YouSaid · the spoken record
Michael I. Jordan
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- 135
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- 2020-02-24
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- 2020-02-24
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Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections
“Part of it's not just top down. So the Silicon Valley has its 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, right? It's everything you said, but really I think missing the kind of culture. So it's literally that 16 year old who's able to create the songs. You don't create that as a Silicon Valley entity. You don't hire them per se. You have to create an ecosystem in which they are wanted and that they belong. And so you have to have some cultural credibility to do things like this. Netflix to their credit wanted some of that credibility. They created shows content. They call it content. It's such a terrible word, but it's culture.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“He needed it, right? So you could create markets between producers and consumers, take 5% cut, your company will be perfectly sound. It'll go forward in 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, you know, if you think about Uber or whatever, you think about the challenges in the real world of doing things like this. And there are actually new principles that are 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 value. You have to think that through. Just like anyone who ran a big auction or, you know, ran a big matching service in economics. We'll think these things through. And so that maybe doesn't get at all the huge issues that can arise when you start to create markets, but it starts for at least for me solidifying.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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. The person who's making the music can say to someone who comes to their shows a lot, hey, I'll quit your daughter's wedding for $10,000. You'll say $8,000. They'll say $9,000. Again, you can now get an income up to $100,000. You're not going to be a millionaire. 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 as 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“At the level of the actual creative acts. The pipelines and Spotify's 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, the simulate a 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, vetible, 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 have been listening to you that you're coming. If you do this right, you could go down there and make $20,000. You do that three times.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“The creators, the so called influencers, or whatever that diminishes who they are, right? So there are people who make extremely good music, especially on 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. Now it gets turned into bits. It's not economically valuable. The information is lost. It gets put up there. People stream it. You walk around in a big city, you see people with headphones, especially young kids listening to music all the time. If you look at the data, none of the, very little of the music they're listening to is the famous people's music and none of its 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 counterexamples the record company is incentivized to pick out a few and highlight them. Long story short, there's a missing market there. There is not a consumer producer relationship.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 tail 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 non-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, you know, 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 music market. And because there is, to first order, there is no music market in the world right now, in our country, for sure. There are something called things called record companies, and they make money.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It helps add 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 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“We gather the data. I could go on and on. I hope you can see. And I think that if you say predictions, everything 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. Jan rightly so has seen how powerful that is. But at the cost of people not being aware that 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 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. 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 about ready to have a heart attack because some number is over 0.7. Even if you had all the data in the world 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 ask what if questions around that. I'm going to want to look at some other possible date 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And we have a lot in common. And so if one wanted to highlight a disagreement, it's not really a fundamental one. I think it's just kind of what 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. 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 very, very 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 kind of mentality and kind of concrete. We both speak French and we speak French when we're together.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 of them 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 got to be some multiple voices. And there's, I didn't, I wasn't hearing enough of the more sober voice.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And he didn't like, you know, no one really liked Norbert Viener. Norbert Viener was kind of an island to himself. And he felt that he had encompassed all this. And in some sense, he did. You look at the language of cybernetics. It was everything we're talking about. It was control theory and single process 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 maybe the personalities. But McCarthy needed a new word to say, I'm different from you. I'm not part of 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”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't want to reclaim it. I want a new word. I think it was a bad choice. I mean, if you read one of my little things, the history was basically that McCarthy needed a new name because cybernetics already existed.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“In the real world? Or am I going to have a medical operation? Am I going to drive down this street? Things where there's 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 adaptively? How do I use computers to help those kind of things go forward? Whatever you want to call that. 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“No, there's no good words for any of this. That's kind of part of the problem. So we can continue in the conversation to use AI for all that. I just want to kind of 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 don't have a clue. We're not trying to build that because we don't have a clue. Eventually, it may emerge. 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 right now. Okay, we're taking data. We're trying to make good decisions based on that. We're trying to scale. We're trying to do it economically viably. We're trying to build markets. We're trying to keep value at that scale. aspect of this will look intelligent. Computers were so dumb before they will seem more intelligent. We will use that buzzword of intelligence. So we can use it in that sense. But 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I don't even like the word Bachino. I think that with the feel 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“It's not pattern recognition and finding patterns. It's all about making decisions in real worlds and having close feedback loops.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Maybe your version of it is that mine doesn't. No, it doesn't. It's very, very open. It does optimization. It does sampling. It does”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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. And that is a perfectly valid, reasonable thing to do. That's not what's happening in this era.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Keep saying machine learning, you keep wanting to say AI just to let you know I don't, you know, I resist that AI really was John McCarthy.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I hope you can perceive that the deeper yet deeper kind of aspects of intelligence are not going to happen. Now, will there be breakthroughs? I think that Google was a breakthrough. I think Amazon's a breakthrough. You know, I think Uber is a breakthrough. 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 at planetary scale and worries about all the externalities, the privacy. We don't have that feel, so we don't think these things through very well. But I see that as emerging and that will be that will, you know, looking back from 100 years, that will be constituted 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So there's going to be all kinds of breakthroughs. I just don't like that terminology. I'm a scientist. 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 Cole Magoras. I might point to the Edisons of the era and maybe Mosk 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 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 like we're having right now and can 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 going to happen. And so from that, you know.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Wild, 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 Kolmogoroff would not be wowed by some of the stunts that you see right now coming from the big companies.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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, 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. In the meantime, they've got to kind of, you know, the train's 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 electro engineering and chemical engineering that originally emerged that had real principles and you really knew what you were 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't do in a way that's going to impress the next. So I don't need to be.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“And I can imagine business models based on that, and even medical applications of that. But from there to understanding the algorithms that allow us to really tie in deeply from the brain to computer, you know, I just know I don't agree with Elon Musk. I don't think that's even, that's not for our generations, not even for the century.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Not my area, so I hope in this 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 human suffering based on brain disease. And we throw things like lithium at the brain. It kind of works. No one has a clue why. That's not quite true. But, you know, 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 were 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. It's just, it's, 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 mouth with your brain? Yeah, absolutely.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“arithmetic computations what we have all these metaphors and they're fun um but that's not real science per se there is neuroscience that's not neuroscience that that's that's like the greek speculating about how to get to the moon fun right and i think that i like to say this fairly strongly because i think a lot of young people think 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 you know breakthroughs are on the horizon and unscrupulous people sometimes who need money for their labs um that's not even say unscrupulous but people will oversell um i need money for my lab i'm gonna i'm studying you know computational neuroscience um i'm gonna oversell it and so there's been too much of that”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 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 a electromicrograph of a single synapse, it's a city of its own. And that's one little thing on a dendritic tree, which is extremely complicated, you know, electrochemical thing. And it's doing these spikes and voltages that have been 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. 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 computation?”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Electrical engineering. Well, the dreams and aspirations, maybe, 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 just a clueless. We're even worse than the Greeks on most anything interesting scientifically of our era.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Field, which is statistics, compute 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 of previous generations”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So people started to try to do that, of course, and some factories worked, some didn't, you know, some were not viable, some exploded. But in parallel, developed a whole field called chemical engineering. And 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 were needed. Same thing with electrical engineering. There was Maxwell's equations, which in some sense 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 developed called electrical engineering. All right, I think that's what's happening right now, is that we have a proto.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 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 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.”
2020-02-24 · Lex Fridman Podcast · #74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI · IDENTIFIED FROM THE TRANSCRIPT · source