Michael I Jordan is 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 Ng, Zoubin Ghahramani, Ben Taskar, and Yoshua Bengio.

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EPISODE LINKS:
(Blog post) Artificial Intelligence -- The Revolution Hasn’t Happened Yet:
https://hdsr.mitpress.mit.edu/pub/wot7mkc1

OUTLINE:
0:00 - Introduction
3:02 - How far are we in development of AI?
8:25 - Neuralink and brain-computer interfaces
14:49 - The term "artificial intelligence"
19:00 - Does science progress by ideas or personalities?
19:55 - Disagreement with Yann LeCun
23:53 - Recommender systems and distributed decision-making at scale
43:34 - Facebook, privacy, and trust
1:01:11 - Are human beings fundamentally good?
1:02:32 - Can a human life and society be modeled as an optimization problem?
1:04:27 - Is the world deterministic?
1:04:59 - Role of optimization in multi-agent systems
1:09:52 - Optimization of neural networks
1:16:08 - Beautiful idea in optimization: Nesterov acceleration
1:19:02 - What is statistics?
1:29:21 - What is intelligence?
1:37:01 - Advice for students
1:39:57 - Which language is more beautiful: English or French?

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The following is a conversation with Michael I.

Jordan, a professor at Berkeley and 1 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 Ng, Zubin Garamani, Ben Tasker, and Yoshua Bengio.

All of this, to me, is as impressive as the over 32,000 points in the 6 NBA championships of the Michael J.

There's a non-zero probability that I talked to the other Michael Jordan, given my connection to and love of the Chicago Bulls in the 90s, but if I had to pick 1, I'm going with the Michael Jordan of statistics and computer science, or as Jan LeCun 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 of 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 it 5 stars on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D-M-A-N.

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

Given that you're 1 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.

But anyway, my favorite is Jan LeCun 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 4 generations of AI successes that I've seen you talk about?

Yeah, first of all, I much prefer Jan's metaphor.

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

So I think I have 1, but it's not just the 1 you lived.

It's the 1 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'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.

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

It's not just engineering, quote unquote.

There was Maxwell's equations, which in some sense were everything you need to 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 1 point to another safely and so on and so forth.

So a whole field was developed called electrical engineering.

All right, 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 it, algorithmic side of it, computer science.

That was enough to start to build 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.

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 of 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 Greeks 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 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 why we, like where we stand in our understanding of the human brain?

And a lot of people say, you know, scientists say we're not very far in understanding the human brain.

But you're like, you're saying we're in the dark here.

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 a hundreds of year task, and they're building it up slowly and surely.

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

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.

And that's 1 little thing on a dendritic tree, which is extremely complicated electrochemical thing.

And it's doing these spikes and voltages are even flying around and then proteins are taking that and taking it down into the DNA and who knows what.

So it is the problem of the next few centuries.

Or is it like a layered set of arithmetic computations?

We have all these metaphors and they're fun.

That's like the Greeks speculating about how to get to the moon.

And 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 gonna oversell it.

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.

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.

That there's a lot of human suffering based on brain disease, and we throw things like lithium at the brain, it kind of works, no 1 has a clue why.

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.

We just, we were really, really completely dim.

So that you might wanna hook up electrodes and try to do some signal processing on that and try to find patterns, fine.

It's just, so it's like kind of sitting in a satellite and watching the emissions from a city and trying to infer things about the microeconomy even though you don't have microeconomic concepts.

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

Can you control a cursor or mouse with your brain?

And then I can imagine business models based on that.

But from there to understanding algorithms that allow us to really tie in deeply from the brain to computer.

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

So just in the hopes of getting you to dream, you've mentioned Kolmogorov and Turing might pop up.

Do you think that there might be breakthroughs that will get you to sit back in 5, 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 a breakthrough.

And people, you know, some people present it as such.

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

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

And I don't wanna diminish all the hard work and engineering that goes behind things like that and the ultimate value to the human race.

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

In the meantime, they've got to kind of, the train's got to run and they got mouths to feed and they got things to do.

I would call that though just engineering.

And I wanna distinguish that between an engineering field like electrical engineering, chemical engineering that originally emerged that had real principles and you really know what you're doing and you have 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 there'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.

Yeah, So there's gonna be all kinds of breakthroughs.

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 Edisons of the era, and maybe Musk is a bit more like that.

But, you know, Musk, God bless him, also will say things about AI that he knows very little about.

And he 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 like we're having right now, ain't gonna happen in our lifetime.

You could fake it, you can mimic, sort of take old sentences that humans use and retread them, but the deep understanding of language, no, it's not gonna happen.

And so from that, I hope you can perceive that deeper, yet deeper kind of aspects of intelligence are not gonna happen.

You know, I think Uber is a breakthrough.

You know, 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 planetary scale and worries about all the externalities, the privacy.

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

But I see that as emerging, and looking back from 100 years, that will be 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 the 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, we kind of threw off the historian hat.

I mean, you briefly said that 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.

So the reason I keep saying AI actually, and I'd love to hear what you think about it, machine learning has a very particular set of methods and tools.

Maybe your version of it is that mine doesn't.

It does optimization, it does sampling, it does.

So systems that learn is what machine learning is.

So it's not just pattern recognition and finding patterns.

It's all about making decisions in real worlds and having close feedback loops.

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

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, a single agent or a single human.

It really immediately goes to the network of decisions.

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

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

I just want to raise the 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're not trying to build that because we don't have a clue.

I don't know if there'll be breakthroughs, but eventually we'll start to get glimmers of that.

It's not what's happening there right now.

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.

We will use that buzzword of intelligence.

But you know, so machine learning, you can scope it narrowly as just learning from data and pattern recognition, but whatever, when I talk about these topics, 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.

You know, things that, where there's scarcity, things that impact other human beings or other, you know, the environment and so on.

How do I use computers to help those kind of things go forward?

So let's call it AI, let's agree to call it AI, but 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 mean, if you read 1 of my little things, the history was basically that McCarthy needed a new name because cybernetics already existed.

And he didn't like, no 1 really liked Norbert Wiener.

Norbert Wiener was kind of an island to himself, and he felt that he had encompassed all this.

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

It was control theory and signal processing and some notions of intelligence and closed feedback loops and data.

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

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

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 1 of the great personalities of machine learning, whatever the heck you call the field.

Do you think science progresses by personalities or by the fundamental principles and theories and research that's outside of personalities?

And I wouldn't say there should be 1 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 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 a little bit too much in ascendance.

And I do think that 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 to do.

And so I think there's just gotta be some multiple voices and 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 LeCun?

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 it kind of mentality and does it work kind of mentality and kind of concrete.

We both speak French and we speak French more together and we have a lot in common.

And so, if 1 wanted to highlight a disagreement, it's not really a fundamental 1, I think it's just kind of what we're emphasizing.

Jan has emphasized pattern recognition and has emphasized prediction.

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?

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.

Moreover, most of us find ourselves during the day in all kinds of situations we had no anticipation of that are kind of various, that are 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 to think about what I might really want here.

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

A lot of our current pattern recognition and prediction systems don't do any risk evaluations.

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 a medical treatment.

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

I might already have a heart attack because some number is over 0.7.

Even if you had all the data in the world that ever been collected about heart attacks better than any doctor ever had, I'm not gonna trust the output of that neural net to predict my heart attack.

I'm gonna wanna ask what if questions around that.

I'm gonna wanna look at some other possible data I didn't have, causal things.

I'm gonna wanna have a dialogue with a doctor about things we didn't think about when we gathered the data.

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Michael I. Jordan: Machine Learning, Recommender Systems, and Future of AI | Lex Fridman Podcast #74