0:02

Hello and welcome to the big Story. Two

0:04

point oh hello, this

0:07

is the first episode of the second season

0:09

and we are your hosts. I am

0:11

Anjali and I am Prateek.

0:14

So Anjali, what's

0:16

new in the season? What is so two point

0:18

about it. So you know how our news

0:20

culture is quite noisy and

0:23

there are so many headlines that come every

0:25

day, this is happening, that is happening. So

0:28

exactly what is happening, that

0:30

is where the story comes in. So

0:33

just like season one, we will

0:35

get experts on the podcast to answer

0:37

all of our burning questions. But

0:39

in this season since we wanted to spend

0:41

more time on longer discussions,

0:44

we are moving to a fortnightly

0:46

series instead of a daily show and the

0:48

episodes are longer as well. We

0:50

have very free wheeling,

0:53

you know long, long discussion with

0:55

our experts so that we get the more

0:58

nuanced and well rounded up that

1:00

is all about the show. But let's talk about

1:03

this episode before we

1:05

go further. I just want to make an appeal

1:07

to our amazing listeners. Please

1:10

listen to and check out all the future

1:12

episodes of big story, all the previous

1:14

episodes of the big story on your

1:16

favorite audio streaming platforms like apple

1:18

podcast, Spotify gone adios everywhere.

1:21

We are on the Queen's website, we're on the Queen's

1:23

Youtube channel. We have taken over the world

1:26

and also check out our other podcasts.

1:28

Yes, we have a movie

1:30

and tv review show. We have

1:34

and we have a show on indian politics called

1:36

C assets. So to check them

1:38

out as well. Right? So

1:41

for our first episode we sort of looked around

1:44

and we try to find

1:46

out what is the one thing that is creating

1:48

headlines and

1:50

you might have guessed it. It is,

1:52

it's artificial intelligence,

1:55

machine learning. So

1:58

there is a lot of curiosity about how

2:00

it works, what it can do, is it

2:02

good? Is it bad? There is a group

2:04

of people who believe that it is going to change the

2:06

world for the better. And

2:08

then there is another group of people saying

2:10

that all is not take

2:12

our jobs to replace

2:15

humans, robots will kill us, we

2:18

will come back. So there's a lot

2:20

of talk and so we wanted

2:22

to answer a lot of noise, like

2:24

genuine, genuine noise probably

2:27

generated by But

2:30

yeah, so we thought, let us

2:32

get to experts from

2:34

different walks of life but

2:36

connected to Ai and

2:38

ask them some questions that

2:41

I would say we have and

2:43

a lot of our listeners would have as well, like we start

2:45

from very basic and then we want

2:47

to go into some, you know, deep questions

2:50

about how it will impact the society at large.

2:52

So Anjali, what were

2:54

some of the questions that you have for our

2:56

guests? So I think I would

2:58

like to start by understanding how does that work?

3:01

Like in very simple language, how

3:04

does it work? What is the

3:06

thing behind there also this emergence

3:09

of these new tools like

3:11

gPT dolly

3:13

and lens are in, you

3:15

know, very common use

3:18

cases like art and essay

3:21

writing and all of that. So it's actually

3:23

a really interesting, you know,

3:25

I think to deep

3:27

dive upon key AI

3:30

and Art co relation

3:32

right? And for

3:35

that, like we have

3:37

our first guest who is

3:39

Jemima claims he is a Youtuber

3:42

and he's also a graphic designer of

3:44

working real human graphic designer,

3:46

not an a a graphic designer. He works

3:48

at weapons. And

3:50

we want to talk to him about his

3:52

journey with art in general

3:55

and how he sees this

3:57

encroachment of AI in

3:59

the space of art. Interestingly

4:02

enough, he actually made a Youtube video about

4:04

this recently where where he sent

4:07

an AI generated design to a client.

4:09

So that

4:12

would be interesting to explore

4:14

what was that? Like what was the whole

4:17

vibe of the clients? Did they know

4:19

that they find out? So that was really nice.

4:21

So we just want to get

4:23

a peek into how

4:25

does the creative world look at ai

4:28

what are the kind of problems that

4:30

AI is solving for them? Do

4:32

they like it? Do they not like it? And

4:34

at the same time we really want to zoom out

4:36

and talk about a more macro view

4:38

at AI for which we have

4:40

Doctor who is

4:42

a professor at iit Bombay and

4:45

who works extensively in

4:47

aI policy. He has

4:49

spoken about this in a lot of places.

4:51

So, I think he'd be the perfect fit for

4:53

us to understand what

4:55

a policy around here should look like, what

4:57

is really doing to the world.

5:00

And should we be

5:02

worried about it's going to be so interesting to have an artist

5:04

and a turkey talk about. All

5:07

right. So let's jump right into the corners. Let's

5:09

jump right into it. Welcome. Sammy. Welcome

5:12

And hello, I want to

5:14

start today's discussion, first of all

5:16

by talking about ai

5:22

ai artificial intelligence

5:25

Dalil bad.

5:28

Let us you know, hold back a

5:30

bit and discuss what

5:32

artificial intelligence is and how

5:35

it works in a sort of simple

5:37

language. Right? But I was thinking instead

5:39

of Anupam telling us

5:41

what it is because he has been working

5:43

closely with the eye and he knows the internal,

5:46

you know, workings of it. How

5:48

about all of us tell

5:50

him what we think Ai

5:52

is and how it works. And then he

5:55

takes our class and we're like, okay,

5:58

we will do our version of

6:00

it. And he'll give us marks who

6:03

wants to start, I am at the bottom of

6:05

the of this pyramid. So I

6:07

but no, no, we'll do we'll do we'll do

6:10

it alphabetically. So Anjali, like

6:12

I said, I'm at the bottom of this pyramid. So my

6:15

definition is going to be the most

6:17

basic. So how I think social

6:19

intelligence works is like a

6:21

told a while back, it is just a

6:23

very sophisticated coding

6:25

where you feel into

6:27

a program, all the information

6:30

that that is on the internet. And

6:32

you ask it to identify

6:35

patterns. And then

6:37

when given a prompt with

6:39

the identification of those patterns

6:41

create a new result. Now

6:43

this can be done for Yeah,

6:51

so helping off of what she is

6:54

saying. I think artificial artificial

6:56

intelligence, how I understand is

6:58

is a way to, as the name

7:00

suggests, artificially create

7:02

the pattern of thinking of her human

7:05

brain I would say because Hamas

7:09

that becomes sort of our data

7:12

observed and then

7:14

we recognize some patterns

7:20

process information

7:24

Intelligence generate

7:27

over the years. It gets more sophisticated

7:30

and that's how

7:32

I understand it in a more most basic

7:34

sense. I mean what do you think? Yeah, obviously

7:37

you guys went a little too technical

7:39

I think I feel will

7:41

correct me if I'm wrong but Ai is

7:43

basically pattern recognition.

7:45

Like it's very good in recognizing patterns.

7:48

That is what Ai is for me in my

7:50

profession because I'll only talk about my

7:52

profession here but more

7:54

or less like when we talk about jobs and all everything

7:57

and if you look at it from

7:59

a bird's eye view, then I think Ai

8:01

is a tool that will make us

8:04

as a species more productive in the future

8:06

and will make our lives easier. So

8:09

that's what Ai is according

8:11

to me. Okay, so

8:14

so first of all, first

8:17

of all, thank you for having

8:19

me here and

8:21

uh thank you for coming.

8:23

So it's very interesting to talk to you

8:26

a number uh

8:31

definition Angelica

8:36

actually

8:37

actually because

8:46

let me try to explain, first

8:49

of all instead of explaining

8:51

ai Kahuta. I want to start

8:54

with a I can write

8:56

because there is a lot of cultural

8:58

opinions on what it is and I think

9:01

we need to cut a little

9:03

bit there. So the first

9:05

thing you have to understand that artificial

9:08

intelligence is a misnomer or rather

9:10

a market term, it

9:13

has pretty much nothing to do with intelligence

9:16

which is where protic, your

9:18

definition flies out of the window.

9:20

There is no neuroscientists

9:24

on earth right now.

9:26

Who knows why

9:28

are we conscious survey? Are we really

9:30

intelligent and

9:32

hence there is no technology which can

9:34

artificially replicate intelligence

9:43

outward patterns exist,

9:48

record any

9:53

machine which can autonomously

9:55

make decisions is called a nail. And

9:57

generally the technology which is being

9:59

used in 95 96%

10:02

of ai is machine learning. Machine

10:05

learning how to, I would say angelica,

10:07

definition more or less character. Machine

10:09

learning data.

10:12

Machine decision

10:24

decision. Later classification.

10:30

Machine orange

10:39

classification machine learning or

10:50

who are quite fruit drawing.

10:52

Bannock is um generative

10:54

viable daily

10:57

charge gpt a generative machine

10:59

learning types but fundamentally

11:02

all machine learning algorithms,

11:05

they eat a lot of data

11:07

which is called training data.

11:09

So sorry first

11:13

are they new like things like dolly and

11:15

charge. Gpt.

11:16

No they are not, they're not.

11:20

I think 2nd 3rd versions

11:22

of like dolly

11:24

actually

11:25

more more than more than 2nd

11:27

3rd versions of that technology particular

11:30

brand names. But

11:38

so it is basically what is

11:40

called in the technical field a

11:43

language model exiled

11:45

language english

11:49

or patterns

11:53

or patterns is

11:56

a language model. Language

11:59

models, basic technology

12:02

nanotechnology. Earlier two thousand's

12:05

major technologies exist but

12:08

joe hardware software

12:12

but Gpus market manager

12:14

of hardware Nous software checked

12:17

earlier research

12:20

or Joey I just

12:23

ca laboratories methodology which

12:25

was something we were working on

12:27

in our fields. They

12:29

suddenly started to stop

12:32

remaining as scientific projects

12:34

and became market commodities

12:36

which started to be converted into products.

12:39

I would say that was a man.

12:41

I mean we were trying to solve some

12:43

specific problems but

12:45

as a I became commercialized

12:48

people also started to realize

12:50

instead of trying to solve specific

12:52

but hard problems, it is much

12:55

easier to throw lots

12:57

of data models, quantity

12:59

has a quality of its own or

13:03

it's a research

13:06

career or what

13:15

problem are they trying to solve? I

13:28

want to I want to pause you for a second because you

13:31

bring up an interesting point now

13:35

this and since we were talking about things

13:37

like dolly and mid journey and

13:39

stable diffusion. Shamim Ur

13:42

a working professional right. You work

13:44

with graphics on a day to day basis. So

13:46

how do you think have

13:48

these tools sort

13:51

of infiltrated in your

13:53

industry and casually and probably problem

13:55

discussed, correct problem

13:57

exists. Tools

14:00

solve career in your industry specifically

14:02

in my industry at this point

14:04

it's making us more productive.

14:07

Like I still remember in

14:09

my initial days, 2007, 8

14:11

at the time of seven was

14:13

I learned in like the very

14:15

like vintage version there. If you

14:17

have to remove people from a background,

14:20

it was a very big task like it was like a full

14:22

day task. Now it's just one click.

14:25

If I have to make a brochure

14:27

then if I'm using five

14:29

images as such, then

14:31

I can create a brochure in one hour, two or

14:34

maximum three years at the time

14:36

that those three years were three days.

14:38

So as a designer

14:41

I'm being more productive, my team

14:43

is being more productive and if you

14:45

talk about all these designing software, they are becoming

14:47

like intelligence

14:50

is not what they are but for us they

14:52

are becoming intelligent in a way that

14:54

they give us solutions easier

14:56

and faster. Do do you think they seem intelligent

14:59

to us looking

15:01

at it as a layman coffee, truly

15:04

intelligent Like they they might not be intelligent

15:06

in the way we are intelligent but

15:09

they seem pretty into

15:14

background removal. I think also

15:17

because for so long people have

15:19

been removing background that is

15:21

something that the software has also been learning

15:23

that when somebody wants to remove a background

15:25

this is the distinction that they are seeing

15:27

between foreground background and

15:29

after we just discussed

15:32

after all of this data has been collected

15:34

now they've developed a thing where

15:36

one click and the software knows

15:39

what do you think is the foreground and the background

15:41

if you see a new update will come

15:43

and that software will add a new

15:46

to background removal. Background extension

15:48

is just one option. Suppose it's

15:50

very difficult to tell you but you

15:52

get multiple options in a very short

15:54

amount of time. But I I don't

15:56

I don't feel it's it's

15:58

a threat because see jobs

16:01

will change the perception

16:03

of jobs will change. I cannot

16:05

be rigid key. No no I will I will

16:07

do the best work. A I cannot do

16:10

the way I will do and I'll

16:12

only do it myself then I will

16:14

be kicked out because other design will come he'll use

16:16

AI and he'll do

16:19

multiple things in the same amount of time.

16:21

Is something like that happening. He

16:23

has. No

16:26

no I haven't seen because it's

16:28

all about productivity. Right. Who wants to work

16:30

till four in everybody is

16:33

a. I being used very prominently.

16:35

Yeah I'll tell you

16:37

one live example. I'll tell you suppose

16:39

you have a holiday

16:42

client. Okay suppose you are working with a client

16:44

who sells holidays. It's not possible for

16:46

us to go and click a hut in the mountain picture

16:48

all the time. So what we do will

16:50

create an image like that in a I suppose

16:53

and then if necessary we'll try to

16:55

regenerate it because we we

16:57

don't have inspiration before right? But the

16:59

Ai has as said no the

17:01

Ai has been fed so many

17:04

images with a mountain in the heart.

17:07

He'll give us options because

17:09

Ai work is still very patchy. Like I

17:11

I don't see it very like

17:13

fine fine. Okay so we'll

17:15

watch this image and we'll get

17:17

inspiration of the lighting and set up and how

17:19

the mountains and our this thing. But

17:21

we'll create it manually so

17:24

it will look better for it will look better

17:26

and it will be in our control.

17:29

So that's what Ai

17:31

is being used now so

17:33

we can create a safer image. Like it

17:35

won't get cooperated because we are not stealing the image

17:38

we are constructing saying you are

17:40

currently using Ai as a starting

17:42

bouncing board of the

17:44

first idea mildew. And then we

17:47

can because you

17:50

also had made a video about this where

17:52

you said that you gave an

17:54

Ai generated design to an actual client.

17:57

So talk to us about that key

18:00

battle. What kind of brief did you get

18:02

and how was that whole experience

18:05

of you know actually going

18:07

through it and and

18:10

what was that like see how

18:13

it works like and first of all, why did you do did you just

18:15

do it as an experiment? Yes.

18:18

Yes, yes. It was an experiment because we

18:20

know okay. We know when we are teaching something.

18:22

No we are throwing

18:24

things like in the dark we don't know

18:26

what will stick. So you show

18:28

what you can do for them And they'll

18:31

say yeah we can do this also. So you

18:33

do things to get rejected

18:35

sometimes if you're lucky then something from

18:37

that only will get selected okay.

18:39

But like 99% of time

18:41

though it's like okay,

18:44

okay these are very good. But we had

18:46

this in our mind and this is the hook point

18:48

in the business, right? If they

18:50

if you get to that point then you have the job

18:53

and those two logo options.

18:55

No, that was my way of

18:57

like reducing the what

18:59

you call beating because if I tell the initial

19:02

exactly if I did,

19:04

you know, I don't know quite

19:07

possible, likely reject. But

19:09

at least the client actually

19:12

yes, exactly. Because the

19:14

best thing about Ai in our businesses like

19:17

you're not stealing anything. It's not

19:19

cooperated yet because

19:21

I will talk about today.

19:23

What is the idea behind stealing

19:25

it because the air generated

19:27

options also know I did some tweaking

19:29

there also it was not properly. I

19:32

we showed it to them and

19:35

if they like you. Okay this is the go

19:37

one and we need 10 poses

19:39

of this one. At least we have one

19:41

basic post. So we'll do more changes and

19:43

you're saying Ai helps in you know, cracking

19:46

that first step. Yes. There will

19:48

be one graphic designer who's better in air

19:50

and there will be one graphic designer who is really who wants

19:52

to do things the old way.

19:55

Okay, okay. It's a simple digital

19:57

camera and the camera, the transition was

19:59

very, very crooked. Okay. The

20:02

old people who were like real camera

20:04

users, they were very, they didn't want

20:06

it to go to digital camera,

20:08

but who did, who did go to the digital

20:10

camera? They survived. Right.

20:12

So this is what it is. The technologists

20:15

will be evolving and you need to

20:17

write the way basically you need to be updated.

20:19

That's that's what I feel I

20:25

was listening. I have

20:27

one question for Shamim in your

20:29

video. Also you mentioned that

20:31

sometimes what an Ai lacks

20:34

is context. So

20:36

and I think this is very cultural also

20:39

that when you have an indian client

20:41

you have the entire cultural

20:44

background that this client is coming from.

20:46

And so you know you

20:48

might not get it in one go what

20:50

the client wants but you have certain

20:53

context which a lot of times

20:55

because the air doesn't know who this person

20:57

feeding in the prompt is

20:59

might not be able to give

21:02

a more accurate

21:05

image. So how does that

21:07

work? If you can explain more about context

21:09

in graphic communication,

21:14

see your question. No, I I

21:16

want to give it to because your question

21:18

answer is hidden is

21:21

Jarvis possible and if

21:23

Jarvis is possible then is Skynet

21:25

possible, Jarvis and Skynet

21:27

as possible before he answers this, I just

21:29

want to interrupt and explain

21:32

to our listeners what Skynet actually is.

21:34

So a terminator movie,

21:36

the Kyonggi Skynet is like

21:38

the villain of that film and it is like

21:41

a I would say which

21:43

gains self awareness and which becomes

21:45

human and at

21:47

the end it decides that

21:50

humans are the biggest

21:52

enemy in the world and they mission

21:55

is to you know kill humans.

21:58

And essentially Skynet is the example

22:00

that is used to illustrate ghetto

22:04

hockey a human, this

22:09

is like the first time

22:12

in popular culture that we at the

22:14

scale. So all of this obviously irobot

22:16

or go India go

22:18

robot shankar androgyny, that

22:22

movie's premier Ai basically

22:25

humans. So um

22:27

yes is Jarvis and

22:30

Skynet possible. Yeah

22:33

then then you have your

22:34

answer again, I used the

22:37

I I used the word when

22:39

I started to describe what Ai

22:41

is and isn't right, I use the word

22:43

and I used it very specifically I

22:46

said think of ai as a parrot,

22:49

Jarvis is

22:51

not a parrot right? A parrot doesn't

22:53

know what it is saying, A parrot just

22:55

says things, it has heard

22:57

patches from things it has heard

23:00

but I want to answer this

23:02

in a bigger detail, you know like

23:04

because you guys are having this conversation and I was

23:06

listening very closely to

23:08

try to sort of so there

23:11

are again things which

23:13

we have to be very clear about

23:15

Shamim, you used the word

23:17

or rather the phrase that

23:20

Ai is coming right

23:22

and you

23:26

would like to make an analogy. Technology

23:29

never comes. People choose

23:31

to use technology and the people

23:33

who have power and money get

23:35

to dictate which technology is

23:37

used in waterway technological

23:39

part three nautical technologies coming

23:41

another phrase we keep hearing is genie's

23:44

out of the bottle. Yeah, it's

23:46

not a genie, it can't be out of the

23:48

bottle. So bad. There

23:51

is a technology, it is

23:53

used in particular ways

23:55

which way's will become popular

23:58

and which ways will not become popular

24:00

depend a lot on

24:02

ai industry or control what

24:05

use cases they fund, What use cases

24:07

the research, what use cases they

24:09

develop an

24:12

individual level. Sure Arabic

24:14

professional to achieve skills

24:18

survived but at

24:20

a macro level and

24:28

as a citizen facial

24:34

recognition tech are

24:37

identified privacy technology.

24:43

So remember at the end of the

24:45

day her technology wider

24:48

context apart, policy,

24:51

political, economics,

24:54

politics or economics. So Tianna

24:56

who control the technology and

24:59

the second thing you guys were talking about

25:02

a plot bad, correct and Anjali said something

25:04

which was actually correctly segmentation

25:07

job, bad background removal. Computer

25:10

vision that problem is called segmentation,

25:17

segmentation, possible segmentation

25:21

millions of times important

25:28

point. Whatever

25:34

value any eye generates

25:36

that value is

25:38

coming out of training

25:41

of data which was at some point

25:43

made by human beings.

25:44

So doesn't this then bring the question

25:46

of ownership

25:48

exactly here

25:50

the question comes at no AI can

25:52

exist without

25:54

data. A lot of data

25:57

and it is

25:59

a shame that right

26:01

now that data is kind

26:04

of being stolen Internet

26:07

Italia. It's that right. I'll give you one

26:09

example. There is a very

26:11

famous facial recognition

26:13

tech company in America evaluation.

26:16

Billions Clearview Clearview

26:19

dataset. They

26:21

just went on the internet on

26:23

America digital law enforcement website

26:27

under trials, photos, Internet,

26:31

social media, facebook, photo

26:34

media natalia right.

26:38

Company company.

26:46

So yeah, ke

26:50

hame jo bad career gives the job

26:52

maker surprise society. It's

26:56

easy. You can't leave

26:58

them to people

27:00

who are going to make money from it. You

27:03

have to have that discussion at

27:05

a societal or at a policy level

27:10

data protection, europe

27:15

had a law, it is called G. D.

27:17

P. R. You might have heard of it. It's a very

27:20

strong law is locked

27:22

in europe. Your data

27:24

cannot be even if it is on the internet, some

27:27

company can't come and randomly.

27:28

So I'm just going

27:30

to pause you here. So in some way

27:32

would you think that what

27:35

Shamim did with his experiment

27:38

that using somebody else's like using

27:41

ai generated images or even I

27:43

have my profile picture as

27:45

many lands and generate Corinthian.

27:47

I use a lot of a yard. So

27:49

would you consider that unethical

27:52

at some levels.

27:54

As a policy academic.

27:57

I try to stay

27:59

away from questions of

28:01

individual ethics, ethics

28:03

are not something an individual can decide.

28:06

Si Shamim when

28:08

he did that Shameka

28:11

demand met Tonia anarchy. I am using an

28:13

Ai ai millions of images. Million

28:16

images does

28:25

in the past

28:29

but as

28:34

an individual but

28:37

company

29:06

I actually had another question

29:08

to ask. The Ai

29:10

tools are using the databases that

29:12

are available on the internet virtually

29:15

for free of cost. Right. So

29:17

then is

29:19

there a problem with the entire

29:22

money making strategy of

29:28

they have collected the data from the

29:30

internet without giving any money to

29:32

the people to collect or

29:34

fruits. Data model train

29:38

a Prada problem.

30:00

You had that in our culture

30:03

and when I say our culture I just wrote me in

30:05

indian culture. I also mean american culture.

30:07

There is a false narrative or a

30:09

false narrative here. Regulation

30:12

innovation or year

30:15

problem here in America

30:18

or India and our country's key.

30:20

We don't have any

30:23

regulation on data or

30:26

even data protection

30:29

data protection bill. Hell

30:31

on him. But even an

30:33

imperfect law would have been progress

30:36

but I'm sorry to survive now.

30:38

We don't have any law to protect

30:40

your on my

30:41

data. Progress and what will happen if

30:43

a law like this comes

30:46

home ari daily life

30:48

depends on what depends

30:50

on what is written in the law. Like

30:52

right now last

30:54

joe draft data protection card

30:58

criticism law,

31:01

citizens sorry, responsibilities

31:04

but state sorry responsibility,

31:07

Jessica

31:08

again, Shamim should

31:10

be king of

31:12

like professor

31:22

science or humanities. Would

31:35

you be right now If you wouldn't have pivoted

31:37

to policy. Would you be working in

31:39

one of these companies now? Yeah

31:41

I would have been working in Silicon Valley in

31:45

2019.

31:54

I wanted to go

31:56

back to that thing like because after

31:59

listening to my, I'm completely

32:02

with and we are on the same page where

32:04

we talk about laws and policies because whenever

32:06

I meet somebody like who wants to be

32:09

a graphics and who sees me, who meets me

32:11

somebody I know I'll tell them our

32:13

professional, our profession is not just a job, it's

32:15

a superpower because all the fake

32:17

news, all the hysteria and all everything that

32:19

is done by people like us.

32:22

Okay and now there's

32:24

all this like

32:26

what you call Deepfakes and now

32:28

the fake news is exactly

32:30

defects and everything. This is done

32:32

by Ai. You don't put

32:34

a law in breathing right? You put

32:36

a law in cutting a tree because that's something

32:39

is dangerous, breathing is not dangerous. I

32:41

don't have problem with Ai. Okay, I have a problem

32:43

with the hysteria that is being created

32:45

against a you

32:48

will eat your job is dangerous this

32:50

and that. So I'm going back to the same

32:52

how dangerous is let's okay,

32:54

let's talk about key danger in

32:56

its worst form, what is the

32:58

harm they can do? And since we

33:00

are recording it on a friday the 13th

33:02

let us go,

33:03

Okay, let's

33:06

let's actually talk about the danger of

33:10

the answer is a very

33:12

contradictory answer to the patients,

33:17

yes Ai is extremely

33:20

dangerous. No. Skynet is not possible

33:22

because it is not dangerous in the way people

33:24

think of its danger then a common

33:26

person thinks of AI is dangerous.

33:49

You

33:50

make eyes also stupid

33:52

act, you've taken all the

33:54

power away from

33:55

it. Yes because but

33:58

I would also say this very stupid

34:00

thing is extremely dangerous in very

34:02

stupid ways. A problem here

34:04

was stupid, basic, basic,

34:09

basic, basic

34:11

examples how dangerous he is

34:13

America joe

34:21

building America

34:26

program technically

34:30

what is the chance? Criminal

34:37

judges or juries? Common

34:41

sense, shoulder case, black

34:53

community but

35:08

but problem

35:18

or

35:27

was this, what year was this

35:29

has been going on for decades.

35:31

This is a huge problem in America

35:34

example, amazon

35:37

warehouses to

35:39

amazon has moved over from

35:41

purely online presence and going to

35:43

brick and mortar industry of a warehouse. Banaras

35:46

jamming carried warehouses,

35:51

amazon, they tried to

35:53

replicate their whole

35:56

hiring process by converting it

35:58

to machine learning. Machine learning

36:03

problem bias

36:10

people, machine learning, machine

36:15

learning women, Black,

36:45

okay, similarly, but

36:59

generally in

37:10

the army police

37:13

departments, Delhi punjab

37:16

assam, Hyderabad Chennai

37:19

police departments, facial

37:22

recognition tech used to start

37:25

machine learning machine learning

37:30

probabilistic up

37:36

a I say inaccuracy nickel,

37:39

it is not a technical

37:41

fault, inaccurate,

37:44

Neykova inaccurate,

37:48

inaccurate right about decision

37:53

the idea. Okay, inaccurate recently,

38:02

ex Chief justice sheriff

38:04

bob, he announced that

38:06

he has decided and the Supreme Court

38:09

judges think indian courts make

38:13

resources, procedure,

38:27

procedure simplify justice

38:29

be a content justice be

38:31

a product of justice,

38:34

A product now

38:41

machine learning, Machine learning, sorry,

38:48

jobs, jobs spear

38:51

member vodka, intelligent

38:55

creative jobs. Economic

39:01

50 60% jobs, manual

39:04

laborers, especially especially

39:11

like India these jobs

39:13

cannot be replaced because the

39:15

worth of a human being in India is very

39:18

low. Let's be very clear

39:20

about jobs, jobs,

39:24

jobs are but never

39:31

color job correct

39:37

companies, companies

39:39

mining or

39:43

jobs, jobs, jobs

39:46

employee to employee. You

39:49

have an entire economic

39:52

in this country where

39:54

increasing automation and increasing

39:56

machine learning has enabled platforms,

39:59

the platforms in a machine learning and

40:02

because of the popularity

40:04

and profitability of these platforms as

40:06

a profitable dot com company

40:10

will be lost because of that.

40:12

And because of no policy

40:14

on gig work whatsoever.

40:16

Our entire workforce

40:19

is slowly changing into gig work and

40:21

this is extremely dangerous,

40:23

short long

40:37

overall jobs.

40:44

But the biggest problem also

40:47

I would want to get Shamim into this

40:49

key. Even

40:51

us when we were talking about it,

40:54

we were only considering

40:56

all of these higher

40:58

level jobs like creatives and all of that.

41:01

But the actual jobs, we

41:03

aren't talking about those kind of jobs,

41:05

right? And when we also don't associate

41:07

them with Ai Ai

41:11

S C jobs could replace Karenga especially

41:17

after listening to after listening to

41:20

my whole point of

41:22

view has changed this problem that has

41:24

said has never come came to my mind.

41:26

Exactly yeah. The question coming

41:28

to my mind is that key, what

41:30

we should do because mom said key individual

41:33

contribution is very less so

41:35

that's why we need laws and policies

41:38

but how we can

41:39

help like at an individual

41:41

level do what is best

41:44

for you at an individual level

41:46

encourage famine because he has a platform.

41:48

You should encourage people to learn these skills

41:50

so that at an individual level.

42:01

But as

42:06

a, as a policy professor as

42:08

somebody who looks at the entire society

42:11

masses of individuals individuals

42:15

hama english, advanced

42:18

degree police.

42:33

Exactly, sarkar

42:35

policies, laws

42:38

at the end of the day, they

42:40

also come from the popular mass

42:56

popular topic local

43:03

insurance, cancel job

43:06

jobs are jobs

43:09

they call her cell jobs but

43:14

online content creator Hamara

43:24

come bath,

43:30

low quality content, high

43:32

quality content. So

43:34

the point I'm trying to make especially with

43:36

Sammy because Sammy has a large audience

43:39

on youtube right? Loco

43:41

encouraged debates,

43:45

just scholarly debates. Academy

43:47

academy a measure debates, policymakers,

43:53

conference fairness, accountability,

43:55

transparency, fact, conference ai

43:59

or policies or actual

44:08

danger technologies

44:11

related her

44:13

actual danger, boring sensation

44:22

gradually. Exactly

44:29

I was just saying because

44:31

you may be

44:43

in the same

44:45

the same way medical

44:47

field maybe like er

44:51

careful because medical

44:54

it is said it will be the most

44:56

useful

44:58

right now. Yes,

45:00

you should be So simple

45:02

reason. India publishes

45:05

a lot of data for free. Some open

45:07

government data whose website,

45:09

52% downloads I think

45:12

health data, health

45:15

data, jokey health

45:17

data, machine learning

45:19

Alana hottest field commercially

45:24

there are big companies

45:28

wings, telemedicine telemedicine

45:32

remotely bad chat vodka

45:34

through doctor. So

45:36

Covid fella basically

45:38

doctors training as far as technology

45:44

and science

45:49

methodology. Techno solution is

45:51

um techno solution is um

45:53

is that bad idea that

45:56

any social problem can be solved with

45:58

technology Dr Valerie

46:09

problem, chat

46:14

board media, stable

46:20

internet connection, smartphone

46:22

in english. Problem

46:28

solved problem DR

46:32

problem solved colleges.

46:38

Education don't call dr medical

46:46

problem already.

46:56

America makes research

46:59

american medical data,

47:01

hospitals machine learning

47:04

use cases patients

47:10

patients have

47:15

to be very patient a

47:25

malnourished, poor or black

47:27

local data time machine

47:31

decision to medical decision.

47:33

There are a lot of time they were medically

47:35

incorrect decision a

47:39

devil's advocate wala. Point comes

47:41

where people say that

47:43

the exclusive

47:53

technology

48:01

main difference healthcare

48:03

healthcare. So

48:11

then

48:12

would you would you advocate

48:14

for removal of things like

48:16

AI in matters of health

48:18

care and education or more?

48:20

I would advocate for

48:22

I would advocate for no

48:25

I would advocate for rational decision

48:27

making on who is owning that

48:29

rate of O.

48:31

A. I. Ca. Use seer of

48:33

profit making kelly. Individuals

48:36

and private companies decide career yet

48:38

as a society. Hm Sabbatical decided

48:41

correct. Use examples

48:44

of where popular

49:06

election useful

49:11

now but

49:16

marlo you

49:31

can't as a customer ask for

49:33

the company to change its policy but

49:35

as a citizen you can ask the government

49:38

to change its policy services

49:55

and citizenship

50:01

to sort of conclude as

50:04

a policy or where government

50:06

comes in ai is to

50:08

regulate for what and

50:10

for what not AI should

50:12

be used. That

50:14

is where the government should come in and

50:16

it should clearly dictate the use

50:19

of AI is okay. So that

50:21

would also cover all of our creative

50:23

fields created exactly

50:27

what in your opinion would be okay use

50:29

for a

50:30

more than okay as I would say that

50:33

first of all this proceed this process

50:35

to decide what the government should do.

50:37

You should not just hand it to the government

50:39

like that. It

50:45

has to be a democratic process right

50:50

down to

50:59

the public is

51:01

response dolly is

51:05

document I think

51:10

with this chat also it was

51:13

our objective. Yes

51:15

we want to talk about dolly and chad

51:17

Gpt and all of these are

51:19

you know certainly nice

51:21

popular issues but as

51:24

I got Amerco views

51:28

the car and even from your

51:30

understanding of it that these are not

51:32

the biggest issues that we should

51:34

be worried about. I think it's episode

51:37

titled We are worried about the wrong.

51:39

Oh yeah, I

51:42

would like to add one thing is that yes,

51:44

as I mentioned in my video also.

51:46

Okay. Somehow I feel

51:48

key we are being targeted like

51:50

the creative people are being targeted. Everybody

51:52

saying your job will go, your job will

51:54

go today. I understood what AI

51:57

is and how AI is dangerous.

51:59

Exactly. So I want to thank you guys

52:01

thank you for doing this with

52:03

me because it was such a privilege because

52:05

um you just opened my mind

52:08

truly like I'm

52:10

feeling good. Also that the way I use

52:12

AI is not the most dangerous one.

52:14

Don't you think this is also

52:16

like a self romanticization?

52:19

A lot of even creative people are thinking

52:22

that homeless victims it

52:27

I don't think that is the case. We

52:29

are the most privileged

52:31

people. Um it's as a toy

52:33

used we have the option

52:36

to choose it to use it or not use the term.

52:38

This is a playground for us right

52:40

now. Exactly. But year

52:43

options taxi driver because

52:48

Uber has out competed the taxes from

52:50

the market. So that is very important.

52:52

This point of our

52:55

ability to confuse

52:57

the actual reality with our reality

52:59

are very small

53:01

group of elite people who consumed

53:07

but I'm very happy protect

53:10

and Angela that you invited me my

53:12

subcommittee criticized Sada

53:16

ham academics problem but

53:22

very few of us talk to the common

53:24

masses. Some paper papers

53:26

have papers, there

53:29

are very few platforms, they have

53:33

issues seriously but okay so first of

53:35

all I would really like to thank, went for

53:38

in take me to give me a small

53:40

chance to translate

53:42

some of these issues to the common

53:44

language. I hope I was a little

53:47

successful at that.

53:48

Thank you. So

53:51

in closing I would just like to ask

53:54

um what is the first thing because obviously

53:56

the recommendation and I would be endless.

53:58

What is the first thing that people

54:00

who have just heard this chat should

54:02

go and read up

54:04

about. So there is

54:06

a paper I would

54:08

like people to read. The

54:10

paper is called on

54:12

the dangers of stochastic parrots,

54:15

stochastic simply means probabilistic.

54:17

This paper was written by a bunch

54:20

of researchers in the

54:22

U. S. It is a very nice introductory

54:25

paper to understand the

54:27

dangers of language model, language

54:29

model charge deputy, anything that

54:31

generates language. Also,

54:33

the paper is written I think in a very

54:35

easy to understand english

54:37

which anybody can read. You don't need to be

54:39

a machine learning professional. This would be an

54:42

interesting paper to start off with

54:44

very realized that these

54:46

artifacts are not operating

54:48

in a vacuum that they have a social

54:51

impact and what those social impacts

54:53

can be. So this could be an interesting paper

54:55

to begin. I wouldn't say this is you

54:58

know the end of the conversation. But

55:00

the beginning of the conversation

55:02

for somebody, I am

55:04

not the first person who calls Ai parrots

55:08

is conversation with

55:12

one thing that I will carry for

55:15

the next my life is a

55:17

I. Tota. The starting question

55:19

we started with was a I. And art

55:21

and creative job van

55:24

logic hold Kotecki

55:27

creative and

55:30

where the point where we ended

55:35

actually justice

55:38

system, labor market.

55:49

So I would like to close

55:51

with you as well and going back to our

55:54

starting questions. What

55:58

do you think after you've heard

56:01

all of this key your art

56:03

club exist exist, what

56:06

I personally feel after this long

56:08

conversation and after learning so much from them

56:10

is that key. We are being targeted

56:13

because we can be targeted because

56:15

some people want a i in everything

56:17

without any regulation and

56:20

we are the easiest people to talk because

56:22

they'll get people like me who will say is

56:24

making a life easier because

56:30

even this actually is so

56:33

interesting that you say, because I consider

56:35

myself a very, you know, tech savvy

56:37

person every time some

56:39

google assistant comes.

56:41

I really, I'm excited, Kara,

56:44

that's why as soon as ai dolly

56:46

all of this came I was very excited and

56:48

obviously I am not alarmist about

56:50

it. I

56:54

had a very favorable view of

56:56

it. So I

57:00

think advertisers

57:08

or any newspaper Mitnick alarmist

57:11

article article

57:15

article because I was talking about some

57:17

actual issues. But Mark,

57:21

I had reproduce

57:23

the results which had read

57:25

in some McKenzie report somewhere

57:30

academic organization that it's a

57:32

company or a company. Yes,

57:40

sensationalism, but or

57:45

boring imagination.

57:53

I wouldn't say

57:59

sensationalism

58:16

trying to do with this podcast.

58:19

The actual issue is

58:22

like much deeper and honestly speaking,

58:24

boring but love Yeah,

58:27

interesting. Which

58:29

is sad. But I think we

58:32

tried our level best to make it an interesting

58:34

conversation and thanks a lot

58:37

for that. Very

58:38

happy to talk to you guys. Also very happy

58:40

to meet you. You look at the channel,

58:42

I find it very nice. So

58:44

I'm glad you are doing what you're doing.

58:46

Thank you. Thank you so much. Thank you. Okay,

58:49

so Anjali thoughts. First

58:51

thoughts more than more than exciting. Jasmine.

58:53

Yeah. What

58:57

what were your expectations before we

59:00

started? I really was expecting

59:03

just you know, tell us about how he's not

59:05

threatened by. I and

59:07

I'm talking about you should be it might

59:09

it might get so powerful that will take over

59:11

your job. But I think it went into a whole

59:14

other direction. Was I was

59:16

expecting like a modification

59:18

of a. Because usually when people

59:20

talk about AI in popular culture entity

59:26

but now my

59:28

opinion on AI has changed

59:31

so much after obviously

59:36

catchphrase of this episode

59:40

and every time, you know, I feel like

59:42

oh it's going to take over the world, it's going to do this and

59:44

that like the image of a cute

59:46

little at

59:51

the same time, I feel that we've discovered it's

59:53

not all that good

59:55

and that it solely depends

59:57

on what it is used for. And I think we've drawn

1:00:00

a very clear distinction between

1:00:02

using our Ai for art and

1:00:04

using ai to replace human

1:00:09

consciousness. I would say. It

1:00:11

was interesting the case study that

1:00:13

tom talked about the 100 trials in

1:00:16

the U. S. Where that

1:00:18

is scary but it's way more

1:00:20

scarier than all your irobot

1:00:22

type stuff and it is such a babe. But

1:00:29

okay, this can have real world implications.

1:00:32

It's definitely something that these

1:00:34

giant corporations are looking at

1:00:37

and we as citizens have to be

1:00:39

aware and we have to be worried about the right

1:00:41

a and listen

1:00:43

to more academicians. Yes.

1:00:46

And that just goes beyond I

1:00:48

think everything that is popularly

1:00:51

discussed, we like

1:00:53

we should not be listening to influencers talk

1:00:55

about it, but listen to academy,

1:00:57

just listen to the big story. Yes,

1:01:00

that's because we will

1:01:02

get you access to these

1:01:05

crazy, crazy intelligent admissions.

1:01:07

So I think that's it for this episode.

1:01:10

It was a great chat chat,

1:01:13

let us know in the comments, what you

1:01:15

thought about it, what you think about, why

1:01:17

do you think under

1:01:19

or what animal do you think it is

1:01:22

and share

1:01:24

this with your friends, share this with your

1:01:26

parents, share this with anyone,

1:01:28

anyone, anyone who talks about,

1:01:35

What are we going to talk about in the

1:01:37

next episode of the big story. So

1:01:40

after the grand opening

1:01:42

of Ai and art and

1:01:45

Policy, let's talk

1:01:47

about media and we want to talk about

1:01:49

a very interesting aspect of media

1:01:51

which is media trials. It's

1:01:53

like this unique combination of

1:01:56

media and the judiciary, what

1:01:58

role they play in each other's functioning

1:02:01

and we're going to break down

1:02:03

the entire concept of popular

1:02:05

media trials that you've seen in the past decade.

1:02:12

Okay, so, tune into the next

1:02:14

episode. We have two amazing

1:02:16

guests with us. So this was

1:02:18

Prateek and this and

1:02:21

this was the big story. Thank you guys,

1:02:23

Bye bye. The

1:02:26

Big Story is a quint original podcast

1:02:29

executive produced by Shelley Value. And this

1:02:32

episode was hosted, produced and edited

1:02:34

by practically do and Anjali payload and

1:02:36

it uses theme music from BMG production

1:02:38

music, A special thanks to our guests,

1:02:41

Dr Anupam Gupta and Sammy Markelis.

1:02:44

Yeah, you

1:02:47

were listening to the Quinns podcast.