1:59

animal communication using AI. We

2:03

had a wide-ranging conversation that started

2:05

with the basic discovery that our world

2:08

is filled with way more animal sounds

2:10

than a lot of people initially thought. And

2:12

from there, we talked about what those sounds might

2:15

mean, whether there's some form of language

2:17

and whether scientists might someday

2:20

be able to use AI to actually

2:22

decipher what animals are saying. We've

2:25

edited this a bit for clarity and length.

2:28

Here's our conversation.

2:35

So just to start,

2:37

before we get into how researchers are

2:39

trying to decode animal sounds today, I

2:42

think it's worth talking about how

2:44

they realized they were worth decoding in the first place.

2:47

So, Karen, you have a story in your book about how European

2:50

and American scientists really started to discover

2:53

that these sounds were even worth

2:55

exploring. You talk about whale song. So

2:58

I wonder if you could just set the scene for us a little. What

3:00

was the state of research on ocean

3:02

sounds, maybe 100 years or so ago?

3:05

Yeah, so cast your mind back to about 100 years

3:07

ago. There's a widespread assumption

3:10

that only humans possess language, and

3:12

moreover, that other species

3:14

do not possess complex communication

3:18

capacity.

3:18

This

3:20

is, of course, a blind spot in Western

3:23

science that we're going to unpack throughout the course

3:25

of the conversation today. But

3:27

at the time, one of the most remarkable

3:30

aspects of Western science

3:32

was really a kind of a cinnabon mission.

3:35

There wasn't a lot of work done recording

3:37

animal sounds. There wasn't a lot of work

3:40

done trying to decode them. And

3:42

that all changed when a

3:44

small number of researchers, some of

3:47

them associated with then classified

3:50

Navy efforts to listen to underwater

3:52

ocean sounds, began attempting

3:55

to decode and categorize

3:58

the really complex underwater sounds.

3:59

hearing in the ocean. So

4:02

these scientists made early

4:04

efforts, mostly to record

4:07

the

4:07

pretty profound and amazing sounds that whales

4:09

make, which are now well known to us. But

4:12

at the time, we're pretty astounding

4:15

to Western scientists, the kind of operatic

4:17

uulations of humpback whales, the very staccato,

4:20

powerful, eardrum blasting sounds of

4:23

sperm whales. There's a

4:25

whole symphony under the ocean, of

4:28

which Western science was largely unaware.

4:30

Their work came to public attention

4:33

when a renegade scientist

4:36

named Roger Payne and his brilliant wife,

4:39

Katie Payne, a classically trained musician, took

4:42

some classified recordings that

4:45

Navy scientists had given them and published them

4:47

as an album, which

4:50

remains the best-selling nature album of all time, went

4:52

platinum, and was one of the major

4:54

things that

4:55

changed the dynamic around

4:58

the campaign to end industrial whaling, which was

5:00

arguably one of the things that saved many

5:03

whale

5:03

species from extinction. So

5:05

whales were our re-entry

5:09

as Western scientists into something indigenous

5:12

cultures had long known, that many,

5:15

many species are capable of complex communication.

5:17

And from there, many

5:19

other

5:20

species have begun essentially

5:22

to reveal themselves to Western science. But I want to emphasize

5:24

right at the start, this has long held

5:26

human knowledge that somehow we have

5:29

forgotten that we had forgotten, and

5:31

we have just begun remembering really

5:34

how to remember.

5:35

So talking about things that we've forgotten,

5:37

or maybe things that we were

5:39

not aware of until

5:42

very recently, it's

5:44

not just these sounds under the water. It's

5:46

not just deep sounds that

5:48

we haven't been able to hear because we didn't have submarines.

5:51

I wonder if you can tell us a bit about

5:54

some of these sounds that we can't hear

5:56

without technology that are around

5:58

us all the time. majority

6:00

of these sounds are inaudible to the

6:02

naked human ear. They are either above

6:04

our hearing range in the high ultrasound or below

6:07

our hearing range in the deep infrasound.

6:10

There is an evolutionary reason

6:11

for this called the acoustic niche hypothesis.

6:14

So in most ecosystems, what

6:16

you get is much like radio

6:19

stations on the radio dial. You'll have

6:21

different species, essentially broadcasting

6:23

acoustic communication at a specific set of frequencies,

6:26

a band, and also being able

6:28

to hear in that same

6:29

frequency range. We hear pretty

6:31

much at the frequencies that we are able to vocalize

6:34

at. So the ability

6:37

to record beyond human hearing

6:38

range is only about a hundred years old for

6:41

ultrasound and infrasound. But what

6:43

AI does is it allows

6:46

the parsing, the categorizing

6:48

of that data at scale. And

6:51

so we reversed a very fundamental constraint

6:54

of 20th century biology.

6:57

We used to have basically a scarcity of data.

7:00

Now we have a hyperabundance of data

7:01

because of cheap digital recording devices, many,

7:04

many such recording devices from the

7:06

Arctic to the Amazon. And now we have AI

7:08

that can do some, not all,

7:10

automated

7:13

tracking, categorizing, parsing.

7:15

That doesn't mean we can actually then make the

7:17

next step to translating, but it gets

7:19

us a lot further. So

7:22

you're talking about now using AI,

7:24

using this more data we've had, using

7:27

AI to analyze it.

7:30

Aiz, I wonder if you can talk a little bit about

7:32

the research you've done with

7:34

using AI to map things like

7:36

shapes of languages. Like how does AI

7:38

help us translate between languages

7:41

when we might not be able to understand them? Yeah.

7:44

So I'm going to tell this story in two parts. And

7:47

the reason why we started Earth Species

7:49

in 2017 was that something fundamental

7:53

changed, right? Because if you're going to try to translate

7:55

a language without a Rosetta stone, that

7:58

didn't exist in human history. up

8:00

until like 2017, then that changed. So

8:04

what changed? AI gained

8:06

the ability to build shapes that

8:08

represent languages. For those of you that are AI people in the

8:11

room, these are called latent spaces or embedding

8:13

spaces, but these shapes are really

8:15

interesting because they turn

8:18

semantic relationships into geometric

8:21

relationships. What does that mean? Think about

8:24

a language like English. Now imagine

8:26

a galaxy where every star is

8:28

a word.

8:29

And words that mean similar things are near each other, and

8:31

then words that share a semantic relationship share

8:33

a geometric relationship. So king

8:35

is to man, as woman is to queen. So in

8:37

this shape, king is the same distance direction to man,

8:40

as woman is to queen. So you just do king minus man,

8:42

that gives you a distance and direction. You add that to boy

8:45

and it'll equal prince. You add that to girl, it'll equal

8:47

princess. Like all the

8:49

internal relationships of a language are

8:51

encoded in this shape. And

8:53

if you think about it, like dog

8:56

has relationship to man and to wolf

8:58

and to fur and to yelp and to howl,

9:01

it sort of fixes it in a point in space

9:03

in this shape.

9:04

And then if you solve the massive multi-dimensional

9:07

no sudoku puzzle of every

9:09

concept to every other concept,

9:12

out pops this rigid structure representing

9:14

all the internal relationships of a language.

9:17

The computer doesn't know what anything means, it just knows how they relate.

9:19

And here was the deep insight from 2017. Could

9:23

the shape of two languages possibly

9:25

be the same?

9:26

So I'm holding a Portuguese word cloud here.

9:29

You're holding an English one. And

9:31

the mathematical relationship between

9:34

the stars and my galaxy

9:36

that represent woman,

9:39

queen, king, man, is

9:41

more or less the same mathematical, that

9:44

is spatial relationship in your word cloud. If

9:46

I were to throw up Cree or in

9:48

Nukh-to-tut, a little bit less

9:50

overlap, but more or less, a lot of

9:52

these concepts are invariant across human languages.

9:56

And that is why we are able to

9:58

really effectively translate. now using

10:00

AI between our different word clouds.

10:02

That's exactly right. You literally just rotate

10:05

one shape on top of the other, and even

10:07

though there are words in one language that don't exist in the other,

10:09

the point which is dog ends up in the same spot in both,

10:11

and you sort of blur your eyes in the same shape, and that works

10:14

for English and Spanish, and you're like, cool,

10:16

those are related languages, obviously, but

10:18

also works for Finnish, which is a weird language, and

10:20

Aramaic and Urdu, Esperanto.

10:23

Every human language sort of roughly fits in this

10:25

universal human meaning shape, and that

10:28

was the moment that we're like,

10:29

there's a path through.

10:31

Do you think if we build this shape for animal

10:33

communication, it fits somewhere

10:36

in the universal human meaning shape, and the parts that

10:38

overlap, we should be able to directly translate into

10:40

the human experience, and the parts that don't overlap, we

10:42

should be able to see complexity, and so

10:44

this gives us the ability to start getting like blurry

10:47

polaroid images of

10:50

things that are beyond the human imagination.

10:52

Let's go back one step to this notion

10:55

that we can translate between our different

10:57

word clouds, you know, English and Inuktutut,

11:02

and then extrapolate that to non-human communication

11:04

systems. So, first of all, you have to imagine

11:06

that scientists now have the ability to create these kind

11:08

of latent

11:09

spaces or word clouds with non-human

11:11

communication regimes. For example,

11:14

there is now an elephant dictionary with

11:17

thousands of sounds, and field

11:19

biologists have painstakingly documented

11:21

what each of those signals mean. And

11:24

elephants, for example, have a specific signal

11:26

for honey bee. They're terrified

11:28

of honey bees. They can get

11:30

into their trunks and their ears and sting them,

11:33

and so there's a very specific behavior

11:35

elephants display when they hear the sound.

11:37

So tested through playback experiments,

11:40

we have this elephant dictionary, but

11:42

there are many ifs here because

11:44

the assumption that the underlying

11:47

worldview, the felt-lived embodied sense

11:51

of an elephant, the umm-veld,

11:53

as researchers call it, is anything

11:55

like a human is one we haven't

11:58

yet proven. And so my word cloud means...

11:59

not contain any concepts that

12:02

actually overlap with ASAs, with the exception

12:05

of very few. So it may

12:07

be that AI translation is either

12:09

a dead end, or it would only

12:11

allow us to develop a small subset

12:14

of translatable concepts, and

12:17

that there are

12:18

other rules governing these non-human

12:20

communication systems that we've yet to figure out. I'll

12:22

just give a couple examples. It

12:25

may be that animal species have different languages

12:27

for different times of year. A little

12:29

bit like if you're familiar with Indian classical music,

12:32

ragas.

12:32

There's the morning raga, the evening

12:34

raga. So you have to throw out all

12:36

of your assumptions about language. Maybe

12:38

they have different languages for different parts of the

12:40

world if they're migratory. The

12:43

Bering Strait may have a different language. Then

12:45

the warm waters of Hawaii,

12:46

where you give birth. So

12:49

we are just really, at the beginning, of

12:52

trying to figure out what ASAs is trying to do is achievable.

12:55

My personal bet, and I'd love to hear your view, is

12:58

that there will be an incomplete translation.

13:01

We will be able to detect names. We'll

13:03

be able to detect alarm calls. And

13:06

we'll be able to detect the labels

13:08

that are given to

13:09

features of the environment that are linguistically

13:11

invariant. But there are many, many

13:13

more complex concepts that we're going to have

13:15

to invent entirely new types of science

13:18

to begin understanding. And those are going to combine field

13:21

observations with AI.

13:22

Absolutely. I just want to say

13:25

that all of our work is built off

13:27

of decades of painstaking

13:29

research done by biologists out in the field. And

13:31

everything we do is hence in collaboration. But

13:34

then just to add a couple of thoughts

13:36

to what you're saying is, one, the way we're describing

13:38

doing this kind of translation with

13:40

rotation, that's 2017 AI tech. It's

13:43

now sort of Stone Age. There are many

13:46

other techniques which I can talk about that that

13:48

becomes just one tool among many.

13:51

But why should we expect, I agree, the unvelte

13:53

of a sperm whale might be so completely different. It

13:55

spends 80% of its life in complete

13:57

darkness, a kilometer deep in the ocean.

13:59

Why should we expect there to be any overlap whatsoever?

14:03

And I'll give two examples for why there might be

14:05

overlap. And then I'll sort of talk

14:07

about the parts that, like why I think this goes even

14:09

beyond language. The

14:11

first example is the mirror

14:14

test. It's like, how do you know

14:16

whether another being has self-awareness? One

14:18

way you might discover that is you would paint

14:21

a dot on them

14:22

where they are unaware of that dot. Then you

14:24

put a mirror in front of them. And

14:27

if they see

14:28

the dot and they start to try to get it off, that

14:30

shows that they're connecting the image in the mirror

14:33

with themselves, that they have a self-image.

14:36

Now, if they don't respond, that doesn't actually

14:38

tell you anything. Researchers thought

14:40

for the longest time that elephants couldn't pass the mirror

14:42

test, but it turns out they're just using small mirrors.

14:45

Right? But

14:49

a number of species do pass this kind

14:51

of mirror test. And that means if they're

14:53

communicating, they may well be communicating

14:55

about a rich interiority,

14:58

like a self-awareness, one of the most profound things

15:00

that we have. Another example, as

15:04

I have this in my presentations, an incredible

15:06

video of lemurs biting

15:09

centipedes to get high. So they're like

15:11

taking hits off of centipedes. They get super

15:13

cuddly. They enter these trance-like states. It's

15:16

sort of like a proto-burning man. And

15:20

like dolphins do the same thing. They will intentionally

15:22

inflate puffer fish also

15:24

to get high off of their venom and pass them around

15:26

in the original puff puff pass. Right?

15:30

So gorillas and chimpanzees will

15:32

spin. They'll hang on a vine and spin to get really dizzy.

15:35

Transcendent states of consciousness appears

15:38

to be a thing that we share and desire

15:40

across many species. So that, too,

15:42

is like a very profound thing that if we communicate or if they

15:45

communicate, they may well communicate about.

15:47

So there are some, I think, really interesting

15:49

areas of overlap.

15:51

This conversation we're having, on one

15:54

sense, it can feel like we're just speeding

15:56

ahead. And there's this one other hurdle we

15:58

have to pass, which is like. like figuring out this translation,

16:01

the AI is almost there. We're

16:03

also using a lot of anthropomorphic

16:06

language, and I feel like that is definitely,

16:10

I understand why we would do that. We don't have

16:12

better words for those concepts. I

16:15

mean, I want to clarify that

16:16

scientists do not use that language.

16:18

They use very technical terms. So

16:20

for example, scientists would not use

16:23

the term name. They

16:25

would say individual vocal label

16:28

or vocal signature. Equally,

16:31

most of the scientists studying the communicative regimes

16:33

of non-human species would

16:35

use the term communication, not

16:38

language. Because language is sufficiently

16:41

anthropocentrically defined in terms

16:43

of complex

16:45

combinatorial capacity, symbolic content,

16:48

syntax, so on and so forth, that

16:51

it is as yet to be proven that other

16:53

species have, quote unquote, language.

16:56

So

16:57

I just want to clarify that

16:59

although Aysa and I, in

17:02

a sort of a public communication of science way, are

17:04

using these terms, the scientific community is

17:07

pretty rigorous, perhaps

17:10

incorrectly so, but nonetheless

17:12

pretty rigorous about setting a

17:15

boundary between humans and

17:18

non-human species. But one of the things that this research

17:20

may eventually do is create

17:22

a sufficient weight of evidence that

17:25

we do indeed say, ah yes,

17:27

other species have language. We

17:29

may need to change our definition of language in order

17:31

to do so. Or, ah yes, other species

17:33

do convey symbolic

17:36

meaning through language. And here's how. We're

17:39

not there yet. So progressively,

17:42

I think this science is going to lead us somewhere very,

17:44

very interesting in terms of asking

17:46

these fundamental questions on the basis

17:49

of a huge amount of

17:51

empirical evidence.

17:53

I wonder, you know, we briefly

17:56

mentioned the Umm Velt question about what

17:58

does it mean to be a bat or a whale. or

18:00

any of these things that perceive the world completely differently.

18:03

And I guess

18:05

I just wonder if we

18:07

could be communicating something to the animal,

18:10

that the animal would be potentially understanding

18:12

in a different way and acting in a way that

18:14

looks like what we expect

18:17

the animal to act, because that's how we are understanding

18:19

things. But will we ever... Is

18:21

it even possible to imagine that we could actually

18:24

communicate where we both know that we

18:26

are understanding each other?

18:28

I mean, the same problem exists between any

18:31

two humans. Yeah. The myth of communication

18:34

is that it ever happened in the first place. Yeah.

18:37

So I think the

18:39

practical, pragmatic scientific responses

18:42

play back experiments. And that

18:44

is how these are tested. We assume this acoustic

18:47

signal means this. We can play

18:49

it back in the field or in lab-controlled conditions.

18:52

We see the response as what we predict. Elephant

18:55

honeybee alarm call leads to very specific

18:57

physical behaviors, the group of elephants coming together,

19:00

dusting themselves.

19:01

Now, beyond that,

19:03

I mean, the act of communication is

19:06

a profound mystery. The ability

19:08

of any two beings on Earth to

19:10

believe they could actually understand one another,

19:13

it is actually quite magical. So science

19:15

is one of the ways

19:16

of approaching some of the great mysteries,

19:19

and communication is one of them.

19:22

But the reason I think this captures so much public

19:24

imagination, Ays and I have talked about this in the past,

19:26

is because this is also a great mystery,

19:29

which has been the subject of much reflection

19:32

in various mythological

19:34

and spiritual

19:34

traditions. And

19:36

so that is some of the richness of this

19:38

work. It's also some of the controversy that it inspires

19:41

in the scientific community.

19:47

We're going to take a quick break from the conversation

19:49

here. When we come back, we're going

19:52

to ask whether we should be trying to translate

19:54

animal communication at all.

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20:30

Chinese fashion behemoth Shein is

20:32

one of the fastest growing companies in

20:34

the world, which means the

20:36

people of planet Earth like it. But

20:40

it has so many critics. Designers

20:42

say Shein steals from them. Shein

20:44

straight up just stole my pirouette

20:47

skort design. Influencers say

20:49

they've been misled. I make mistakes.

20:51

I'm imperfect. I'm forever

20:53

growing and evolving, and that's like probably

20:56

one of my favorite things about myself. I'm sorry

20:58

about that trip to China. Congress

21:00

said Shein's being deceptive about who

21:02

makes the clothes. Consumers don't, all

21:04

things being equal, want to purchase goods

21:07

that are contaminated by slave labor or forced

21:09

labor. And a Vogue editor says the

21:11

clothes are just not that good. These are not very

21:14

well made clothing. They are not made to last.

21:17

As Shein flirts with going public, why

21:20

is this hated company so

21:22

beloved? And why is this beloved

21:24

company so hated? Today Explained,

21:27

it's in your feeds every weekday at 2

21:29

p.m.

21:38

One issue we haven't touched on is sort

21:41

of a should issue. Yeah. And

21:44

assuming we can use

21:46

AI to analyze these language shapes,

21:49

to figure out in some basic sense

21:52

what animals may be saying to each other, maybe

21:54

we can communicate with them, maybe we can listen

21:56

to them, knowing

21:58

humans... I can imagine there

22:01

would be some maybe not friendly ways

22:03

to approach that situation. And I'm wondering what you

22:05

think about the danger

22:08

of being able to understand animals

22:10

and being able to communicate with them.

22:12

So I want to give a really specific example of

22:18

the new responsibility that we

22:20

as a species are going to have to show up

22:22

to in the very, very near future.

22:25

So you guys, I'm sure, have encountered Chat

22:27

GPT. You can

22:29

build chat bots

22:31

like Chat GPT in Chinese, even

22:33

if you do not speak Chinese. And

22:36

you've probably also seen all of the deep fake stuff.

22:40

Now, it is possible with just three seconds of anyone's

22:42

voice to continue to speak in their voice and

22:44

say what they were saying. And what

22:46

this means is that within months

22:50

to short number of years,

22:52

we will be able to build, essentially,

22:55

synthetic chat bots, synthetic whales, synthetic

22:57

belugas, synthetic tool

23:00

using crows that can speak in

23:03

a way that

23:04

they don't understand. They're not speaking to

23:06

one of their own. Imagine you had the superpower,

23:08

and your superpower was able to walk up to

23:10

somebody whose language you don't understand.

23:13

You sort of cock your ear. And you listen, and you're like, OK,

23:15

I see this pattern after this pattern. You start to babble

23:18

with those patterns. And you don't know what you're saying, but

23:20

the other person's like, yeah,

23:21

wow. It's Douglas Adams' Babelfish. It

23:23

is, except here's the plot twist. You'll

23:26

be able to communicate before

23:29

you understand. And so this is actually

23:31

the case. We're actually starting our first

23:33

experiments with Zebrafinch likely later this

23:36

year. We're doing real time, two

23:38

way communication with captive

23:40

population to see can

23:42

we do, start to

23:44

cross this communications barrier

23:47

by being able to speak before we

23:49

understand. And this is fascinating.

23:51

It obviously lets us start to get to decode much

23:54

faster. But Humpback

23:56

whale song goes viral, right? Like songs

23:58

sung off the coast of Australia. Australia can

24:01

go 1,000 kilometers. The

24:04

Humpback whale song will be picked up by the world population

24:07

within a season or two sometimes. And

24:09

so if we're not careful, if we just create a

24:11

whale that starts to sing, especially before

24:13

we understand what it means, we

24:17

could be messing up with wisdom tradition, creating

24:19

a whale QAnon. We don't know. And

24:22

that means before that

24:25

happens, because that means it's

24:27

a very crazy thing to think

24:29

of. I didn't think we were going to get here this quickly. In

24:32

the next 12 months, five years, certainly before 2030,

24:35

we will have the capacity to

24:37

do real time, two way communication,

24:40

animal to AI, not necessarily

24:42

animal to human.

24:45

And

24:46

we need to have a kind of Geneva

24:48

convention for cross species communication, a

24:51

prime directive, sets of norms,

24:53

ways that IRBs review. There are a whole

24:56

bunch of things we

24:58

need to set up. And I think you can talk about them too. Yeah, and

25:00

I think there are even more nefarious uses. Precision

25:02

hunting, precision

25:04

fishing, of course. Yeah, poaching. Yeah,

25:06

poaching. This will enable the

25:10

acceleration of the kind of cat and mouse

25:12

game between poachers and game keepers,

25:15

no doubt. There

25:17

also is the specter of being able

25:19

to domesticate species that

25:22

were formerly not domesticatable by

25:24

humans. So we may

25:26

be able to use this in certain contexts,

25:29

and this is what my next book is about, for

25:31

biodiversity conservation goals.

25:34

At the same time, it could allow

25:36

bad actors, and

25:39

keep in mind how big

25:41

the multi-billion dollar global

25:43

illegal wildlife trade is, right, to

25:45

further capitalize on their ability to ensnare

25:48

animals

25:48

that have so far been out of reach. So

25:51

the Geneva Convention long term for multi-species

25:54

dialogue, great. Prior

25:56

to that, I think we've got a

25:59

more immediate problem.

25:59

on our hands given the biodiversity crisis

26:02

with respect to nefarious

26:04

uses of these technologies. The

26:07

only saving grace is that the

26:09

AI may not be really as good as we

26:12

think. So you know, first

26:14

of all, we're being very self-centered

26:17

here as usual, we're humans. We're assuming

26:19

other species actually want to talk to us. They

26:22

may be like boring, you know,

26:24

or they may just assume that

26:26

these sounds which are gibberish, you know,

26:29

are to be avoided, rightfully so. Or they

26:31

may simply be able to detect it's not being made

26:33

by another living member of their

26:35

species and avoid. So

26:38

my hope is that they're

26:39

going to reveal, we're going to reveal ourselves to be slightly

26:42

stupider than we think. They're going to reveal

26:44

themselves to be smarter than we believe. And

26:46

maybe that'll create a bit more breathing room. But no

26:48

doubt, longer term,

26:50

deep fake AI technology creates a whole

26:52

bunch of risks. And do you think we should, given

26:54

these risks, you think this is something that

26:57

scientists should push forward? Yeah.

26:58

I mean, I believe we should have a moratorium. Ays

27:00

and I don't agree on this point, I think.

27:03

I think there are certainly thresholds that if we cross,

27:05

we should have a moratorium. We should stop. Absolutely.

27:09

Here's the thing. We are mutilating

27:11

the tree of life. And at some point,

27:14

we are going to cut the branch upon which humanity

27:16

depends. Right?

27:18

So we are in the land of Hail

27:21

Mary passes.

27:23

The hope for,

27:25

I think, working on

27:28

showing and really, the

27:30

point is not really to talk to animals. The

27:32

point is really to understand and listen. And along

27:34

the path to that, we are creating the

27:36

technology that solves the fundamental

27:38

problems we see across all of conservation,

27:42

biology and ethology research. Every

27:45

biologist we talk to needs to do classification,

27:48

denoising, detection of signals

27:50

to understand biodiversity, to understand their

27:53

behaviors. And so the tools we build

27:55

as we head towards decoding

27:58

are the fundamental tools we use. that

28:00

are accelerating conservation biology,

28:02

which to the extent that conservation science

28:05

accelerates conservation, like

28:08

we're trying to broad scale do that. But

28:10

then there are these moments when we get shifts

28:12

in perspective and that changes everything. We talked about

28:14

songs of the humpback whale, but also when human

28:17

beings went to the moon and

28:19

when human beings were dosed with that overview effect

28:22

and seeing us as a pale blue dot suspended

28:24

in space, planet, spaceship earth,

28:26

right? That's when the EPA came into existence. NOAA

28:30

was born, modern environmental movement started, clean

28:32

air acts was passed, and that was in the Nixon

28:35

era, right? And so the goal here

28:37

is

28:38

like there are moments in

28:40

history which superpower

28:42

movements. There are no silver bullets, but maybe

28:44

there's silver buckshot. And maybe

28:47

if

28:47

we know this is coming, we

28:49

can arm every other conservation

28:52

org out there, rights for nature, personhood

28:55

for non-humans, E.O.

28:57

Wilson's half-earth, much bigger marine

29:00

protected zones. When this becomes the thing

29:02

that the entire world sees

29:05

and becomes the top of politicians'

29:08

priority list, suddenly I think

29:11

we can accelerate and be a force multiplier

29:13

for every other conservation

29:17

and climate action out there. And

29:19

that I think is the reason why it's worth pursuing.

29:21

I wonder if, yeah, I mean, just before we finish, I wonder,

29:24

could you just say a bit about why you think we should have a moratorium?

29:28

I will, but I do also want to build on Asia's

29:30

point. So

29:31

the climate change and biodiversity

29:34

crisis are intimately interrelated. And

29:37

the fundamental

29:40

challenge of the next 20 or 30 years,

29:42

as we add a couple billion more humans to

29:45

the planet, is a sort of arc, Noah's

29:47

arc-like challenge. How many species

29:51

will be around at the end of our

29:53

lifetimes? And

29:56

acoustics, regardless of whether

29:59

we actually achieve it, we can do it. to achieve interspecies communication

30:02

is a powerful tool in the conservationists

30:05

toolkit. Because simply

30:07

through the use of digital bioacoustics, you

30:09

have a very low cost, very effective

30:12

monitoring regime that

30:14

is much less invasive than human

30:16

monitoring. And this

30:18

is now something that is being set up around

30:20

the world.

30:22

I can't go into the technical details for lack of

30:24

time, but very simply, bio

30:26

and ecoacoustic

30:27

and disease allow us to tell

30:30

simply by listening the

30:32

extent to which climate change is disrupting species,

30:35

species migrations and movements, and

30:39

species abundance or disappearance, et cetera. So

30:41

we may never achieve interspecies communication,

30:45

but what we can do and we all should be doing if we're

30:47

interested in environmental work is supporting

30:49

the inclusion of digital acoustics,

30:52

bio and ecoacoustics, into conservation

30:54

work as a low cost, minimally invasive,

30:56

very powerful tool. So

30:59

I hope that's a take home message for all of

31:01

you. The question

31:03

of the moratorium, I think, is really

31:06

one about something

31:08

humans are not very good at. That

31:11

is

31:11

having

31:12

the ability

31:13

to

31:16

create a space in

31:18

between what we think we're capable of doing

31:21

and reaching for that thing. So

31:24

there is a

31:25

in the tech community, in the scientific community,

31:28

there is a can do ethos. I

31:32

can do that. And so I want to do

31:34

it. But there is a should

31:36

do question here that I think

31:38

requires very, very careful consideration.

31:41

And I see no, and

31:43

I hear I disagree with you, compelling reason

31:45

right now to continue the

31:48

work that could be so damaging to other species

31:51

that could

31:52

lead to precision hunting and poaching. Without

31:55

getting a lot of the ethical frameworks

31:58

in place, it would mean updating the

31:59

Convention on International Trade in Endangered Species,

32:02

updating a lot of international environmental regulatory

32:05

frameworks. AI governance poses a more

32:07

general

32:07

problem, right? So

32:09

my view is we need to get our house in order on that.

32:11

And just like from time to time human

32:14

genomics research has hit pause

32:16

and there's certain no-go areas, like cloning

32:19

humans, I think we can come

32:21

up with a set of no-go areas for

32:24

AI science in this regard that

32:26

would allow technical progress to still be made,

32:29

but not be invoking the kind of risks

32:31

that we barely have even begun to understand.

32:33

And on that we actually I think agree,

32:36

which is that as we show up to

32:41

the new responsibility of the power, you have

32:43

to ask how is that power bound to

32:45

wisdom and how knowing

32:48

that this technology is coming, because

32:50

regardless the ability to emulate

32:52

any signal,

32:54

that's being pushed by

32:56

the market forces on the human domain.

32:59

So we need to accelerate as fast

33:01

as possible

33:02

all of the ethics and legal

33:04

updates.

33:05

It's Pandora's box, right?

33:08

Every new technology creates a new responsibility.

33:11

I think there's a small enough set of researchers doing

33:13

this that we could actually do a better job this time

33:16

at sorting out the responsibility before we unleash

33:18

the technology. I completely agree.

33:21

Thanks so much to Aza Raskin and Karen

33:23

Bacher and to the Aspen Institute. If you

33:26

wanna read more

33:26

about the history here, I recommend Karen's

33:28

book, The Sounds of Life. It's got

33:31

the whale story we mentioned, but also some fascinating

33:33

stories about everything from

33:35

chatty turtles to bee communication. You

33:38

can also watch a video of the full discussion we had at

33:40

aspenideas.org. This

33:43

episode was produced by the Aspen Institute and

33:46

the Aspen Institute Foundation. This

33:49

episode was produced by Bird Pinkerton. It

33:51

was edited by Brian Resnick and Meredith Hodnut,

33:54

who also manages our team. We

33:56

had mixing

33:56

from Christian Ayala, music from me, Serena

33:59

Solon, check the facts. and we're so happy to

34:01

have her on the team. And Manding Nguyen

34:03

is not afraid of spiders. If

34:05

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34:10

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