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you can find a link in the show notes for our August

1:00

30th episode.

1:05

There are lots of stories about mind

1:07

reading. Stories

1:10

about people who can eavesdrop

1:12

on your thoughts. I can read every

1:15

mind in this room.

1:17

Stories about aliens who can communicate

1:19

telepathically. You can read my mind,

1:21

I can read yours. Even stories about machines

1:24

built to make thoughts more transparent.

1:27

We'll be measuring the tiniest electrical impulses

1:29

of your brain, and we'll be sending

1:31

impulses back into the box.

1:33

But one thing these stories all have in

1:35

common is that they are just

1:38

stories. Until pretty

1:40

recently, most mainstream scientists

1:42

agreed that reading minds was the

1:44

stuff of fiction.

1:46

But now... New research shows

1:48

that tech can help read

1:51

people's private thoughts.

1:53

They're training AI to essentially

1:55

read your mind. In the last

1:58

few decades, we've been able to extract more.

1:59

and more things from people's minds.

2:03

And last May, a study was published in

2:05

the journal Nature that got a lot of play

2:07

in news outlets. In that paper,

2:10

a group of Texas scientists revealed that they'd

2:12

been able to translate some of people's thoughts

2:15

into words on a screen.

2:17

The thing that you could call mind

2:19

reading. But do we want machines

2:22

reading human minds? I'm

2:24

Bird Pinkerton, and on this episode

2:25

of Unexplainable, how

2:28

much can these researchers actually

2:30

see inside of our heads? Will

2:32

they be able to see more someday

2:34

soon? And what does all this

2:37

mean for our privacy?

2:54

I reached out to one of the co-authors on this

2:56

paper to get some answers to these questions.

2:59

This guy named Alex Huth, who

3:01

researches how the brain processes

3:04

language.

3:05

And Alex has a word

3:06

of caution on the terminology

3:09

here. A lot of people call this mind reading. We

3:12

don't use that term in general because I think it's

3:14

vague and what does that

3:16

mean? He prefers a more descriptive

3:18

word, which is decoding.

3:21

So basically, when the brain processes

3:23

language or sounds or emotions, whatever, it

3:26

generates this huge flurry of activity.

3:29

And we can capture that activity with a

3:31

variety of tools. So like electroencephalography,

3:35

for example, which is EEG, that

3:37

reads electrical impulses from the

3:39

mind. Or fMRI machines

3:41

will take pictures of our brain at

3:43

work as we react to the things that we experience.

3:47

But then researchers like Alex have

3:49

to decode the cryptic

3:51

signals that come from these machines.

3:53

And in Alex's case, their lab is trying

3:56

to parse exactly how the brain

3:58

processes language.

3:59

So,

4:00

For them, decoding means taking

4:02

the brain responses and then trying to figure out

4:04

like what were the words, what was the

4:07

story that elicited these brain responses.

4:09

So how do you do that? That

4:11

is what this paper from May was all about.

4:15

Step one in their process of decoding the

4:17

mind is, and I swear

4:19

I'm not making this up, listening

4:22

to lots of podcasts.

4:25

So we just had people go in the MRI

4:27

scanner over and over and over and over

4:29

and listen to stories. That was

4:32

it. Alex and his fellow researchers, they took seven

4:34

people and they played them a variety of shows.

4:36

This is the Moth Radio Hour from PRX. So

4:40

Moth Stories, right? The Moth Radio Hour. And

4:42

also the Modern Love Podcast from the New York

4:44

Times.

4:45

So we're just listening to tons and tons of these

4:47

stories, hours and hours and hours. Which sounds kind

4:49

of fun. Right? It's like, it's

4:51

not that bad. It's a dream experiment, really.

4:54

So that's it for this episode of the

4:56

Moth Radio Hour. But then things got

4:58

a little less

4:59

dreamy because the researchers actually had

5:01

to decode all this very

5:03

fun data to kind of match up

5:05

words and phrases from these podcasts

5:08

to the signals coming from the brain. Which

5:11

might sound easy, but unfortunately,

5:14

fMRI has one small problem.

5:17

Which is

5:19

that what it measures sucks.

5:23

fMRI measures blood flow in the brain

5:25

and the amount

5:26

of oxygen in that blood. It

5:28

turns out that when you have a burst of neural activity,

5:31

if your neurons are active, they call

5:33

out to nearby capillaries and say like, hey, I need

5:35

more energy. So let's say you hear the word

5:38

unexplainable, for example. A bunch

5:40

of neurons in different parts of your brain

5:42

will fire and call for energy, which comes

5:45

to them via blood. And over the

5:47

next three-ish seconds,

5:49

you see this increase in blood flow in that

5:51

area. And then over the next five seconds, you see a slow

5:54

decrease.

5:55

But it's not like your brain is only firing one

5:58

thought at a time and then kind of waiting for

6:00

blood flow to clear an area, right? It's potentially

6:02

hearing lots of words, even whole

6:05

sentences in that 8 to 10 second period. Like

6:07

maybe it's hearing, thanks so much for

6:10

listening to unexplainable. Please

6:12

leave a review. And all

6:14

those words could trigger activity in the

6:16

brain, which leaves researchers

6:18

like Alex with this very messy

6:21

scrambled picture to decode. Because

6:23

that means that every brain image that we measure is really some mushy

6:25

combination over

6:28

stuff that happened over the last 10 seconds.

6:32

So if every brain image that you see is like a

6:34

mushy combination of 20, 30 words, like

6:37

how the hell can you do any kind of decoding? For

6:42

a while, the answer was you could

6:44

not really do very much decoding.

6:47

This was a huge roadblock to this research

6:50

until around 2017, when

6:53

we got the first real seeds of something

6:56

you've almost certainly heard about in

6:58

the news. This chatbot

7:00

called ChatGPT. It's a large

7:03

language model, AI, trained

7:05

on a large amount of text across the internet. The

7:08

language model that powers ChatGPT is

7:11

much more advanced than what Alex's team

7:13

started using. They were working with something

7:15

called GPT-1, which is like

7:17

a much more basic model

7:19

that came out in 2018. But

7:22

this model did help Alex and his team sort

7:24

of sort through the mushy,

7:27

noisy pictures that they were getting from FMRI

7:30

scans and sharpen

7:32

the

7:33

image a little bit.

7:34

It was still hard, like even with a

7:37

language model helping him, it took one of Alex's

7:39

grad students, this guy named Jerry Tang,

7:42

years to really perfect this. But

7:45

finally, after some testing, some

7:47

retesting, checking their work, they

7:49

were successful. They could pop someone

7:51

into an FMRI machine, play them a

7:53

podcast, scan their brain, and

7:57

decode the signals coming from

7:59

their brain. back into language

8:01

on a screen.

8:04

The decoding here was not perfect. Like,

8:07

for example, here's a sentence from a story

8:09

that the researchers played for a subject.

8:12

I don't have my driver's license yet, and I just

8:14

jumped out right when I needed to.

8:16

Their decoder interpreted the brain scans and

8:19

came up with this.

8:20

She's not even started to learn to drive yet.

8:22

I had to push her out of the car. Again, the

8:25

story that was played. She says, well, why don't

8:27

you come back to my house and I'll give you a ride? I

8:30

said, we will take her home now. The story?

8:32

I say, OK. The decoder? And

8:34

she agreed. So as

8:36

you can hear, in the decoder's translations,

8:38

pronouns get mixed up. In other examples

8:41

that the researchers provide in their paper, some ideas

8:44

get lost. Others get garbled. But

8:46

still, overall, the decoder

8:49

is picking up on the kind of main gist

8:51

of the story here. And it's not likely

8:53

that it was just lucky. Like, it does

8:55

seem to be reading these signals.

8:59

And that would be amazing enough. But

9:01

the researchers did not stop there. Jerry

9:03

designed a set of experiments to test

9:06

how far can we go? For example,

9:08

they wanted to see if they could decode the signals

9:11

coming from someone's brain if the person was

9:13

just thinking about a story and not

9:15

hearing it. So they ended up having

9:18

people memorize a story. And then

9:20

instead of playing them a podcast, they

9:23

just asked them to think about the

9:25

story while they were in an

9:27

fMRI machine.

9:28

And then we tried to decode that

9:31

data. And? It worked pretty

9:33

well, which I think was

9:36

kind of a surprise, the fact that that worked.

9:38

Because this meant that this tool wasn't

9:40

just detecting what a person was hearing,

9:43

but also what they were imagining.

9:46

Which is also interesting, because it suggests

9:49

that there's some kind of parallel, potentially,

9:51

between hearing something and

9:53

just thinking about it. Like, our brains are

9:55

doing something similar when we listen

9:57

to speech and when we think about it. And

10:00

the researchers found other interesting parallels

10:02

too. Like they tried this other

10:04

experiment. Which was just weird

10:08

and I still think it's kind of wild that it worked.

10:10

We had the subjects go in the scanner and watch little

10:12

videos.

10:13

Silent videos with no speech, no

10:15

language involved. They were actually using Pixar

10:18

shorts. And again,

10:20

they collected people's brain activities while

10:23

they were watching these things and then

10:25

popped that activity into

10:27

their decoder. And it turns out that the

10:29

decoded things were quite good.

10:31

For example, one video is about a girl

10:34

raising a baby dragon and

10:36

then the decoding example that they give. There

10:38

are definitely moments that the decoder is way off.

10:41

Like at one point something falls out of the sky

10:43

in kind of a surprising way. And the decoded

10:45

description is, quote, my mom

10:47

brought out a book and she was like, wow, look

10:49

what I made. Which is not super related.

10:53

But other moments do sync up pretty well.

10:55

Like at one point the girl gets hit by

10:57

a dragon tail and falls over

11:00

and the decoded text is, quote, I

11:02

see a girl that looks just like me get hit

11:05

on her back and then she is knocked

11:06

off. And that was wild.

11:12

And also potentially says something really

11:15

interesting about the brain, right? Like that

11:17

even as we watch something that doesn't involve

11:20

language at all, on some

11:22

level our brains seem to be processing

11:25

it into language, sort of descriptions

11:27

of what's on screen.

11:28

That was like exciting

11:30

and weird. And I don't know that

11:32

I expected that to work as well as it did.

11:34

Now this research is part of a longer

11:36

line of work. Like other researchers

11:38

have been able to do stuff that's sort of similar to

11:41

this by implanting devices into the

11:43

brain, for example. They've even been able

11:45

to use fMRI machines

11:47

to reconstruct images and sounds

11:49

that brains have been thinking about. But

11:52

Alex and his lab, they've really taken an impressive

11:54

step towards decoding part

11:57

of this sort of messy chaos

11:59

of free. revealing thought that runs

12:01

through someone's head. And

12:04

that's kind of wild.

12:05

You know, the first response to seeing this was

12:08

like, oh, this is really exciting. And then the second response

12:10

was like, oh, this is actually kind of scary too, that this

12:12

works.

12:13

It's especially unsettling, at least to me, from

12:16

a privacy

12:16

perspective. Like right

12:18

now, I

12:19

can think pretty much whatever I want and

12:22

nobody can probe those thoughts unless I

12:24

choose to share them.

12:26

And

12:27

to be clear, it's not obvious

12:29

that this technology is going to change that.

12:31

There are a lot of barriers in place right

12:33

now keeping our brains private. Like

12:36

these decoders have to be tailored to one

12:38

individual brain, for example. You

12:41

can't take the,

12:42

whatever, many hours of another person sitting in the scanner

12:44

and use it to predict this person's brain

12:47

responses or to decode this person's brain responses.

12:49

So unless you're currently in an fMRI

12:51

machine having your brain scanned

12:54

and you also recently spent many

12:56

hours in an fMRI machine listening to podcasts,

13:00

you probably don't need to worry too much that

13:02

someone is reading your thoughts. And

13:04

even if you are in an fMRI machine

13:06

listening to, I don't know, this podcast,

13:09

you could still keep your thoughts from being

13:11

read because Alex and his team

13:14

tested whether someone had to cooperate

13:16

in order for the decoder to work.

13:18

Like if they actively try to make it not

13:20

work, does it fail? And it turns out that, yes, it does

13:22

fail in that situation.

13:23

Like if a subject refuses

13:25

to listen does math

13:27

in their head, for example, like takes a number and

13:29

keeps adding seven to it, the decoder

13:32

does a really bad job of

13:34

reading their thoughts as a result. Like its

13:36

answers become much more random. Still,

13:39

barriers like this, like the need for a bespoke

13:42

decoder for each person's brain

13:44

or the ability to block a decoder with

13:47

one's thoughts. That's definitely

13:49

not a fundamental limitation, right? That's definitely

13:51

not something that's like

13:53

never going to change. Maybe

13:55

it won't, maybe that'll still be necessary, but

13:58

that doesn't seem like a fundamental thing.

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17:47

The mind of

17:49

this young

17:50

researcher is as frantic and

17:52

busy as a, say,

17:55

as a city.

17:59

that can look at a bunch of brain data and

18:02

translate it to tell researchers

18:04

what a subject is hearing or thinking.

18:07

It's amazing, but at least

18:09

for now, it involves a lot of clunky

18:12

technology, a lot of time, and

18:14

a lot of cooperation from the person whose

18:16

mind is being decoded. So

18:19

most people

18:19

are probably not going to have machines spitting

18:22

out all their exact thoughts anytime soon.

18:25

But... Don't let that come for you. Nita

18:28

Farhani is still concerned. She

18:30

is a bioethicist who studies the effects

18:32

of new technologies, sort of what they mean for

18:34

all of us legally, ethically,

18:37

and culturally. And recently,

18:39

she published a whole book about

18:41

tools that read the brain. I was

18:44

somebody who had already been following this stuff for a long time,

18:46

and as I dove into the research for

18:48

the book, I mean, I

18:50

was like, what? Really? Nita

18:53

is less focused on fMRI research

18:55

trying to get at exact thoughts, and

18:57

instead, most of her book focuses on different

19:00

brain reading tools, these tools that are becoming more

19:03

and more commonplace. Everyday

19:05

wearables, primarily that are reading

19:07

electrical activity in the brain. Basically,

19:10

when you think, or when your brain

19:12

sends instructions to your body, your

19:14

neurons give off a little electrical discharge.

19:17

And because hundreds of thousands of neurons

19:20

are firing on your brain at the same time, you

19:22

can pick up using brain sensors

19:25

the broader signals that are

19:27

happening. This is electroencephalography,

19:30

or EEG, this technology we've mentioned

19:32

before. It's less precise than

19:35

something like fMRI. It doesn't tell

19:37

you where in the brain the signals are coming from. But

19:40

it also doesn't require you to sit in a loud

19:42

machine for hours. EEG devices

19:44

can take readings by being applied to the head. And

19:47

also, when the brain sends signals out into the body,

19:49

like, say, into the wrist, your sensors

19:52

can measure the electrical activity of the

19:54

muscles that happens as a result. And

19:57

they can be miniaturized and put into earbuds

19:59

and... watches and headphones.

20:02

Because the level of detail is lower, there isn't a

20:04

way, at least right now, to kind of use

20:06

EEG readings to do what Alex can do with

20:08

an fMRI machine, to decode brain

20:11

activity into words running through people's

20:13

heads. But these devices

20:15

can detect things like alertness, tiredness,

20:18

focus, or reactions

20:19

to stimuli.

20:21

And these readings aren't always very precise,

20:24

but as Nita dove into her research,

20:27

she found that these devices are already

20:29

being used in

20:31

all kinds of contexts. It

20:33

would be like, oh, imagine if it was used

20:35

in this way. And then I would find

20:37

an example of it being used in that way. And I'm like,

20:39

what? You know? Some of

20:41

the uses or potential uses for

20:43

these EEG tools are actually

20:45

kind of promising. They could help

20:48

people track their sleep better, potentially

20:50

track cognitive deterioration. Nita

20:53

says they could maybe help people with epilepsy

20:57

get alerts about changes in their brain

20:59

that could mean a seizure, and they

21:01

could help people measure their own pain

21:03

more accurately. But they also

21:06

have a lot of uses that feel a little closer

21:08

to invasions of privacy. So,

21:12

for example, these wearable EEGs

21:14

can be used to measure recognition. Like

21:17

when your brain sees something, any kind of

21:19

stimulus, like a house or

21:21

a face or a goose,

21:23

you say. Your brain reacts to

21:25

the stimulus and it reacts differently

21:28

if you recognize it versus if you don't

21:30

recognize it.

21:31

It does this super fast, like even before you're

21:33

consciously aware of it. And if you recognize

21:36

that goose or face or house, your

21:39

brain then fires a signal that says, I

21:41

know that goose or face or house.

21:44

And because an EEG reader can

21:46

then detect that signal, a

21:48

researcher named Dawn Song, along with some

21:51

collaborators, showed that this can be used

21:53

in pretty concerning ways. What

21:56

they did was, as people were playing video

21:59

games...

21:59

wearing EEG devices, subliminally

22:03

they flashed up images of

22:05

numbers and they were able to go

22:07

through and figure out recognition

22:09

of numbers without the person even knowing

22:12

that the numbers were being flashed up in the video game. And

22:15

just by doing this, just by supplying sort of subliminal

22:18

prompts and then measuring reactions, these

22:21

researchers were able to get some pretty personal

22:23

data. Things like your PIN

22:25

number, even home addresses through

22:27

this recognition-based interrogation

22:30

of the brain.

22:33

That same recognition measurement has also

22:35

been used in criminal investigations.

22:38

Police have interrogated criminal suspects

22:41

to see whether or not they recognize details

22:44

of crime scenes. This is not a new

22:46

thing. Like as early as 1999, a

22:49

researcher in the U.S. claimed that he could use

22:52

an EEG lie detector test to see

22:54

if convicted felons recognize

22:56

details of crimes. This has

22:58

been used by the Singapore police force

23:00

and by investigators in India as

23:03

evidence in criminal trials. And

23:05

there are lots of arguments that the data that comes

23:07

from these machines is not good enough or

23:09

reliable enough to base a criminal

23:11

conviction on. But whether

23:14

or not this technology really works,

23:17

if people believe the results of an EEG

23:19

lie detector like this, it can have

23:21

really serious consequences.

23:24

And not just in the court system. Like

23:26

an Australian company came up with a hat

23:28

that monitors EEG signals

23:31

of employees. There's already a lot

23:33

of employers worldwide who've required employees

23:36

to wear devices that monitor

23:38

their brain activity for whether they're tired

23:41

or wide awake, like for commercial drivers.

23:44

It's also big in the mining industry. Reports

23:46

like this have been worn by workers not just

23:48

in Australia but across the world. And

23:51

while that might seem worthwhile if it prevents

23:54

accidents, some places have started monitoring

23:57

more than just

23:58

tiredness. Like there are reports...

23:59

of Chinese companies rolling out hats

24:02

for their employees. Testing for

24:04

boredom and engagement. Even depression

24:06

or anxiety. The

24:08

reporting around these suggests that EEG

24:11

is way too limited to do a great job

24:13

at reliably detecting those kinds of

24:15

emotions. But again, these

24:18

tools don't need to work well to have professional

24:21

or privacy

24:21

consequences.

24:23

There's risks on the side of if it's really accurate

24:26

and what it reveals. And then there's risks

24:29

on it not being perfectly accurate and

24:31

how people will use or misuse or

24:33

misinterpret that information.

24:35

I think this workplace stuff is especially startling

24:37

to me. Because when I first started reading

24:40

about these EEG devices, I thought, OK,

24:43

I will simply never purchase

24:46

a watch that monitors my brainwaves. Problem

24:48

solved. Yeah, I mean, so most people's

24:52

first reaction to hearing about this stuff is like,

24:54

OK, I'm just never going to use one of those. Great,

24:58

thank you for letting me know. I will avoid

25:00

it at all costs. But if you

25:02

have to have one of these for work, that

25:04

takes away that element of choice. Or

25:07

similarly, Mita told me about this

25:09

EEG tool in the works right now that lets you type

25:12

just by thinking. And if something

25:14

like that becomes the default way

25:16

of typing, then maybe

25:19

having a brain monitoring tool like this also

25:21

becomes the default. Like having

25:24

a cell phone. Technically, you can live

25:26

without one, but it is logistically

25:28

difficult.

25:29

It both becomes inescapable. And

25:32

people are generally

25:35

outraged by the idea that most companies

25:37

require the commodification of

25:40

your personal data to use them as

25:42

free services, whether that's a Google search

25:44

or that's a Facebook app or a

25:46

different social media app. And

25:48

then they seem to forget about it and do it anyway.

25:50

And so there's all kinds of evidence that

25:52

people trade their personal

25:54

privacy for the convenience

25:58

all the time. Right. This

26:00

is why Anita says that we should think

26:02

seriously about the implications of technologies

26:04

like these EEG readers right now,

26:07

as well as the implications of more

26:10

advanced thought-reading technologies

26:12

like the fMRI-based ones

26:14

that researchers like Alex are working on.

26:17

It's really exciting

26:19

to make our brains transparent to ourselves,

26:21

but once we make our brains transparent to ourselves,

26:24

we've also made it transparent to other people. At

26:28

the simplest level, that's terrifying.

26:31

I think from my perspective, there

26:34

is nothing more fundamental

26:36

than the basic sanctity

26:38

of our own minds. What

26:40

we're talking about is a world in which we

26:43

had assumed that that was inviolate

26:47

and it's not.

26:48

All this made me wonder, should

26:51

we shut all this down? Should

26:53

we stop trying to find ways to read minds

26:55

and just tell researchers like Alex Huth

26:57

to stop working on stuff like his

27:00

fMRI brain decoder? For

27:02

Alex, it's tricky because this research

27:05

isn't like working on the nuclear bomb,

27:07

for example. It's not a tool that

27:09

is pretty much only good for killing people.

27:12

I think it's more like, I don't know,

27:15

computers themselves.

27:17

We have shown that computers can be used for bad

27:19

things, right? They can be used to survey lists

27:21

or collect data about us as

27:23

we browse the internet.

27:24

They're also very good. They're used in all kinds of ways that are very

27:26

good. Similarly,

27:27

if EEG devices

27:29

are used to monitor brain waves

27:31

and then detect problems like Alzheimer's

27:34

or concussions, that would

27:36

be a win. If the fMRI

27:39

work in Alex's lab helps us understand

27:41

the fundamental workings of the brain, how our

27:44

mind processes language, I think that's good,

27:47

and other versions of brain reading talk are being used

27:49

to help people with paralysis communicate.

27:52

I think in the same way that it's like

27:54

something can be big and have

27:56

implications in a lot of different ways, it kind

27:59

of matches that.

27:59

mold rather than like nuclear bomb mold.

28:02

But he does worry. After his paper

28:04

came out, Alex actually reached out to Nita

28:06

to ask about the

28:08

ethical implications of his work.

28:10

And he was not particularly surprised

28:12

when she told him that decoding minds could

28:14

lead to pretty concerning consequences for

28:16

privacy. Yeah, I mean, I've

28:20

been reading her books, so I think

28:22

I kind of knew what page she was on. The

28:25

thing that did surprise him was

28:26

when he started asking

28:27

her about some further experiments

28:29

his team is considering.

28:31

Right now, for example, Alex says

28:33

their decoder can pick up the stories

28:35

someone is hearing, but not

28:38

the stray random thoughts they're

28:40

having about that story. Like incidental

28:42

thoughts? It's not clear whether or not

28:44

it's even possible to pick up those kinds of thoughts.

28:47

But when Alex was talking to Nita,

28:49

he asked her, should

28:51

he try and figure out if it's possible?

28:54

Like, should he try and

28:55

probe deeper into people's minds?

28:57

Are there things that we shouldn't do? Like, is this a thing that

28:59

we shouldn't do?

29:00

He thought she'd say, Alex,

29:03

shut it down.

29:04

Like stop going deeper.

29:07

But she didn't.

29:09

If we don't have the facts, it's

29:12

very difficult to know what the ethics

29:14

should be.

29:15

Her view was,

29:18

you

29:19

know, her community, the ethicists,

29:22

philosophers, so on, lawyers,

29:25

they need data.

29:27

They need information to do what they do. And

29:30

they need information like, is this possible or

29:33

not?

29:33

You know, unless you know what you're

29:35

dealing with, how do you develop effective

29:37

countermeasures? How do you develop effective safeguards?

29:40

So she was like, you should do that. Like you kind

29:42

of have a responsibility to do that.

29:44

So now Alex is in a kind of an odd

29:46

position.

29:47

It's a little weird.

29:49

It's a little weird, like feeling maybe we have a responsibility

29:51

to do these things now that are

29:54

creepier because I

29:56

don't know. So we can see like what the limits are and

29:58

we can talk to people about that openly instead of

30:00

somebody just going and doing it and hiding it away.

30:02

I don't know.

30:03

I don't know either. But

30:06

I do understand this argument, that

30:08

it's important to figure out the unknowns

30:10

here. Some of this stuff still feels

30:12

kind of like science fiction to me, and

30:15

it's hard to know really how

30:17

far this tech will advance or how

30:19

transparent it

30:20

could make our brains.

30:22

But I do think there is at least a case

30:24

here for mapping things

30:26

out, right? To understand what the limits

30:29

of this technology might be so

30:31

that we can put safeguards in place

30:33

if we need to.

30:41

Yudhaparahani is the author of The

30:43

Battle for Your Brain, Defending the Rights

30:45

to Think Freely in the Age of Neurotechnology.

30:48

If you want to hear more from her, Fox's Sigal

30:51

Samuel did a great interview with her on

30:53

the Gray Area podcast.

30:54

Look for Your Brain Isn't

30:56

So Private Anymore. And Sigal

30:59

also has a great text piece

31:00

about mind decoding on our site,

31:02

vox.com. You can

31:04

find out more about Alex Huth's work by

31:07

looking up the Huth Lab at the University

31:09

of Texas at Austin. This

31:12

episode was produced by me, Bird Pinkerton.

31:14

It was edited by Brian Resnick and Meredith

31:16

Hodnot, who also manages our team. We

31:19

had sound design and mixing from Christian

31:21

Ayala and music from Noam Hassenfeld.

31:24

Serena Solon checked our facts and

31:26

Manning Len's

31:27

favorite fruit is mango.

31:29

This podcast and all of Vox is

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free in part because of gifts

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from our readers and listeners. You

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can go to vox.com slash give

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that we should do in the future, please email

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week.