2: to get away from you. And so when you get big,

666
00:32:53,200 --> 00:32:55,600
Speaker 2: you get noisy, and people develop ears and then they

667
00:32:55,680 --> 00:32:58,240
Speaker 2: use those to hunt, and so he thinks predation is

668
00:32:58,280 --> 00:33:03,040
Speaker 2: totally inevitable. Directly like, do you think aliens eat each other?

669
00:33:03,080 --> 00:33:06,280
Speaker 1: And he was like, oh, yeah, absolutely, okay, So at

670
00:33:06,360 --> 00:33:09,080
Speaker 1: least that argues that if aliens visit Earth, they shouldn't

671
00:33:09,120 --> 00:33:12,560
Speaker 1: find us too apart, although maybe they'd be shocked that

672
00:33:12,600 --> 00:33:16,040
Speaker 1: we eat lobsters because gross.

673
00:33:17,120 --> 00:33:20,719
Speaker 2: Lobsters are like just huge ocean conchroaches. I don't get it,

674
00:33:20,760 --> 00:33:22,200
Speaker 2: Like they look really gross to me.

675
00:33:22,320 --> 00:33:26,040
Speaker 1: Oh yeah, I'll pass. Sorry, I'm not into it. So

676
00:33:26,160 --> 00:33:29,400
Speaker 1: to be honest, when I was researching hypotheses for why

677
00:33:29,760 --> 00:33:32,960
Speaker 1: intelligence pops up from time to time, I didn't come

678
00:33:33,000 --> 00:33:37,520
Speaker 1: across this predation hypothesis often. The hypothesis that I came

679
00:33:37,560 --> 00:33:42,040
Speaker 1: across the most often is what's called the social intelligence hypothesis.

680
00:33:42,280 --> 00:33:44,760
Speaker 1: This is what Daniel was talking about earlier, which is

681
00:33:44,800 --> 00:33:48,320
Speaker 1: that group living selects for intelligence, and you need group

682
00:33:48,400 --> 00:33:51,400
Speaker 1: living to do things like track members of a group.

683
00:33:51,480 --> 00:33:54,360
Speaker 1: So remember, like, you know, when I was cooperating with Beth,

684
00:33:54,920 --> 00:33:56,640
Speaker 1: you know, I pulled some ticks off her back, and

685
00:33:56,680 --> 00:33:58,600
Speaker 1: the next day she pulled some ticks off my back.

686
00:33:58,680 --> 00:34:00,680
Speaker 1: So that's great, I'll keep working with her. But I

687
00:34:00,720 --> 00:34:03,360
Speaker 1: pulled some ticks off Frank's back, and that jerk never

688
00:34:03,400 --> 00:34:05,000
Speaker 1: returned to the favor. So I'm not going to work

689
00:34:05,000 --> 00:34:08,279
Speaker 1: again with Frank. And so it helps you like keep

690
00:34:08,360 --> 00:34:11,400
Speaker 1: track of individuals in your group and how you should

691
00:34:11,760 --> 00:34:13,200
Speaker 1: react with each of them differently.

692
00:34:13,800 --> 00:34:17,839
Speaker 2: Essentially, politics is complicated and it takes real intelligence, and

693
00:34:17,880 --> 00:34:21,040
Speaker 2: in a society, politics are important for survival. You have

694
00:34:21,080 --> 00:34:24,000
Speaker 2: to build coalitions, you have to worry about your enemies,

695
00:34:24,120 --> 00:34:26,640
Speaker 2: you have to worry about being betrayed by your allies.

696
00:34:27,080 --> 00:34:28,160
Speaker 2: It's complicated stuff.

697
00:34:28,239 --> 00:34:30,600
Speaker 1: Well, and you need to communicate with each other so

698
00:34:30,640 --> 00:34:32,600
Speaker 1: that if you're you know, a bee, for example, you

699
00:34:32,640 --> 00:34:35,080
Speaker 1: can waggle and tell everyone where the good food source is.

700
00:34:35,239 --> 00:34:37,719
Speaker 1: Or you need to if your wolves coordinate so you

701
00:34:37,719 --> 00:34:39,840
Speaker 1: can take down the moose so that you can feed everyone.

702
00:34:40,440 --> 00:34:44,160
Speaker 1: So social behavior requires a lot of intelligence to work out.

703
00:34:44,480 --> 00:34:47,200
Speaker 2: Have you ever read the books about the Wolves of Yellowstone?

704
00:34:47,520 --> 00:34:48,200
Speaker 1: Oh? Have you?

705
00:34:48,640 --> 00:34:51,680
Speaker 2: Yeah, there's a fascinating series of books written by a

706
00:34:51,719 --> 00:34:54,239
Speaker 2: guy who spent like forty years getting up at like

707
00:34:54,280 --> 00:34:56,920
Speaker 2: three in the morning and just watching the wolves and

708
00:34:57,080 --> 00:34:59,160
Speaker 2: each of a number, and he just got to know

709
00:34:59,239 --> 00:35:03,080
Speaker 2: them all and wrote these books about their politics. And

710
00:35:03,120 --> 00:35:07,120
Speaker 2: it's like a soap opera. You know, betrayals and arguments

711
00:35:07,160 --> 00:35:10,040
Speaker 2: and you know, love affairs and all sorts of crazy

712
00:35:10,080 --> 00:35:12,279
Speaker 2: stuff and wolve's going off to start their own packs

713
00:35:12,280 --> 00:35:14,440
Speaker 2: and then coming back and fighting their parents and like

714
00:35:14,800 --> 00:35:17,600
Speaker 2: it's dramatic stuff, and it leaves you with this sense

715
00:35:17,840 --> 00:35:20,799
Speaker 2: of like, wow, this is a society. You know, these

716
00:35:20,800 --> 00:35:23,640
Speaker 2: are intelligent creatures and they have all the same kind

717
00:35:23,680 --> 00:35:26,440
Speaker 2: of politics that we do. It really is a soap opera.

718
00:35:26,560 --> 00:35:27,800
Speaker 2: It's fascinating reading.

719
00:35:27,960 --> 00:35:31,640
Speaker 1: Robert Sipolski wrote a book about baboons called a Primates Memoir,

720
00:35:31,680 --> 00:35:33,919
Speaker 1: which is sort of the same. It's really becomes clear

721
00:35:33,960 --> 00:35:36,359
Speaker 1: how they're a society that has a lot of similarities

722
00:35:36,360 --> 00:35:39,000
Speaker 1: to how humans work. And also he's a fantastic writer.

723
00:35:39,080 --> 00:35:40,400
Speaker 1: I really enjoyed that memoir.

724
00:35:40,600 --> 00:35:42,920
Speaker 2: So I really recommend these books there by Rick McIntyre,

725
00:35:42,960 --> 00:35:44,880
Speaker 2: one of them has called, for example, the Rise of

726
00:35:44,960 --> 00:35:49,720
Speaker 2: Wolf eight. They're fantastic books. Really couldn't recommend them higher anyway,

727
00:35:49,719 --> 00:35:52,480
Speaker 2: Go ahead, tell us about other intelligent critters.

728
00:35:52,640 --> 00:35:55,640
Speaker 1: Yeah. So in the nineteen nineties it was observed that

729
00:35:55,680 --> 00:35:59,800
Speaker 1: there was this positive correlation between neo coortec side. So

730
00:35:59,840 --> 00:36:01,879
Speaker 1: this is like the outer part of our brain that's

731
00:36:01,920 --> 00:36:04,239
Speaker 1: close to our skull, and it's thought to be sort

732
00:36:04,239 --> 00:36:09,000
Speaker 1: of like a more recent mammalian thing. And so monkeys

733
00:36:09,000 --> 00:36:13,160
Speaker 1: and apes that had bigger neo cortexes were also thought

734
00:36:13,200 --> 00:36:14,880
Speaker 1: to be smarter. And this part of the brain is

735
00:36:14,960 --> 00:36:17,280
Speaker 1: used for things like language and sensory perception.

736
00:36:17,560 --> 00:36:19,800
Speaker 2: Can I stop you right there and be a little skeptical.

737
00:36:20,000 --> 00:36:22,040
Speaker 2: Anytimes somebody says something like that like this part of

738
00:36:22,040 --> 00:36:24,439
Speaker 2: the brain is used for XYZ, I'm like, we don't

739
00:36:24,560 --> 00:36:28,439
Speaker 2: understand the brain. Isn't this some like correlational study using

740
00:36:28,560 --> 00:36:30,879
Speaker 2: fMRI And it's like, well, there's more blood flow here

741
00:36:30,960 --> 00:36:33,600
Speaker 2: when we show them this picture of a baboon and

742
00:36:33,640 --> 00:36:36,719
Speaker 2: so therefore, right, it's all pretty fuzzy. I feel like

743
00:36:36,719 --> 00:36:39,400
Speaker 2: this should be a huge asterisk anytime somebody says this

744
00:36:39,480 --> 00:36:40,560
Speaker 2: part of the brain does.

745
00:36:40,600 --> 00:36:43,520
Speaker 1: X OMG, yes, yeah, no, I totally agree with you,

746
00:36:43,560 --> 00:36:45,399
Speaker 1: and so we'll move this point up a little bit.

747
00:36:45,440 --> 00:36:47,000
Speaker 1: One of the issues with this field is that to

748
00:36:47,040 --> 00:36:49,799
Speaker 1: try to support the hypothesis, they'll do things like look

749
00:36:49,800 --> 00:36:54,400
Speaker 1: at relative brain size and correlate that with things that

750
00:36:54,440 --> 00:36:56,719
Speaker 1: are related to social things like if you are a

751
00:36:56,760 --> 00:36:59,479
Speaker 1: species that tends to live in groups of five rather

752
00:36:59,520 --> 00:37:02,239
Speaker 1: than you know, by yourself, they'll look to see, do

753
00:37:02,280 --> 00:37:04,960
Speaker 1: you have a bigger brain if you live in groups

754
00:37:04,960 --> 00:37:06,840
Speaker 1: of five, or if you're a species that lives in

755
00:37:06,880 --> 00:37:09,560
Speaker 1: a group of just one. Does that make sense? And

756
00:37:09,600 --> 00:37:13,120
Speaker 1: so there's a lot of these correlations, but there's a

757
00:37:13,160 --> 00:37:16,279
Speaker 1: lot of argument about what you should be measuring in

758
00:37:16,320 --> 00:37:18,840
Speaker 1: a brain to begin with, like, what is the relevant

759
00:37:18,840 --> 00:37:20,440
Speaker 1: part of the brain you should be measuring. Is it

760
00:37:20,480 --> 00:37:23,880
Speaker 1: the whole brain, is it the neocortex? Is it actually

761
00:37:23,920 --> 00:37:26,759
Speaker 1: the density of neurons and a very particular part of

762
00:37:26,800 --> 00:37:29,640
Speaker 1: the brain that matters. And additionally, you know, there are

763
00:37:29,719 --> 00:37:34,040
Speaker 1: animals that can do similarly smart tasks that have very

764
00:37:34,040 --> 00:37:36,080
Speaker 1: different kinds of brains. You know, how do you compare,

765
00:37:36,160 --> 00:37:39,520
Speaker 1: for example, a primate brain and a bird brain. And

766
00:37:39,600 --> 00:37:41,560
Speaker 1: so one of the big critiques in this field is

767
00:37:41,600 --> 00:37:44,640
Speaker 1: that they do rely a lot on different measures of

768
00:37:44,680 --> 00:37:48,239
Speaker 1: brains that have not necessarily been locked in as the

769
00:37:48,400 --> 00:37:51,279
Speaker 1: correct thing to be measuring when you're interested in intelligence.

770
00:37:51,800 --> 00:37:54,239
Speaker 2: Right, And we mentioned these as critiques of the field

771
00:37:54,320 --> 00:37:56,719
Speaker 2: not because we think these folks don't know what they're doing. Yeah,

772
00:37:56,760 --> 00:37:59,120
Speaker 2: they're following a glorious tradition in science, which is like

773
00:37:59,440 --> 00:38:02,480
Speaker 2: tackling and possibly hard question by doing the only things

774
00:38:02,520 --> 00:38:04,799
Speaker 2: they know how to do. And often this leads to

775
00:38:04,920 --> 00:38:07,120
Speaker 2: breakthroughs you didn't expect. It's better than just sitting on

776
00:38:07,120 --> 00:38:09,839
Speaker 2: the couch and saying, well, it's impossible, let's not do it.

777
00:38:10,239 --> 00:38:13,239
Speaker 2: But another glorious tradition in science is being open about

778
00:38:13,280 --> 00:38:15,480
Speaker 2: the critiques of what we really do know and what

779
00:38:15,560 --> 00:38:18,640
Speaker 2: we don't know, and being careful about drawing too broad

780
00:38:18,680 --> 00:38:19,280
Speaker 2: a conclusion.

781
00:38:19,480 --> 00:38:22,920
Speaker 1: Yeah, exactly. There have been a lot of correlations where

782
00:38:23,040 --> 00:38:27,680
Speaker 1: they have found that complicated social behaviors in insects and

783
00:38:27,719 --> 00:38:31,239
Speaker 1: birds and mammals are correlated with either bigger brains or

784
00:38:31,280 --> 00:38:35,040
Speaker 1: bigger brain parts. And that's at least suggestive. And again,

785
00:38:35,080 --> 00:38:37,759
Speaker 1: it's really hard to compare between two different species. What

786
00:38:37,840 --> 00:38:40,239
Speaker 1: is the right measurement for how intelligent they are? How

787
00:38:40,239 --> 00:38:42,640
Speaker 1: do you rank them? You know, as you're sort of

788
00:38:43,280 --> 00:38:46,200
Speaker 1: stumbling around in a dark room with no light on,

789
00:38:46,640 --> 00:38:48,880
Speaker 1: you've got to start somewhere. And I feel like they

790
00:38:48,960 --> 00:38:51,960
Speaker 1: have made some exciting progress and they're having these conversations

791
00:38:51,960 --> 00:38:54,600
Speaker 1: about what is the right thing to be measuring, and

792
00:38:54,800 --> 00:38:56,160
Speaker 1: you know, where do we go from here? And we

793
00:38:56,239 --> 00:38:59,040
Speaker 1: found something tantalizing here, how do we follow up and

794
00:38:59,080 --> 00:39:01,319
Speaker 1: get like a better answer based on this sort of

795
00:39:01,320 --> 00:39:05,160
Speaker 1: tantalizing initial result. That's pretty much where we are right now.

796
00:39:05,200 --> 00:39:07,640
Speaker 1: There's this interesting correlation, but we're still trying to figure

797
00:39:07,640 --> 00:39:10,359
Speaker 1: out what to measure and how to measure it.

798
00:39:10,880 --> 00:39:13,160
Speaker 2: So when you say correlation, you're saying there's a correlation

799
00:39:13,239 --> 00:39:18,000
Speaker 2: between brain size and like social activity, and that suggests

800
00:39:18,080 --> 00:39:21,520
Speaker 2: that bigger brains are smarter and social activities connected to

801
00:39:21,600 --> 00:39:22,120
Speaker 2: being smarter.

802
00:39:22,680 --> 00:39:24,719
Speaker 1: Yeah, that if you've got a bigger brain, you are

803
00:39:24,840 --> 00:39:27,480
Speaker 1: more able to, for example, live in a bigger social

804
00:39:27,520 --> 00:39:30,480
Speaker 1: group where you presumably have more you know, sort of

805
00:39:30,520 --> 00:39:33,120
Speaker 1: peers or conspecifics that you need to keep track of

806
00:39:33,160 --> 00:39:35,600
Speaker 1: so that you can remember their prior behaviors and you

807
00:39:35,640 --> 00:39:39,640
Speaker 1: can tell the difference between them. And so more complicated

808
00:39:39,680 --> 00:39:44,280
Speaker 1: social systems tend to be correlated with bigger brains relative

809
00:39:44,320 --> 00:39:48,839
Speaker 1: to body size or bigger brain parts. Sometimes this falls apart.

810
00:39:48,920 --> 00:39:52,480
Speaker 1: For example, little owls, which is Athena noctua. It's a

811
00:39:52,600 --> 00:39:55,239
Speaker 1: species of owl that is super cute. They are not

812
00:39:55,400 --> 00:39:58,680
Speaker 1: social like at all, but they got real big four brains,

813
00:39:58,760 --> 00:40:01,279
Speaker 1: and so it's kind of surprise that they're like, not

814
00:40:01,440 --> 00:40:03,880
Speaker 1: super social, but they got big old breaks. Why we

815
00:40:03,920 --> 00:40:04,279
Speaker 1: don't know.

816
00:40:04,600 --> 00:40:07,160
Speaker 2: Maybe they're sitting there thinking about eleven dimensional string theory

817
00:40:07,200 --> 00:40:08,200
Speaker 2: and maybe they know the answer.

818
00:40:08,200 --> 00:40:09,880
Speaker 1: Oh my god, we have to learn to talk to

819
00:40:09,920 --> 00:40:10,760
Speaker 1: the little owls.

820
00:40:12,680 --> 00:40:15,200
Speaker 2: Tell me, little owl what is the nature of the universe?

821
00:40:15,440 --> 00:40:15,600
Speaker 4: Oh?

822
00:40:15,680 --> 00:40:17,320
Speaker 1: That should be a poem like that sounds like the

823
00:40:17,360 --> 00:40:18,799
Speaker 1: kind of poem you sing to a kid when they're

824
00:40:18,800 --> 00:40:19,279
Speaker 1: going to bed.

825
00:40:22,040 --> 00:40:23,839
Speaker 2: All right, let's write that children's book.

826
00:40:23,920 --> 00:40:26,879
Speaker 1: All right. Okay, So now we're going to take a break,

827
00:40:26,880 --> 00:40:28,640
Speaker 1: and when we get back, we're going to talk about

828
00:40:28,680 --> 00:40:31,600
Speaker 1: one of the other hypotheses for the evolution of intelligence

829
00:40:31,880 --> 00:40:34,320
Speaker 1: as we answer our third listener question.

830
00:40:51,160 --> 00:40:54,560
Speaker 2: Okay, we are back, and we're talking about smartness across

831
00:40:54,600 --> 00:40:58,319
Speaker 2: the animal and alien kingdom? Is intelligence inevitable? What can

832
00:40:58,360 --> 00:41:01,160
Speaker 2: we learn from life on Earth and the patterns of

833
00:41:01,200 --> 00:41:05,719
Speaker 2: intelligence or deer and fly like dumbness on Earth? Is

834
00:41:05,760 --> 00:41:08,279
Speaker 2: there something we can tell about why humans ended up

835
00:41:08,520 --> 00:41:10,440
Speaker 2: so dang smart?

836
00:41:11,000 --> 00:41:13,640
Speaker 5: We as humans have long looked to the stars with

837
00:41:13,719 --> 00:41:16,560
Speaker 5: wonder and all trying to figure out exactly what the

838
00:41:16,640 --> 00:41:19,880
Speaker 5: universe is, what is it made of, what is our

839
00:41:19,920 --> 00:41:22,799
Speaker 5: place in it? And ultimately, I guess to try to

840
00:41:22,840 --> 00:41:26,080
Speaker 5: answer the age old question are we alone in the universe?

841
00:41:27,000 --> 00:41:31,120
Speaker 5: So if one of our Mars rovers or Europa Clipper

842
00:41:31,280 --> 00:41:34,879
Speaker 5: found even microbial signs of life out there, it would

843
00:41:34,880 --> 00:41:38,080
Speaker 5: of course be a game changer, a seismic event, to

844
00:41:38,160 --> 00:41:41,880
Speaker 5: be sure. And while it would be scientifically exciting, I

845
00:41:41,920 --> 00:41:45,680
Speaker 5: think it could also be somewhat disappointing because when most

846
00:41:45,680 --> 00:41:47,960
Speaker 5: of us look to the stars in hopes of finding

847
00:41:48,040 --> 00:41:51,200
Speaker 5: signs of life, I think we envision not just a

848
00:41:51,239 --> 00:41:57,040
Speaker 5: bacterial colony, but an actual, functioning civilization. So my question

849
00:41:57,160 --> 00:42:00,000
Speaker 5: to you is this, as you look at life here

850
00:42:00,040 --> 00:42:04,160
Speaker 5: on Earth, is there any indication at all that intelligence

851
00:42:04,680 --> 00:42:08,480
Speaker 5: is inevitable? I mean, given enough time for evolution to

852
00:42:08,520 --> 00:42:11,799
Speaker 5: do its thing, do you believe sentient beings would be

853
00:42:11,840 --> 00:42:15,440
Speaker 5: the ultimate result? And to put a finer point on that,

854
00:42:16,160 --> 00:42:19,200
Speaker 5: imagine if the asteroids that wiped out the dinosaurs had

855
00:42:19,280 --> 00:42:22,960
Speaker 5: narrowly missed the Earth and that extinction event never occurred.

856
00:42:23,600 --> 00:42:26,480
Speaker 5: Let's say mammals remained as nothing more than small, furry,

857
00:42:26,480 --> 00:42:29,520
Speaker 5: little rodent like creatures and did not lead to the

858
00:42:29,600 --> 00:42:33,400
Speaker 5: rise of the human species. Under those circumstances, do you

859
00:42:33,440 --> 00:42:36,200
Speaker 5: have any reason to believe that there might be an

860
00:42:36,200 --> 00:42:41,920
Speaker 5: intelligent civilization developed by reptilian creatures instead of warm blooded

861
00:42:41,920 --> 00:42:45,160
Speaker 5: things like us. Do you think dinosaurs could be running

862
00:42:45,160 --> 00:42:46,800
Speaker 5: the show these days?

863
00:42:47,600 --> 00:42:48,000
Speaker 2: Anyway?

864
00:42:48,520 --> 00:42:52,239
Speaker 5: Thank you for your consideration in answering this, and I

865
00:42:52,400 --> 00:42:54,759
Speaker 5: look forward to hearing what you say. Love the show,

866
00:42:54,880 --> 00:42:55,800
Speaker 5: Keep up the good work.

867
00:42:56,120 --> 00:43:00,920
Speaker 1: So Howard wanted to know if intelligence is inevitable and

868
00:43:01,080 --> 00:43:04,240
Speaker 1: man I don't know the answer, and it probably depends

869
00:43:04,239 --> 00:43:06,600
Speaker 1: on what the bar is for intelligence. But if you're

870
00:43:06,600 --> 00:43:10,160
Speaker 1: asking about like human level intelligence, there's over two million

871
00:43:10,239 --> 00:43:14,560
Speaker 1: described species on the planet, but only one human, and

872
00:43:14,640 --> 00:43:17,680
Speaker 1: so apparently you can have a lot of organisms without

873
00:43:17,680 --> 00:43:22,000
Speaker 1: getting us, you know, rocket level smart species. You only

874
00:43:22,000 --> 00:43:24,120
Speaker 1: get that once out of two million. So what do

875
00:43:24,200 --> 00:43:26,520
Speaker 1: you think Daniel would t Rex have been coming up

876
00:43:26,520 --> 00:43:29,759
Speaker 1: with Shakespearean style pros if that asteroid mister Earth?

877
00:43:30,120 --> 00:43:32,200
Speaker 2: Well, first, I want to quibble with your claim that

878
00:43:32,239 --> 00:43:35,440
Speaker 2: there's only one intelligent species. Okay, right, I think that's

879
00:43:35,480 --> 00:43:38,960
Speaker 2: a little bit definitional. Like we think, probably fifty thousand

880
00:43:39,040 --> 00:43:42,880
Speaker 2: years ago, there were multiple intelligent species. We just like killed, raped,

881
00:43:43,040 --> 00:43:46,120
Speaker 2: or ate the other ones, right, And so in some sense,

882
00:43:46,160 --> 00:43:48,880
Speaker 2: and you get smart enough, you're likely, I think, to

883
00:43:49,000 --> 00:43:51,759
Speaker 2: just kill off all the other intelligent competition un till

884
00:43:51,800 --> 00:43:53,799
Speaker 2: it becomes part of your in group. So you might

885
00:43:53,840 --> 00:43:56,719
Speaker 2: inevitably just get to one. But it seems like the

886
00:43:56,760 --> 00:43:59,400
Speaker 2: evidence suggests that there were multiple strands at least of

887
00:43:59,480 --> 00:44:01,520
Speaker 2: human life intelligence on Earth. Weren't there?

888
00:44:02,000 --> 00:44:04,319
Speaker 1: So usually when a listener asked the question, they mean,

889
00:44:04,440 --> 00:44:07,280
Speaker 1: like humans like as smart as us, you know, creating

890
00:44:07,360 --> 00:44:11,719
Speaker 1: rockets nine dimensional math. I don't know that even our

891
00:44:11,800 --> 00:44:14,960
Speaker 1: ancestors were that smart, you know, one hundred thousand years ago,

892
00:44:15,280 --> 00:44:18,040
Speaker 1: and so I'm not quite sure that there was you know,

893
00:44:18,160 --> 00:44:20,520
Speaker 1: songs were being sung by other species of humans. We

894
00:44:20,600 --> 00:44:22,960
Speaker 1: might never know, but you're right, they probably were very

895
00:44:23,000 --> 00:44:25,960
Speaker 1: smart relatively relative to other organisms on the planet.

896
00:44:26,040 --> 00:44:28,560
Speaker 2: Yeah, you're right that we don't know biologically. Do they

897
00:44:28,600 --> 00:44:32,879
Speaker 2: really have the same capacity and Neanderthals, for example, or

898
00:44:33,280 --> 00:44:35,880
Speaker 2: other variations. It's hard to know. And maybe they had

899
00:44:35,920 --> 00:44:38,879
Speaker 2: a different kind of smartness, right, maybe they would be like, hey,

900
00:44:38,880 --> 00:44:41,399
Speaker 2: eleven dimension string theory, that's the wrong way to go, man,

901
00:44:41,480 --> 00:44:43,080
Speaker 2: you should be doing loop quantum gravity.

902
00:44:43,200 --> 00:44:44,319
Speaker 1: That's right, that's right.

903
00:44:46,080 --> 00:44:48,960
Speaker 2: But yeah, the really deep question is like, is intelligence

904
00:44:49,000 --> 00:44:51,840
Speaker 2: inevitable in some creature? Are you likely to have it

905
00:44:51,920 --> 00:44:54,880
Speaker 2: no matter what happens? If the afteroid hadn't hit or

906
00:44:54,960 --> 00:44:56,960
Speaker 2: something else has gone different? On Earth, would you have

907
00:44:57,000 --> 00:45:02,440
Speaker 2: superintelligent spiders or whales dominating the planet, or reptiles or something?

908
00:45:02,840 --> 00:45:04,520
Speaker 2: And boy, I wish I knew the answer that, because

909
00:45:04,560 --> 00:45:06,840
Speaker 2: that would tell us a lot about the nature of

910
00:45:06,840 --> 00:45:10,520
Speaker 2: intelligence out there in the universe. Right, whether it's likely

911
00:45:10,560 --> 00:45:13,200
Speaker 2: when we land on alien planets to just find a

912
00:45:13,239 --> 00:45:16,240
Speaker 2: bunch of fun. Guy or something's going to be smart

913
00:45:16,280 --> 00:45:18,200
Speaker 2: on those planets. And I think the answer that we

914
00:45:18,239 --> 00:45:21,760
Speaker 2: really need a better understanding of how intelligence arose on Earth.

915
00:45:21,800 --> 00:45:24,360
Speaker 2: And as you could tell from this whole episode, we

916
00:45:24,440 --> 00:45:25,800
Speaker 2: don't know the answer to that either.

917
00:45:26,400 --> 00:45:28,960
Speaker 1: That's true. And you mentioned super intelligent spiders. Have you

918
00:45:29,000 --> 00:45:30,360
Speaker 1: read the Children of Time series?

919
00:45:30,920 --> 00:45:34,160
Speaker 2: Of course I have, Yes, I love that book. Yeah, fantastic.

920
00:45:34,560 --> 00:45:37,560
Speaker 2: We had Adrian Tchaikowsky on the Daniel and Jorge podcast,

921
00:45:37,840 --> 00:45:39,879
Speaker 2: really fun conversations. You guys should check that out.

922
00:45:40,120 --> 00:45:44,080
Speaker 1: Awesome. So, as you've pointed out, the other theories are

923
00:45:44,080 --> 00:45:47,319
Speaker 1: not exactly ironclad that we've talked about yet. There is

924
00:45:47,600 --> 00:45:50,680
Speaker 1: one additional hypothesis for why you get intelligence that we

925
00:45:50,719 --> 00:45:53,240
Speaker 1: haven't talked about yet that I saved for now because

926
00:45:53,239 --> 00:45:57,399
Speaker 1: it's possibly the one that's most relevant to space. It's

927
00:45:57,400 --> 00:45:59,080
Speaker 1: a bit of a stretch, but you know, I try

928
00:45:59,120 --> 00:46:02,120
Speaker 1: to have an answer for every question, no matter how

929
00:46:02,160 --> 00:46:04,400
Speaker 1: confused I am, So all right, here's my best shot.

930
00:46:05,239 --> 00:46:11,200
Speaker 1: So this hypothesis is called the cognitive buffer hypothesis, and

931
00:46:11,239 --> 00:46:16,280
Speaker 1: the idea is that how variable your environment is determines

932
00:46:16,320 --> 00:46:18,680
Speaker 1: whether or not you need to be intelligent to survive

933
00:46:18,719 --> 00:46:22,680
Speaker 1: in it. So maybe more variable planets would be more

934
00:46:22,760 --> 00:46:25,279
Speaker 1: likely to produce intelligent aliens. But so let's dig in

935
00:46:25,280 --> 00:46:27,080
Speaker 1: for a second and revisit that idea at the end.

936
00:46:27,480 --> 00:46:31,360
Speaker 1: So one of the ideas is that seasonality is important.

937
00:46:31,800 --> 00:46:34,480
Speaker 1: So if you live in an area where sometimes it's

938
00:46:34,560 --> 00:46:37,839
Speaker 1: really hot and sometimes it's really cold, maybe you need

939
00:46:37,880 --> 00:46:41,480
Speaker 1: to be smart to survive that transition between temperatures. So,

940
00:46:41,520 --> 00:46:46,560
Speaker 1: for example, migratory birds have two pretty smart solutions to

941
00:46:46,640 --> 00:46:50,279
Speaker 1: this problem. Some of them migrate and have amazing long

942
00:46:50,400 --> 00:46:54,040
Speaker 1: term spatial memory relative to non migratory ones, so they

943
00:46:54,120 --> 00:46:57,360
Speaker 1: essentially figure out how to leave when it gets bad

944
00:46:57,440 --> 00:46:59,760
Speaker 1: and then how to come back later, and that requires

945
00:47:00,120 --> 00:47:04,040
Speaker 1: some incredible brain abilities. And then there are other birds

946
00:47:04,080 --> 00:47:07,560
Speaker 1: that don't leave, but they have amazing memories for where

947
00:47:07,600 --> 00:47:11,160
Speaker 1: they've cached food, so even when food is rare, they've

948
00:47:11,280 --> 00:47:13,600
Speaker 1: hidden it when times are good, and they remember where

949
00:47:13,640 --> 00:47:15,439
Speaker 1: they can go to find it when times are bad.

950
00:47:15,960 --> 00:47:17,960
Speaker 1: And so the idea here is that living in a

951
00:47:18,000 --> 00:47:22,000
Speaker 1: super variable environment requires you to become intelligen so that

952
00:47:22,040 --> 00:47:24,080
Speaker 1: you can survive. There was a study looking at twelve

953
00:47:24,120 --> 00:47:27,400
Speaker 1: hundred bird species that found that you get bigger brains

954
00:47:27,640 --> 00:47:30,360
Speaker 1: and birds that live in environments that are more variable,

955
00:47:30,719 --> 00:47:32,920
Speaker 1: So suggesting that if things change more, you got to

956
00:47:32,920 --> 00:47:34,920
Speaker 1: be smarter to make it. What do you think, Daniel,

957
00:47:34,960 --> 00:47:35,840
Speaker 1: Do you buy it?

958
00:47:35,840 --> 00:47:38,000
Speaker 2: It makes some sense. It connects to the idea we

959
00:47:38,000 --> 00:47:41,960
Speaker 2: were talking about earlier that intelligence is about generalization, like

960
00:47:42,040 --> 00:47:44,720
Speaker 2: being able to solve new problems you hadn't seen before.

961
00:47:45,120 --> 00:47:48,360
Speaker 2: So if your environment is changing around you, ice ages

962
00:47:48,400 --> 00:47:52,160
Speaker 2: are ending, climate is changing something, you can't just rely

963
00:47:52,560 --> 00:47:55,040
Speaker 2: on the techniques handed down to you by ancestors. You

964
00:47:55,040 --> 00:47:58,280
Speaker 2: have to come up with new ideas to solve new problems,

965
00:47:58,320 --> 00:48:00,920
Speaker 2: and that requires intelligence. So it makes sense to me

966
00:48:01,080 --> 00:48:03,520
Speaker 2: that a climate with a lot of variation, or an

967
00:48:03,680 --> 00:48:06,720
Speaker 2: environment with a lot of variability to it would require

968
00:48:06,760 --> 00:48:09,760
Speaker 2: more intelligence in order to survive. On the other hand,

969
00:48:10,239 --> 00:48:13,240
Speaker 2: I don't know that. I consider like geese that's smart.

970
00:48:13,239 --> 00:48:15,400
Speaker 2: I mean, yes, they can do something amazing, which is

971
00:48:15,440 --> 00:48:18,799
Speaker 2: like navigate the planet with their magnetic sense in their eyeballs.

972
00:48:18,840 --> 00:48:21,239
Speaker 2: It's incredible. I couldn't do that. But I don't know

973
00:48:21,320 --> 00:48:24,440
Speaker 2: if that makes them smart. They seem like hyper optimized

974
00:48:24,560 --> 00:48:28,920
Speaker 2: to this one situation, like if for example, the planet

975
00:48:29,000 --> 00:48:31,920
Speaker 2: change the magnetic field flipped or where they need to

976
00:48:31,960 --> 00:48:34,239
Speaker 2: migrate to changed. Do you think geese would be able

977
00:48:34,280 --> 00:48:35,719
Speaker 2: to adapt to that situation?

978
00:48:36,239 --> 00:48:38,880
Speaker 1: So, the last time I was reading about birds and

979
00:48:38,920 --> 00:48:42,160
Speaker 1: their ability to sense the magnetic fields, my understanding is

980
00:48:42,160 --> 00:48:46,480
Speaker 1: that we don't actually understand that very well, and that

981
00:48:46,600 --> 00:48:49,640
Speaker 1: a lot of animals that we suspect rely on magnetic

982
00:48:49,719 --> 00:48:53,040
Speaker 1: fields also have other memory cues that they rely on

983
00:48:53,120 --> 00:48:54,120
Speaker 1: to get to where they're going.

984
00:48:54,320 --> 00:48:56,560
Speaker 2: Did you read that study about how whales might be

985
00:48:56,600 --> 00:49:00,120
Speaker 2: looking at the stars and navigating by the stars. Oh amazing,

986
00:49:00,239 --> 00:49:00,759
Speaker 2: Oh my god.

987
00:49:00,760 --> 00:49:02,640
Speaker 1: Okay, we should talk about that in a future show

988
00:49:02,680 --> 00:49:04,040
Speaker 1: because that's amazing. I'd love to read that.

989
00:49:04,280 --> 00:49:04,480
Speaker 2: Yeah.

990
00:49:04,560 --> 00:49:07,399
Speaker 1: Yeah, I guess that goes back to the question how

991
00:49:07,440 --> 00:49:09,160
Speaker 1: do you measure intelligence? And how do you make sure

992
00:49:09,160 --> 00:49:11,680
Speaker 1: that you're not measuring intelligence in a too human centric way?

993
00:49:11,760 --> 00:49:13,840
Speaker 1: Or maybe it doesn't matter because maybe if you're specifically

994
00:49:13,840 --> 00:49:17,000
Speaker 1: interested in how did you get human intelligence? Then maybe

995
00:49:17,000 --> 00:49:20,600
Speaker 1: focusing on human intelligence is fine. But I get lost,

996
00:49:21,040 --> 00:49:23,680
Speaker 1: like going to the grocery store sometimes, Like I use

997
00:49:23,760 --> 00:49:25,960
Speaker 1: my GPS in the town that I've been living in

998
00:49:26,000 --> 00:49:28,000
Speaker 1: for half a decade all the time.

999
00:49:28,120 --> 00:49:31,000
Speaker 2: And so GPS your goose positioning system.

1000
00:49:33,880 --> 00:49:36,719
Speaker 1: So I'm impressed in these long distance migrants no matter

1001
00:49:36,719 --> 00:49:38,719
Speaker 1: how they do it, because I would get lost. I'd

1002
00:49:38,800 --> 00:49:39,319
Speaker 1: be in a lake.

1003
00:49:39,680 --> 00:49:42,960
Speaker 2: Again the same way, like flies are impressive. They're very responsive.

1004
00:49:43,000 --> 00:49:45,239
Speaker 2: They're faster off the starting block than I am, or

1005
00:49:45,239 --> 00:49:47,680
Speaker 2: probably seen bolt is. But I don't know that. I

1006
00:49:47,719 --> 00:49:50,080
Speaker 2: call it smart and so, but maybe you're right, maybe

1007
00:49:50,120 --> 00:49:53,120
Speaker 2: I'm being too humanistic there. I'm looking for the kind

1008
00:49:53,120 --> 00:49:55,200
Speaker 2: of intelligence that I know the same way like I

1009
00:49:55,239 --> 00:49:57,480
Speaker 2: see in my dog, a lot of the kind of

1010
00:49:57,520 --> 00:49:59,799
Speaker 2: intelligence I see in people. And that's what impresses me.

1011
00:50:00,640 --> 00:50:03,399
Speaker 2: Sea patterns and respond to them and understand the world

1012
00:50:03,480 --> 00:50:05,440
Speaker 2: around him and adapt to it.

1013
00:50:05,560 --> 00:50:07,319
Speaker 1: Yeah, and appear to be able to read like the

1014
00:50:07,320 --> 00:50:11,480
Speaker 1: emotions of people around them. Yeah, agreed. You've got this

1015
00:50:11,560 --> 00:50:15,120
Speaker 1: seasonality components, which is about like you know, escaping extreme

1016
00:50:15,160 --> 00:50:19,640
Speaker 1: temperatures and stuff. Another component of this idea is resource availability. So,

1017
00:50:19,680 --> 00:50:23,000
Speaker 1: for example, if you are a monkey species that eats fruits,

1018
00:50:23,880 --> 00:50:26,600
Speaker 1: fruits kind of ripen at different times, so they're available

1019
00:50:26,640 --> 00:50:28,919
Speaker 1: sometimes not at other times. You need to remember where

1020
00:50:28,920 --> 00:50:31,799
Speaker 1: the fruit trees are. And so there's some evidence that

1021
00:50:32,120 --> 00:50:35,279
Speaker 1: monkeys that rely on fruit are smarter than monkeys that

1022
00:50:35,320 --> 00:50:38,200
Speaker 1: can eat leaves because leaves are just always available, they're

1023
00:50:38,239 --> 00:50:41,239
Speaker 1: easy to find, they're just much more reliable. So the

1024
00:50:41,280 --> 00:50:44,799
Speaker 1: idea is that if seasons are changing your environment, or

1025
00:50:45,040 --> 00:50:47,560
Speaker 1: resources that you need, or sort of popping in and

1026
00:50:47,600 --> 00:50:52,239
Speaker 1: out of existence, either of those things are important for intelligence.

1027
00:50:52,800 --> 00:50:55,359
Speaker 1: But you know, when you read about any of these hypotheses,

1028
00:50:55,440 --> 00:50:58,640
Speaker 1: like the recent papers that I was reading, everybody's got

1029
00:50:58,680 --> 00:51:02,960
Speaker 1: like their favorite hypothesis, point out limitations of the other hypothesis,

1030
00:51:03,440 --> 00:51:04,960
Speaker 1: and at the end of the day, it could be

1031
00:51:05,000 --> 00:51:07,000
Speaker 1: some combination of things. You know, it could be if

1032
00:51:07,040 --> 00:51:10,239
Speaker 1: you're in a hyper variable environment and you've got a

1033
00:51:10,239 --> 00:51:14,080
Speaker 1: big social group, that's when you get the super smart individuals.

1034
00:51:14,160 --> 00:51:16,479
Speaker 1: Or maybe it's you know, the three things we've talked

1035
00:51:16,480 --> 00:51:19,200
Speaker 1: about today, those are all important, plus a fourth thing

1036
00:51:19,280 --> 00:51:21,680
Speaker 1: that nobody's thought about yet, and so at the end

1037
00:51:21,680 --> 00:51:23,360
Speaker 1: of the day, we don't really have the answer.

1038
00:51:23,560 --> 00:51:25,799
Speaker 2: So what I like to think about sometimes is like

1039
00:51:25,920 --> 00:51:28,960
Speaker 2: fantasy data, Like if you had infinite resources and you've

1040
00:51:29,000 --> 00:51:31,400
Speaker 2: asked me these kind of questions before you could go

1041
00:51:31,440 --> 00:51:33,880
Speaker 2: all the way around the galaxy, whatever, is there a

1042
00:51:33,880 --> 00:51:37,600
Speaker 2: way we could answer this question someone definitively with infinite

1043
00:51:37,800 --> 00:51:41,719
Speaker 2: scientific powers you know, do you think actually seeing aliens

1044
00:51:41,800 --> 00:51:45,240
Speaker 2: and surveying intelligence across the galaxy would answer this question

1045
00:51:45,640 --> 00:51:47,320
Speaker 2: or or did just make it murky again?

1046
00:51:48,000 --> 00:51:52,200
Speaker 1: Wouldn't surprise me that on a different planet there could

1047
00:51:52,280 --> 00:51:55,759
Speaker 1: be some different thing that selects for intelligence relative to

1048
00:51:55,760 --> 00:51:57,960
Speaker 1: what we find here on Earth. You'd think that if

1049
00:51:57,960 --> 00:52:01,839
Speaker 1: you could find enough alien civilizations that had enough like

1050
00:52:02,239 --> 00:52:06,520
Speaker 1: equivalents of New Caledonian crows and Homo sapiens, and you know,

1051
00:52:06,640 --> 00:52:10,040
Speaker 1: enough different kinds of intelligence and enough variability and intelligence,

1052
00:52:10,440 --> 00:52:13,759
Speaker 1: you'd be able to find some patterns. But in terms

1053
00:52:13,760 --> 00:52:16,719
Speaker 1: of what you could do here on Earth, I think

1054
00:52:16,760 --> 00:52:18,799
Speaker 1: we're doing what you can do here on Earth. Like

1055
00:52:18,840 --> 00:52:20,960
Speaker 1: I think we're doing the best we can right now.

1056
00:52:21,320 --> 00:52:23,480
Speaker 2: No, I think the alien question is the fascinating one,

1057
00:52:23,520 --> 00:52:25,799
Speaker 2: and I think a lot of people feel like, of

1058
00:52:25,840 --> 00:52:28,480
Speaker 2: course you do. If we could visit those planets and

1059
00:52:28,520 --> 00:52:30,839
Speaker 2: see those then we would know, and you know, we'd

1060
00:52:30,880 --> 00:52:33,399
Speaker 2: get some answers, like if we saw there's alien life,

1061
00:52:33,440 --> 00:52:35,880
Speaker 2: we could be like, Okay, very clearly life is not

1062
00:52:35,920 --> 00:52:38,400
Speaker 2: as rare, but for intelligence, it's so much harder to

1063
00:52:38,440 --> 00:52:42,800
Speaker 2: define that unless we find alien life and it's intelligent

1064
00:52:42,840 --> 00:52:45,560
Speaker 2: and exactly the same way we are, which would tell

1065
00:52:45,600 --> 00:52:48,680
Speaker 2: us like, Okay, this kind of intelligence is weirdly inevitable.

1066
00:52:48,920 --> 00:52:51,280
Speaker 2: That instead we're going to find like a huge variety

1067
00:52:51,320 --> 00:52:53,920
Speaker 2: of different kinds of intelligences and we're going to get

1068
00:52:53,920 --> 00:52:56,400
Speaker 2: stuck in the same question, just now on a bigger scale.

1069
00:52:56,560 --> 00:52:59,480
Speaker 2: And it might be this question doesn't really have an answer.

1070
00:53:01,080 --> 00:53:05,680
Speaker 1: Yeah, I mean it could be that there's no one answer.

1071
00:53:05,840 --> 00:53:08,239
Speaker 1: I do feel like if you had thirty planets that

1072
00:53:08,280 --> 00:53:10,879
Speaker 1: intelligence arose on all of them, you'd probably be able

1073
00:53:10,880 --> 00:53:15,239
Speaker 1: to say, like, Okay, in every case where there's seasons,

1074
00:53:15,920 --> 00:53:19,400
Speaker 1: you find some animals that are intelligent in this way.

1075
00:53:19,480 --> 00:53:22,120
Speaker 1: Maybe it's like they're better at remembering where the good

1076
00:53:22,120 --> 00:53:25,319
Speaker 1: food was or something. I imagine that we'd find some

1077
00:53:25,719 --> 00:53:30,319
Speaker 1: common threads. But would we ever answer all of our questions. No,

1078
00:53:30,360 --> 00:53:33,040
Speaker 1: probably not. Probably we'd get a lot more questions from

1079
00:53:33,040 --> 00:53:35,439
Speaker 1: those twenty or thirty planets, But that would be exciting too.

1080
00:53:36,520 --> 00:53:38,799
Speaker 2: I mean, we can't even agree on this podcast about

1081
00:53:38,800 --> 00:53:42,680
Speaker 2: whether flies are intelligent. So I feel like what we're

1082
00:53:42,719 --> 00:53:46,400
Speaker 2: really asking is when we find aliens, are they going

1083
00:53:46,440 --> 00:53:48,680
Speaker 2: to have culture the way we have? Are they going

1084
00:53:48,760 --> 00:53:51,640
Speaker 2: to develop technology? Are they going to be scientific? Are

1085
00:53:51,640 --> 00:53:53,279
Speaker 2: they going to be exploring the universe? Are they going

1086
00:53:53,360 --> 00:53:55,560
Speaker 2: to be like us? So in the end, I think,

1087
00:53:55,719 --> 00:53:59,080
Speaker 2: really what these questions, deep down or asking is is

1088
00:53:59,120 --> 00:54:01,160
Speaker 2: our experience universe. So so I think it really is a

1089
00:54:01,239 --> 00:54:03,600
Speaker 2: human like thing. We could find aliens that are really

1090
00:54:03,640 --> 00:54:06,000
Speaker 2: super duper smart, but so different from us that we

1091
00:54:06,000 --> 00:54:08,320
Speaker 2: don't call it intelligence. We call it something else, schmid

1092
00:54:08,320 --> 00:54:11,800
Speaker 2: intelligence or something. I think, really this is probing about

1093
00:54:11,880 --> 00:54:12,879
Speaker 2: human intelligence.

1094
00:54:13,160 --> 00:54:15,040
Speaker 1: Yeah, So first I want to reiterate that I do

1095
00:54:15,080 --> 00:54:16,839
Speaker 1: feel like you should be in charge of naming new

1096
00:54:16,880 --> 00:54:20,840
Speaker 1: things in just about any fields shima in front of it,

1097
00:54:21,880 --> 00:54:24,200
Speaker 1: and I think that's great. Actually my first blog was

1098
00:54:24,239 --> 00:54:27,920
Speaker 1: Fungilius schmundolous because I think schma is a great thing

1099
00:54:27,920 --> 00:54:29,279
Speaker 1: to end in front of other words. But you know,

1100
00:54:29,320 --> 00:54:31,640
Speaker 1: we didn't even talk about all of the hypotheses today.

1101
00:54:31,640 --> 00:54:34,680
Speaker 1: So you mentioned culture. There's a cultural intelligence hypothesis that

1102
00:54:34,719 --> 00:54:38,520
Speaker 1: focuses in particular on learning and like learning in a

1103
00:54:38,560 --> 00:54:40,920
Speaker 1: cultural context and how that might be really important for

1104
00:54:40,960 --> 00:54:43,439
Speaker 1: things like humans, And so I agree with what you said.

1105
00:54:43,480 --> 00:54:45,759
Speaker 1: There's even more hypotheses we didn't get a chance to

1106
00:54:45,760 --> 00:54:48,480
Speaker 1: talk about today. So let's take a step back and

1107
00:54:48,520 --> 00:54:51,520
Speaker 1: see if we've answered all of the questions that we

1108
00:54:51,520 --> 00:54:54,480
Speaker 1: were asked today. And so, you know, Tom wanted to

1109
00:54:54,520 --> 00:54:58,799
Speaker 1: know what makes rocket level intelligence and I threw up

1110
00:54:58,840 --> 00:55:03,160
Speaker 1: my arms and trucks, I don't know. But Tom wanted

1111
00:55:03,200 --> 00:55:06,120
Speaker 1: to know if there's things that select for intelligence. And

1112
00:55:06,160 --> 00:55:08,400
Speaker 1: we've talked about a couple things that we think maybe

1113
00:55:08,400 --> 00:55:11,960
Speaker 1: select for intelligence. Yeah, and he pointed out that intelligence

1114
00:55:12,160 --> 00:55:14,279
Speaker 1: might yet be unlucky for us, and you know, maybe

1115
00:55:14,280 --> 00:55:17,440
Speaker 1: we will all blow up because of our nuclear weapons.

1116
00:55:17,520 --> 00:55:17,960
Speaker 1: I hope not.

1117
00:55:19,239 --> 00:55:20,400
Speaker 2: I don't know why I'm laughing at that.

1118
00:55:20,760 --> 00:55:23,479
Speaker 1: Yeah, well, sometimes you laugh or you cry. Laughing is better.

1119
00:55:23,800 --> 00:55:26,040
Speaker 1: And then we addressed Mark's question about whether or not

1120
00:55:26,080 --> 00:55:29,799
Speaker 1: predation is necessary. Some people think it's important, but you know,

1121
00:55:29,800 --> 00:55:31,839
Speaker 1: we don't know if it's the only thing. Then we

1122
00:55:31,840 --> 00:55:35,799
Speaker 1: looked at Howard's question about whether or not intelligence is inevitable,

1123
00:55:35,840 --> 00:55:38,760
Speaker 1: and I yet again threw up my arms and shrugs.

1124
00:55:39,040 --> 00:55:40,560
Speaker 1: But you know, we had fun imagining it.

1125
00:55:40,840 --> 00:55:43,200
Speaker 2: And I think that these are really fascinating questions. I

1126
00:55:43,280 --> 00:55:46,480
Speaker 2: love the folks ask them, even if we don't have answers.

1127
00:55:46,520 --> 00:55:48,920
Speaker 2: I think it's exciting to think about what efforts people

1128
00:55:48,960 --> 00:55:52,280
Speaker 2: are making and tackling these really big, really deep questions,

1129
00:55:52,719 --> 00:55:55,040
Speaker 2: because you know, those first steps are the hardest ones.

1130
00:55:55,440 --> 00:55:57,800
Speaker 2: Like when you look back what the Greeks did or

1131
00:55:57,840 --> 00:56:00,840
Speaker 2: what ancient civilizations have done to take the first steps

1132
00:56:00,840 --> 00:56:04,120
Speaker 2: towards science, they seem obvious in hindsight maybe, but it's

1133
00:56:04,239 --> 00:56:07,160
Speaker 2: difficult to take those first steps when you're up against

1134
00:56:07,200 --> 00:56:10,400
Speaker 2: the abyss of our ignorance about these deep questions.

1135
00:56:10,560 --> 00:56:11,960
Speaker 1: Yeah, and at the end of the day, I think

1136
00:56:11,960 --> 00:56:14,880
Speaker 1: it's amazing that there is a species on our planet

1137
00:56:14,960 --> 00:56:17,800
Speaker 1: that's asking these questions and are trying to buddle through.

1138
00:56:17,920 --> 00:56:21,440
Speaker 1: And you know, I, when I looked at the neuroanatomy textbook,

1139
00:56:21,480 --> 00:56:24,080
Speaker 1: threw up my hands and said, I'm studying something else.

1140
00:56:24,480 --> 00:56:27,319
Speaker 1: Thank goodness. There are people who are like, I'm going

1141
00:56:27,360 --> 00:56:30,040
Speaker 1: to spend my whole life banging my head against this

1142
00:56:30,160 --> 00:56:32,520
Speaker 1: problem and make a little bit a headway and move

1143
00:56:32,600 --> 00:56:35,520
Speaker 1: us all ahead. And so I lately feel like there's

1144
00:56:35,520 --> 00:56:37,440
Speaker 1: a lot of reasons to be down on our species.

1145
00:56:37,440 --> 00:56:39,480
Speaker 1: But it is pretty cool that we do this kind

1146
00:56:39,520 --> 00:56:41,480
Speaker 1: of stuff, and I find the science that we do

1147
00:56:41,560 --> 00:56:42,520
Speaker 1: super inspirational.

1148
00:56:42,719 --> 00:56:45,000
Speaker 2: Yes, although I think we can agree that the smartest

1149
00:56:45,000 --> 00:56:46,960
Speaker 2: people on the planet are the folks who listen to

1150
00:56:46,960 --> 00:56:47,600
Speaker 2: this podcast.

1151
00:56:47,680 --> 00:56:50,680
Speaker 1: Right, agreed, and that is the perfect note to end on.

1152
00:56:52,960 --> 00:56:55,319
Speaker 2: And so before we sign off, we were curious did

1153
00:56:55,360 --> 00:56:58,399
Speaker 2: we answer Tom and Mark and Howard's questions. We sent

1154
00:56:58,560 --> 00:57:01,360
Speaker 2: this to them and hear what they had to say.

1155
00:57:02,239 --> 00:57:05,759
Speaker 6: Thanks Daniel and Kelly for answering this question. You know,

1156
00:57:05,760 --> 00:57:07,560
Speaker 6: one of the reasons I love your podcast so much

1157
00:57:07,640 --> 00:57:12,400
Speaker 6: is because no matter how complete your answers are, you

1158
00:57:12,440 --> 00:57:16,440
Speaker 6: always offer a more beautiful question at can't. And I

1159
00:57:16,440 --> 00:57:19,400
Speaker 6: guess one of the reasons I was focused on rockets

1160
00:57:19,520 --> 00:57:24,520
Speaker 6: is because aliens were more likely to kind of discover

1161
00:57:24,600 --> 00:57:27,880
Speaker 6: alien technology than we are. Aliens first, and I imagine

1162
00:57:27,880 --> 00:57:33,640
Speaker 6: that rockets are pretty important in getting off planet. I'm

1163
00:57:33,640 --> 00:57:38,040
Speaker 6: not aware of any other way to do that, but yeah,

1164
00:57:38,080 --> 00:57:42,920
Speaker 6: I mean, I very satisfied with your discussion, and I

1165
00:57:43,080 --> 00:57:48,120
Speaker 6: look forward to finding out what this field is discovering

1166
00:57:48,120 --> 00:57:54,240
Speaker 6: about intelligence and different kinds of intelligences and what we

1167
00:57:54,360 --> 00:57:57,400
Speaker 6: might expect to find out there in the universe.

1168
00:57:58,160 --> 00:58:02,520
Speaker 3: Thank you, Hi, Daniel Cally. Yes, you answered my question.

1169
00:58:03,360 --> 00:58:06,040
Speaker 3: And while I was hoping the answer would be that

1170
00:58:06,080 --> 00:58:09,560
Speaker 3: there could be some world out there where all the

1171
00:58:09,640 --> 00:58:15,320
Speaker 3: creatures hold hands or tentacles or whatever and sing Kumbaya

1172
00:58:15,320 --> 00:58:18,120
Speaker 3: and are cool to each other. It sounds like the

1173
00:58:18,160 --> 00:58:22,760
Speaker 3: real answer is wherever there's life, whether it's intelligent or not,

1174
00:58:23,600 --> 00:58:27,000
Speaker 3: something is gone, sooner or later get the idea to

1175
00:58:27,000 --> 00:58:31,200
Speaker 3: eat something else. So thanks for taking my question and

1176
00:58:31,200 --> 00:58:33,919
Speaker 3: thanks for the book suggestion. I will definitely check it out,

1177
00:58:34,120 --> 00:58:36,720
Speaker 3: and of course thanks for the wonderful show.

1178
00:58:37,720 --> 00:58:40,560
Speaker 5: Thank you Daniel and Kelly. This is Howard and I

1179
00:58:40,600 --> 00:58:42,960
Speaker 5: want to thank you for taking on my question. I

1180
00:58:43,000 --> 00:58:46,200
Speaker 5: know it can be challenging when the question has no

1181
00:58:46,360 --> 00:58:50,160
Speaker 5: definitive answer, but I kind of knew that going in.

1182
00:58:50,240 --> 00:58:55,080
Speaker 5: But what I really appreciated was listening to your thought processes,

1183
00:58:55,160 --> 00:58:57,480
Speaker 5: how you break it down, how you would approach it,

1184
00:58:57,560 --> 00:59:02,640
Speaker 5: what hilarities are there that might apply.

1185
00:59:02,520 --> 00:59:07,200
Speaker 2: To it, And that was invigorating. Thank you so much.

1186
00:59:07,280 --> 00:59:12,880
Speaker 2: I appreciate it. A quick non science announcement. May tenth,

1187
00:59:13,000 --> 00:59:16,800
Speaker 2: twenty twenty five is the Stamp Out Hunger Drive, organized

1188
00:59:16,840 --> 00:59:19,960
Speaker 2: in tandem with the US Postal Service. It's very easy

1189
00:59:20,000 --> 00:59:22,600
Speaker 2: to help fill up your local food banks. Just leave

1190
00:59:22,800 --> 00:59:26,400
Speaker 2: non perishable food at your mailbox and your carrier will

1191
00:59:26,440 --> 00:59:28,560
Speaker 2: collect it for you. That's all you have to do.

1192
00:59:29,000 --> 00:59:32,280
Speaker 2: Leave non perishable foods by your mailbox on May tenth,

1193
00:59:32,360 --> 00:59:34,760
Speaker 2: twenty twenty five to help stamp out hunger.

1194
00:59:41,480 --> 00:59:45,320
Speaker 1: Daniel and Kelly's Extraordinary Universe is produced by Iheartreading. We

1195
00:59:45,360 --> 00:59:47,800
Speaker 1: would love to hear from you, We really would.

1196
00:59:47,960 --> 00:59:50,680
Speaker 2: We want to know what questions you have about this

1197
00:59:50,880 --> 00:59:52,560
Speaker 2: Extraordinary Universe.

1198
00:59:52,760 --> 00:59:55,720
Speaker 1: Want to know your thoughts on recent shows, suggestions for

1199
00:59:55,800 --> 00:59:58,880
Speaker 1: future shows. If you contact us, we will get back

1200
00:59:58,920 --> 00:59:59,240
Speaker 1: to you.

1201
00:59:59,320 --> 01:00:03,040
Speaker 2: We really mean we answer every message. Email us at

1202
01:00:03,120 --> 01:00:05,920
Speaker 2: Questions at Danielandkelly.

1203
01:00:05,160 --> 01:00:07,200
Speaker 1: Dot org, or you can find us on social media.

1204
01:00:07,280 --> 01:00:11,080
Speaker 1: We have accounts on x, Instagram, Blue Sky and on

1205
01:00:11,160 --> 01:00:13,120
Speaker 1: all of those platforms. You can find us at D

1206
01:00:13,560 --> 01:00:15,040
Speaker 1: and K Universe.

1207
01:00:15,200 --> 01:00:16,720
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