Speaker 1: This is Bloomberg Business Week with Carol Messer and Tim
Speaker 1: Stenebek on Bloomberg Radio.
Speaker 2: All Right, when someone says AI, they can be referring
Speaker 2: to a lot of different things, you know of that, right,
Speaker 2: They can be talking about chatbots such as chat Gibt
Speaker 2: or Google Bard, which are examples of LM's or large
Speaker 2: language models. We talked. We talked about this a little
Speaker 2: bit with Mendeep earlier.
Speaker 1: Exactly. Okay, what about when it comes to AGI, artificial
Speaker 1: general intelligence. It's the holy grail of AI. It's not
Speaker 1: around yet, but everyone is working on it. What that
Speaker 1: means is that AI can perform as well or better
Speaker 1: than humans on most tasks.
Speaker 2: All right, so machine learning is something else. It's the
Speaker 2: focus of our next guest, new book. Eric Siegel is
Speaker 2: a consultant and former Columbia University professor. He's also the
Speaker 2: author of a new book. It is entitled The AI Playbook,
Speaker 2: Mastering the Rare Art of Machine Learning Deployment. It is
Speaker 2: out today. Eric joins us on Zoom from the Bay Area. Eric, congratulations,
Speaker 2: Let's start with the basics, because I think sometimes we
Speaker 2: all throw around phrases assuming everybody gets it and they
Speaker 2: don't necessarily. How do you define machine learning?
Speaker 3: Well, thanks Carol. Machine learning is technology that learns from
Speaker 3: experience in order to make predictions in order to target
Speaker 3: and improve large scale operations. So who to target to
Speaker 3: for marketing based on predicting who's going to buy which transaction?
Speaker 3: To audit for fraud based on predicting which is going
Speaker 3: to be turned out to be fraudulent. Who to approve
Speaker 3: for credit application based on who's going to be the
Speaker 3: most reliable debtor, etc. Etc. Which satellite to investigate for
Speaker 3: potentially running out of a battery based on whether it's
Speaker 3: going to where to drill for oil? Pretty much this
Speaker 3: is the type of AI, and you could call it
Speaker 3: predictive AI or predictive analytics to differentiate it from generative AI.
Speaker 3: This is the type of AI you turn to when
Speaker 3: you want to improve pretty much any and all of
Speaker 3: your large scale operations, which are which consists of many decisions,
Speaker 3: and prediction is the holy grail for improving decisions.
Speaker 1: Would you consider the way that content is surfaced on
Speaker 1: a social media platform like x slash, Twitter or Instagram?
Speaker 1: Would you consider that machine learning?
Speaker 3: Oh? I see? Yeah?
Speaker 1: As far as the ordering of your feed, yeah, like
Speaker 1: if I log in, you know, if I start to
Speaker 1: follow someone new, Like if I follow someone new on
Speaker 1: Instagram and then I log in Instagram again, that like
Speaker 1: an old post from that person is going to be
Speaker 1: like the first thing that comes up because Instagram thinks
Speaker 1: I'm now interested in that.
Speaker 3: Right, that's a prediction task. And the same thing with
Speaker 3: the ordering of your Facebook feed the default feed, assuming
Speaker 3: you leave it at that, and the same thing in
Speaker 3: the ordering of your Google search results. It's all based
Speaker 3: on predictive models. That's what machine learning generates from data.
Speaker 3: Is a model that captures the patterns, that's the discoveries
Speaker 3: it's made from data. That helps it predict. Predict is
Speaker 3: the action. So you're predicting in order to say which
Speaker 3: of these content ten items is going to be of
Speaker 3: most interest or most relevant, whether it's for Internet search
Speaker 3: like with Google, or the ordering of your news feed,
Speaker 3: or the ordering of your search results for properties on Airbnb.
Speaker 2: All right, whiteboard it for me. So AI big umbrella
Speaker 2: machine learning a form of AI. How do we stack
Speaker 2: up and make sense in this environment of because we
Speaker 2: do throw around AI, which has been around for a
Speaker 2: long time, but now we're talking about you know, generative AI.
Speaker 2: Give me the whiteboard on it. You're teaching a class, like,
Speaker 2: how do you lay it all out in terms of
Speaker 2: AI what it means or machine learning how it'll play
Speaker 2: a role.
Speaker 3: Well, the typical hierarchy is that machine learning is part
Speaker 3: of AI, and that's a more bigger umbrella term. But
Speaker 3: my opinion varies from a lot of the mainstream, although
Speaker 3: more people are jumping on board that AI is really
Speaker 3: an amorphous, ill defined term. We're trying to ascribe the
Speaker 3: word intelligence to a machine. That's quite problematic to nail down.
Speaker 3: But if you don't define something well, you can't pursue it.
Speaker 3: For engineering, AI is the story we hear about machine
Speaker 3: learning is the technology that we have, and machine learning
Speaker 3: in all those ways. I just describe where you're predicting
Speaker 3: for each individual customer healthcare client, as far as their
Speaker 3: disease progression, or where to drill for oil which I'll
Speaker 3: like to investigate in which transaction to audit on that
Speaker 3: individual level. It's the same core technology that drives generative
Speaker 3: AI for its ability to generate first drafts of writing
Speaker 3: or code, or of images, and in those cases what
Speaker 3: it's doing is predicting what should the next word be Okay,
Speaker 3: well it's actually the next token, but it's on that
Speaker 3: level of detail. What should the next word be? How
Speaker 3: should I change this individual pixel in an iteration? As
Speaker 3: I'm rendering this image, I being the computer in this case.
Speaker 3: It's the same core technology learning from data to predict.
Speaker 1: You know, it's funny, Carol, when you're so just a
Speaker 1: little behind the scenes. Eric, It's like, we use this
Speaker 1: Google doc to prepare the show, and we both work
Speaker 1: on it and work on different things. And Google now
Speaker 1: has predictive texts, And when I was writing this one
Speaker 1: for Eric's intro, it actually got some stuff wrong, which
Speaker 1: I thought was really funny because here we are talking
Speaker 1: about the technology that it's getting wrong. When I'm like,
Speaker 1: you know, how do you writing a question in the doc?
Speaker 1: I mean, have you noticed that?
Speaker 2: Yeah? No, you're absolutely right if you're very careful, because
Speaker 2: it jumps ahead right if you don't catch it. So, yeah,
Speaker 2: how do we make sure all this stuff that were
Speaker 2: it feels like increasingly moving towards relying on make sure
Speaker 2: whether it's machine learning, that it's accurate and predicting right
Speaker 2: things or the right outcomes or the smart outcomes. How
Speaker 2: do we make sure I know. Is it just the
Speaker 2: data sets that go into it? Is it that simple
Speaker 2: and that complicated? No?
Speaker 3: I mean no, no, because we're not headed with definitiveness
Speaker 3: towards reliability when it comes to that type of generated text. Look,
Speaker 3: these models are so seemingly human like. They're amazing. I
Speaker 3: spent six years of my career in the Natural Language
Speaker 3: Processing Research Group at Columbia, and I never thought I
Speaker 3: would see what we're seeing today. It's so amazing. The
Speaker 3: way it creates often cohesive content, can talk about anything,
Speaker 3: use expressions the humans use, and because it's trained over
Speaker 3: so much data and the actual modeling itself is so advanced. However,
Speaker 3: what it's trained to do is essentially on that per
Speaker 3: word level of detail, which really gives it a human
Speaker 3: like aura. But that doesn't mean that it was developed
Speaker 3: to pursue higher order human goals like being correct. That's
Speaker 3: a whole nother thing. The fact that it's seemingly human
Speaker 3: like doesn't mean it's a step towards general human behavior.
Speaker 3: So you know you earlier Tim mentioned artificial general intelligence.
Speaker 3: I'm actually sort of a disbeliever in that.
Speaker 1: I don't think it's Why are you a disbeliever.
Speaker 3: Yeah, I don't think it's technically impossible that someday, But
Speaker 3: I do not believe that any of the advancements, as
Speaker 3: impressive and valuable as they are, actually represent a concrete
Speaker 3: step towards general human level capabilities. Where the machine is basically,
Speaker 3: let's call it what it is in the story, it's
Speaker 3: an artificial human. You can onboard them like an employee
Speaker 3: at like a human employee, and let it rip. They
Speaker 3: can run a fortune five hundred company, whatever it is.
Speaker 3: That is a science fiction fantasy. I do not believe
Speaker 3: that we're taking concrete steps in that direction.
Speaker 1: So you're not concerned about the rise.
Speaker 3: Of the bots, right, And that's they call it critic hype,
Speaker 3: right where you say, hey, look this stuff is so
Speaker 3: good it could kill all of us. It's really just
Speaker 3: another way to sort of mismanage expectations. And there's a
Speaker 3: variety of reasons why people do that. Some genuinely believe it.
Speaker 3: I'm trying to calm the world down a little bit here.
Speaker 3: The stuff is extremely valuable and in what it can
Speaker 3: do today, and the story that it's becoming human like
Speaker 3: is it over sells? In other words, it's hype and
Speaker 3: that gap between what's real and what's plausible from the
Speaker 3: stories is bad. And it's with mismanaged expectations that's when
Speaker 3: you have the downfall, the disappointment. The disillusion meant also
Speaker 3: in the more extreme case called an AI winter, and
Speaker 3: the problem there is you throw the baby out with
Speaker 3: the bathwater. You throw the value of generative AI for
Speaker 3: first drafts and predictive AI, which by the way, is
Speaker 3: still a much bigger industry right now, out with the bathwater.
Speaker 1: You know.
Speaker 2: Our producer Paul Brennan said, Yep, you're gonna want to
Speaker 2: talk to this guy for a long time. We have
Speaker 2: unfortunately run out of time, so promise you will come
Speaker 2: back soon because I feel like this is a conversation
Speaker 2: we need to continue. Just making so much sense, Eric,
Speaker 2: thank you so much. Eric Siegel, he's a consultant, former
Speaker 2: Columbia University professor. He's got a new book at the
Speaker 2: AI Playbook, Mastering the rare art of Machine Learning Deployment.