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you get your podcasts. This

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is a CBC Podcast. Hi,

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I'm Nora Young. This is Spark. Over

0:43

the years, we've talked a lot about the

0:45

data-driven turn in AI and how

0:47

a deep learning approach has given us everything

0:49

from image recognition to chat GPT. But

0:52

what about the ongoing ethical questions about the

0:54

kinds of data machines are learning on? And

0:57

beyond that, what if we're starting

0:59

to run out of data? This

1:01

time, tracking the data limits of AI. Ever

1:17

since chat GPT took off, Google, Meta

1:19

and OpenAI have been in a race

1:21

to build ever more powerful generative AI

1:23

systems. Systems that rely on enormous

1:25

amounts of data to train them. Especially

1:28

the kind of human-created, high-quality

1:30

data that large language models

1:32

like chat GPT need to

1:34

produce impressive results. But now,

1:38

there's concern that these companies are running out

1:40

of data to train their new, large language

1:42

models. That high-quality,

1:44

human-produced information is finite. And

1:48

that the internet isn't the endless source of data they

1:50

once thought it was. I

1:52

think that there's a real reason to think that we've

1:54

maybe reached a period of diminishing returns. So a year

1:56

ago, it looked like we were going to be able

1:58

to or maybe on

2:00

an exponential, things were rising really fast. This

2:03

is Gary Marcus. He's a cognitive scientist and

2:06

leading voice in artificial intelligence. He's

2:08

the author of Rebooting AI, Building Artificial

2:10

Intelligence We Can Trust, and the

2:12

forthcoming book Taming Silicon Valley, How We

2:15

Can Ensure That AI Works for Us.

2:18

Well, I think of large language models as being

2:20

like bulls in a china shop. They're wild, reckless

2:22

beasts that do amazing things, but we don't really

2:24

know how to control them. Back

2:27

in 2022, Gary warned that we

2:29

were nearing this deep learning data wall.

2:33

And he's also written a lot about the limits

2:35

of large language models. They're

2:38

not very good at reasoning. They're not very

2:40

good at planning. They hallucinate or confabulate might

2:42

be a better word frequently. And

2:44

there's also an issue that they're very greedy about

2:47

data. And we're running up,

2:49

I think, against the fact that people have already

2:51

used essentially every bit of data they can get

2:53

their hands on. A

2:55

recent piece in The New York Times

2:57

reported that a team at OpenAI, which

3:00

included President Greg Brockman, had actually collected

3:02

and transcribed over a million hours

3:04

of YouTube videos to train their

3:06

chat GPT-4. Last

3:09

year, Metta also reportedly discussed acquiring

3:11

Simon & Schuster to gain access

3:13

to the publishing house's long-form works.

3:16

I mean, there's almost a desperation about trying to

3:18

get more data. And there's not that much

3:20

more good data. You can always make up

3:22

bad data. You can have chat GPT, which

3:24

hallucinates or confabulates makeup data. But some of

3:27

that data is not going to be any

3:29

good. So there's actually a concern about kind

3:31

of polluting the internet with bad information. If

3:34

you plotted things on a graph on

3:36

your favorite benchmark, how well are we doing? None of

3:38

them are perfect. But if you took whatever your favorite

3:41

one is and looked at like the difference between 2020

3:43

and 2022, you'd see a huge difference. And

3:47

a huge difference between 2022 and 2023, and you'd say, hey, we're in this period of exponential

3:52

returns. But that

3:54

growth hasn't really sustained. Gary says that GPT-4,

3:57

which came out in March, 2020, is a

3:59

big difference. 23 was a

4:01

huge and impressive leap. Since

4:03

then, there have been several competing

4:06

models with huge financial investment, time

4:08

investment, and massive amounts of data,

4:10

but they're not really any better.

4:13

While generative AI may have reached

4:15

a point of diminishing returns, Gary

4:17

says that doesn't mean AI itself is

4:19

going to be indefinitely stuck, but

4:22

it does mean we'll need to come up with

4:24

new approaches to how we train these systems. My

4:28

view is this has been a productive

4:30

path, but also a blind alley in

4:32

a certain way. The whole

4:35

notion of these systems is that you

4:37

statistically predict what people would say in

4:39

certain circumstances based on experience, but these

4:41

systems have always been poor at outliers,

4:44

cases that are different from what they've

4:46

been trained on before. We saw this

4:48

whole movie before with driverless cars, where

4:51

I and a couple other people pointed out in 2016

4:54

that you have outliers with driverless cars, unfamiliar

4:56

circumstances, and that the kinds of techniques we

4:58

know how to build in AI now are just not that

5:00

good at those. We

5:02

said, driverless cars might not be as imminent as

5:04

you thought, and lots of people got excited. Investors

5:07

put in $100 billion, but in the end

5:09

of the day, there are still lots of

5:11

unpredictable circumstances, weird placements of traffic cones or

5:13

people with hand-lettered signs that the driverless cars

5:15

still don't do very well with. I think

5:17

we're seeing the same thing with large language

5:19

models. If you ask a question a lot

5:21

of people have asked before, you're probably all

5:23

set. If it's subtly different from a question

5:26

that's been asked before, they might miss that

5:28

subtlety. It's not clear that

5:30

the generative AI systems are ever

5:32

going to be able to deal with

5:34

the unfamiliar in an effective and systematic

5:36

way. That doesn't mean no approach to

5:39

AI will ever get there. I

5:41

think we're in this blind alley

5:43

where it's all statistical approximation, and we

5:45

need systems that are in fact based

5:47

on facts and reasoning. Neural networks

5:49

that are popular right now are basically good

5:52

at something that's a little bit like intuition,

5:54

but they're bad at the deliberate stuff. They

5:56

really can't reason reliably. They can't plan

5:58

reliably. some other

6:01

approach to do that. So

6:03

just to explain

6:05

what synthetic data is. Sure, you make stuff up. So

6:07

a great example of this is, I mean, really, truly, I didn't

6:09

mean to be, to ridicule the idea. I mean, it's actually a good idea

6:12

as far as it can take you, but it doesn't

6:14

take you far enough sometimes. So a classic example, I

6:16

would say, is in driverless cars around 2016 or so,

6:18

people started realizing they didn't have enough data

6:23

from actual cars and they started making up

6:26

data in different ways. So, I think it

6:29

started making up data in video games like Grand Theft Auto and

6:31

sometimes their own version of

6:38

that. So you would have a simulated car in some

6:40

weird circumstance and try to get data

6:42

from that in order to feed the system. There's a whole company

6:45

that's, I think, Canadian-based that's

6:47

trying to do that. And there are probably multiple companies that are

6:49

trying to do this in various ways. And I would say it's

6:51

helped, but I would say it

6:54

hasn't helped enough. And it's partly because you

6:56

don't know which data to store and

6:58

you don't know which data to simulate. In the

7:00

real world, there are many, many instances where nobody

7:02

anticipates the data that you might need. So if

7:04

you can anticipate exactly what people are going to

7:07

need, you could do that. It would be a

7:09

really stupid use of a large language model to

7:11

make it do arithmetic because they're just not very

7:13

good at it. But you could say, well, they're

7:15

not very good at it, but if I give

7:18

them more data, they'll be better. And so you

7:20

could synthesize all the math data that you want

7:22

in principle and you could improve it to some

7:24

extent. But, for example, if you're

7:26

dealing with irrational numbers, there's just never

7:28

going to be enough synthetic data. You're

7:30

not really going to solve that problem

7:32

that way. Synthetic data has been compared

7:35

to the computer science version of inbreeding. What

7:37

do you make of that analogy? I

7:39

think there's something even more like inbreeding,

7:41

which is what Ernie Davis and I

7:43

once called the echo chamber effect, which

7:45

is having the models train on their

7:47

own output or having Google train on

7:49

open AI's output. So it is a

7:51

kind of inbreeding that's going on where

7:53

these models are making synthetic data and

7:55

then training on that. And so errors

7:57

get in there like a crazy one.

8:00

was somebody asked one of these systems, I

8:02

might get the details wrong, but I think

8:04

asked OpenAI how many

8:06

African countries begin with the letter K

8:09

and it said none. And then,

8:11

sorry about that Kenya, and

8:13

then Google trained on

8:15

OpenAI's output. So that's a kind of inbreeding where

8:17

the one system is training on the other and

8:20

the whole quality of the information ecosphere is going

8:22

down because then other people ask and that error

8:25

percolates. Again, these are kind of like contrived test

8:27

examples, we call them red teaming. But they're so

8:29

easy to generate that we're sure that they're happening

8:32

in the real world which parenthetically

8:34

points to something else, which is transparency. We don't

8:36

actually know how these systems get used in the

8:38

real world because the companies don't want to share

8:40

it. And governments should actually

8:42

be demanding logs. Like for example, do

8:44

people use these systems to make decisions

8:46

about jobs, loans, prison sentences? It was

8:48

just a study that showed in carefully

8:50

controlled circumstances if you speak to them

8:52

in African American English, you get a

8:55

different set of answers than if you

8:57

speak to them in standard English. So

8:59

we know this from the lab, we would like to

9:02

know does this happen in the real world. We don't

9:04

have that transparency right now. So the examples I give

9:06

you are a little contrived, but they show in principle

9:08

this kind of inbreeding thing that we call the echo

9:10

chamber effect and so forth. So we know

9:13

from kind of doing science as best we

9:15

can on the limited data that's available that

9:17

there are all these serious problems. And that

9:19

we don't know how far they go in

9:21

the actual world. Just to

9:23

throw out one case where we do know

9:25

in the actual world, there was a piece

9:27

in the New York Times today showing that

9:29

in the case of child born, there's so

9:31

much of it being created by generative AI

9:34

that one of the nonprofits, I guess the

9:36

tracks is overwhelmed now because suddenly there's just

9:38

so much out there. So sometimes we have

9:40

some way of measuring in the real world what's

9:42

going on and sometimes we don't. Yeah,

9:44

but this is what I've wondered is even if we're not using

9:47

sort of specifically synthetic

9:50

data to train, if

9:53

we have these systems that are generating content

9:55

and that's filling the internet, doesn't that mean

9:57

a lot of the data that gets used

9:59

to... train next generations of models isn't

10:01

going to be human created anyway. Well,

10:04

I mean, what's happening is the companies are stealing from

10:06

each other. And so the

10:08

stuff that they're stealing is no longer

10:11

pure. I mean, we always

10:13

have problems with people generating misinformation for

10:15

political reasons and so forth. But

10:17

the situation has gotten worse because there is

10:19

this mad craze for more data. So one

10:21

of the ways in which people get data

10:24

now is they use each other's models. And

10:26

the terms of service tell them not to

10:28

do that, but they've all violated each other's

10:30

terms of service. So YouTube doesn't say that

10:32

OpenAI can use their data, but apparently GPT-4,

10:35

maybe, so we're trained on.

10:37

So you have this kind of

10:39

mad mess of recycling each other's

10:41

data rather than what you really

10:43

want is like authentic human

10:45

created data from like the New

10:48

York Times, ideally licensed, where

10:51

some human writer has written an article, some

10:53

fact-checking team has verified it, or you want,

10:55

you know, the Britannica, whether it was hard

10:58

work or Wikipedia. They are taking Wikipedia, but

11:00

they're taking all this other garbage too. And

11:03

I mean, there is this old saying, computer science, like

11:05

somebody should remember this, garbage

11:07

in, garbage out, right? And

11:10

the proportion of garbage is going up.

11:32

You are listening to Spark. Everything

11:35

is a sort of a fun house. Nothing

11:37

is as it ordinarily is. And

11:41

all possibilities are open

11:43

to exploration. This is

11:45

Spark. From CBC. I'm

11:57

Nora Young, and today on Spark we're talking about the

11:59

limitations of our current approach to data intensive

12:01

AI and the ways AI giants

12:04

are trying to get around the data wall.

12:06

Right now my guest is Gary

12:08

Marcus, a cognitive scientist and founder

12:10

of Robust AI and Geometric AI.

12:12

He says there's both an underlying

12:14

technical problem and business problem when

12:16

it comes to all the competition

12:18

and hype around AI right now. So

12:22

the technical problem is the kind of AI that

12:24

we know how to build now, which I think

12:26

will look laughable 30 years from now. Like old

12:29

flip phones look a little bit laughable to us now.

12:32

It's very greedy in terms of how much data

12:34

it uses. And I pointed this out in

12:36

2018, I think people ignored me, but that's now

12:38

coming home to roost. It is

12:41

changing the moral fiber of these companies

12:43

and it's maybe leading to the diminishing

12:45

returns and so may undermine the whole

12:47

project. So on the technical side, these

12:50

systems just aren't as efficient with data

12:52

as human children. I have a 9 and an

12:54

11 year old show them something once and they

12:56

understand that they can put it to use. You

12:59

show them the rules of a new game and they

13:01

get it. These systems need a lot of data for

13:03

most of what they do. And I

13:06

don't think that's anywhere near the limit of what we

13:08

could do with AI. It's just the limit of what

13:10

we know how to do with AI today. Just like

13:14

we didn't know how to build efficient gasoline

13:16

or electric, gasoline engines or electric motors once

13:18

upon a time and we learned to make

13:21

things more efficiently, sometimes by changing the entire

13:23

structure. In this case, I think the entire

13:25

algorithm is just not the right way to

13:27

do things efficiently. It's just built as a

13:30

way of mimicking things, not as a way

13:32

of deeply comprehending things. And the reason my

13:34

kids are so much more efficient is

13:36

they build models of the world and how

13:38

it works, causal models of what

13:41

supports their weight or why this thing

13:44

works this way in this game. And

13:46

these systems just aren't really doing that.

13:48

So the technical limitation that then drives

13:51

a business thing, so the business thing

13:53

is complicated. It starts with the fact

13:55

that people think there's a lot of money to be made,

13:57

which may not actually be true. We might want to talk

13:59

about it. about that. But there is a widespread

14:01

belief that many people are acting on that

14:04

there's a ton of money being made and

14:06

so people are rushing. They want to be

14:08

first or more prominent. They want to be

14:10

Coca-Cola rather than Pepsi. And so that's driving

14:12

things. And then the fact

14:14

that there's no known method

14:16

for doing better besides getting more data

14:19

has led to this mad dash for

14:21

data which has led to a lot

14:24

of copyright infringement to companies doing a

14:26

lot of really shady things. And so

14:28

a bunch of these companies actually started

14:30

out wanting to do AI ethically and responsibly. And

14:32

now they're kind of like screwing artists and writers

14:35

left, right and center. They've kind of lost their

14:37

moral compass and a lot of the loss of

14:39

that moral compass has really been driven around the

14:41

mad dash for data. Like they've kind of forgot

14:43

where they came from and what they're supposed to

14:46

do. Like I have lost my faith in a

14:48

number of companies over the last year and a

14:50

half and a lot of it is the things

14:52

that they have done to try to get ahead

14:54

in this race. So what

14:57

would it take for generative AI to

14:59

make real progress from where we are

15:01

today if there's a diminishing return? My

15:03

view as a generative AI is not to paraphrase

15:05

Star Wars, the droids we're looking for. That

15:08

generative AI is almost like a mirage. I mean

15:11

you can use it for some things but a

15:13

lot of things that people wanted to use it

15:15

for are not reliable. And I

15:18

think AI is much harder than a lot of people

15:20

think. Like I don't think it's an impossible problem. You

15:22

know our brains are essentially computers. I know a lot

15:24

of people get mad but I think that's correct. But

15:27

our brains do a lot of

15:29

amazing things. They also make mistakes. They

15:31

could be improved upon. But our brains

15:33

are capable of approaching new problems adaptively

15:35

and flexibly. That's what I think the

15:37

center of intelligence is. This particular algorithm

15:39

just isn't. It's popular but I think

15:41

it's on the wrong track. I think

15:43

when we look 20 years

15:45

from now, look back at 2024, we're

15:47

going to say, well, in that era

15:49

people figured out one thing which is how

15:51

amazing AI could be, how it could spectacularly

15:54

transform our lives but they didn't really know

15:56

how to do it. In fact, they spent

15:58

too much time on that one thing. kind

16:00

of stifled research into anything else. They

16:02

put in billions and billions

16:04

of dollars and this other thing that

16:06

got developed in 2030 or whatever

16:08

it is, I wish they could have

16:11

developed it sooner because if we had this technology in

16:13

2025 instead of waiting until 2035, a lot

16:15

of lives could have been saved

16:19

because it was so good at solving medicine

16:21

and so forth. But people were

16:23

obsessed with the wrong tool. They didn't recognize it

16:25

was the wrong tool. I've

16:28

argued for something more like a hybrid approach. Do

16:30

you think that that's the path forward where we're

16:32

using generative AI for the things that generative AI

16:34

is good at and we're using things that have

16:37

more of a semantic understanding of the world around

16:39

them together in the same system or that we

16:41

triage problems and separate this

16:44

is a generative AI problem and this is not?

16:46

I think we need to do a lot of

16:48

that. I wrote in 2018 about deep learning, which

16:50

is generative AI is a form

16:52

of. I said it's one tool among many. We

16:54

shouldn't throw it away, but we

16:57

have to understand a large complement of tools.

17:00

It's like if somebody was building a house and they

17:02

discovered power screwdrivers and they'd be like, these are amazing,

17:04

but that doesn't mean you want to forget

17:07

that you have hammers and chisels and you might need

17:09

to build a custom tool for this one thing that

17:11

you do a lot. I mean, that's

17:13

kind of what's happening right now. It's like

17:15

the best power screwdriver ever invented. It really

17:17

is amazing. I mean, I'm often criticizing, but

17:19

it's amazing. There's a question about it. It's

17:21

amazing. The question is, is it the right

17:23

tool for the job and which jobs is the right tool for? Ultimately,

17:27

if you want a general intelligence that

17:29

can be like the Star Trek computer, it's

17:31

reliable. You can trust it with whatever kind

17:33

of problem you want to pose, you're going

17:35

to need something that has a broader array

17:38

of tools. I love the word semantic. It's

17:40

not common in these kinds of conversations, but

17:42

it's right. The semantics, the comprehension, the

17:45

meaning in generative AI is

17:47

very limited. Simple

17:49

AI, although it's limited in other ways,

17:51

symbolic AI is better representing semantics, the

17:54

meanings of things, reasoning about those relationships.

17:56

We're certainly going to need elements of

17:58

both. I don't think ... that's enough. I

18:00

wrote an article called The Next Decade

18:02

in AI which came out just before

18:04

the pandemic and the argument I

18:07

made there was that we need this thing,

18:09

hybrids, called neurosymbolic AI but that that's itself

18:11

only part of the solution. So we also

18:13

need a lot of knowledge. We need better

18:15

reasoning techniques. We need our systems to build

18:17

models of the world in the way that

18:19

you do when you go to a movie

18:21

and you learn about each character and their

18:23

motivations and what they're setting is you build

18:25

an internal model of what's going on there.

18:28

Current systems don't really do that in a

18:30

careful and robust way. So you can't kind

18:32

of ask them what's going on. They can't work

18:35

on that. So I said we need to

18:37

tackle four different problems. One of them is this

18:39

hybrid that you're talking about and I've devoted

18:41

a lot of my career to. And even

18:43

on the hybrid I would say we kind

18:46

of sort of know what that might look like

18:48

but not exactly. There's a lot of best practices

18:50

we have to learn and we're

18:52

kind of mostly ignoring that right now. There

18:54

was a very nice paper by DeepMind last

18:56

year that was a neurosymbolic approach to math

18:58

problems that could solve some international math

19:01

Olympian problems called alpha geometry. So there's

19:03

a bit of work in that area

19:05

but it's underfunded compared to the rest.

19:07

So we've probably as a field put

19:09

in close to $100 billion, certainly well

19:11

over 50 on the neural

19:13

network side and the rest of it's getting like

19:15

2% of that

19:18

or something like that. You could

19:20

think like an investor wants to diversify their

19:22

holdings. They want some stocks. You want some

19:24

bonds. You want some real estate. Right now

19:27

there's an intellectual monoculture in AI where only

19:29

one idea is being pursued hard and that

19:31

idea is generative AI. We need some other

19:33

ideas to flourish before we get to I

19:36

think AI that we can trust and that really

19:38

is transformative in the way that we're all hoping.

19:40

So do you think that given that hitting

19:43

a kind of data wall might

19:45

be a good thing at least temporarily? Yeah.

19:47

I mean there is

19:49

a sense in which I think that's right. Right

19:51

now people are resisting. They're saying well give it

19:53

another year, another two years. Some people may kind

19:55

of stick to the wrong horse for a really

19:57

long time. We'll see. I

20:00

think hitting a wall might actually

20:02

turn out to be good in just the

20:04

way that you're saying because it might force

20:06

us to a more reliable, more trustworthy substrate

20:08

for AI. There's a saying or a phrase

20:10

in the field that the current stuff that

20:12

we have, they're called foundation models, but they're

20:14

terrible foundation, right? The point of a foundation

20:16

in a house is you build the rest

20:18

of the line, you know that it's going

20:20

to be stable. And what

20:22

we have now is an unstable foundation.

20:24

If what it takes to get people

20:27

to widely acknowledge the instability of that

20:29

foundation is a period of

20:31

slower progress so that we kind of finally

20:34

say, hey, we're not quite doing this

20:36

right, what else can we do? Then

20:38

yeah, a short-term slowdown might lead to

20:41

a longer-term acceleration and a longer-term more

20:44

stable way of doing AI. A lot of people

20:46

think that I hate AI and it's not true.

20:48

It's not at all true. You hate it. I

20:51

really don't, right? I mean, I built an AI

20:53

company and sold it. I've been working on it since

20:55

I was eight years old. I actually love AI. I

20:57

spend most of my discretionary time

20:59

thinking about AI. Mostly don't even do this

21:01

for pay. I mostly just want the world

21:03

to be in the right place. But I really

21:05

do kind of hate the way that generative AI

21:08

has been positioned. Like as a lab curiosity, it's

21:10

fine. People should look at different

21:12

approaches, but it is so

21:14

much sucking the life from everything else and

21:16

it is so unreliable that it's just not

21:19

a good way to do AI. OpenAI is

21:21

like instead of like saving lives, it's mostly

21:23

in the near term going to be used

21:25

to surveil people. OpenAI wants

21:28

to suck up all your documents and

21:30

your calendar entries. It's going

21:32

to be like the greatest surveillance tool ever made,

21:34

but that's not why I went into AI. OpenAI

21:38

CEO Sam Altman said at a conference last

21:40

year that we were coming to an end

21:43

of the era where we keep relying on

21:45

these giant data models and that we'd make

21:47

them better in other ways. So do you

21:49

think that the kinds of limitations

21:51

in the current approaches to generative AI are

21:54

acknowledged within the AI community? Well, I mean

21:56

it's hilarious that he said that because when

21:58

I first said that... in

22:00

2022. He posted on Twitter a meme that

22:03

looked like my article, Deep Learning is Hitting

22:05

a Wall, saying, God, give me the strength

22:07

of something like that of the mediocre deep

22:09

learning skeptic. So he came after me hard

22:11

for saying this stuff, but I think he's

22:13

come around. I think a few people have

22:15

come around. I think people who have really

22:18

looked at the problem of what intelligence is

22:20

almost uniformly recognize how far away we actually

22:22

are. Gary, thanks so much for

22:24

your insights on this. Sure. My pleasure. Gary

22:27

Marcus is a cognitive scientist, entrepreneur

22:29

and professor emeritus at New York

22:31

University. His forthcoming book is called

22:33

Teaming Silicon Valley. It's out September

22:35

24th, 2024. You

22:47

are listening to Spark. Democratizing

22:49

culture to me means not

22:51

just letting us shout into

22:54

the void of the internet. This

22:57

is Spark with Nora Yun on

22:59

CBC Radio. On

23:08

last week's show about tech and

23:10

music, Inongo Lumumba-Kassongo talked about technological

23:12

transformation in the history of hip

23:14

hop. Inongo is an associate

23:17

professor of music at Brown University. We

23:19

had such an engaging talk, but we didn't

23:22

have time for it all. So we decided

23:24

to play more from that conversation, especially because

23:26

it speaks directly to how data gathered from

23:28

hip hop artists work is used by generative

23:30

AI and the ethical problems that

23:33

poses. It also lets

23:35

us reflect not only on how AI challenges

23:37

what music is for, but also

23:39

the importance of lived human

23:41

experiences. The

23:47

thing our music prof is also a rapper. And

23:53

I go by the name Samus when I'm performing. I

24:01

started making beats in high school. In part,

24:03

I wanted to score a video game because

24:05

I love video games. And so

24:08

my older brother showed me how to

24:10

make beats on my laptop. And from there, I

24:12

started making these sort of little songs.

24:14

And then eventually that expanded into

24:16

me rapping over those songs. I

24:19

wasn't formally musically trained. So I felt like, OK, I

24:21

know how to make beats. And I have my voice.

24:23

What can I do? And

24:25

so rap became this really awesome mode for

24:28

me to be able to share things that

24:30

I was thinking were important. They trying to

24:32

bleed. The psycho on the right

24:34

folks trying to sight bloke. Say my

24:36

company's easy. In 2022, Inongo wrote

24:39

a piece for Public Books where she explored

24:41

the emergence of high tech blackface

24:43

and digital blackface, the

24:46

idea that digital technologies allow non-black

24:48

people to adopt the personas of

24:50

black artists online. One of

24:52

the examples she highlights is the case of

24:54

FN Mecha. So

24:57

FN Mecha had this almost

24:59

like Icarus tale, Rise

25:01

and Fall. So a set

25:04

of kind of creative technologists, or

25:06

really only one sort of entrepreneur

25:09

and another creative technologist, I think

25:11

around 2019, 2020 started developing the

25:13

idea to

25:16

create a kind of rap

25:18

avatar who would take on

25:21

rap, our hip hop mannerisms, and

25:24

promote music, and be sort

25:26

of the first quote unquote AI

25:29

rapper. And I say AI rapper

25:31

in quotes because it was not

25:33

actually ever made clear how AI

25:35

was being engaged in this context,

25:38

but it was clearly important for

25:40

the developers of this character to

25:43

place AI in dialogue with the

25:45

way that this character was being

25:47

developed. There was a recognition that

25:49

this signals, at the very least,

25:51

that there's a kind of innovation

25:54

happening here that other musicians and

25:56

record labels will want to sort of invest in.

25:58

And so this character of FN Mecha, Maca

26:01

started putting out music, which we later learned was

26:03

actually recorded by a black rapper

26:11

named Kyle the Hooligan. He

26:15

was sort of voicing the character but

26:17

was not properly compensated. And

26:20

this was the voice of F.N. Maca.

26:22

And he was sort of developing a

26:24

presence online on Instagram and on TikTok,

26:26

kind of performing this

26:28

prototypical rap persona where, you

26:30

know, he has lots of

26:32

cars and lots of jewelry.

26:35

And questions started to emerge

26:37

around who was the creative

26:39

force behind this avatar, right?

26:42

And I think part of that awareness has

26:44

been this understanding in the digital age

26:47

that stepping into black personhood is

26:50

particularly kind of easy through

26:52

some of the forms of the digital world.

26:55

And so there was an already kind

26:57

of a caution and suspicion on the

26:59

part of listeners and, you

27:01

know, folks who would be in

27:03

that space. Despite those

27:06

suspicions and its ethically dubious

27:08

origins, F.N. Maca's popularity

27:10

continued to grow with over one

27:12

billion views on TikTok and millions

27:14

of followers. And then in 2022,

27:16

the AI rapper was signed

27:20

to Capitol Records, the first time an

27:22

AI-generated musical artist was signed to a

27:24

major record label. And

27:26

was subsequently dropped within months

27:28

of being signed because so

27:30

many people responded with

27:32

concerns about what sort of image

27:35

of a rapper this avatar was

27:38

conveying. And again, questions about

27:40

transparency. Who is making decisions

27:42

about who this AI

27:45

or avatar rapper is

27:47

sort of how he moves through

27:49

the space and how he's understood. I think

27:51

there's a lot of healthy suspicion that this

27:54

was sort of a cash grab that was

27:56

not invested in the actual communities from

27:58

which the art form

28:01

and even the mannerisms were sort

28:03

of coming from. Yeah, yeah. And

28:05

you've argued that this is part of a long

28:07

history of black sound. Can you dig into that a little

28:09

bit for me? Absolutely. So

28:11

Matthew D. Morrison, who's a

28:14

musicologist, really brilliant thinker, has

28:17

asked for us to think

28:19

about the context of how

28:22

we engage with the work

28:24

and material of black

28:26

musical artists in our contemporary moment

28:29

by thinking back to the formation

28:31

of the music industry, particularly within

28:33

the US context. And so he

28:36

asks us to think about the

28:38

emergence of black-based minstrelsy,

28:40

which is this racist theatrical form

28:42

that emerges in the 1820s

28:46

and involves the

28:48

performance caricaturing of

28:51

enslaved Africans as well as free

28:53

black folks by white performers

28:55

who would don black face paint

28:58

and step into these caricatures of

29:00

these figures. And it was a

29:02

way not just to

29:05

express kind of fear and

29:08

revulsion around relationships

29:11

to black folks in

29:13

the US. It was also a way

29:15

to transgress and play with some of

29:17

the sort of gendered and class hierarchies

29:20

that were emerging at that time as

29:22

well. And so I think that dialectic

29:24

is really important to note because when

29:26

we think about digital black face, it's

29:29

not about sort of just mocking or

29:32

playing with representations of blackness

29:34

that are about demeaning black

29:36

folks, right? In a lot of

29:38

ways, these representations are ways that

29:41

non-black people can play with

29:43

transgression or trying new

29:46

modes of expression without

29:48

having to sort of deal with

29:50

the consequences of what that might

29:52

look like without doing so in

29:54

the body of a figure that

29:56

is commonly understood as transgressive just

29:58

as a matter matter of fact.

30:00

And so there's a kind

30:03

of play that's happening there that's

30:05

really harmful because folks get to

30:07

step in and out of presentations

30:09

and performances of black modes of

30:11

expression and thought without having to

30:13

deal with how being black shapes

30:16

one's life outside of that

30:18

context. You

30:21

know, it seems to me that in the sort of popular

30:23

conversation around this, there's been a lot of focus

30:25

on extremely high profile artists, people like Drake or

30:27

The Weeknd, you know, whose

30:29

voices and likenesses are being used. But ultimately,

30:31

who do you think really stands out to

30:33

lose in all this? I

30:36

mean, it's interesting because like you said,

30:39

the way in which this is

30:41

sort of unfolding, the people who

30:43

are at the moment the most

30:45

vulnerable when I think about these

30:47

kind of AI voice filters where

30:49

folks are able to really sound,

30:51

you know, like audio deepfakes to

30:53

really step into the sound of

30:55

a Drake or The Weeknd, you

30:58

know, because they have this kind of

31:00

cultural cachet built into the timbre of

31:02

their voice, it enables

31:04

people to step in and

31:07

to generate capital and clout

31:09

because their voice means something. So for

31:11

an artist who's just starting out, their

31:14

voice doesn't mean what Drake's voice means,

31:16

just the sound of it, right? Just

31:18

the sound of it is doing something

31:20

important. And so I think in many

31:23

ways, artists who are, you know, at

31:25

that sort of upper echelon, they're really

31:27

vulnerable because their voice, A, is

31:30

everywhere. Yeah, a lot of

31:32

training data there. So much, there's

31:34

so much material. And B,

31:37

their voice has a kind of value

31:39

pop culturally. I mean, I think

31:41

about the ways that when an

31:44

artist features on another artist's track,

31:46

the excitement about hearing these two

31:48

voices be in conversation

31:50

because this voice is meaningful to

31:53

us. So it's not

31:55

as, I think, overtly

31:57

destructive in the more deep.

32:00

DIY spaces or the spaces where

32:02

an artist hasn't yet developed a voice

32:05

or a timbre of a voice that's

32:07

recognizable. But again, I think

32:09

how that impacts artists who are

32:11

sort of on the underground is

32:14

that when we think about the possibilities

32:16

for how working musicians can

32:18

build a life, it's very,

32:21

very difficult at this moment to be

32:23

a working artist. I think every single

32:25

rapper friend that I have or music,

32:28

you know, just more generally folks who

32:30

work in music, they have

32:32

like five hustles. I mean, I myself

32:34

am a professor, and I'm also a

32:37

rapper. And, you know, I value

32:39

and appreciate being an academia and having

32:42

these conversations. And in

32:44

part, this has been a strategy to be

32:46

able to build a sustainable art practice, because

32:48

were I to just be actively pursuing music,

32:50

I would be subject to the whims of

32:52

the market. And that's a really, really difficult

32:55

position to be in as an artist. And

32:57

as an artist who doesn't want to just

32:59

make whatever is profitable on the

33:01

radio, like this is a really,

33:03

really difficult position to be in. And

33:05

so with the advent of AI in

33:08

the music space, again, I think about

33:10

questions of risk and who can afford

33:12

to absorb creating new kinds of sounds

33:15

or trying to make it. My worry

33:17

is that artists who are just starting

33:19

out or who are, you

33:21

know, creeping around the DIY basement

33:23

space, is that they don't even

33:25

see a possibility or a way

33:28

forward. Because what the sort of

33:30

large record labels do impacts what

33:32

the middle tier record labels do and

33:34

who they invest in. And

33:36

if the sort of Warner Music Groups

33:38

of the world are reflecting the message

33:41

that it's not really worth investing in

33:43

real human artists, and instead, maybe what

33:45

we should do is invest in tools

33:47

that enable us to take

33:50

on the personhood of artists, artists who we

33:52

don't then have to be accountable to in

33:54

the ways that we have to be accountable

33:56

to human artists. You know, I

33:58

can see that impacting the decision. making on

34:00

the part of everyone else

34:03

in the music industry. So I

34:05

think I'm worried about the culture

34:07

around how we view the work

34:09

of being a musician, that it's

34:12

devalued in this process. And that

34:14

devaluation actually significantly impacts

34:16

who sees themself as being

34:18

able to pursue a life

34:20

as an artist. Yeah.

34:22

Well, no, just from a technical point of

34:24

view, I mean, what do you

34:26

make of their ability to replicate sounds

34:28

from different genres, different forms of music?

34:32

I think that the tools that

34:34

I've engaged with are there's

34:37

a range of levels of sophistication.

34:39

So for example, if I were

34:41

to go into chat GPT and

34:43

say, write me a rhyme in

34:45

the style of Samus, myself. And

34:49

it'll generate this pretty

34:51

mundane, childish rhyme that

34:53

has a really not

34:56

a particularly innovative rhyme scheme. There's

34:58

not sort of like metrical complexity

35:00

to it. And the material

35:03

itself reflects sort

35:05

of like a shadow of who I am

35:07

as a rapper generally based on what exists

35:09

in the world. So a lot of

35:11

my music deals with metaphors around technology

35:13

and video games. And so there's some

35:16

reflection of that being important to me.

35:18

But it's very unspecific

35:21

and not particularly compelling. However,

35:24

with some of these sort

35:26

of tools that

35:28

allow folks to use

35:30

AI to create a filter for a

35:33

particular person's voice so they can rap

35:35

as themselves and then sort of put

35:37

this filter on so that it becomes,

35:39

as we've heard, Drake or The Weeknd,

35:42

that enables you to step into

35:45

the kind of flow and real

35:47

expressive qualities of what makes a

35:49

rap song, a rap song, or

35:51

what makes a rap interesting. So

35:54

the level of sophistication there, I

35:56

think, is troubling and does

35:59

sort of like. on a technical level, I

36:01

think we're moving into a space

36:03

where it will become really, really

36:05

difficult to kind of figure out

36:08

who's authoring what. And actually, it's really interesting.

36:10

We're seeing that happen right now with Drake,

36:12

who's in a bit of a beef with

36:14

a number of different artists. And

36:17

very, very recently, a track

36:19

was released and a real

36:21

discourse online was is this

36:23

diss track an AI track?

36:25

Like, did Drake actually write

36:27

this track? And there's so

36:29

many implications around that. You

36:32

know, if Drake says, I didn't write this

36:34

track, like if it is an AI track,

36:37

the next thing that he writes will be

36:39

compared to this other AI track. So as

36:41

an artist, he's kind of having to interface

36:44

with this shadow version of himself. But

36:46

then there's also the misinformation elements

36:49

of this where, you know, with a

36:51

diss track, or in the context of

36:53

a beef, this can have real implications

36:55

for people's relationships with the other people

36:57

in the music industry or with their

37:00

peers. And if it's not clear, whether

37:02

this was generated by some outside force

37:05

or by the artists themselves, it can

37:07

start to get really challenging interpersonally. So

37:09

it's we already see how

37:11

it's manifesting in the public sphere. Yeah,

37:13

I mean, historically, people have used songwriting

37:16

as ways to sort of, you

37:18

know, document their lives to

37:20

work through their feelings and their thoughts.

37:23

Does generative AI for music come

37:25

into conflict with that history? Like,

37:27

and the importance of just lived

37:29

human experience in that type of

37:31

storytelling? Absolutely. And I think

37:33

that there's a particular way in which

37:36

the rap context is interesting to

37:39

study because within the world of

37:41

rap, the sort of like subjectivity

37:43

of the rapper is so

37:45

critical to our understanding and love

37:47

of or engagement with that person.

37:49

So like the rapper saying, this

37:51

is me, this is my story,

37:54

even if it's not right, even

37:56

if there is

37:58

embellishment, which of course, of course for

38:01

all artists, we're telling stories. So there's

38:03

some artists are more committed to kind of

38:06

telling the story of their life in a

38:08

way that really reflects sort of the events

38:10

of it. And other artists have more of

38:12

a sort of playful relationship with their sense

38:14

of truth. But within the rap context, there's

38:17

very much a sort of understanding that what

38:19

you present is who you are. So

38:21

much so that the practice of ghostwriting

38:24

is frowned upon, right? That's just not

38:26

something you do. And in other songwriting

38:28

contexts, you know, we know Beyonce has

38:30

a team of songwriters. We know that

38:32

other artists work with songwriters. And what

38:35

we expect of them or desire of

38:37

them is that they implement

38:39

or use their own

38:41

capacity as a performer

38:44

to give the song life or

38:46

infuse their story with it. But

38:48

with the rap context, there

38:50

really is an expectation that the

38:53

rapper does all of that sort

38:55

of labor of writing and performing

38:57

and being. So when you bring

38:59

in these tools of generative AI

39:01

that really question authorship, it

39:04

kind of throws the

39:06

whole hip-hop project into question. Like what

39:08

do we think is the most important

39:10

value in this space? Is it okay

39:13

to have a

39:15

person who is a really

39:17

incredible performer but their words

39:20

that they're performing have come from a

39:22

context that is not of their lived

39:24

experience? I think in this

39:26

moment, many sort of rap fans would

39:28

say that's unacceptable. But I also think

39:30

a growing number of people who are

39:32

getting familiar with these tools would argue

39:34

that that's actually, that's okay. It's okay

39:36

to sort of play with authorship

39:39

in new ways. And maybe we don't

39:41

have to be so beholden to that

39:43

mode of being. So yeah, it

39:45

definitely pulls apart, I think, as some

39:48

of the central tenets of what we

39:50

think of as being constitutive of like

39:52

rap music. Yeah. Fascinating. Inango, thanks

39:54

so much for your insights on this. Thank

39:56

you so much for having me. Inango

39:59

Lumumba. is assistant professor of

40:01

music at Brown University, chief rap officer

40:03

at Glow Up Games, and a

40:06

rapper. Hello,

40:08

I'm Jess Milton. For 15 years,

40:10

I produced The Vinyl Cafe with the late,

40:12

great Stuart McLean. Every week, more

40:15

than 2 million people tuned in to hear

40:17

funny, fictional, feel-good stories about Dave and his

40:19

family. We're excited to welcome you back to

40:21

the warm and welcoming world of The

40:23

Vinyl Cafe with our new podcast, Backstage at

40:25

The Vinyl Cafe. Each week,

40:28

we'll share two hilarious stories by Stuart, and for

40:30

the first time ever, I'll tell you what it

40:32

was like behind the scenes. Subscribe

40:34

for free whenever you get your podcasts.

40:53

Hello, I'm Nora Young, and today on Spark, we're talking

40:56

about some of the limits in how we use

40:58

data in training AI, and

41:00

how we might think differently about how we

41:02

create, train, and use these systems. Models

41:05

are what they eat. They ultimately regurgitate the data

41:07

that you show them. So if you show them

41:09

high-quality data, they're going to be high-quality. If

41:12

you show them low-quality data, they're going to be low-quality. This

41:15

is Ari Morcos. He's the CEO

41:17

and co-founder of a data selection

41:19

tool startup called Datology AI, which

41:22

he formed after a career working at

41:24

Meta Platforms and Google's DeepMind unit. We

41:27

help companies train better models faster by optimizing

41:29

the quality of the data that they train

41:31

on. So at a high

41:34

level, we can exploit other models

41:36

to describe the relationships between billions

41:38

of data points, and use those

41:40

models to identify what data are

41:42

good, bad, redundant, etc. But

41:44

ultimately, it's a lot of various algorithms

41:46

that take into account the relationships between

41:49

data points to figure this out. In

41:52

2022, Ari co-authored

41:54

a landmark paper called Beyond Neural Scaling

41:56

Laws, which challenges the widespread notion that more data

41:58

can be used to solve the problem of data. data equals

42:01

better models. Not

42:03

all data are created equal. Some data teach the

42:05

model a lot, and some data teach the model

42:07

a little. The amount of information you learn

42:09

from a piece of data also depends on how much data

42:12

you've seen already. So if you've seen a

42:14

little bit of data, then the next data point is

42:16

probably going to teach you something new. But if you've

42:18

seen a ton of data already, then that next data

42:20

point is probably not going to teach you something new,

42:22

because it's likely to be similar to something you've seen

42:25

before. And in many data sets, we observe this distribution

42:27

where most of the data is focused

42:29

on a pretty small set of concepts. And then

42:31

you have this long tail of more esoteric concepts

42:33

that are really the most informative for the model

42:35

and teach the model the most. But naively, if

42:37

you were to just train on all the data

42:40

or just acquire as much data as possible, those

42:43

long tail data points that are really

42:45

informative would be massively underrepresented in the

42:48

data sets. This comes up commonly in

42:50

a lot of different use cases. And ultimately,

42:52

what's important to get models that are really

42:54

high quality is to identify what are the

42:56

most informative data points, what's the data that

42:59

teaches the model the most, and enrich your

43:01

data sets so that those data points are

43:03

most prevalent in training. So

43:05

what are the practical implications of looking at,

43:07

for example, the data that tells you not

43:09

the 1,000 times the chicken crossed the

43:11

road, but the one time the chicken didn't cross the

43:13

road? What is that actually giving you in practical terms?

43:16

Yeah, that's ultimately what teaches the model

43:18

to be robust and to be able

43:21

to generalize to lots of different situations.

43:23

There's another huge practical implication of this,

43:25

which is that it dramatically slows down

43:27

training and makes training far more expensive

43:29

to get much worse models. Because what

43:32

happens as a result of this is

43:34

that most data that a model is

43:36

looking at doesn't teach it anything at

43:38

all. But it costs money. It costs

43:40

compute to look at that data. And

43:43

it takes time. And ultimately,

43:45

we're in a regime now where we have

43:47

so much data that no model is actually

43:49

learning everything about the data that's presented to

43:51

us. We decide to stop training a model

43:53

because we ran out of money. So we have a budget

43:56

for how much we're willing to spend to train a model.

43:58

And we run out of math. say optimizing

44:00

the quality of the data that goes into a

44:02

model, what you're effectively doing is making it

44:04

so that the model learns faster. And

44:07

if the model learns faster, that provides what

44:09

we call a compute multiplier, but that

44:11

leads to what also is called a quality multiplier,

44:14

because if the model learns faster, then you can

44:16

get to the same performance much faster, but you

44:18

can also get to much better performance in the

44:20

same budget. So this is ultimately

44:23

critical to getting models that work robustly

44:25

across lots of situations in

44:27

which we can train in a cost-effective way. So

44:30

how does this thinking inform what you're

44:32

doing at Datology AI? Yeah.

44:34

So ultimately, our goal at Datology is

44:36

to make curating high-quality data easy for

44:38

everyone. This is a frontier research problem,

44:40

as you noted, kind of in many

44:43

ways. My company is based off of

44:45

this paper that we had in 2022,

44:47

Beyond Neural Scaling Laws. But

44:49

there's a ton of nuance and challenge into

44:51

how you do this. And this is an

44:53

area where there's been very little published research

44:56

in general. This is ultimately the secret sauce

44:58

that divides the best models from the average

45:00

models. Data quality really is everything.

45:03

Most of the big frontier model companies are

45:05

using the same architecture. Ultimately

45:07

what differentiates the quality of the model is

45:10

which data they show it. But of course,

45:12

they're strongly disincentivized to share with anybody how

45:14

they do that, because that is a secret

45:16

sauce. So what that means is, if you

45:19

wanted to train your own model, you would

45:21

not have access to this really critical part

45:23

of the AI infrastructure stack that's really quite

45:25

challenging and difficult and has a lot of

45:27

nuance in how you identify this data at

45:30

scale automatically. So that's what we

45:32

do at Datology. We make that easy for everybody

45:34

by automatically curating massive data sets up

45:36

to petabytes that in order to make

45:38

the data as high-quality and informative as

45:40

possible and make models train

45:43

much faster and to much better performance. But

45:45

doesn't the entire sort of big data

45:48

machine learning project rely on kind

45:50

of probabilistic outcomes of large amounts

45:52

of even sort of messy data?

45:54

I understand the importance of the outliers, the long tail,

45:57

but don't we need to know what mostly

45:59

happens? as well? This gets into this

46:01

notion of redundancy and redundancy is actually

46:03

good to a point. And

46:05

different concepts have different amount of complexity, which

46:08

means that they need different amounts of

46:10

redundancy. So I'll give you an example.

46:12

Imagine trying to understand elephants versus dogs.

46:14

Okay, elephants are pretty stereotypes, right? They're

46:17

all gray. They all have wrinkly skin.

46:19

They all have big floppy ears. They're

46:21

bigger and smaller elephants, African and Asian,

46:23

respectively. But ultimately, most elephants are pretty

46:25

similar to one another. Whereas dogs, you

46:27

have tons of variation. So the amount

46:29

of redundancy that I need in order

46:31

to understand what an elephant is is much

46:33

smaller than the amount of redundancy that I

46:35

need in order to understand what a dog

46:38

is. So if I were to use the

46:40

right amount of redundancy for elephants, for dogs,

46:42

then I'd end up doing very well on elephants,

46:44

but I would not fully understand dogs in my

46:47

model. Right. And if I were to do the

46:49

opposite, I would understand dogs perfectly well, but I

46:51

would have wasted a ton of compute, looking

46:53

and learning about elephants far beyond where I

46:55

need to. So the challenge here is that

46:58

you absolutely need redundancy about the common concepts,

47:00

but you need the appropriate amount of redundancy

47:02

for a given complexity. So what we have

47:04

to do given a massive data set that's

47:06

unlabeled, that doesn't have, it doesn't say this

47:08

is an elephant or this is a dog.

47:10

It just here's a bunch of data, we

47:12

have to identify automatically what are those concepts,

47:14

figure out how complicated are each of those

47:16

concepts. And then based off of that, determine

47:19

the right amount of data to remove from

47:21

each of those concepts, in addition to removing

47:23

the right data there, because obviously, even within

47:25

a concept of elephants, not all elephant data

47:27

is equally informative, some is going to be

47:30

better than others. One

47:32

of the things we've talked about on the

47:34

show in the past is not only the

47:36

cost of training these things, but the environmental

47:38

cost of these very, very data intensive models,

47:40

like deep learning, do you think this approach

47:42

has potential to address the end just a straight

47:44

up energy costs of this approach to computing? Absolutely.

47:46

And I think that's a big part of our

47:48

mission as well as to help with the compute

47:50

costs of these models, both on the training side,

47:52

but also on the inference side. During

47:55

training, by reducing the amount of data you

47:57

need to train models on, we can reduce

47:59

that currently by two to to 4X and

48:01

we're getting better at that every day. So

48:03

that already means that you can now train

48:05

a model with 2 to 4X less environmental

48:07

impact, which is obviously significant.

48:09

But one of the things that we can

48:11

also do with higher quality data is train

48:14

smaller models to the same performance. And in

48:16

the scheme of things, ultimately models are actually

48:18

gonna be run in what's called

48:20

inference, which is when you're actually using a model

48:22

in deployment or something like that, far more often

48:25

than they're gonna be used in training. And if

48:27

you deploy a model to inference that's bigger than

48:29

it needs to be because it didn't

48:31

see high quality data, then that's a

48:33

massively increased environmental and compute costs as

48:35

well. So better quality data both helps

48:37

to cut training costs of models, but

48:39

also helps you to train models that

48:42

are smaller and better optimized so that

48:44

the inference cost at deployment time is

48:46

also much lower, which is very helpful

48:48

from a business standpoint, but also clearly

48:50

has massive environmental impact. You

49:04

are listening to Spark. The idea that

49:06

we're somehow making proto humans and

49:09

that may approach or exceed us on

49:11

some mythical scale of intelligence or

49:13

decide they don't need us anymore, there's no they

49:15

there. This is Spark from

49:17

CBC. Hi,

49:29

I'm Nora Young. Today on Spark, we're talking

49:31

about the data limitations of some AI and

49:34

whether the way around the data wall is

49:36

to focus on data quality rather than quantity.

49:39

Right now, my guest is AI researcher, Ari

49:41

Morcos. His company, Datology AI, is

49:43

building tools to improve data selection, which

49:45

could help lower the amount of data

49:48

needed to train these systems. One

49:51

reason we wanted to talk to you is

49:54

that we've been hearing about concerns that data-hungry

49:56

AI like large language models will hit a

49:58

cap of good quality training data. So

50:00

if we don't rethink how to train these

50:02

systems, do you think large language models

50:05

are going to hit a plateau? I

50:07

think there's a ton more we can

50:09

do by just gumming up with better

50:11

quality metrics for our existing datasets. Obviously

50:14

more data is better given the same quality,

50:16

but if we look at the models that

50:18

we have right now, they're still getting better

50:21

with more data. They're not converging yet, even

50:23

on the data that we've already shown them.

50:25

So there's a lot of gains still to

50:27

be had from showing the model higher quality

50:30

data more times over so that it learns

50:32

it. Think about how you might do flashcards

50:34

if you're trying to study for a test.

50:37

You put all the different questions on your flashcards,

50:39

and then when you get one correct, you take

50:42

it out of the pack. When you get it

50:44

incorrect, you put it at the back, and then

50:46

you see it over and over again. So doing

50:48

things where we actually present the data that's most

50:51

difficult for the model or that teaches the model

50:53

the most multiple times is still an area where

50:55

I think we can get a ton of gains

50:57

and one that we've just really barely exploited. For

50:59

a number of cultural reasons, the field of machine

51:02

learning has largely ignored studying data. Part

51:04

of that is because data has often been viewed as kind of

51:06

boring or the plumbing.

51:09

In many cases, part of it is also that

51:11

in a lot of the competition style machine learning

51:13

research data is viewed as a given. So it's

51:15

like given a dataset, how can you create a

51:18

model that's going to do the best on that

51:20

dataset? As a result of

51:22

that, the field is mostly focused on advances

51:24

in modeling rather than advances in data. A

51:27

metaphor I like for this is

51:29

that there's this tree that's barren

51:31

that's surrounded by a bunch of

51:33

professors prodding their grad students to

51:35

climb this barren thorny tree to

51:38

reach up to find a shriveled apple

51:40

that is some site improvement in a

51:42

modeling advance. Meanwhile, just out of sight,

51:44

there's a lush orchard of

51:47

trees that are literally dropping fruit

51:49

onto the floor in the realm of

51:51

ways we can better improve data. So

51:53

I think this is an area that

51:56

just has been so massively understudied relative

51:58

to its potential impact. that

52:00

I think that even if we hit the

52:02

limits of what's available with respect to public

52:05

data, there's still far more we

52:07

can do by making better use

52:09

of the data that we already have.

52:11

I'll also note that the data that's

52:13

in public is a heavy

52:15

minority of the total data that's present in

52:17

the world, right? The majority of data is

52:19

private. So there's also a lot of

52:21

opportunities, I think, to get that private data and exploit

52:23

that. And I think that's one of the things that

52:26

a lot of businesses are thinking now, hey, we're sitting

52:28

on these hordes of data that could be really valuable.

52:30

How can we use that to make models better for

52:32

ourselves? And personally,

52:34

a lot of companies are concerned about their

52:36

proprietary data outside

52:39

of their proprietary wall as well,

52:41

right? Absolutely. They wanna make sure

52:43

that that advantage doesn't get ceded

52:45

to everyone. Right.

52:48

How widespread a problem do you think

52:50

this sort of potential data shortage is?

52:53

Like much of the conversation has been about chat,

52:55

GPT, and large language models, but is

52:57

this sort of issue with growing

53:00

data potentially kind of an existential issue for

53:02

a deep learning approach to AI

53:04

in general? How broad are we talking about

53:07

here? Yeah, I actually don't think

53:09

the data shortage is as big of an

53:11

issue as people make it out to be

53:13

in general. And in large part,

53:15

that's for the reasons we've been discussing, that there's just

53:17

a lot more we can do by making better use

53:19

of the data we have available. And I think if

53:21

you go to companies, many

53:23

enterprises have too much data. They have

53:26

petabytes or exabytes of data that they've

53:28

been collecting, most of which is mostly

53:31

useless because it's not very high

53:33

quality. And the problem is, right, that they

53:35

don't know how do I make the best use of that data?

53:37

How do I find the data that's actually gonna teach me the

53:39

most? But

53:41

I think for the largest frontier models

53:43

that you see coming out of OpenAI,

53:48

ultimately the path forward is going to be

53:50

to try to acquire more high quality data,

53:52

right? They've started doing a lot of licensing

53:54

deals with various data providers in

53:56

order to acquire new data that has some sort of

53:58

quality guarantee. and then also

54:00

by pushing forward a lot of research to do

54:03

better at identifying the right data, of course, which

54:05

they will not share with anybody else.

54:09

All right. Thanks so much for your insights on this.

54:11

Absolutely. Thank you for having me. Ari

54:14

Morkos is an AI researcher and founder

54:16

of Datology AI. The

54:24

show is made by Michelle Parisi, Samarit

54:26

Yohannes, Megan Carty and me, Nora

54:28

Young and by Gary

54:30

Marcus, Inongo Lumumba Kasongo and Ari

54:32

Morkos. Subscribe to Spark

54:34

on the free CBC Listen app or your favourite podcast

54:36

app. I'm Nora Young. Talk to you soon. For

55:04

more CBC podcasts,

55:06

go to cbc.ca/podcasts.