The following is a conversation with Michael I. Jordan, a professor at Berkeley and one

of the most influential people in the history of machine learning, statistics, and artificial

intelligence.

He has been cited over 170,000 times and has mentored many of the world class researchers

defining the field of AI today, including Andrew Eng, Zubin Garamani, Bantaskar, and

Yoshio Benjo.

All this, to me, is as impressive as the over 32,000 points in the six NBA championships

of the Michael J. Jordan of basketball fame.

There's a nonzero probability that I'd talk to the other Michael Jordan given my connection

to and love of the Chicago Bulls of the 90s, but if I had to pick one, I'm going with the

Michael Jordan of statistics and computer science, or as Jan Lacoon calls him, the Miles

Davis of machine learning.

In his blog post titled Artificial Intelligence, The Revolution Hasn't Happened Yet, Michael

argues for broadening the scope or the artificial intelligence field.

In many ways, the underlying spirit of this podcast is the same, to see artificial intelligence

as a deeply human endeavor, to not only engineer algorithms and robots, but to understand and

empower human beings at all levels of abstraction, from the individual to our civilization as

a whole.

This is the Artificial Intelligence Podcast.

If you enjoy it, subscribe on YouTube, give us five stars at Apple Podcast, support it

on Patreon, or simply connect with me on Twitter, at Lex Friedman, spelled F R I D M A N.

As usual, I'll do one or two minutes of ads now and never any ads in the middle that can

break the flow of the conversation.

I hope that works for you and doesn't hurt the listening experience.

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the world.

And now, here's my conversation with Michael I. Jordan.

Given that you're one of the greats in the field of AI, machine learning, computer science,

and so on, you're trivially called the Michael Jordan of machine learning, although as you

know, you were born first, so technically MJ is the Michael I. Jordan of basketball,

but anyway, my favorite is Yanlacoon calling you the Miles Davis of machine learning, because

as he says, you reinvent yourself periodically and sometimes leave fans scratching their

heads after you change direction.

So can you put at first your historian hat on and give a history of computer science and

AI as you saw it, as you experienced it, including the four generations of AI successes that

I've seen you talk about?

Sure.

Yeah.

First of all, I much prefer Yan's metaphor.

Miles Davis was a real explorer in jazz, and he had a coherent story.

So I think I have one, but it's not just the one you lived.

It's the one you think about later.

What a good historian does is they look back and they revisit.

I think what's happening right now is not AI.

That was an intellectual aspiration that's still alive today as an aspiration.

But I think this is akin to the development of chemical engineering from chemistry or

electrical engineering from electromagnetism.

So if you go back to the 30s or 40s, there wasn't yet chemical engineering.

There was chemistry.

There was fluid flow.

There was mechanics and so on.

But people pretty clearly viewed interesting goals to try to build factories that make

chemicals products and do it viably, safely, make good ones, do it at scale.

So people started to try to do that, of course, and some factories worked, some didn't.

Some were not viable, some exploded.

But in parallel, developed a whole field called chemical engineering.

Chemical engineering is a field.

It's no bones about it.

It has theoretical aspects to it.

It has practical aspects.

It's not just engineering, quote, unquote.

It's the real thing, real concepts are needed.

Same thing with electrical engineering.

There was Maxwell's equations, which in some sense were everything you know about electromagnetism.

But you needed to figure out how to build circuits, how to build modules, how to put

them together, how to bring electricity from one point to another safely and so on and

so forth.

So a whole field that developed called electrical engineering.

I think that's what's happening right now is that we have a proto field, which is statistics,

computer, more of the theoretical side of the algorithmic side of computer science.

That was enough to start to build things.

But what things?

Systems that bring value to human beings and use human data and mix in human decisions.

The engineering side of that is all ad hoc.

That's what's emerging.

In fact, if you want to call machine learning a field, I think that's what it is.

That's a proto form of engineering based on statistical and computational ideas in previous

generations.

But do you think there's something deeper about AI in his dreams and aspirations as

compared to chemical engineering and electrical engineering?

Well, the dreams and aspirations may be, but those are 500 years from now.

I think that that's like the Greek sitting there and saying, it would be neat to get

to the moon someday.

I think we have no clue how the brain does computation.

We're just a clue.

We're even worse than the Greeks on most anything interesting scientifically of our era.

Can you linger on that just for a moment because you stand not completely unique, but a little

bit unique in the clarity of that.

Can you elaborate your intuition of where we stand in our understanding of the human

brain?

A lot of people say, and your scientists say, we're not very far in understanding human

brain, but you're saying we're in the dark here.

Well, I know I'm not unique.

I don't even think in the clarity, but if you talk to real neuroscientists that really

study real synapses or real neurons, they agree.

They agree.

It's 100 years, hundreds of year task, and they're building it up slowly, surely.

What the signal is there is not clear.

We have all of our metaphors.

We think it's electrical, maybe it's chemical, it's a whole soup.

It's ions and proteins, and it's a cell, and that's even around like a single synapse.

If you look at an electron micrograph of a single synapse, it's a city of its own.

That's one little thing on a dendritic tree, which is extremely complicated, electrochemical

thing, and it's doing these spikes and voltages are flying around, and then proteins are taking

that and taking it down into the DNA, and who knows what.

It is the problem of the next few centuries.

It is fantastic, but we have our metaphors about it.

Is it an economic device?

Is it like the immune system, or is it like a layered set of arithmetic computations?

We have all these metaphors, and they're fun, but that's not real science per se.

There is neuroscience.

That's not neuroscience.

That's like the Greek speculating about how to get to the moon, fun.

I think that I like to say this fairly strongly, because I think a lot of young people think

that we're on the verge, because a lot of people who don't talk about it clearly, let

it be understood that, yes, we kind of, this is brain inspired, we're kind of close, breakthroughs

are on the horizon, and unscrupulous people sometimes who need money for their labs.

As I'm saying, unscrupulous, but people will oversell.

I need money from a lab.

I'm studying computational neuroscience.

I'm going to oversell it, and so there's been too much of that.

So I'll step into the gray area between metaphor and engineering with, I'm not sure if you're

familiar with brain computer interfaces.

So a company like Elon Musk has Neuralink that's working on putting electrodes into

the brain and trying to be able to read both read and send electrical signals, just as

you said, even the basic mechanism of communication in the brain is not something we understand.

But do you hope, without understanding the fundamental principles of how the brain works,

we'll be able to do something interesting at that gray area of metaphor?

It's not my area.

So I hope in the sense like anybody else hopes for some interesting things to happen from

research, I would expect more something like Alzheimer's will get figured out from modern

neuroscience.

There's a lot of humans offering based on brain disease, and we throw things like lithium

at the brain.

It kind of works.

It's a blue eye.

That's not quite true, but mostly we don't know.

And that's even just about the biochemistry of the brain and how it leads to mood swings

and so on.

How thought emerges from that.

We're really, really completely dim.

So that you might want to hook up electrodes and try to do some signal processing on that

and try to find patterns, fine, by all means go for it.

It's just not scientific at this point.

So it's like kind of sitting in a satellite and watching the emissions from a city and

trying to affirm things about the microeconomy, even though you don't have microeconomic concepts.

I mean, it's really that kind of thing.

And so yes, can you find some signals that do something interesting or useful?

Can you control a cursor or a mouse with your brain?

Yeah.

Absolutely.

And then I can imagine business models based on that and even medical applications of

that.

But from there to understanding algorithms that allow us to really tie in deeply from

the brain to computer, I just, no, I don't agree with Elon Musk.

I don't think that's even, that's not for our generation, it's not even for the century.

So just in the hopes of getting you to dream, you've mentioned Komogorov and touring might

pop up.

Do you think that there might be breakthroughs that will get you to sit back in five, 10 years

and say, wow.

Oh, I'm sure there will be, but I don't think that there'll be demos that impress me.

I don't think that having a computer call a restaurant and pretend to be a human is

breakthrough and people, you know, some people presented as such.

It's imitating human intelligence.

It's even putting coughs in the thing to make a bit of a PR stunt.

And so fine that the world runs on those things too.

And I don't want to diminish all the hard work and engineering that goes behind things

like that and the ultimate value to the human race.

But that's not scientific understanding.

And I know the people that work on these things, they are after scientific understanding, you

know, in the meantime, they've got to kind of, you know, the trains got to run and they

got mouths to feed and they got things to do.

And there's nothing wrong with all that.

I would call that though, just engineering.

And I want to distinguish that between an engineering field like electrical engineering

that originally that originally emerged that had real principles and you really know what

you're doing and you had a little scientific understanding, maybe not even complete.

So it became more predictable and it was really gave value to human life because it was understood.

And so we don't want to muddle too much these waters of what we're able to do versus what

we really can do in a way that's going to impress the next.

So I don't need to be wowed, but I think that someone comes along in 20 years, a younger

person who's absorbed all the technology and for them to be wowed, I think they have to

be more deeply impressed.

A young Kolmogorov would not be wowed by some of the stunts that you see right now coming

from the big companies.

The demos, but do you think the breakthroughs from Kolmogorov would be and give this question

a chance?

Do you think they'll be in the scientific fundamental principles arena?

Or do you think it's possible to have fundamental breakthroughs in engineering?

Meaning you know, I would say some of the things that Elon Musk is working with SpaceX

and then others sort of trying to revolutionize the fundamentals of engineering of manufacturing

of saying, here's a problem, we know how to do a demo of and actually taking it to scale.

So there's going to be all kinds of breakthroughs.

I just don't like that terminology.

I'm a scientist and I work on things day in and day out and things move along and eventually

say, wow, something happened, but I don't like that language very much.

Also I don't like to prize theoretical breakthroughs over practical ones.

I tend to be more of a theoretician and I think there's lots to do in that arena right now.

And so I wouldn't point to the Kolmogorovs, I might point to the Edison's of the era

and maybe Musk is a bit more like that.

But you know, Musk, God bless him also, we'll say things about AI that he knows very little

about and he doesn't know what he, he is, you know, it leads people astray when he talks

about things he doesn't know anything about.

Trying to program a computer to understand natural language, to be involved in a dialogue

we're having right now, that can happen in our lifetime.

You could fake it, you can mimic, sort of take old sentences that humans use and retread

them with the deep understanding of language, no, it's not going to happen.

And so from that, you know, I hope you can perceive that deeper, yet deeper kind of aspects

and intelligence are not going to happen.

Now will there be breakthroughs?

You know, I think that Google was a breakthrough, I think Amazon is a breakthrough.

You know, I think Uber is a breakthrough, you know, that bring value to human beings

at scale in new brand new ways based on data flows and so on.

A lot of these things are slightly broken because there's not a kind of a engineering

field that takes economic value in context of data and, you know, planetary scale and

worries about all the externalities, the privacy.

You know, we don't have that field, so we don't think these things through very well.

But I see that as emerging and that will be constant, that will, you know, looking back

from a hundred years, that will be constantly a breakthrough in this era, just like electrical

engineering was a breakthrough in the early part of the last century and chemical engineering

was a breakthrough.

So the scale, the markets that you talk about and we'll get to will be seen as sort of breakthrough

and we're in very early days of really doing interesting stuff there.

And we'll get to that, but it's just taking a quick step back.

Can you give, kind of throw off the historian hat, I mean, you briefly said that in the

history of AI kind of mimics the history of chemical engineering, but I keep saying machine

learning, you keep wanting to say AI, just to let you know, I don't, you know, I resist

that.

I don't think this is about AI really was John McCarthy as almost a philosopher.

Saying, wouldn't it be cool if we could put thought in a computer?

If we could mimic the human capability to think or put intelligence in in some sense

into a computer.

That's an interesting philosophical question.

And he wanted to make it more than philosophy.

He wanted to actually write down logical formula and algorithms that would do that.

And that is a perfectly valid reasonable thing to do.

That's not what's happening in this era.

So the reason I keep saying AI actually, and I'd love to hear what you think about it,

machine learning has a has a very particular set of methods and tools.

Maybe your version of it is that mine doesn't know it doesn't very, very open.

It does optimization.

It does sampling.

It does.

So systems that learn is what machine learning is systems that learn and make decisions and

make decisions.

So what is pattern recognition and, you know, finding patterns is all about making decisions

in real worlds and having close feedback loops.

So something like symbolic AI expert systems, reasoning systems, knowledge based representation

and all of those kinds of things search.

Does that neighbor fit into what you think of as machine learning?

So I don't even like the word machine learning.

I think that with the field you're talking about is all about making large collections

of decisions under uncertainty by large collections of entities.

Yes.

Right.

And there are principles for that at that scale.

You don't have to say the principles are for a single entity that's making decisions,

single agent or single human.

It really immediately goes to the network of decisions.

Is a good word for that?

No, there's no good words for any of this.

That's kind of part of the problem.

So we can continue the conversation, use AI for all that.

I just want to kind of raise our flag here that this is not about, we don't know what

intelligence is and real intelligence.

We don't know much about abstraction and reasoning at the level of humans.

We don't have a clue.

We're not trying to build that because we don't have a clue.

Eventually it may emerge.

They'll make, I don't know if they'll be breakthroughs, but eventually we'll start to get glimmers

of that.

It's not what's happening right now.

Okay.

We're taking data.

We're trying to make good decisions based on that.

We're trying to do a scale.

We're trying to do it economically, viably.

We're trying to build markets.

We're trying to keep value at that scale.

And aspects of this will look intelligent.

They will look, computers were so dumb before, they will see more intelligent.

We will use that buzzword of intelligence.

So we can use it in that sense, but you know, so machine learning, you can scope it narrowly

is just learning from data and pattern recognition.

But whatever I, when I talk about these topics, I maybe data science is another word you could

throw in the mix.

It really is important that the decisions are, as part of it, it's consequential decisions

in the real world.

Am I going to have a medical operation?

Am I going to drive down the street, you know, things that were, they're scarcity, things

that impact other human beings or other, you know, the environment and so on.

How do I do that based on data?

How do I do that?

I definitely how do I use computers to help those kinds of things go forward, whatever

you want to call that.

So let's call it AI.

Let's agree to call it AI, but it's, let's, let's not say that what the goal of that

is, is intelligence, the goal of that is really good working systems at planetary scale that

we've never seen before.

So reclaim the word AI from the Dartmouth conference from many decades ago of the dream

of human.

I don't want to reclaim it.

I want a new word.

I think it was a bad choice.

I mean, I, you know, I, if you read one of my little things, the history was basically

that McCarthy needed a new name because cybernetics already existed.

And he didn't like, you know, no one really liked Norbert Wiener.

Norbert Wiener was kind of an island to himself.

And he felt that he had encompassed all this and in some sense he did.

If you look at the language of cybernetics, it was everything we're talking about.

It was control theory and single processing and some notions of intelligence and close

feedback loops and data.

It was all there.

It's just not a word that lived on partly because of the, maybe the personalities.

But McCarthy needed a new word to say, I'm different from you.

I'm not part of your, your show.

I got my own invented this word.

And again, as a kind of a thinking forward about the movies that would be made about

it, it was a great choice, but thinking forward about creating a sober academic and

real world discipline.

It was a terrible choice because it led to promises that are not true, that we understand.

We understand artificial perhaps, but we don't understand intelligence.

It's a small tangent because you're one of the great personalities of machine learning,

whatever the heck you call the field.

The, do you think science progresses by personalities or by the fundamental principles and theories

and research that's outside of personality?

Yeah, both.

And I wouldn't say there should be one kind of personality.

I have mine and I have my preferences and I have a kind of network around me that feeds

me and some of them agree with me and some disagree, but you know, all kinds of personalities

are needed.

Right now, I think the personality that it's a little too exuberant, a little bit too ready

to promise the moon is, is a little bit too much in ascendance.

And I do, I do think that that's, there's some good to that.

It certainly attracts lots of young people to our field, but a lot of those people come

in with strong misconceptions and they have to then unlearn those and then find something

in, you know, to do.

And so I think there's just got to be some, you know, multiple voices and there's, I didn't,

I wasn't hearing enough of the more sober voice.

So as a continuation of a fun tangent and speaking of vibrant personalities, what would

you say is the most interesting disagreement you have with Jan Lacoon?

So Jan's an old friend and I just say that I don't think we disagree about very much

really.

He and I both kind of have a let's build that kind of mentality and does it work and kind

of mentality and kind of concrete.

We both speak French and we speak French more together and we have, we have a lot, a lot

in common.

And so, you know, if one wanted to highlight a disagreement, it's not really a fundamental.

When I think it's just kind of where we're emphasizing, Jan has emphasized pattern recognition

and has emphasized prediction.

All right.

So, you know, and it's interesting to try to take that as far as you can.

If you could do perfect prediction, what would that give you kind of as a thought experiment?

And I think that's way too limited.

We cannot do perfect prediction.

We will never have the data sets that allow me to figure out what you're about ready to

do, what question you're going to ask next.

I have no clue.

I will never know such things.

Moreover, most of us find ourselves during the day in all kinds of situations we had

no anticipation of that are kind of various, their novel in various ways.

And in that moment, we want to think through what we want.

And also, there's going to be market forces acting on us.

I'd like to go down that street, but now it's full because there's a crane in the street.

I got it.

I got to think about that.

I got to think about what I might really want here.

And I got to sort of think about how much it costs me to do this action versus this

action.

I got to think about the risks involved.

You know, a lot of our current pattern recognition and prediction systems don't do any risk evaluations.

They have no error bars, right?

I got to think about other people's decisions around me.

I got to think about a collection of my decisions, even just thinking about like a medical treatment.

You know, I'm not going to take the prediction of a neural net about my health, about something

consequential.

I'm not about ready to have a heart attack because some number is over.7.

Even if you had all the data in the world, they've ever been collected about heart attacks

better than any doctor ever had.

I'm not going to trust the output of that neural net to predict my heart attack.

I'm going to want to ask what if questions around that.

I'm going to want to look at some other possible data I didn't have.

Causal things.

I'm going to want to have a dialogue with a doctor about things we didn't think about

when we gathered the data.

You know, I could go on and on.

I hope you can see.

And I don't, I think that if you say predictions, everything that, that, that you're missing

all of this stuff.

And so prediction plus decision making is everything, but both of them are equally important.

And so the field has emphasized prediction.

Yon rightly so has seen how powerful that is.

But at the cost of people not being aware of the decision making is where the rubber

really hits the road, where human lives are at stake, where risks are being taken, where

you got to gather more data.

You got to think about the air bars.

You got to think about the consequences of your decisions on others.

You got about the economy around your decisions, blah, blah, blah, blah.

I'm not the only one working on those, but we're a smaller tribe.

And right now we're not the one that people talk about the most.

But you know, if you go out in the real world and industry, you know, at Amazon, I'd say

half the people there are working on decision making and the other half are doing, you know,

the pattern recognition.

It's important.

And the words of pattern recognition and prediction, I think the distinction there, not to linger

on words, but the distinction there is more a constrained sort of in the lab data set

versus decision making is talking about consequential decisions in the real world under the messiness

and the uncertainty of the real world.

And just the whole of it, the whole mess of it that actually touches human beings and

scale, like you said, market forces, that's the distinction.

It helps add those, that perspective, that broader perspective.

You're right.

I totally agree.

On the other hand, if you're a real prediction person, of course you want it to be in the

real world.

You want to predict real world events.

I'm just saying that's not possible with just data sets, that it has to be in the context

of, you know, strategic things that someone's doing, data they might gather, things they

could have gathered, the reasoning process around data.

It's not just taking data and making predictions based on the data.

So one of the things that you're working on, I'm sure there's others working on it, but

I don't hear often it talked about, especially in the clarity that you talk about it.

And I think it's both the most exciting and the most concerning area of AI in terms of

decision making.

So you've talked about AI systems that help make decisions that scale in a distributed

way, millions, billions of decisions, sort of markets of decisions.

Can you, as a starting point, sort of give an example of a system that you think about

when you're thinking about these kinds of systems?

Yeah.

So first of all, you're absolutely getting into some territory, which I will be beyond

my expertise.

And there are lots of things that are going to be very not obvious to think about.

Just like, again, I like to think about history a little bit, but think about, put yourself

back in the 60s.

There was kind of a banking system that wasn't computerized really.

There was database theory emerging.

And database people had to think about, how do I actually not just move data around, but

actual money, and have it be valid, and have transactions at ATMs happen that are actually

all valid, and so on and so forth.

So that's the kind of issues you get into when you start to get serious about things

like this.

I like to think about as kind of almost a thought experiment to help me think something

simpler, which is the music market, because to first order, there is no music market in

the world right now in our country, for sure.

There are things called record companies, and they make money, and they prop up a few

really good musicians, and make them superstars, and they all make huge amounts of money.

But there's a long tale of huge numbers of people that make lots and lots of really

good music that is actually listened to by more people than the famous people.

They are not in a market.

They cannot have a career.

They do not make money.

The creators.

The creators.

The creators.

The so called influencers or whatever.

The managers who they are, right?

So there are people who make extremely good music, especially in the hip hop or Latin

world these days.

They do it on their laptop.

That's what they do on the weekend, and they have another job during the week, and they

put it up on SoundCloud or other sites.

Eventually, it gets streamed.

It down gets turned into bits.

It's not economically valuable.

The information is lost.

It gets put up.

There are people stream it.

You walk around in a big city.

You see people with headphones all, you know, especially young kids listening to music all

the time.

Especially the data, none of them, very little of the music they listen to is the famous

people's music.

And none of it's old music.

It's all the latest stuff.

But the people who made that latest stuff are like some 16 year old somewhere who will

never make a career out of this, who will never make money.

Of course, there will be a few counter examples.

The record companies incentivize to pick out a few and highlight them.

Long story short, there's a missing market there.

There is not a consumer producer relationship at the level of the actual creative acts.

The pipelines and spotifies of the world that take this stuff and stream it along, they

make money off of subscriptions or advertising and those things.

They're making the money, right?

And then they will offer bits and pieces of it to a few people again to highlight that,

you know, they're the simulator market.

Anyway, a real market would be if you're a creator of music that you actually are somebody

who's good enough that people want to listen to you.

You should have the data available to you.

There should be a dashboard showing a map of the United States.

So in last week, here's all the places your songs were listened to.

It should be transparent, vettable so that if someone down in Providence sees that you're

being listened to 10,000 times in Providence, that they know that's real data.

You know it's real data.

They will have you come give a show down there.

They will broadcast to the people who've been listening to you that you're coming.

If you do this right, you could, you could, you know, go down there and make $20,000.

You do that three times a year, you start to have a career.

So in this sense, AI creates jobs.

It's not about taking away human jobs.

It's creating new jobs because it creates a new market.

Once you've created a market, you've now connected up producers and consumers.

You know, the person who's making the music can say to someone who comes to their shows

a lot, hey, I'll play your daughter's wedding for $10,000.

You'll say $8,000.

They'll say $9,000.

Then you, again, you can now get an income up to $100,000.

You're not going to be a millionaire, all right.

And now even think about really the value of music is in these personal connections, even

so much so that a young kid wants to wear a T shirt with their favorite musician's signature

on it, right?

So if they listen to the music on the internet, the internet should be able to provide them

with a button that they push and the merchandise arrives the next day.

We can do that, right?

And now why should we do that?

Well, because the kid who bought the shirt will be happy, but more the person who made

the music will get the money.

There's no advertising needed, right?

So you can create markets between producers and consumers, take 5% cut.

Your company will be perfectly sound, it'll go forward into the future, and it will create

new markets and that raises human happiness.

Now this seems like it was easy, just create this dashboard, kind of create some connections

and all that.

But if you think about Uber or whatever, you think about the challenges in the real world

of it doing things like this, and there are actually new principles going to be needed.

You're trying to create a new kind of two way market at a different scale that's ever

been done before.

There's going to be unwanted aspects of the market, there'll be bad people, there'll

be the data will get used in the wrong ways, it'll fail in some ways, it won't deliver

about, you have to think that through.

Just like anyone who ran a big auction or ran a big matching service in economics will

think these things through.

And so that maybe didn't get at all the huge issues that can arise when you start to create

markets, but it starts for me solidify my thoughts and allow me to move forward in my

own thinking.

Yeah, so I talked to, had a research at Spotify actually, I think their long term goal they've

said is to have at least one million creators make a comfortable living putting on Spotify.

So I think you articulate a really nice vision of the world and the digital and the cyberspace

of markets.

What do you think companies like Spotify or YouTube or Netflix can do to create such

markets?

Is it an AI problem?

Is it an interface problems or interface design?

Is it some other kind of, is it an economics problem?

Who should they hire to solve these problems?

Well, part of it's not just top down.

So the Silicon Valley has this attitude that they know how to do it.

They will create the system just like Google did with the search box that will be so good

that they'll just everyone will adopt that.

It's not, it's everything you said, but really I think missing the kind of culture.

So it's literally that 16 year old who's, who's able to create the songs.

You don't create that as a Silicon Valley entity.

You don't hire them per se, right?

You have to create an ecosystem in which they are wanted and that they're belong, right?

And so you have to have some culture credibility to do things like this, you know, Netflix

to their credit wanted some of that credibility and they created shows, you know, content.

They call it content.

It's such a terrible word, but it's culture, right?

And so with movies, you can kind of go give a large sum of money to somebody graduate

from the USC film school.

It's a whole thing of its own, but it's kind of like rich white people's thing to do,

you know, and you know, American culture has not been so much about rich white people.

It's been about all the immigrants, all the, you know, the Africans who came and brought

that culture and those, those rhythms and that to this world and created this whole

new thing, you know, American culture.

And so companies can't artificially create that.

They can't just say, Hey, we're here.

We're going to buy it up.

You got a partner, right?

And so, but anyway, you know, not to denigrate these companies are all trying and they should

and they, they are, I'm sure they're asking these questions and some of them are even

making an effort, but it is partly a respect the culture as you were a, as a technology

person, you got to blend your technology with cultural, with cultural, you know, meaning.

How much of a role do you think the algorithm machine learning has in connecting the consumer

to the creator sort of the recommender system aspect of this?

Yeah.

It's a great question.

I think pretty high recommend, you know, there's no magic in the algorithms, but a good

recommender system is way better than a bad recommender system and recommender systems

is a billion dollar industry back even, you know, 10, 20 years ago.

And it continues to be extremely important going forward.

What's your favorite recommender system just so we can put something well, just historically

I was one of the, you know, when I first went to Amazon, you know, I first didn't like

Amazon because they put the book people are out of business or the library, you know,

the local booksellers went out of business.

I've come to accept that there, you know, there probably are more books being sold now

and poor people reading them than ever before.

And then local book stores are coming back.

So you know, that's how economics sometimes work.

You go up and you go down.

But anyway, when I finally started going there and I bought a few books, I was really pleased

to see another few books being recommended to me that I never would have thought of.

And I bought a bunch of them, so they obviously had a good business model, but I learned things

and I still to this day kind of browse using that service.

And I think lots of people get a lot, you know, that is a good aspect of a recommendation

system.

I'm learning from my peers in an indirect way.

And their algorithms are not meant to have them impose what we learn.

It really is trying to find out what's in the data.

It doesn't work so well for other kind of entities, but that's just the complexity of human life

like shirts.

I'm not going to get recommendations on shirts, but that's interesting.

If you try to recommend restaurants, it's hard.

It's hard to do it at scale.

But a blend of recommendation systems with other economic ideas, matchings and so on

is really, really still very open, research wise, and there's new companies that are going

to emerge that do that well.

What do you think was going to the messy, difficult land of, say, politics and things

like that that YouTube and Twitter have to deal with in terms of recommendation systems?

Being able to suggest, I think Facebook just launched Facebook News, so having recommend

the kind of news that are most likely for you to be interesting.

Do you think this is AI solvable, again, whatever term you want to use, do you think it's a

solvable problem for machines or is it a deeply human problem that's unsolvable?

I don't even think about it at that level.

I think that what's broken with some of these companies, it's all monetization by advertising.

They're not at least Facebook.

I want to critique them.

They didn't really try to connect a producer and a consumer in an economic way.

No one wants to pay for anything.

They all started with Google and Facebook.

They went back to the playbook of the television companies back in the day.

Everyone wanted to pay for this signal, they will pay for the TV box, but not for the signal,

at least back in the day.

Advertising kind of filled that gap and advertising was new and interesting and it somehow didn't

take over our lives quite.

Fast forward, Google provides a service that people don't want to pay for.

Somewhat surprisingly in the 90s, they ended up making huge amounts to the corner of the

advertising market.

It didn't seem like that was going to happen, at least to me.

These little things on the right hand side of the screen just did not seem all that economically

interesting, but companies had maybe no other choice.

The TV market was going away and billboards and so on.

They got it.

I think that, sadly, that Google was doing so well with that and making it so much more.

They didn't think much more about how, wait a minute, is there a producer or consumer

relationship to be set up here, not just between us and the advertisers market to be created?

Is there an actual market between the producer and consumer?

There, the producer is the person who created that video clip.

The person that made that website, the person who couldn't make more such things, the person

who could adjust it as a function of demand, the person on the other side who's asking

for different kinds of things.

You see glimmers of that now, there's influencers and there's a little glimmering of a market,

but it should have been done 20 years ago, it should have been thought about.

It should have been created in parallel with the advertising ecosystem.

Then Facebook inherited that and I think they also didn't think very much about that.

Fast forward and now they are making huge amounts of money off of advertising and the

news thing and all these clicks is just feeding the advertising.

It's all connected up to the advertising.

You want more people to click on certain things because that money flows to you, Facebook.

You're very much incentivized to do that and when you start to find it's breaking, people

are telling you, well, we're getting into some troubles, you try to adjust it with your

smart AI algorithms and figure out what are bad clicks though, maybe shouldn't be clicked

through the radar.

I find that pretty much hopeless, it does get into all the complexity of human life

and you can try to fix it, you should, but you could also fix the whole business model

and the business model is that really, what are, are there some human producers and consumers

out there?

Is there some economic value to be liberated by connecting them directly?

Is it such that it's so valuable that people will be going to pay for it?

All right.

Like micro payment, like small payment.

Micro, but even after you micro, so I like the example, suppose I'm going, next week

I'm going to India, never been to India before, right?

I have a couple of days in, in Mumbai, I have no idea what to do there, right?

And I could go on the web right now and search, it's going to be kind of hopeless.

I'm not going to find, you know, I'll have lots of advertisers in my face, right?

What I really want to do is broadcast to the world that I am going to Mumbai and have someone

on the other side of a market look at me and, and there's a recommendation system there.

So they're not looking at all possible people coming to Mumbai, they're looking at the people

who are relevant to them.

So someone in my age group, someone who kind of knows me in some level, I give up a little

privacy by that.

But I'm happy because what I'm going to get back is this person can make a little video

for me or they're going to write a little two page paper on, here's the cool things

that you want to do and move by this week, especially, right?

I'm going to look at that.

I'm not going to pay a micro payment.

I'm going to pay, you know, a hundred dollars or whatever for that.

It's real value.

It's like journalism.

And as an odd subscription, it's that I'm going to pay that person in that moment.

I mean, it's going to take 5% of that and that person has now got it.

It's a gig economy, if you will, but, you know, done for it, you know, thinking about

a little bit behind YouTube, there was actually people who could make more of those things.

If they were connected to a market, they would make more of those things independently.

You don't have to tell them what to do.

You don't have to incentivize them in any other way.

And so yeah, these companies, I don't think have thought long, long and heard about that.

So I do distinguish on, you know, Facebook on the one side who just not thought about

these things at all.

They were thinking that AI will fix everything and Amazon thinks about them all the time

because they were already out in the real world.

They were delivering packages to people's doors.

They were worried about a market.

They were worried about sellers and, you know, they worry and some things they do are great.

Some things maybe not so great, but, you know, they're in that business model.

And then I'd say Google sort of hovers somewhere in between.

I don't think for a long, long time they got it.

I think they probably see that YouTube is more pregnant with possibility than they might

have thought and that they're probably heading that direction.

But you know, Silicon Valley has been dominated by the Google, Facebook kind of mentality

and the subscription and advertising and that is, that's the core problem, right?

The fake news actually rides on top of that because it means that you're monetizing with

clip through rate and that is the core problem.

You got to remove that.

So advertisement, if we're going to linger on that, I mean, that's an interesting thesis.

I don't know if everyone really deeply thinks about that.

So you're right.

The thought is the advertisement model is the only thing we have, the only thing we'll

ever have.

So we have to fix, we have to build algorithms that despite that business model, you know,

find the better angels of our nature and do good by society and by the individual.

But you think we can slowly, you think, first of all, there's a difference between should

and could.

So you're saying we should slowly move away from the advertising model and have a direct

connection between the consumer and the creator.

The question I also have is, can we, because the advertising model is so successful now

in terms of just making a huge amount of money and therefore being able to build a big company

that provides, has really smart people working that create a good service.

Do you think it's possible?

And just to clarify, you think we should move away?

Well, I think we should.

Yeah.

But we is the, you know, me.

Society.

Yeah.

Well, the companies.

I mean, so first of all, full disclosure.

I'm doing a day a week at Amazon because I kind of want to learn more about how they

do things.

So, you know, I'm not speaking for Amazon in any way, but, you know, I did go there

because I actually believe they get a little bit of this or trying to create these markets.

And they don't really use, advertising is not a crucial part of Amazon.

That's a good question.

So it has become not crucial, but it's become more and more present if you go to Amazon

website.

And, you know, without revealing too many deep secrets about Amazon, I can tell you

that, you know, a lot of people come to question this and there's a huge questioning going

on.

You do not want a world where there's zero advertising.

That actually is a bad world.

Okay.

So here's the way to think about it.

You're a company that like Amazon is trying to bring products to customers, right?

And the customer and you get more, you want to buy a vacuum cleaner, say, you want to

know what's available for me.

And, you know, it's not going to be that obvious.

You have to do a little bit of work at it.

The recommendation system will sort of help, right?

But now suppose this other person over here has just made the world, you know, they spend

a huge amount of energy.

They had a great idea.

They made a great vacuum cleaner.

They know they really did it.

They nailed it.

They, you know, whiz kid that made a great new vacuum cleaner, right?

It's not going to be in the recommendation system.

No one will know about it.

The algorithms will not find it and AI will not fix that.

Okay.

At all.

Right.

How do you allow that vacuum cleaner to start to get in front of people?

Be sold.

Well, advertising and here what advertising is, it's a signal that you're, you believe in

your product enough that you're willing to pay some real money for it.

And to me as a consumer, I look at that signal, I say, well, first of all, I know these are

not just cheap little ads because we have now right now that I know that, you know, these

are super cheap, you know, pennies.

If I see an ad where it's actually, I know the company is only doing a few of these and

they're making real money is kind of flowing and I see an ad, I may pay more attention

to it.

And I actually might want that because I see, Hey, that guy spent money on his vacuum cleaner.

Maybe there's something good there.

So I will look at it.

And so that's part of the overall information flow in a good market.

So advertising has a role.

But the problem is, of course, that that signal is now completely gone because it just, you

know, dominated by these tiny little things that add up to big money for the company.

You know, so I think it will just, I think it will change because societies just don't,

you know, stick with things that annoy a lot of people and advertising currently annoys

people more than it provides information.

And I think that a Google probably is smart enough to figure out that this is a dead, this

is a bad model, even though it's a hard huge amount of money and they'll have to figure

out how to pull it away from it slowly, and I'm sure the CEO there will figure it out.

But they need to do it and they needed it.

So if you reduce advertising, not to zero, but you reduce it at the same time you bring

up producer, consumer, actual real value being delivered.

So real money is being paid and they take a 5% cut.

That 5% could start to get big enough to cancel out the lost revenue from the kind of the

poor kind of advertising.

And I think that a good company will do that, will realize that.

And they're, you know, Facebook, you know, again, God bless them.

They bring, you know, grandmothers, you know, they bring children's pictures into grandmothers

lives.

It's fantastic.

But they need to think of a new business model and that's the core problem there.

Until they start to connect producer, consumer, I think they will just continue to make money

and then buy the next social network company and then buy the next one.

And the innovation level will not be high and the health issues will not go away.

So I apologize that we kind of returned to words.

I don't think the exact terms matter, but in sort of defensive advertisement, don't

you think the kind of direct connection between consumer and creator, producer is the best,

like the, is what advertisement strives to do, right?

So it is best advertisement is literally now the Facebook is listening to our conversation

and heard that you're going to India and we'll be able to actually start automatically for

you making these connections and start giving this offer.

So like, I apologize if it's just a matter of terms, but just to draw a distinction,

is it possible to make advertisements just better and better and better algorithmically

to where it actually becomes a connection almost a direct connection?

That's a good question.

So let's put it on that first of all, what we just talked about, I was defending advertising.

Okay.

So I was defending it as a way to get signals into a market that don't come any other way,

especially algorithmically.

It's a sign that someone spent money on it is a sign they think it's valuable.

And if I think that if other things, someone else thinks it's valuable, then if I trust

other people, I might be willing to listen.

I don't trust that Facebook though is who's an intermediary between this.

I don't think they care about me.

Okay.

I don't think they do.

And I find it creepy that they know I'm going to India next week because of our conversation.

Why do you think that?

Can we just put your PR hat on?

Why do you think you find Facebook creepy and not trust them as do majority of the population?

So they're out of the Silicon Valley companies.

I saw like, not approval rate, but there's ranking of how much people trust companies

and Facebook is in the gutter.

In the gutter, including people inside of Facebook.

So what do you attribute that to?

Because when I...

Come on.

You don't find it creepy that right now we're talking that I might walk out on the street

right now that some unknown person who I don't know kind of comes up to me and says,

I hear you going to India.

I mean, that's not even Facebook.

That's just a...

I want transparency in human society.

I want to have...

If you know something about me, there's actually some reason you know something about me.

It's something that if I look at it later and audit it kind of, I approve.

You know something about me because you care in some way.

There's a caring relationship even or an economic one or something.

Not just that you're someone who could exploit it in ways I don't know about or care about

or I'm troubled by or whatever.

And we're in a world right now where that happened way too much and that Facebook knows

things about a lot of people and could exploit it and does exploit it at times.

I think most people do find that creepy.

It's not for them.

It's not...

Facebook does not do it because they care about them in any real sense.

And they shouldn't.

They should not be a big brother caring about us.

That is not the role of a company like that.

Why not?

Wait, not the big brother part but the caring, the trust thing.

I mean, don't those companies...

Just to linger on it because a lot of companies have a lot of information about us.

I would argue that there's companies like Microsoft that has more information about us

than Facebook does and yet we trust Microsoft more.

But Microsoft is pivoting.

Microsoft has decided this is really important.

We don't want to do creepy things.

Really want people to trust us to actually only use information in ways that they really

would approve of, that we don't decide.

And I'm just kind of adding that the health of a market is that when I connect to someone

who produced or consumers, not just a random producer or consumer, it's people who see

each other.

They don't like each other but they sense that if they transact, some happiness will

go up on both sides.

If a company helps me to do that and moments that I choose of my choosing, then fine.

So and also think about the difference between browsing versus buying, right?

There are moments in my life, I just want to buy a gadget or something.

I need something for that moment.

I need some ammonia for my house or something because I got a problem in this bill.

I want to just go in.

I don't want to be advertised at that moment.

I don't want to be led down very straight.

That's annoying.

Let's just go and have it extremely easy to do what I want.

Other moments I might say, no, it's like today I'm going to the shopping mall.

I want to walk around and see things and see people and be exposed to stuff.

So I want control over that though.

I don't want the company's algorithms to decide for me, right?

And I think that's the thing.

It's a total loss of control if Facebook thinks they should take the control from us of deciding

when we want to have certain kinds of information, when we don't, what information that is, how

much it relates to what they know about us that we didn't really want them to know about

us.

I don't want them to be helping me in that way.

I don't want them to be helping them by they decide they have control over what I want

and when.

I totally agree.

So Facebook, by the way, I have this optimistic thing where I think Facebook has the kind

of personal information about us that could create a beautiful thing.

So I'm really optimistic of what Facebook could do.

It's not what it's doing, but what it could do.

So I don't see that, I think that optimism is misplaced because you have to have a business

model behind these things.

Create a beautiful thing is really, let's be clear.

It's about something that people would value.

And I don't think they have that business model.

And I don't think they will suddenly discover it by what, you know, a long hot shower.

I disagree.

I disagree in terms of, you can discover a lot of amazing things in a shower.

So I didn't say that.

I said they won't come.

They won't.

But in the shower, I think a lot of other people will discover it.

I think that this guy, so I should also full disclosure, there's a company called United

Masters, which I'm on their board and they've created this music market and they have 100,000

artists now signed on and they've done things like gone to the NBA and the NBA, the music

you find behind NBA Eclipse right now is their music, right?

That's a company that had the right business model in mind from the get go, right, executed

on that.

And from day one, there was value brought to, so here you have a kid who made some songs

who suddenly their songs are on the NBA website, right?

That's really economic value to people.

And so, you know.

So you and I differ on the optimism of being able to sort of change the direction of the

Titanic, right?

So I.

Yeah.

I'm older than you.

So I think the Titanic's crash.

Got it.

But just to elaborate, because I totally agree with you and I just want to know how difficult

you think this problem is of, so for example, I want to read some news and I would, there's

a lot of times in the day where something makes me either smile or think in a way where

I like consciously think this really gave me value.

Like I sometimes listen to the daily podcast in the New York Times, way better than the

New York Times themselves, by the way, for people listening.

That's like real journalism is happening for some reason in the podcast space.

It doesn't make sense to me.

But often I listen to it 20 minutes and I would be willing to pay for that, like $5,

$10 for that experience and how difficult.

That's kind of what you're getting at is that little transaction.

How difficult is it to create a frictionless system like Uber has, for example, for other

things?

What's your intuition there?

So first of all, I pay little bits of money to, you know, to send there's something called

courts that does financial things.

I like medium as a site, I don't pay there, but I would.

You had a great post on medium.

I would have loved to pay you a dollar and not others.

I wouldn't have wanted it per se because there should be also sites where that's not actually

the goal.

The goal is to actually have a broadcast channel that I monetize in some other way if I chose

to.

I mean, I could now.

People know about it.

I could.

I'm not doing it, but that's fine with me.

Also the musicians who are making all this music, I don't think the right model is that

you pay a little subscription fee to them, right?

Because people can copy the bits too easily and it's just not that somewhere the value

is.

The value is that a connection was made with real between real human beings, then you can

follow up on that, right?

And create yet more value.

So no, I think there's a lot of open questions here.

Hot open questions, but also, yeah, I do want good recommendation systems that recommend

cool stuff to me.

But it's pretty hard, right?

I don't like them to recommend stuff just based on my browsing history.

I don't like that they're based on stuff they know about me, quote unquote.

That's unknown about me is the most interesting.

So this is the really interesting question.

We may disagree, maybe not.

I think that I love recommender systems and I want to give them everything about me in

a way that I trust.

Yeah, but you don't.

Because so for example, this morning I clicked on, you know, I was pretty sleepy this morning.

I clicked on a story about the Queen of England, right?

I do not give a damn about the Queen of England.

I really do not.

But it was clickbait.

It kind of looked funny and I had to say, what the heck are they talking about there?

I don't want to have my life, you know, heading that direction.

Now that's in my browsing history.

The system and any reasonable system will think that I care about Queen of England.

But that's browsing history.

Right, but you're saying all the trace, all the digital exhaust or whatever, that's been

kind of the models.

If you collect all this stuff, you're going to figure all of us out.

Well, if you're trying to figure out like kind of one person, like Trump or something,

maybe you could figure him out.

But if you're trying to figure out, you know, 500 million people, you know, no way, no way.

Do you think so?

No, I do.

I think so.

I think we are humans are just amazingly rich and complicated.

Every one of us has our little quirks.

Everyone else has our little things that could intrigue us, that we don't even know and will

intrigue us.

And there's no sign of it in our past.

But by God, there it comes and, you know, you fall in love with it.

And I don't want a company trying to figure that out for me and anticipate that.

Okay, well, I want them to provide a forum, a market, a place that I kind of go and by

hook or by crook, this happens.

You know, I'm walking down the street and I hear some Chilean music being played and

I never knew I liked Chilean music.

Wow.

So there is that side and I want them to provide a limited but, you know, interesting place

to go.

Right.

And so don't try to use your AI to kind of, you know, figure me out and then put me in

a world where you figured me out, you know, no, create huge spaces for human beings where

our creativity and our style will be enriched and come forward and it'll be a lot of more

transparency.

I won't have people randomly, anonymously putting comments up and especially based on

stuff they know about me, facts that, you know, we are so broken right now.

If you're, you know, especially if you're a celebrity, but, you know, it's about anybody

that anonymous people are hurting lots and lots of people right now.

And that's part of this thing that Silicon Valley is thinking that, you know, just collect

all this information and use it in a great way.

So, you know, I'm not a pessimist, I'm very much an optimist, my nature, but I think that's

just been the wrong path for the whole technology to take.

Be more limited, create, let humans rise up.

Don't try to replace them.

That's the AI mantra.

Don't try to anticipate them.

Don't try to predict them because you're not going to, you're not going to be able to do

those things.

You're going to make things worse.

Okay.

So, right now, just give this a chance.

Right now, the recommender systems are the creepy people in the shadow watching your

every move.

So they're looking at traces of you.

They're not directly interacting with you, sort of your close friends and family, the

way they know you is by having conversation, by actually having interactions back and forth.

Do you think there's a place for recommender systems, sort of to step, because you just

emphasize the value of human to human connection.

But yeah, let's give it a chance, AI human connection.

Is there a role for an AI system to have conversations with you in terms of, to try

to figure out what kind of music you like, not by just watching what you listen to, but

actually having a conversation, natural language or otherwise.

Yeah.

So I'm not against this, I just wanted to push back against the, maybe you were saying,

you have options for Facebook.

So there I think it's misplaced.

But I think that distributing...

I'm the one that's depending on Facebook.

Yeah.

No.

So good for you.

Go for it.

That's a hard spot to be.

Yeah.

No.

Good.

Human interaction, like on our daily, the context around me in my own home is something that

I don't want some big company to know about at all.

But I would be more than happy to have technology help me with it.

Which kind of technology?

Well, you know, just...

Alexa.

Amazon.

Well, Alexa's done right.

I think Alexa's a research platform right now more than anything else.

But Alexa done right, you know, could do things like I leave the water running in my garden

and I say, hey, Alexa, the water's running in my garden.

And even have Alexa figure out that that means when my wife comes home that she should be

told about that.

That's a little bit of a reasoning.

I would call that AI and by any kind of stretch, it's a little bit of reasoning.

And it actually kind of would make my life a little easier and better.

And you know, I wouldn't call this a wow moment, but I kind of think that overall rice is human

happiness up to have that kind of thing.

And not when you're lonely, Alexa knowing loneliness.

No, no, I don't want Alexa to feel intrusive.

And I don't want just the designer of the system to kind of work all this out.

I really want to have a lot of control and I want transparency and control.

And if the company can stand up and give me that in the context of technology, I think

they're going to first of all be way more successful than our current generation.

And like I said, I was measuring Microsoft, you know, I really think they're pivoting

to kind of be the trusted old uncle.

You know, I think that they get that this is a way to go, that if you let people find

technology empowers them to have more control and have control, not just over privacy, but

over this rich set of interactions, that that people are going to like that a lot more.

And that's, that's the right business model going forward.

What does control over privacy look like?

Do you think you should be able to just view all the data that?

No, it's much more than that.

I mean, first of all, it should be an individual decision.

Some people don't want privacy.

They want their whole life out there.

Other people's want it.

Privacy is not a zero one.

It's not a legal thing.

It's not just about which date is available, which is not.

I like to recall to people that, you know, a couple of hundred years ago, everyone, there

was not really big cities.

Everyone lived in on the countryside and villages and in villages, everybody knew everything

about you.

Very, you didn't have any privacy.

Is that bad?

Are we better off now?

Well, you know, arguably no, because what did you get for that loss of at least certain

kinds of privacy?

Well, people helped each other because they know everything about you.

They know something bad's happening.

They will help you with that.

Right?

And now you live in a big city, no one knows the amount.

You get no help.

So it kind of depends the answer.

I want certain people who I trust and there should be relationships.

I should kind of manage all those, but who knows what about me?

I should have some agency there.

It shouldn't be a drift in a city of technology where I have no agency.

I don't want to go reading things and checking boxes.

So I don't know how to do that.

And I'm not a privacy researcher per se.

I recognize the vast complexity of this.

It's not just technology.

It's not just legal scholars meeting technologists.

There's got to be kind of a whole layers around it.

And so when I alluded to this emerging engineering field, this is a big part of it.

Well, like an electrical engineering come game, I'm not running around in the time,

but you just didn't plug electricity into walls and all kind of work.

You know, I had to have like underwriters laboratory that reassured you that that plug

is not going to burn up your house and that that machine will do this and that and everything.

There'll be whole people who can install things.

There'll be people who can watch the installers.

There'll be a whole layers, you know, an onion of these kind of things.

And for things as deep and interesting as privacy, which is as least as interested as electricity,

that's going to take decades to kind of work out, but it's going to require a lot of new

structures that we don't have right now.

So it's kind of hard to talk about it.

And you're saying there's a lot of money to be made if you get it right.

So I should look at a lot of money to be made.

And all these things that provide human services and people recognize them as useful parts

of their lives.

So yeah.

So yeah, the dialogue sometimes goes from the exuberant technologists to the no technology

is good kind of.

And that's, you know, in a public discourse, you know, in newsrooms, you see too much of

this kind of thing.

And the sober discussions in the middle, which are the challenging ones to have or where

we need to be having our conversations.

And you know, it's just not, actually, there's not many forum for those.

You know, there's, that's, that's kind of what I would look for.

Maybe I could go and I could read a comment section of something and it would actually

be this kind of dialogue going back and forth.

You don't see much of this, right?

Which is why actually there's a resurgence of podcasts out of all, because people are

really hungry for conversation, but there's technology is not helping much.

So comment sections of anything, including YouTube is not hurting and not helping.

And you think technically speaking is possible to help?

I don't know the answers, but it's a less anonymity, a little more locality, you know,

worlds that you kind of enter in and you trust the people there in those worlds so that when

you start having a discussion, you know, not only is that people are not going to hurt

you, but it's not going to be a total waste of your time because there's a lot of wasting

of time that, you know, a lot of us, I pulled out of Facebook early on because it was clearly

going to waste a lot of my time, even though there was some value.

And so yeah, worlds that are somehow you enter in, you know what you're getting, and it's

kind of appeals to you, new things might happen, but you kind of have some trust in that world.

And there's some deep, interesting, complex psychological aspects around anonymity, how

that changes human behavior that's quite dark.

Quite dark, yeah.

I think a lot of us are, especially those of us who really loved the advent of technology,

I loved social networks when they came out, I didn't see any negatives there at all.

But then I started seeing comment sections, I think it was maybe, you know, the CNN or

something.

And I started to go, wow, this, this darkness I just did not know about, and our technology

now amplifying it.

So sorry for the big philosophical question, but on that topic, do you think human beings,

because you've also, out of all things, had a foot in psychology too, do you think human

beings are fundamentally good?

Like all of us have good intent that could be mined, or is it, depending on context and

environment, everybody could be evil?

So my answer is fundamentally good, but fundamentally limited.

All of us have very, you know, blinkers on.

We don't see the other person's pain that easily.

We don't see the other person's point of view that easily.

We're very much in our own head, in our own world.

And on my good days, I think that technology could open us up to more perspectives and

more, less blinkered and more understanding, you know, a lot of wars in human history happened

because of just ignorance.

They didn't, they thought the other person was doing this, well, that person wasn't

doing this, and we have a huge amounts of that.

But in my lifetime, I've not seen technology really help in that way yet.

And I do, I do, I do believe in that, but, you know, no, I think fundamentally humans

are good.

People suffer.

People have grieves, and so you have grudges, and those things cause them to do things they

probably wouldn't want.

They regret it often.

So no, I, I, I think it's a, you know, part of the progress that technology is to indeed

allow it to be a little easier to be the real good person you actually are.

Well, but do you think individual human life or society can be modeled as an optimization

problem?

Um, not the way I think typically, I mean, that's, you're talking about one of the most

complex phenomena in the whole, you know, in all, which individual human life or society

is a whole.

Both.

Both.

I mean, individual human life is, is amazingly complex and, um, so, uh, you know, optimization

is kind of just one branch of mathematics that talks about certain kind of things.

And, uh, it just feels way too limited for the complexity of, uh, such things.

What properties of optimization problems do you think, so do you think most interesting

problems that could be solved through optimization, uh, what kind of properties does that surface

have nonconvexity, convexity, linearity, all those kinds of things, saddle points.

Well, so optimization is just one piece of mathematics.

You know, there's like, you just, even in our era, we're aware that say sampling, um,

it's coming up examples of something, um, come in with a distribution.

What's optimization?

What's sampling?

Well, you think you can, if you're a kind of a certain kind of math, but you can try

to blend them and make them seem to be sort of the same thing.

But optimization is roughly speaking, trying to, uh, find a point that, um, a single point

that is the optimum of a criterion function of some kind, um, and sampling is trying to,

from that same surface, treat that as a distribution or density and find prop points that have

high density.

So, um, I want the entire distribution and the sampling paradigm and I want the, um,

you know, the single point, that's the best point in the, in the sample, in the, uh, optimization

paradigm.

Now, if you were optimizing in the space of probability measures, the output of that could

be a whole probability distribution.

So you can start to make these things the same, but in mathematics, if you go too high

up that kind of abstraction hierarchy, you start to lose the, uh, you know, the ability

to do the interesting theorems.

So you kind of don't try that.

You don't try to overly over abstract.

So as a small tangent, what kind of world do you, do you find more appealing?

One that is deterministic or stochastic?

Uh, well, that's easy.

I mean, I'm a statistician, you know, the world is highly stochastic.

Wait, I don't know what's going to happen in the next five minutes.

Right.

Cause what you're going to ask, what we're going to do, what I'll say, massive uncertainty,

you know, massive uncertainty.

And so the best I can do is have come rough sense or probability distribution on things

and somehow use that in my reasoning about what to do now.

So how does the distributed at scale, when you have multi agent systems, uh, look like,

so optimization can optimize sort of, it makes a lot more sense sort of, uh, at least mine

from a robotics perspective, for a single robot, for a single agent, trying to optimize

some objective function, uh, what, when you start to enter the real world, this game theory

ready concepts starts popping up and that's how do you see optimization in this?

Cause you've talked about markets and a scale.

What does that look like?

Do you see it as optimization?

Do you see it as a sampling?

Do you see like how, how should you, yeah, these all blend together.

Um, and a system designer thinking about how to build an incentivized system will have

a blend of all these things.

So you know, a particle in a potential well is optimizing a functional called a Lagrangian.

Right.

The particle doesn't know that.

There's no algorithm running that does that.

It just happens.

It's, it's, so it's a description mathematically of something that helps us understand as analysts,

what's happening.

All right.

And so the same thing will happen when we talk about, you know, mixtures of humans and

computers and markets and so on and so forth, there'll be certain principles that allow

us to understand what's happening and whether or not the actual algorithms are being used

by any sense is not clear.

Um, now at, at some point I may have set up a multi agent or market kind of system and

I'm now thinking about an individual agent in that system and they're asked to do some

task and they're incentivized and somewhere they get certain signals and they, they have

some utility.

Maybe what they will do at that point is they just won't know the answer.

They may have to optimize to find an answer.

Okay.

So an autos could be embedded inside of an overall market, uh, you know, and game theory

is, is very, very broad.

Um, it is often studied very narrowly for certain kinds of problems.

Um, but it's roughly speaking, this is just the, I don't know what you're going to do.

I kind of anticipate that a little bit and you anticipate what I'm anticipating and we

kind of go back and forth in our own minds.

We run kind of thought experiments.

You've talked about this interesting point in terms of game theory, you know, most optimization

problems really hate saddle points.

Maybe you can describe what saddle points are, but I had heard you kind of mentioned

that there's a, there's a branch of optimization that you could try to explicitly look for

saddle points as a good thing.

Oh, not optimization.

That's just game theory.

That's, so, uh, there's all kinds of different equilibrium game theory and some of them are

highly explanatory behavior.

They're not attempting to be algorithmic.

They're just trying to say, if you happen to be at this equilibrium, you would see certain

kind of behavior and we see that in real life.

That's what an economist wants to do, especially behavioral economists, um, uh, in, in continuous,

uh, differential game theory, you're in continuous spaces, a, um, some of the simplest

equilibria are saddle points and Nash equilibrium as a saddle point, it's a special kind of

saddle point.

So, uh, classically in game theory, you were trying to find Nash equilibria and algorithmic

game theory, you're trying to find algorithms that would find them, uh, and so you're trying

to find saddle points.

I mean, so that's, that's literally what you're trying to do.

Um, but you know, any economist knows that Nash equilibria, uh, have their limitations.

They are definitely not that explanatory in many situations.

You're not what you really want.

Um, there's other kind of equilibria and there's names associated with these cause they came

from history with certain people working on them, but there'll be new ones emerging.

So you know, one example is a Stackelberg equilibrium.

So you know, Nash, you and I are both playing this game against each other or for each other,

maybe it's cooperative and we're both going to think it through and then we're going to

decide and we're going to do our thing simultaneously.

You know, in a Stackelberg, no, I'm going to be the first mover.

I'm going to make a move.

You're going to look at my move and then you're going to make yours.

Now, since I know you're going to look at my move, I anticipate what you're going to

do.

And so I don't do something stupid, but, and, but then I know that you were also anticipating

me.

So we're kind of going back and so forth.

Why, but there is then a first mover thing.

And so there's a different equilibria, all right.

And, uh, so just mathematically, yeah, these things have certain topologies and certain

shapes that are like salivates and then algorithmically or dynamically, how do you move towards them?

How do you move away from things?

Um, you know, so some of these questions have answers.

They've been studied.

Others do not, and especially if it becomes stochastic, especially if there's large numbers

of decentralized things, there's just, uh, you know, young people get in this field who

kind of think it's all done because we have, you know, TensorFlow.

Well, no, these are all open problems and they're really important and interesting.

And it's about strategic settings.

How do I collect data?

I suppose I don't know what you're going to do because I don't know you very well, right?

Well, I got to collect data about you.

So maybe I want to push you in a part of the space where I don't know much about you.

So I can get data.

And then later I'll realize that you'll never, you'll never go there because of the way the

game is set up.

But, you know, that's part of the overall, you know, data analysis context.

Yeah.

Even the game of poker is fascinating space.

Yeah.

Whenever there's any uncertainty or lack of information, it's, it's a super exciting

space.

Yeah.

Uh, just a lingering optimization for a second.

So when we look at deep learning, it's essentially a minimization of a complicated loss function.

So is there something insightful or hopeful that you see in the kinds of function surface

that lost functions that deep learning in, in the real world is trying to optimize over?

Is there something interesting as it's just the usual kind of problems of optimization?

I think from an optimization point of view, that surface first of all, it's pretty smooth.

And secondly, if there's over, if it's over parameterized, there's kind of lots of paths

down to reasonable optima.

And so kind of the getting downhill to the, to an optimum is, is viewed as not as hard

as you might have expected in high dimensions.

The fact that some optima tend to be really good ones and others not so good and you tend

to, it's not, sometimes you find the good ones is, is sort of still needs explanation.

Yes.

But, but the particular surface is coming from the particular generation of neural nets.

I kind of suspect those will, those will change in 10 years.

It will not be exactly those surfaces.

There'll be some others that are, an optimization theory will help contribute to why other surfaces

or why other algorithms.

Layers of arithmetic operations with a little bit of nonlinearity, that's not, that didn't

come from neuroscience per se.

I mean, maybe in the minds of some of the people working on it, they were thinking about brains,

but they were arithmetic circuits in all kinds of fields, you know, computer science control

theory and so on.

And that layers of these could transform things in certain ways and that if it's smooth, maybe

you could, you know, find parameter values, you know, it's a big, is a, is a, is a, is

a sort of big discovery that it's, it's working.

It's able to work at this scale, but I don't think that we're stuck with that and we're,

we're certainly not stuck with that because we're understanding the brain.

So in terms of, on the algorithm side, sort of gradient descent, do you think we're stuck

with gradient descent as variants of it?

What variants do you find interesting or do you think there'll be something else invented

that is able to walk all over these optimization spaces in more interesting ways?

So there's a co design of the surface and they are the architecture and the algorithm.

So if you just ask if we stay with the kind of architectures that we have now, not just

neural nets, but, you know, phase retrieval architectures or maybe completion architectures

and so on.

You know, I think we've kind of come to a place where a stochastic gradient algorithms

are dominant and there are versions that, you know, they're a little better than others.

They, you know, have more guarantees, they're more robust and so on and there's ongoing

research to kind of figure out which is the best time for which situation.

But I think that that'll start to co evolve, that that'll put pressure on the actual architecture

and so we shouldn't do it in this particular way, we should do it in a different way because

this other algorithm is now available if you do it in a different way.

So that I can't really anticipate that co evolution process, but you know, gradients

are amazing mathematical objects.

They have a lot of people who sort of study them more deeply mathematically or kind of

shocked about what they are and what they can do.

I mean, to think about this way, if I suppose that I tell you, if you move along the x axis,

you get, you know, you go uphill in some objective by, you know, three units, whereas if you move

along the y axis, you go uphill by seven units, right now, I'm going to only allow you to

move a certain, you know, unit distance, right?

What are you going to do?

Well, the most not people will say, I'm going to go along the y axis, I'm getting the biggest

bang for my buck, you know, and my buck is only one unit.

So I'm going to put all of it in the y axis, right?

And why should I even take any of my strength, my step size and put any of it in the x axis

because I'm getting less bang for my buck.

That seems like a completely, you know, clear argument and it's wrong because the gradient

direction is not to go along the y axis, it's to take a little bit of the x axis.

And that, to understand that, you have to, you have to know some math.

And so even a, you know, trivial, so called operator like gradient is not trivial and

so, you know, exploiting its properties is still very, very important.

Now we know that just creating descent has got all kinds of problems that get stuck in

many ways and it had never, you know, good dimension dependence and so on.

So my own line of work recently has been about what kinds of stochasticity, how can we get

dimension dependence?

How can we do the theory of that?

And we've come up with pretty favorable results with certain kinds of stochasticity.

We have sufficient conditions generally.

We know if you, if you do this, we will give you a good guarantee.

We don't have necessary conditions that it must be done a certain way in general.

So stochasticity, how much randomness to inject into the, into the walking along the gradient.

And what kind of randomness?

Why is randomness good in this process?

Why is stochasticity good?

Yeah.

So I can give you simple answers, but in some sense, again, it's kind of amazing.

Stochasticity just, you know, particular features of a surface that could have hurt

you if you were doing one thing, you know, deterministically won't hurt you because,

you know, by chance, you know, there's very little chance that you would get hurt.

And, you know, so here stochasticity, you know, it's just kind of saves you from some

of the particular features of surfaces that, you know, in fact, if you think about, you

know, surfaces that are discontinuous in a first derivative, like, you know, absolute

value function, you will go down and hit that point where there's non differentiability.

Right.

And if you're running a deterministic argument at that point, you can really do something

bad.

Right.

Where stochasticity just means it's pretty unlikely that's going to happen.

You're going to get, you're going to hit that point.

So, you know, it's, again, not trivial to analyze, but, especially in higher dimensions,

also stochasticity, our intuition isn't very good about it, but it has properties that

kind of are very appealing in high dimensions for kind of law of large number of reasons.

So it's all part of the mathematics to kind of, that's what's fun to work in the field

is that you get to try to understand this mathematics.

But long story short, you know, partly empirically, it was discovered stochastic gradient is very

effective and theory kind of followed, I'd say, that, but I don't see that we're getting

it clearly out of that.

What's the most beautiful, mysterious, or profound idea to you in optimization?

I don't know the most, but let me just say that, you know, Nesterov's work on Nesterov

acceleration to me is pretty surprising and pretty deep.

Can you elaborate?

Well, Nesterov acceleration is just that, I suppose that we are going to use gradients

to move around into space for the reasons I've alluded to, there are nice directions

to move.

And suppose that I tell you that you're only allowed to use gradients, you're not going

to be allowed to use this local person, it can only sense kind of the change in the surface.

But I'm going to give you kind of a computer that's able to store all your previous gradients.

And so you start to learn something about the surface.

And I'm going to restrict you to maybe move in the direction of like a linear span of

all the gradients.

So you can't kind of just move in some arbitrary direction, right?

So now we have a well defined mathematical complexity model.

There's a certain classes of algorithms that can do that and others that can't.

And we can ask for certain kinds of surfaces, how fast can you get down to the optimum?

So there's an answers to these.

So for a, you know, a smooth convex function, there's an answer, which is one over the number

of steps squared is that you will be within a ball of that size after K steps.

Gradient descent in particular has a slower rate.

It's one over K. Okay.

So you could ask, is gradient descent actually, even though we know it's a good algorithm,

is it the best algorithm in the sense of the answer is no, but well, well, not clear yet

because one over K score is a lower bound.

That's probably the best you can do.

Gradient is one over K, but these are something better.

And so I think as a surprise to most, the Nesterov discovered a new algorithm that has got two

pieces to it.

It uses two gradients and puts those together in a certain kind of obscure way.

And the thing doesn't even move downhill all the time.

It sometimes goes back uphill.

And if you're a physicist, that kind of makes some sense.

You're building up some momentum and that is kind of the right intuition, but that that

intuition is not enough to understand kind of how to do it and why it works.

But it does.

It achieves one over K squared and it has a mathematical structure and it's still kind

of to this day, a lot of us are writing papers and trying to explore that and understand it.

So there are lots of cool ideas and optimization, but just kind of using gradients, I think

is number one that goes back, you know, 150 years.

And then Nesterov, I think has made a major contribution with this idea.

So like you said, gradients themselves are in some sense mysterious.

Yeah, they're not, they're not as trivial as not as much as coordinate descent is more

of a trivial one.

You just pick one of the coordinates.

That's how we think that's how our human minds think and gradients are not that easy

for our human mind to grapple with.

An absurd question, but what is statistics?

So here it's a little bit, it's somewhere between math and science and technology.

It's somewhere in that convex hole.

So it's a set of principles that allow you to make inferences that have got some reason

to be believed and also principles that allow you to make decisions where you can have some

reason to believe you're not going to make errors.

So all of that requires some assumptions about what do you mean by an error?

What do you mean by, you know, the probabilities and, but, you know, after you start making

some assumptions, you're led to conclusions that, yes, I can guarantee that, you know,

if you do this in this way, your probability of making an error will be small.

Your probability of continuing to not make errors over time will be small.

And probability you found something that's real will be small, will be high.

So decision making is a big part of that?

So decision making is a big part, yeah.

So the original, so statistics, you know, short history was that, you know, it's kind

of goes back sort of as a formal discipline, you know, 250 years or so.

It was called inverse probability because around that era, probability was developed

sort of especially to explain gambling situations.

Of course.

And interesting.

So you would say, well, given the state of nature is this, there's a certain roulette

board that has a certain mechanism in it.

What kind of outcomes do I expect to see?

And especially if I do things longer, longer amounts of time, what outcomes will I see

and the physicists start to pay attention to this?

And then people say, well, given, let's turn the problem around.

What if I saw certain outcomes, could I infer what the underlying mechanism was?

That's an inverse problem.

And in fact, for quite a while, statistics was called inverse probability.

That was the name of the field.

And I believe that it was Laplace, who was working in Napoleon's government, who was

trying, who needed to do a census of France, learn about the people there.

So he went and gathered data and he analyzed that data to determine policy and said, let's

call this field that does this kind of thing, statistics, because the word state is in there.

And in French, that's état, but it's the study of data for the state.

So anyway, that caught on and it's been called statistics ever since.

But by the time it got formalized, it was sort of in the 30s.

And around that time, there was game theory and decision theory developed nearby.

People in that era didn't think of themselves as either computer science or statistics or

control or econ.

They were all, they were all the above.

And so, you know, von Neumann is developing game theory, but also thinking of that as

decision theory, Wald is an econometrician, developing decision theory, and then, you

know, turning that into statistics.

And so it's all about, here's not just data and you analyze it, here's a loss function,

here's what you care about, here's the question you're trying to ask.

Here is a probability model and here's the risk you will face if you make certain decisions.

And to this day, in most advanced statistical curricula, you teach decision theory as the

starting point, and then it branches out into the two branches of Bayesian and Frequentist.

But that's, it's all about decisions.

In statistics, what is the most beautiful, mysterious, maybe surprising idea that you've

come across?

Yeah, good question.

I mean, there's a bunch of surprising ones, there's something that's way too technical

for this thing, but something called James Stein estimation, which is kind of surprising

and really takes time to wrap your head around.

Can you try to maybe?

I think I don't want to even want to try.

Let me just say a colleague at Steven Stigler at University of Chicago wrote a really beautiful

paper on James Stein estimation, which is helps to, its views of paradox, it kind of

defeats the mind's attempts to understand it, but you can, and Steve has a nice perspective

on that.

So one of the troubles with statistics is that it's like in physics, that are in quantum

physics, you have multiple interpretations.

There's a wave and particle duality in physics and you get used to that over time, but it's

still kind of haunts you that you don't really, you know, quite understand the relationship.

The electrons of wave and electrons of particle, well, well, same thing happens here.

There's Bayesian ways of thinking and Frequentist and they are different.

They, they often, they sometimes become sort of the same in practice, but they are physically

different.

And then in some practice, they are not the same at all.

They give you rather different answers.

And so it is very much like wave and particle duality and that is something that you have

to kind of get used to in the field.

Can you define Bayesian and Frequentist?

Yeah.

In decision theory, you can make, I have a, like I have a video that people could see

it's called, are you a Bayesian or a Frequentist and kind of help try to, to, to make it really

clear.

It comes from decision theory.

So, you know, decision theory, you're talking about loss functions, which are a function

of data X and parameter theta.

So there are a function of two arguments, okay?

Neither one of those arguments is known.

You don't know the data a priori.

It's random and the parameters unknown, all right?

So you have this function of two things you don't know and you're trying to say, I want

that function to be small.

I want small loss, right?

Well, what are you going to do?

So you sort of say, well, I'm going to average over these quantities or maximize over them

or something so that, you know, I turn that uncertainty into something certain.

So you could look at the first argument and average over it or you could look at the second

argument average over it.

That's Bayesian Frequentist.

The Frequentist says, I'm going to look at the X, the data, and I'm going to take that

as random and I'm going to average over the distribution.

So I take the expectation loss under X, theta is held fixed, all right?

That's called the risk.

And so it's looking at all the data sets you could get, all right?

And say how well will a certain procedure do under all those data sets?

That's called a Frequentist guarantee, all right?

So I think it is very appropriate when you're building a piece of software and you're shipping