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Speaker 1: Hello the Internet, and welcome to Season three h five,

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Speaker 1: Episode two of dar Day Zai Guys Day production of iHeartRadio.

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Speaker 1: This is a podcast where we take a deep dive

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Speaker 1: into America's share consciousness. And it is Tuesday, September nineteenth.

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Speaker 2: Three Oh yeah you know, oh yeah, National It Professionals Day.

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Speaker 2: It's also National Voter Registration today. Hey, make sure you

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Speaker 2: richter to vote because a lot of people would probably

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Speaker 2: hope you aren't doing that. And also National butter Scotch

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Speaker 2: Flitting Day. Shout out to all of my lovers of

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Speaker 2: the butter scotch flavor, including myself.

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Speaker 1: Yeah, I think that's one of the better putting flavors.

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Speaker 2: It's also it's also Taco like a Pirate Day. But

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Speaker 2: I just I.

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Speaker 1: Didn't want to, you know, you don't want to do

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Speaker 1: that to everybody with children?

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Speaker 2: No, No, is that a thing you would do at

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Speaker 2: the parent would You'd be like, Hey, guys, it's talk

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Speaker 2: like a pirate day. We ready to annoy ourselves to death.

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Speaker 1: In this household. We look down on international days that

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Speaker 1: are made up for no reason. We scoff at them.

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Speaker 2: Miles right right, right, right right. We're out here living

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Speaker 2: in the real world. There's real consequences to our.

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Speaker 1: Actions, and when we scoff at them, we say horror, horror,

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Speaker 1: because it's always international to the part day. My name

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Speaker 1: is Jack O'Brien aka L Jacko. That's courtesy of Max

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Speaker 1: Her twelve sixteen, because they think I look like L

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Speaker 1: Chapo's son. And oh okay, yeah, there was a new

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Speaker 1: picture of El Chapo's son being extradited to the US

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Speaker 1: on board of plane in handcuffs of people. Once again,

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Speaker 1: anytime a picture of that fellow hits the news, I

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Speaker 1: am reminded, oh okay, hey this you anyways, it is

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Speaker 1: not I'm thrilled to be joined as always by my

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Speaker 1: co host mister Miles.

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Speaker 2: Great, well break, Okay, I'm far out of range.

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Speaker 3: One bar is how I feel my own a searching hard,

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Speaker 3: lying naked on my thigh, irradiation changed my guts do

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Speaker 3: something real.

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Speaker 1: They're wide awaken. I can't see a quato has.

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Speaker 4: Been born.

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Speaker 2: To study on discord. Yes, I how that iPhone twelve

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Speaker 2: had higher levels of radiation, yeah, than the than the

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Speaker 2: French government would allow h And I thought, hey, maybe

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Speaker 2: that would just create a little total recall quato type character.

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Speaker 1: And that's fun that's a fun one for you to have,

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Speaker 1: you know, fun news story for you to have a

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Speaker 1: laugh with, because you what what I turned out?

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Speaker 2: Turned out? I had an eleven, I got that twelve, baby, But.

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Speaker 1: No fair, you have to name your name your quato

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Speaker 1: after me. Give me that quato, Miles. Yes, we are thrilled, fortunate,

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Speaker 1: blessed to be joined in our third seat by an

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Speaker 1: assistant Professor of Technology, Operations and Statistics at NYU, where

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Speaker 1: his research focuses on the intersection of machine learning and

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Speaker 1: natural language processing, which, of course means you know, his

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Speaker 1: interests include conversational agents, hierarchical models, deep learning, spectral clustering.

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Speaker 1: You guys are all probably finishing this sentence with yeah,

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Speaker 1: of course, spectral estimation of hidden Markov models, yes, obviously,

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Speaker 1: and of course time series analysis. Please welcome doctor Joao, sid,

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Speaker 1: Doc doctor Joe.

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Speaker 4: Hi. Thank you, Yeah, thank you Jack and Moles for

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Speaker 4: having me. It's a pleasure to be here, the pleasure

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Speaker 4: to have you.

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Speaker 2: We're glad you're here because we got we got a

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Speaker 2: lot of questions about a lot of stuff, some some

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Speaker 2: to mostly to do with what your area of expertise is.

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Speaker 2: Some things I just need a second opinion on just around.

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Speaker 1: The career of Alan Iverson. Is it a bad joke

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Speaker 1: if it's been used in like a cable I believe

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Speaker 1: that it's a Direct TV commercial is using They're like

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Speaker 1: and you we have our very own AI that helps

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Speaker 1: you select shows, and then Alan Iversons sitting on the

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Speaker 1: couch with the person. Wait, does that make it off limits?

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Speaker 2: Yeah?

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Speaker 1: Yeah, that's that's a real commercial. And I'm going to

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Speaker 1: be running with that joke all day today because I'm

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Speaker 1: not any better than the comedy writers at Direct TV.

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Speaker 2: I know is Yeah, but what doctor said, Doc?

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Speaker 1: Is that good? Doctor said Doc? Is that a good

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Speaker 1: way to address you?

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Speaker 4: Yeah? Sure, I mean I'm very easy about things. Okay, okay,

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Speaker 4: how about Well, I've never been called that. I had

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Speaker 4: some students in my class called me ja wow Wow. Yes, yeah,

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Speaker 4: is it jo so? So the pronunciation is so, it's

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Speaker 4: Brazilian Portuguese. That's what I named.

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Speaker 1: I go by.

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Speaker 4: Joo jook about it?

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Speaker 2: Doctor j even doctor j.

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Speaker 1: Amazing?

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Speaker 4: All right?

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Speaker 1: Well, yeah, I mean the main thing we we've brought

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Speaker 1: you here today to ask the big questions around AI,

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Speaker 1: such as what is that?

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Speaker 2: What is AI?

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Speaker 1: And we'll follow from there but you know, questions that

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Speaker 1: reveal that we're smart people.

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Speaker 2: Obviously.

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Speaker 1: No, I actually do think that the fact that AI

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Speaker 1: is used as the phrase to describe a bunch of

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Speaker 1: different types of technology probably has something to do with

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Speaker 1: where people's fear comes from and where people's like kind

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Speaker 1: of confusion, where my confusion up to this point has

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Speaker 1: come from. But yeah, so we're going to talk to

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Speaker 1: you about a bunch of that stuff plenty more. But

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Speaker 1: first we do like to ask our guests, what is

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Speaker 1: something from your search history that is revealing about who

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Speaker 1: you are or where you are in life.

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Speaker 4: So I think the most revealing thing about my current

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Speaker 4: search history is probably me searching for the which of

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Speaker 4: Pfizer or Maderna had the highest amount of mRNA in

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Speaker 4: the vaccine, which took me like forever to figure out.

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Speaker 4: And that was, you know, a wasted thirty minutes of

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Speaker 4: my day where I could have just slacked someone and

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Speaker 4: had the answer.

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Speaker 2: And wait, why were you trying to discern who which

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Speaker 2: had a higher concentration of m marna?

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Speaker 4: And yeah, I have a feeling that, like, you know,

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Speaker 4: so that was kind of like, okay, the one with

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Speaker 4: the higher dosage might still have me slightly more effective.

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Speaker 4: The new vaccines have come or have been approved just

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Speaker 4: a couple of days ago. Right trying to it.

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Speaker 2: Seemed like on a spectrum like MODERNA was having the

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Speaker 2: better results, right like on a continuum, I felt like

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Speaker 2: the new one, or just in general, because I remember

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Speaker 2: when the vaccine first came out, it was like, oh,

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Speaker 2: you got Johnson and we got that Jay and J

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Speaker 2: and then I was kind of like I had Maderna,

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Speaker 2: but as an American consumer, I'm like, but I know

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Speaker 2: the brand Peiser more. Yeah, And then I ended up

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Speaker 2: being like, Okay, good, I got I got that good one,

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Speaker 2: the Moderna it turned out, or at least better than

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Speaker 2: the Pfiser slightly more effective there, I.

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Speaker 4: Mean, both are extremely effective.

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Speaker 2: But yeah, right right.

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Speaker 4: Now, that was my That was probably the thing in

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Speaker 4: my today's browsing history that told me the most about me,

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Speaker 4: which is that I'm still after this many years, paranoid

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Speaker 4: about something. I meanach everybody in the wrest of the

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Speaker 4: world is sort of moved on about.

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Speaker 2: Yeah, for better or worse. But yeah, it does seem

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Speaker 2: like that. Yeah, I'm still I'm with you on that

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Speaker 2: because like I still somehow have an ego like that

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Speaker 2: attached to what brand of fucking vaccine I got, Like, well,

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Speaker 2: you know, I got maderna, So you know, I'll sit

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Speaker 2: over there with the elevated folks.

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Speaker 1: And so it's not you're not googling to see which

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Speaker 1: has the more emini or what's it called, m RNA

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Speaker 1: mr in it because you are worried that it's going

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Speaker 1: to kill you or cause you to start shaking uncontrollably

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Speaker 1: like the people in those TikTok videos.

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Speaker 4: Just to be clear, I mean, the vaccines have adverse

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Speaker 4: side effects, but no, I'm in it for Hey, I'm

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Speaker 4: already you know, old enough that you know that's not

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Speaker 4: going to really matter. I'm more worried about, you know,

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Speaker 4: adverse effects actually getting the disease.

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Speaker 2: So yeah, you know, but very scientific answer.

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Speaker 4: You know. I was thinking about it, and you know,

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Speaker 4: it was funny because I was asking the chat bots

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Speaker 4: and so one thing as I work on this stuff

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Speaker 4: that's both funny and somewhat annoying to my wife is

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Speaker 4: that like I'll do a search and then I'll play

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Speaker 4: around with like three different chat bots, and then like

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Speaker 4: half an hour later, she's like, have you not been

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Speaker 4: listening to me? Right? And I haven't. I've been going

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Speaker 4: trying to figure out.

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Speaker 1: Where we're going to order from yet.

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Speaker 2: I don't know, but being AI thinks that this chain

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Speaker 2: is still in New York. It's not.

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Speaker 4: Foolish foolishness one exactly. Yeah, and so it's you know, yeah,

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Speaker 4: I do tend to get distracted while playing with you know,

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Speaker 4: all the cool new things. And I mean both. I

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Speaker 4: mean every once in a while, you learn something by

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Speaker 4: anecdotally testing something, or you know, somebody making a statement like, oh,

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Speaker 4: you know, did you notice like you know, this kind

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Speaker 4: of behavior, and I'm like, oh, that's really cool. Okay,

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Speaker 4: you know, and then if you go down the path

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Speaker 4: of like trying to understand.

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Speaker 2: It, right, cool?

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Speaker 1: What what's something you think is over it?

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Speaker 4: I think that in terms of the things that really overrated,

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Speaker 4: like you know, probably artificial general intelligence also known as

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Speaker 4: a g I occasionally people think about it like the singularity,

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Speaker 4: where you know, we start to assume that, you know,

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Speaker 4: the machines are going to take over and we're going

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Speaker 4: to be in a terminator world. I think, you know,

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Speaker 4: the likelihood of that risk is like super overstated.

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Speaker 2: Mm hmm okay, yeah, okay, yeah, that's what that's what I.

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Speaker 2: That's what I that's what I learned.

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Speaker 1: Well, you actually just answered the only question we had

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Speaker 1: on this episode, So I think, sorry, no, no, Yeah.

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Speaker 1: So I think coming in like and we'll get to

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Speaker 1: this a little bit. But coming into this week's episode,

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Speaker 1: like I had, you know, read a couple of articles

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Speaker 1: on AI, but I think the artificial general intelligence, like

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Speaker 1: the idea of like some chatbot or you know, language

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Speaker 1: model or you know, some sort of artificial intelligence deep

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Speaker 1: brain model, whatever it is, like gains sentience and that

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Speaker 1: then it like takes over weapons systems immediately was like

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Speaker 1: the threat that I had in my mind, and after

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Speaker 1: like doing some research, is no longer something that is it? Well,

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Speaker 1: it doesn't seem to be the one that most people

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Speaker 1: are are worried about at least, and then there are

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Speaker 1: the people like the well we'll we'll get into it.

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Speaker 1: But I do think, yeah, that's an important distinction and

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Speaker 1: kind of gets it that first question of like what

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Speaker 1: what is AI? Because people use it to stand in

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Speaker 1: for a bunch of different things. What what something you

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Speaker 1: think is underrated?

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Speaker 4: This is maybe mildly underrated, But I think that the

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Speaker 4: amount of upheaval that AIM let this generation of technologies

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Speaker 4: is going to have in the next couple of years.

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Speaker 4: This means that like, you know, certain jobs, so a

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Speaker 4: lot of technology, like in the industrial age, has gone

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Speaker 4: from lower jobs, like jobs that don't require you know,

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Speaker 4: having skill sets that require a large amount of education.

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Speaker 4: You know, I think chetchipt already has surfaced evidence of

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Speaker 4: being able to either replace or facilitate somebody to be

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Speaker 4: much more productive, and that this will actually cause a

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Speaker 4: reasonably large amount of change in the workforce globally. You

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Speaker 4: know that I think will have real impact, right and

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Speaker 4: could cause problems, you know, with more erosion of middle

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Speaker 4: class jobs and you know, needing to have like sort

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Speaker 4: of deeper retraining.

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Speaker 1: Interesting, what are the yeah, I guess, I guess let's

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Speaker 1: let's dig into it, Like what are the jobs that

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Speaker 1: you see being replaced, because like the ones that we've

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Speaker 1: heard most about specifically, and I've seen sort of poor

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Speaker 1: results for are the writing jobs, Like when people are like, no,

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Speaker 1: we're just gonna have AI like write these articles for

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Speaker 1: us now, and they seem like they do kind of

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Speaker 1: a bad job and they have a tough time with

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Speaker 1: like fact checking. What what where are you going?

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Speaker 4: Where do you.

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Speaker 1: Expect to see the kind of changes in the workforce first,

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Speaker 1: because I get I guess I will caveat that by

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Speaker 1: saying that it doesn't seem like a lot of companies

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Speaker 1: care if the technology does a bad job. They're just like, yeah,

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Speaker 1: but it's it's good enough to put butts in the seats,

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Speaker 1: and it's less money, and the entire model of capitalism

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Speaker 1: currently because of private equity, and you know, just that

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Speaker 1: being how the incentive system is set up is like

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Speaker 1: to cut costs. So maybe it won't matter and it'll

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Speaker 1: just like make journalism shittier and have all journalists jobs

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Speaker 1: replaced by these ais. But what where is that? Is

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Speaker 1: that kind of where you see it? Or where where

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Speaker 1: do you see the AI replacing jobs?

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Speaker 4: Well, so, okay, so I think creatively true creative writing,

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Speaker 4: right like things that JOURNALI writing from journalists, or let's

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Speaker 4: say creative writing, songwriting, poetry. These are require such deep

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Speaker 4: understanding of people and theory about how people interpret the

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Speaker 4: words and the language and sort of the reactions and

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Speaker 4: emotions that are evolved that I don't think AI is

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Speaker 4: going to help very much with that. I think, sorry,

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Speaker 4: replace right, I think it can help right and make

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Speaker 4: you more productive but I don't think it's really going

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Speaker 4: to be let's say, a replacement for a journalist. But

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Speaker 4: some stuff that people do that are performa like a

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Speaker 4: performer report, right, that's you know, almost template based, but

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Speaker 4: not quite you know. So somebody's got to do some filing. Well, okay,

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Speaker 4: CHT GBT can do this, you know, well enough for

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Speaker 4: you know, for me that Okay, here's a filing. Very

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Speaker 4: few people are probably going to look at it. Let

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Speaker 4: me use chat GUPT, you know, and maybe not need

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Speaker 4: to hire someone to do that, right. I think the

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Speaker 4: other thing where we're going to see a lot of

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Speaker 4: changes also in you know, coding. Here, I think we're

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Speaker 4: seeing already the impact of it, where there's this tool

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Speaker 4: called Copilot which is driven by similar technology as chatchept,

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Speaker 4: which is able to create massive amounts of code for people.

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Speaker 4: So it's making a certain subset of coders just way

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Speaker 4: more efficient, you know. I see that in even with

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Speaker 4: my students. So I teach a undergraduate data science class.

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Speaker 4: Last semester, the projects that my students were able to

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Speaker 4: do was actually much more impressive than the previous semester,

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Speaker 4: and part of that was just them being able to

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Speaker 4: use chat GUPT to be able to make better code

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Speaker 4: to make their projects better.

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Speaker 2: It is like I was just reading like an op

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Speaker 2: ed from a former IBM employee that he's like describing

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Speaker 2: himself as like a tech evangelist and had always been

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Speaker 2: you know, you know, say like cheering on like the

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Speaker 2: advent or the arrival of chat, GPT and things like that,

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Speaker 2: And he said, and I only it only ended up

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Speaker 2: actually taking a lot of work from me, like because

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Speaker 2: some companies who were less interested in good writing were like,

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Speaker 2: we're actually gonna use a lot of this for like

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Speaker 2: content generation, so we don't need you know.

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Speaker 1: Like your services as much. He said.

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Speaker 2: It hasn't completely replaced him, but it's a signific He's

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Speaker 2: he has noticed this more than significant amount of like

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Speaker 2: businesses sort of be like, you know.

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Speaker 1: What, we're kind of gonna just sort of lean.

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Speaker 2: Into this thing that costs like way less money, and

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Speaker 2: are these kind of like the stories that are like

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Speaker 2: canaries in the coal mine. But I feel like, sort

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Speaker 2: of like in the industrial age, we've always seen like

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Speaker 2: there was just automation right where people who had jobs

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Speaker 2: at a bank to do specific things like file things

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Speaker 2: or put things in a ledger, ended up like those

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Speaker 2: jobs ended up going away because of automation or just

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Speaker 2: you think of customer service now a lot of that

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Speaker 2: is automated. And I was also reading about how a

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Speaker 2: lot of local governments are now adopting or really interested

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Speaker 2: in chat bots as a way to like sort of

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Speaker 2: replace bureaucracies at certain levels. Like is that kind of

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Speaker 2: is that sort of where you see it ending or

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Speaker 2: what like what's the sort of fifty year outlook, because

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Speaker 2: I think to Jack's point two, we always look at

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Speaker 2: this as like, well, when we're trying to reconcile that

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Speaker 2: with like capitalism and the corporation's need to like always

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Speaker 2: look at their bottom line, is it a thing that

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Speaker 2: there we can strike a balance where it's like, yeah,

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Speaker 2: we have these human employees who use this as a

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Speaker 2: tool obviously because it makes them better or or is

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Speaker 2: it like the cynical version is like they're going to

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Speaker 2: get as much as they can done with it, only

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Speaker 2: up until the point that they probably need human workers

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Speaker 2: to kind of really fine tune the processes.

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Speaker 4: Yeah, I think the latter. I mean, I think you know,

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Speaker 4: the company is their interest is to optimized, so there,

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Speaker 4: I mean, the truth of the matter is that the

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Speaker 4: tool right now, right is just not good enough for

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Speaker 4: most you know, really for most tasks. I think, you know,

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Speaker 4: fifty years from now is a really hard thing for

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Speaker 4: me to predict.

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Speaker 1: I know, but you have to know, you know, it's

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Speaker 1: a wild speculation, but sound extremely common because because this.

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Speaker 2: Is going to go on TikTok and we're going to

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Speaker 2: scare the heck out of a lot of young people.

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Speaker 2: So go ahead, you're saying, how sky net is imminent?

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Speaker 4: Yees, sky net is imminent. You know, I'm kidding. So

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Speaker 4: I think that you know, looking forward, like looking far

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Speaker 4: forward into where I think it will be in you know,

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Speaker 4: maybe ten or fifteen years. Sure, this technology is just

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Speaker 4: really just going to improve. And what we're going to

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Speaker 4: see is that, you know, we will have certain jobs

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Speaker 4: which were done by people for a very long time

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Speaker 4: go away. That will probably mean the new jobs will

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Speaker 4: open up and people will just be more productive. That's

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Speaker 4: the sort of optimist in me, right.

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Speaker 1: That's also how everything has happened to this point, right,

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Speaker 1: is that like people are at first scared of new technology,

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Speaker 1: like airplanes are going to be really scary, and like

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Speaker 1: make it so the you know, nobody has to ever

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Speaker 1: take a train and it's like, well, then there's like

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Speaker 1: the entire aerospace jet propulsion industry, and you know, I

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Speaker 1: guess that one's a little easier to foresee, but like,

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Speaker 1: it does feel like there's always fear of new technology

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Speaker 1: and like belief that it's going to end the economy

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Speaker 1: as we know it, and it does, but it's always

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Speaker 1: replaced by a new a new version, right.

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Speaker 4: Yep, exactly, And so I think, you know, we're going

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Speaker 4: to see something similar in this some questions of factuality.

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Speaker 4: Sometimes people call this hallucinations in our models. How difficult

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Speaker 4: is that going to be to really fix those sort

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Speaker 4: of corner cases. But you know, given the amount of

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Speaker 4: money and people that are working on this, you know,

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Speaker 4: I think that within the next ten to fifteen years

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Speaker 4: we're going to see that you know, a lot of

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Speaker 4: that gets solved, or at least the likelihood of it

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Speaker 4: coccurring is so small that it's you know, more that

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Speaker 4: the likelihood of it, you know, lying to you or

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Speaker 4: saying something that is untrue is going to be you know,

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Speaker 4: way way way less than any person. Right, So that's

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Speaker 4: kind of where where I think we're going. And so

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Speaker 4: we'll see, you know, some jobs like you know, entry

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Speaker 4: level coders, make away certain jobs like you know, certain

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Speaker 4: types of business analysts, even some form of middle management

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Speaker 4: I think is at risk. You know, lots of various

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Speaker 4: amounts of places I think are at risk. I do

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Speaker 4: want to say one other thing, which I think is

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Speaker 4: going to be amazing. And we're seeing this in higher education,

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Speaker 4: but in education in general. Is it Probably the place

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Speaker 4: that's been most impacted by chat GPT has been education.

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Speaker 4: And I think what we'll see is in the next

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Speaker 4: ten to fifteen years, education is just going to dramatically reform, right,

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Speaker 4: hopefully for the better, but like we're going to see

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Speaker 4: like major changes in how we teach students, how we

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Speaker 4: assess students. Hopefully this will lead to you know, just

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Speaker 4: better quality of education for everyone.

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Speaker 2: Right, because is it sort of like the main I

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Speaker 2: guess I feel like the point of our traditional educational

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Speaker 2: system is like almost like memory recall. It's like how

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Speaker 2: good is your factor recall? Memory recall and things like

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Speaker 2: that at or can you sit through reading this thousand

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Speaker 2: page historical textbook to glean like these eight points really

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Speaker 2: that we want you to come away with it come

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Speaker 2: away with and I see, like how much that distillation

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Speaker 2: of information how quickly that occurs with like you know, chat,

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Speaker 2: GPT and things like that. So I guess it does

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Speaker 2: become more like, Okay, granted, if this is the information

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Speaker 2: we want people to learn, then how are we now

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Speaker 2: taking that next step to make sure it's sort of

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Speaker 2: ingested properly for people to know that they are making

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Speaker 2: sense of it, rather than like being like, yeah, here

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Speaker 2: are the eighteen things you need to say to pass

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Speaker 2: like a like a historical course on the Colombian Exchange,

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Speaker 2: and more like Okay, we know you know how to

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Speaker 2: get to those answers, but how can you demonstrate that knowledge?

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Speaker 2: And I guess that's sort of where the I guess

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Speaker 2: excitement is in academia, or at least that's the challenge, right.

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Speaker 4: Yeah, yeah, it's the challenge, the worry, the you know,

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Speaker 4: and I guess excitement goes in both ways positive and

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Speaker 4: negative depending on what you are and how you want

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Speaker 4: to deal with this. But I think that like this

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Speaker 4: has this type of technology has the potential of being

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Speaker 4: you know, a tutor and a tutoring system that is

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Speaker 4: almost as good as you know, the best tutor, where

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Speaker 4: we can help people learn better and just be able

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Speaker 4: to interactively one on one, you know, sort of teach

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Speaker 4: certain concepts and.

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Speaker 1: Like with an AI teacher or you're saying it would

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Speaker 1: help teachers teach.

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Speaker 4: Help teachers, help teachers. So it'd be like a tutor, right,

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Speaker 4: so you know, the teacher is still going to teach,

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Speaker 4: but the tutor is going to be able to, like

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Speaker 4: the AI tutor is going to be able to, you know,

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Speaker 4: help the student with Okay, I didn't understand this, right,

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Speaker 4: Like I mean, and one thing that we do know

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Speaker 4: about chatbots is that in general, you know, people have

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Speaker 4: this impression that chat bots have less judgment, right, and

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Speaker 4: so people are willing to ask you know, in air quotes,

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Speaker 4: stupid questions.

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Speaker 2: Thank you, Yeah, because no questions are stupid.

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Speaker 4: Yeah, we're in a classical classroom. You know, they wouldn't

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Speaker 4: be able to they wouldn't be willing to ask that

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Speaker 4: to a person, and they're willing to ask you to

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Speaker 4: the AI. Yeah. I don't know.

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Speaker 1: As a parent, I would be very worried just based

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Speaker 1: on the work I've seen from chatbots up to this point,

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Speaker 1: Like like what at this point, we've covered Yeah, up

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Speaker 1: to this point, like we've covered the Columbus dispatch like

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Speaker 1: recap of like of football game, like high school football game,

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Speaker 1: and it's just like such absolute shit. It's like one

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Speaker 1: of the worst articles I've ever read, you know, the

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Speaker 1: sports the Star Wars one that Gis Moto put up

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Speaker 1: is terrible. So I don't like I I believe that,

436
00:25:50,080 --> 00:25:53,680
Speaker 1: like the chatbots will continue to get better, but I

437
00:25:53,720 --> 00:25:56,680
Speaker 1: guess I have questions about what they're going to get

438
00:25:56,720 --> 00:25:58,919
Speaker 1: better at, whether they're going to get better at like

439
00:25:59,080 --> 00:26:02,760
Speaker 1: fooling us into thinking that they have like human intelligence,

440
00:26:02,840 --> 00:26:05,760
Speaker 1: or whether they're gonna get like more more accurate, because

441
00:26:05,800 --> 00:26:08,720
Speaker 1: it seems like within the world of AI, like there's

442
00:26:08,720 --> 00:26:12,040
Speaker 1: still people who are like I think it was somebody

443
00:26:12,080 --> 00:26:16,680
Speaker 1: who worked it open AI was like Wikipedia level accuracy

444
00:26:17,040 --> 00:26:20,919
Speaker 1: is like years off at this point, and like like

445
00:26:20,960 --> 00:26:23,959
Speaker 1: Wikipedia is suddenly being used as this phrase for like

446
00:26:23,960 --> 00:26:27,919
Speaker 1: something that is like the gold standard, whereas like before,

447
00:26:28,080 --> 00:26:32,480
Speaker 1: outside of the context of AI training, it's something that

448
00:26:32,520 --> 00:26:36,800
Speaker 1: we like joke about being easy, easily editable and stuff.

449
00:26:36,840 --> 00:26:40,960
Speaker 1: So yeah, it worries me a little bit like that.

450
00:26:41,240 --> 00:26:44,600
Speaker 1: I think people's faith is being misplaced in the language

451
00:26:44,640 --> 00:26:50,040
Speaker 1: models because language is inherently an abstractive system that is

452
00:26:50,080 --> 00:26:54,639
Speaker 1: designed to lie like essentially, it's I mean, it's to

453
00:26:54,720 --> 00:26:58,320
Speaker 1: tell you a story that gives you meaning. But it

454
00:26:58,359 --> 00:27:02,720
Speaker 1: feels like from a philosophical level like that, I see that.

455
00:27:02,960 --> 00:27:05,239
Speaker 1: I see where people who are skeptical that this is

456
00:27:05,280 --> 00:27:09,200
Speaker 1: the path to like really useful stuff are coming from.

457
00:27:09,200 --> 00:27:12,000
Speaker 1: But let's take a quick break and we'll come back.

458
00:27:12,000 --> 00:27:14,200
Speaker 1: And I just I do just want to like kind

459
00:27:14,200 --> 00:27:17,440
Speaker 1: of nail down two different things that are being called

460
00:27:17,480 --> 00:27:19,480
Speaker 1: AI that seem vastly different to me.

461
00:27:19,600 --> 00:27:22,360
Speaker 5: So let's let's take a quick break. We'll be right back,

462
00:27:32,760 --> 00:27:34,119
Speaker 5: and we're back.

463
00:27:34,920 --> 00:27:38,679
Speaker 1: So yeah, I guess I think Chad GBT seems to

464
00:27:38,680 --> 00:27:43,560
Speaker 1: be the thing that everyone has been taken by like

465
00:27:43,760 --> 00:27:48,959
Speaker 1: very like interested in right like that, it seems to

466
00:27:48,960 --> 00:27:53,000
Speaker 1: have reached a new level where people can do things

467
00:27:53,080 --> 00:27:59,240
Speaker 1: and have conversations that are remarkably like human seeming and

468
00:28:00,119 --> 00:28:06,760
Speaker 1: can do right really a good maybe B minus term

469
00:28:06,840 --> 00:28:09,960
Speaker 1: papers and then use that to kind of scrap together

470
00:28:10,080 --> 00:28:12,720
Speaker 1: something or depending you know, maybe maybe it's a higher

471
00:28:12,800 --> 00:28:15,439
Speaker 1: level of term paper. I don't grade term papers, so

472
00:28:15,480 --> 00:28:19,359
Speaker 1: I wouldn't know, But just based on the journalistic work

473
00:28:19,359 --> 00:28:21,960
Speaker 1: I've seen it, Dove, I haven't been impressed. But so

474
00:28:22,080 --> 00:28:25,960
Speaker 1: that's the main thing that I'm hearing referred to as AI,

475
00:28:26,000 --> 00:28:28,400
Speaker 1: and they're like this, there's a new future upon us.

476
00:28:28,600 --> 00:28:32,600
Speaker 1: Look at chat GPT, and then there's like when you

477
00:28:32,680 --> 00:28:35,840
Speaker 1: look into some of the things that are being done

478
00:28:35,880 --> 00:28:41,000
Speaker 1: with machine learning, like accurately predicting how proteins fold, which

479
00:28:41,200 --> 00:28:45,840
Speaker 1: is like a problem that has been just input too

480
00:28:45,920 --> 00:28:48,720
Speaker 1: complex for humans to solve on their own, just like

481
00:28:48,920 --> 00:28:54,600
Speaker 1: too slow for humans to know the atomic shape of proteins,

482
00:28:54,640 --> 00:28:57,560
Speaker 1: Like they just kind of fold in ways that he

483
00:28:57,920 --> 00:29:01,200
Speaker 1: is like too small, too complicated for humans to predict,

484
00:29:01,240 --> 00:29:05,800
Speaker 1: and like through machine learning and like this deep brain

485
00:29:06,400 --> 00:29:10,200
Speaker 1: system at Google, they were able to like knock it

486
00:29:10,240 --> 00:29:13,520
Speaker 1: out within a couple of years, and like they gave

487
00:29:13,560 --> 00:29:17,080
Speaker 1: it to the public. They were like here, here is

488
00:29:17,120 --> 00:29:20,520
Speaker 1: the shape of the proteins. And so if you're using

489
00:29:20,600 --> 00:29:25,160
Speaker 1: AI to describe both of those things, I think that

490
00:29:26,280 --> 00:29:29,040
Speaker 1: is that's where the creepiness comes in, because you're like, Okay,

491
00:29:29,120 --> 00:29:32,680
Speaker 1: it has this like godlike science ability, and then it

492
00:29:32,720 --> 00:29:37,560
Speaker 1: also has this like child personality where it like can

493
00:29:37,640 --> 00:29:39,680
Speaker 1: like talk to you and flirt with you a little bit,

494
00:29:40,080 --> 00:29:45,840
Speaker 1: and it's like that that mismatch seems uncanny and weird

495
00:29:46,080 --> 00:29:48,600
Speaker 1: to me. Like, and I think that that is what

496
00:29:48,760 --> 00:29:51,800
Speaker 1: is happening in people's minds when they're like, oh, yeah,

497
00:29:51,840 --> 00:29:56,840
Speaker 1: AI is scary. It's this like massively powerful thing, and

498
00:29:56,920 --> 00:29:59,800
Speaker 1: it is massively powerful. I think it's just more massively

499
00:30:00,000 --> 00:30:03,040
Speaker 1: powerful when it has a task and like a specific

500
00:30:03,120 --> 00:30:06,560
Speaker 1: thing that it's seeking, which is not what you can

501
00:30:06,680 --> 00:30:10,000
Speaker 1: really have with the language models, which are like predictive. Right.

502
00:30:10,600 --> 00:30:15,479
Speaker 4: Yeah. I think I think it's kind of unfair in

503
00:30:15,600 --> 00:30:19,440
Speaker 4: the way that people characterize the language models as like, oh,

504
00:30:19,480 --> 00:30:22,360
Speaker 4: they just predict the next word, right, I mean, in

505
00:30:22,400 --> 00:30:28,760
Speaker 4: some sense, it's what we know of what the language

506
00:30:28,760 --> 00:30:31,800
Speaker 4: model is trying to do, is it is trying to

507
00:30:31,840 --> 00:30:35,320
Speaker 4: predict some sequence of text, and in doing so, it's

508
00:30:35,680 --> 00:30:41,160
Speaker 4: learning a whole bunch of the knowledge in the world

509
00:30:41,200 --> 00:30:45,680
Speaker 4: and how the world is connected. It's learning some seemingly

510
00:30:46,200 --> 00:30:53,120
Speaker 4: probabilistic logic rules. Yeah. However, yeah, I mean this is

511
00:30:54,280 --> 00:31:01,840
Speaker 4: oftentimes you see this weird sort of disconnect between chatjepte

512
00:31:02,280 --> 00:31:05,719
Speaker 4: or that technology, which we call large language models in

513
00:31:05,800 --> 00:31:10,960
Speaker 4: general being so smart and so stupid at the same.

514
00:31:10,760 --> 00:31:14,280
Speaker 1: Time, right, Right, that's kind of our specialty on the show.

515
00:31:16,720 --> 00:31:22,600
Speaker 4: Well, and and I think as we as like society

516
00:31:23,360 --> 00:31:27,160
Speaker 4: interact with the technology more and more, we're going to

517
00:31:27,240 --> 00:31:32,080
Speaker 4: get sort of a better mental model of the technology,

518
00:31:32,920 --> 00:31:38,240
Speaker 4: right and in some sense, like right now, a lot

519
00:31:38,280 --> 00:31:41,880
Speaker 4: of people, you know, I ask my students, okay, like

520
00:31:42,040 --> 00:31:44,520
Speaker 4: what do you think of Like, how do you reason

521
00:31:44,720 --> 00:31:46,000
Speaker 4: about chat GPT?

522
00:31:46,520 --> 00:31:46,720
Speaker 2: Right?

523
00:31:46,840 --> 00:31:50,120
Speaker 4: How do you reason about the quality of the technology

524
00:31:50,200 --> 00:31:54,840
Speaker 4: and how it you know, is you know, understanding the

525
00:31:54,840 --> 00:31:57,920
Speaker 4: input and giving you output. And some people think about

526
00:31:57,960 --> 00:32:02,440
Speaker 4: it kind of like this person, and you know, reason

527
00:32:02,520 --> 00:32:05,000
Speaker 4: about it like a person. Other people see it like

528
00:32:05,320 --> 00:32:08,440
Speaker 4: Google Search, but just a really really interactive version of

529
00:32:08,480 --> 00:32:14,120
Speaker 4: Google Search. Sure, and somehow it's somewhere in between, which

530
00:32:14,160 --> 00:32:17,160
Speaker 4: I think is like the weird sort of place that

531
00:32:17,200 --> 00:32:21,000
Speaker 4: we're at. And you know, I think that the fact

532
00:32:21,240 --> 00:32:25,280
Speaker 4: that the same type of deep learning and machine learning

533
00:32:25,280 --> 00:32:32,280
Speaker 4: models can predict protein folding and find new, you know,

534
00:32:32,400 --> 00:32:36,000
Speaker 4: ways of inverting matrices that are even more efficient and

535
00:32:36,840 --> 00:32:40,400
Speaker 4: doing all this very very incredible intelligent stuff at the

536
00:32:40,440 --> 00:32:45,720
Speaker 4: same time as you know, making incredibly dumb statements is

537
00:32:46,520 --> 00:32:49,560
Speaker 4: something that we will like people are going to have

538
00:32:49,640 --> 00:32:53,400
Speaker 4: to deal with rights as Okay, you know, there's this

539
00:32:53,520 --> 00:32:57,440
Speaker 4: weird disconnect. You know, if you think about it like

540
00:32:57,480 --> 00:33:00,720
Speaker 4: a person, it just it's not a person, right, it's

541
00:33:00,880 --> 00:33:05,680
Speaker 4: not a you know, it doesn't map on Like in general,

542
00:33:05,760 --> 00:33:08,560
Speaker 4: we like to take technologies or different types of constructs

543
00:33:08,560 --> 00:33:11,680
Speaker 4: and map them onto what we already know. And in

544
00:33:11,880 --> 00:33:15,480
Speaker 4: some sense it's not mapping well onto what we already know,

545
00:33:16,520 --> 00:33:20,160
Speaker 4: and so we're gonna have to figure out how to

546
00:33:20,920 --> 00:33:23,640
Speaker 4: sort of properly map it onto its own new sort

547
00:33:23,680 --> 00:33:24,480
Speaker 4: of construct.

548
00:33:25,000 --> 00:33:28,920
Speaker 1: Yeah, I get like the one thing that you know

549
00:33:29,000 --> 00:33:31,880
Speaker 1: in talking about it as like well, it's just predicting

550
00:33:31,880 --> 00:33:36,240
Speaker 1: the next word, or you know, it's doing like when

551
00:33:36,280 --> 00:33:40,600
Speaker 1: you describe the very specific tasks that's being programmed to

552
00:33:40,720 --> 00:33:45,280
Speaker 1: do versus the emergent abilities that are coming out of

553
00:33:45,320 --> 00:33:50,600
Speaker 1: that task. Like the analogy that I heard used that

554
00:33:50,600 --> 00:33:53,520
Speaker 1: that made senses Like, I guess it's a quote from

555
00:33:53,920 --> 00:33:58,200
Speaker 1: Darwin that like from so simple a beginning, endless forms

556
00:33:58,240 --> 00:34:03,040
Speaker 1: most beautiful, like that that quote being applied to basically

557
00:34:03,120 --> 00:34:06,040
Speaker 1: the argument is like that's all the human mind is doing. Also,

558
00:34:06,360 --> 00:34:08,840
Speaker 1: like in some ways, like the human mind is just

559
00:34:09,360 --> 00:34:13,759
Speaker 1: can can be seen as being programmed to like look

560
00:34:13,880 --> 00:34:19,880
Speaker 1: for lions in the bushes and move towards food and procreation,

561
00:34:20,520 --> 00:34:26,040
Speaker 1: and then like from all of these like complicated synapses firing,

562
00:34:26,239 --> 00:34:30,319
Speaker 1: you get this amazing thing called consciousness, and that you know,

563
00:34:30,440 --> 00:34:35,160
Speaker 1: just by describing AI in a reductive way, you're ignoring

564
00:34:35,440 --> 00:34:39,320
Speaker 1: the fact that, like it could be these very basic

565
00:34:39,360 --> 00:34:42,960
Speaker 1: things and also be moving towards you know, what we

566
00:34:43,080 --> 00:34:48,160
Speaker 1: describe as what we think of as consciousness. So yeah,

567
00:34:47,480 --> 00:34:52,000
Speaker 1: I think, yeah, it's a super interesting conversation. That was

568
00:34:52,040 --> 00:34:55,000
Speaker 1: Like I think I was scared to like read into

569
00:34:55,440 --> 00:34:58,880
Speaker 1: like to get like to research because it's just so

570
00:34:59,080 --> 00:35:02,840
Speaker 1: massive and like when I was I was a philosophy

571
00:35:02,920 --> 00:35:06,799
Speaker 1: major in college, and when I was studying philosophy back

572
00:35:06,840 --> 00:35:11,080
Speaker 1: in like the early two thousands, like AI, Like the

573
00:35:11,160 --> 00:35:13,080
Speaker 1: question of AI and what was going to happen was

574
00:35:13,160 --> 00:35:17,600
Speaker 1: like a major question in philosophy all the way back then.

575
00:35:17,960 --> 00:35:20,000
Speaker 1: And it's like all of the all of the ways

576
00:35:20,040 --> 00:35:22,400
Speaker 1: that philosophers have been like thinking about and asking all

577
00:35:22,440 --> 00:35:25,920
Speaker 1: the questions they've been asking about AI for you know,

578
00:35:25,960 --> 00:35:30,720
Speaker 1: since like the sixties are now like coming to a forum,

579
00:35:30,800 --> 00:35:35,720
Speaker 1: like as we've gotten closer to the moment, we still

580
00:35:35,760 --> 00:35:38,960
Speaker 1: don't really have any clearer of an idea of like

581
00:35:39,080 --> 00:35:43,360
Speaker 1: what exactly is going to happen. So it's it's a

582
00:35:43,400 --> 00:35:46,040
Speaker 1: little scary. I do want to. I do want to

583
00:35:46,080 --> 00:35:47,759
Speaker 1: take a break and then come back and just talk

584
00:35:47,760 --> 00:35:50,960
Speaker 1: about like kind of some specific ways, just like take

585
00:35:51,040 --> 00:35:53,840
Speaker 1: some guesses as to like specific ways that the future

586
00:35:54,920 --> 00:35:59,319
Speaker 1: might look different, that we might not be that I

587
00:35:59,440 --> 00:36:02,600
Speaker 1: at least going into like researching this hadn't hadn't been

588
00:36:03,200 --> 00:36:06,520
Speaker 1: taken into account as a possibility. So we'll be right

589
00:36:06,560 --> 00:36:21,759
Speaker 1: back and we're back and Miles, I mean we we

590
00:36:21,800 --> 00:36:23,960
Speaker 1: talk a lot about how we we what we think

591
00:36:24,000 --> 00:36:25,560
Speaker 1: this future is going to look like?

592
00:36:25,680 --> 00:36:29,520
Speaker 2: Right, Yeah, I mean I share the name with Miles Dyson.

593
00:36:30,000 --> 00:36:30,799
Speaker 1: Ever heard of him.

594
00:36:31,320 --> 00:36:33,880
Speaker 2: I started a little thing called the sky net, you

595
00:36:33,880 --> 00:36:35,279
Speaker 2: know that keeps me up all night?

596
00:36:35,560 --> 00:36:39,400
Speaker 1: Well but seriousness, I hey, he went out of here, Miles,

597
00:36:39,480 --> 00:36:40,480
Speaker 1: you know I did. I did.

598
00:36:41,400 --> 00:36:43,239
Speaker 2: Look it's all about altruism, you know at the end.

599
00:36:44,080 --> 00:36:46,279
Speaker 2: But like I think for me personally, right, so much

600
00:36:46,320 --> 00:36:49,080
Speaker 2: of my so much of my understanding of science is

601
00:36:49,160 --> 00:36:54,120
Speaker 2: derived from film and television because I'm American, and I

602
00:36:54,160 --> 00:36:58,120
Speaker 2: think with like with Ai, I think whenever I would

603
00:36:58,120 --> 00:36:59,839
Speaker 2: think of like, oh my, like he's like, you don't

604
00:36:59,840 --> 00:37:02,879
Speaker 2: know this thing's gonna go. The place I think it's

605
00:37:02,920 --> 00:37:05,960
Speaker 2: gonna go is sky net from Terminator.

606
00:37:05,920 --> 00:37:08,480
Speaker 1: Just see the shot from the sky of all the

607
00:37:08,520 --> 00:37:11,200
Speaker 1: missiles being launched just coming down.

608
00:37:12,080 --> 00:37:14,040
Speaker 2: I'm trying to have a nice day at the park

609
00:37:14,080 --> 00:37:16,360
Speaker 2: with my kids. I'm watching through a chain link, and

610
00:37:16,440 --> 00:37:16,759
Speaker 2: next some.

611
00:37:16,920 --> 00:37:20,960
Speaker 1: Ladies over there shaking the chain link and skeletons rattling

612
00:37:21,000 --> 00:37:22,000
Speaker 1: the kids over there.

613
00:37:22,120 --> 00:37:25,040
Speaker 2: Get her out of here. But I'm you know, first

614
00:37:25,040 --> 00:37:28,520
Speaker 2: of all, it's sort of twofold question, how how far

615
00:37:28,640 --> 00:37:31,760
Speaker 2: off base is the idea of like how many jumps

616
00:37:31,760 --> 00:37:33,719
Speaker 2: am I making in my brain to be like Chad

617
00:37:33,800 --> 00:37:38,800
Speaker 2: gbt next stop sky net? And and then also because

618
00:37:38,800 --> 00:37:41,400
Speaker 2: of that, what are all of the ways that people

619
00:37:41,480 --> 00:37:45,920
Speaker 2: like me are not considering what those actual, real tangible

620
00:37:45,920 --> 00:37:48,680
Speaker 2: effects will be of. And I don't want to be

621
00:37:48,719 --> 00:37:51,960
Speaker 2: like the most cynical version, but you know, if we

622
00:37:52,000 --> 00:37:55,799
Speaker 2: aren't careful and we have very sort of individualistic or

623
00:37:55,920 --> 00:38:00,000
Speaker 2: profit minded sort of motivations to develop this kind of technology,

624
00:38:00,120 --> 00:38:03,520
Speaker 2: like what does that worst case sort of look like

625
00:38:03,640 --> 00:38:05,560
Speaker 2: or not worst case? But what are the ways I'm

626
00:38:05,560 --> 00:38:07,560
Speaker 2: actually not thinking of because I'm too busy thinking about

627
00:38:07,600 --> 00:38:08,400
Speaker 2: T one thousands?

628
00:38:08,719 --> 00:38:11,000
Speaker 1: Right, Like I just to like add on to that,

629
00:38:11,040 --> 00:38:14,240
Speaker 1: when when I think about the computer revolution, like for

630
00:38:14,320 --> 00:38:18,280
Speaker 1: decades it was these people in San Francisco talking about

631
00:38:18,320 --> 00:38:21,120
Speaker 1: how the future was going to be completely different and

632
00:38:21,160 --> 00:38:26,160
Speaker 1: done on computers or something called the Internet, And we

633
00:38:26,160 --> 00:38:29,920
Speaker 1: were just down here looking at a computer that was

634
00:38:29,960 --> 00:38:34,920
Speaker 1: like green dots, like not that good of a screen display.

635
00:38:35,440 --> 00:38:38,680
Speaker 1: And by the time it like filtered down to us

636
00:38:38,760 --> 00:38:41,759
Speaker 1: and the world does look totally different, it's like, well, shit,

637
00:38:42,239 --> 00:38:45,160
Speaker 1: like that's that turns out that was a big deal.

638
00:38:45,600 --> 00:38:51,600
Speaker 1: So yeah, I feel like I want to be constantly,

639
00:38:51,880 --> 00:38:54,080
Speaker 1: like on our show, just asking me a question like

640
00:38:54,400 --> 00:38:58,680
Speaker 1: where is this actually taking us? Because some in the past,

641
00:38:58,719 --> 00:39:02,000
Speaker 1: I feel like it's been hard to predict and when

642
00:39:02,040 --> 00:39:04,520
Speaker 1: it did come, it wasn't exactly what the you know,

643
00:39:04,640 --> 00:39:07,480
Speaker 1: tech oracles said it was going to be, and it

644
00:39:07,600 --> 00:39:11,279
Speaker 1: was like unexpected and weird and beautiful and banala and

645
00:39:11,360 --> 00:39:14,680
Speaker 1: weird ways. And so I'm curious to hear your thoughts

646
00:39:14,760 --> 00:39:20,000
Speaker 1: on like once this technology reaches the level of consumers,

647
00:39:21,080 --> 00:39:24,560
Speaker 1: like of just your average ordinary person who's like not

648
00:39:25,320 --> 00:39:28,680
Speaker 1: teaching machine learning at m YU, like, what what is

649
00:39:28,719 --> 00:39:29,600
Speaker 1: it going to look like?

650
00:39:29,920 --> 00:39:34,719
Speaker 2: But first but obviously first, but first guy, I.

651
00:39:34,680 --> 00:39:40,799
Speaker 4: Think it's kind of okay. So so I think the

652
00:39:40,840 --> 00:39:49,160
Speaker 4: idea of worrying about some version of you know, this

653
00:39:49,280 --> 00:39:54,759
Speaker 4: technology going out of control, right is there's so many

654
00:39:54,920 --> 00:40:01,360
Speaker 4: checks and balances of ways in which we are thinking

655
00:40:01,400 --> 00:40:06,719
Speaker 4: about and the technology is sort of moving forward. That

656
00:40:07,200 --> 00:40:10,840
Speaker 4: is kind of you know, the idea of something emergent

657
00:40:11,120 --> 00:40:13,880
Speaker 4: and then not only as an emergent, but it's going

658
00:40:13,960 --> 00:40:18,800
Speaker 4: to take over and try to destroy civilization.

659
00:40:18,800 --> 00:40:22,760
Speaker 2: And almost send and also send cybernetic organisms into the

660
00:40:22,800 --> 00:40:25,759
Speaker 2: past to ensure that those people do not grow up

661
00:40:25,800 --> 00:40:29,160
Speaker 2: to take arms. I can't kind of like John Connor and.

662
00:40:29,520 --> 00:40:33,759
Speaker 4: Yeah, yeah, So I'm a firm believer that in being

663
00:40:33,760 --> 00:40:37,000
Speaker 4: able to send people to the future, I still don't

664
00:40:37,080 --> 00:40:38,919
Speaker 4: think that we're not going to be able to send

665
00:40:38,920 --> 00:40:45,759
Speaker 4: people in the past. So that's that's maybe one problem.

666
00:40:44,520 --> 00:40:50,600
Speaker 1: But that's just like your opinion. Man, all right, I'm

667
00:40:51,960 --> 00:40:55,799
Speaker 1: is gonna happen. And by the way, I'm the Doc

668
00:40:55,880 --> 00:41:00,000
Speaker 1: Brown character. I'm gonna be friend a young child, Yeah right, exactly,

669
00:41:00,160 --> 00:41:06,960
Speaker 1: Like Marty and Doc been friends like probably like it

670
00:41:07,000 --> 00:41:09,799
Speaker 1: seems like eight years so like when Marty was like

671
00:41:09,880 --> 00:41:13,279
Speaker 1: nine years old, they became best friends. Anyways, sorry about

672
00:41:13,320 --> 00:41:18,839
Speaker 1: that forgetting sidetracks so well, So I think you know,

673
00:41:19,480 --> 00:41:22,200
Speaker 1: so what I'm what I'm thinking about when we think

674
00:41:22,239 --> 00:41:24,879
Speaker 1: about this kind of technology and how it could be,

675
00:41:25,520 --> 00:41:28,600
Speaker 1: how the technology itself could go wrong.

676
00:41:29,440 --> 00:41:34,560
Speaker 4: I don't think that that kind of integration is likely, right.

677
00:41:34,719 --> 00:41:39,160
Speaker 4: I think that the concept of you know, the singularity,

678
00:41:39,440 --> 00:41:42,920
Speaker 4: it's so far out, and I don't think that we

679
00:41:43,000 --> 00:41:48,160
Speaker 4: have a good ability or understanding of like consciousness. And

680
00:41:48,280 --> 00:41:50,640
Speaker 4: I also don't think we have a really good understanding

681
00:41:50,680 --> 00:41:54,880
Speaker 4: of like, Okay, you know, why would you know these

682
00:41:55,920 --> 00:41:59,520
Speaker 4: large language models start, you know, trying to harm us.

683
00:42:00,680 --> 00:42:04,959
Speaker 4: So there's all those steps and jumps and leaps and

684
00:42:05,080 --> 00:42:10,719
Speaker 4: all the intermediate pieces. That just seems so incredibly unlikely. Now.

685
00:42:10,760 --> 00:42:14,360
Speaker 4: I know that a lot of the ai AGI people

686
00:42:14,360 --> 00:42:17,480
Speaker 4: who worry, they worry is, oh, well, we got to

687
00:42:17,520 --> 00:42:20,279
Speaker 4: worry about this tail race, great, and a human level

688
00:42:20,320 --> 00:42:23,840
Speaker 4: extinction event is worth worrying about. Yeah, and some people

689
00:42:23,840 --> 00:42:27,120
Speaker 4: worrying about that is probably not a bad thing. But

690
00:42:27,160 --> 00:42:32,120
Speaker 4: I just think it's very unlikely for the reasons of

691
00:42:32,200 --> 00:42:35,480
Speaker 4: those of all the chain that would need to happen,

692
00:42:36,560 --> 00:42:41,160
Speaker 4: and we're not very good at robots. Also, robots are

693
00:42:41,280 --> 00:42:44,320
Speaker 4: you know, actually much further away.

694
00:42:44,360 --> 00:42:47,680
Speaker 1: But that is one Okay, well, we'll talk about it,

695
00:42:47,719 --> 00:42:49,560
Speaker 1: but let put a pin in that because I want

696
00:42:49,560 --> 00:42:52,560
Speaker 1: to come back to that to bad robots, to bad robots,

697
00:42:53,080 --> 00:42:55,000
Speaker 1: and how easy I just want to brag about how

698
00:42:55,000 --> 00:42:56,600
Speaker 1: easy it would be for me to beat one up.

699
00:42:56,760 --> 00:43:00,520
Speaker 2: But dynamics, Yeah, yeah, I got my on you in

700
00:43:00,520 --> 00:43:00,839
Speaker 2: that fight.

701
00:43:01,400 --> 00:43:04,399
Speaker 4: But the thing that I do want to point out

702
00:43:04,520 --> 00:43:07,800
Speaker 4: is that, you know, the concept of starting to build

703
00:43:07,880 --> 00:43:11,839
Speaker 4: some of these rules into the large language models, you know,

704
00:43:12,120 --> 00:43:15,880
Speaker 4: to try to say, okay, well, you know, try to

705
00:43:16,120 --> 00:43:20,760
Speaker 4: be benign, don't do harm. Like that's a really super

706
00:43:20,800 --> 00:43:25,120
Speaker 4: good idea, right, And I think that, you know, even

707
00:43:25,160 --> 00:43:29,719
Speaker 4: if you don't believe in sky Neet, actually believing in

708
00:43:29,760 --> 00:43:33,920
Speaker 4: trying to incorporate responsibility into the large language models, I

709
00:43:33,960 --> 00:43:37,520
Speaker 4: think that is something that's very important moving into some

710
00:43:37,560 --> 00:43:41,280
Speaker 4: of the dangers though, Like I think actually large language

711
00:43:41,320 --> 00:43:45,000
Speaker 4: models and people like Opening Eye was actually worried about

712
00:43:45,000 --> 00:43:49,160
Speaker 4: this in one of its first iterations of GPT was

713
00:43:49,880 --> 00:43:54,799
Speaker 4: the ability for the large language models to create misinformation

714
00:43:55,120 --> 00:44:00,680
Speaker 4: and disinformation. And you know, I think that that's a

715
00:44:00,800 --> 00:44:05,759
Speaker 4: really bad use of the technology and very very potentially harmful.

716
00:44:05,480 --> 00:44:08,799
Speaker 1: And it already seems to be what it's being used for.

717
00:44:09,160 --> 00:44:13,279
Speaker 1: Like the technology, like the ways that like companies are

718
00:44:13,280 --> 00:44:17,800
Speaker 1: trying to replace journalism with it, or you know, clickbait article,

719
00:44:17,920 --> 00:44:20,560
Speaker 1: like just generate tons of clickbait articles that are like

720
00:44:20,600 --> 00:44:23,680
Speaker 1: targeted at people. Is like it, it feels like it's

721
00:44:23,719 --> 00:44:27,600
Speaker 1: already training in that in a lot of ways.

722
00:44:27,960 --> 00:44:33,120
Speaker 4: Yeah, Unfortunately, I think you're right like that, that there

723
00:44:33,600 --> 00:44:38,040
Speaker 4: is this that there is this bad use of the

724
00:44:38,120 --> 00:44:41,160
Speaker 4: technology where it's like, oh, let's make this so that

725
00:44:41,239 --> 00:44:44,919
Speaker 4: it could be as persuasive as possible. You know, there's

726
00:44:45,000 --> 00:44:47,640
Speaker 4: obviously been a ton of research in marketing on like

727
00:44:47,680 --> 00:44:50,680
Speaker 4: figuring out, okay, how do we you know, position this

728
00:44:50,800 --> 00:44:53,840
Speaker 4: product so that it's you know, you're more likely to

729
00:44:53,880 --> 00:44:56,000
Speaker 4: buy it, right, And the same thing can be now

730
00:44:56,040 --> 00:45:00,279
Speaker 4: applied to language of like okay, you know, for Jacob Ryan,

731
00:45:00,480 --> 00:45:04,040
Speaker 4: how am I going to make this tailored advertisement just

732
00:45:04,160 --> 00:45:06,920
Speaker 4: for him to be able to do to make him

733
00:45:06,960 --> 00:45:10,080
Speaker 4: buy this particular product that I'm selling, or to make

734
00:45:10,239 --> 00:45:13,520
Speaker 4: him not vote right? Yeah? Right, you know those kind

735
00:45:13,520 --> 00:45:16,480
Speaker 4: of that's I mean, I think that this is scary,

736
00:45:16,520 --> 00:45:19,000
Speaker 4: and I think that this is in the now, right,

737
00:45:19,280 --> 00:45:23,160
Speaker 4: which you know, is something that in some sense is

738
00:45:23,280 --> 00:45:27,879
Speaker 4: going to be hard to like really stop people from

739
00:45:28,000 --> 00:45:33,279
Speaker 4: using this technology in that direction without things like legislation,

740
00:45:34,000 --> 00:45:40,560
Speaker 4: you know, there's just some some components that need, you know,

741
00:45:40,719 --> 00:45:44,080
Speaker 4: more legislation, and I think that's going to be hard

742
00:45:44,120 --> 00:45:46,959
Speaker 4: to do but probably necessary. Right.

743
00:45:47,719 --> 00:45:51,040
Speaker 2: Yeah, I could even see, like just even in politics,

744
00:45:51,160 --> 00:45:53,800
Speaker 2: you can be like, Okay, I need to actually figure

745
00:45:53,840 --> 00:45:57,400
Speaker 2: out the best campaign plan for this very specific demographic

746
00:45:57,440 --> 00:46:00,160
Speaker 2: that lives in this part of this state, and sure,

747
00:46:00,440 --> 00:46:03,080
Speaker 2: and then just imagine what happens. But I guess is

748
00:46:03,080 --> 00:46:04,960
Speaker 2: that also part of the slippery slope is that like

749
00:46:05,320 --> 00:46:07,520
Speaker 2: the reliance sort of gives way to like sort of

750
00:46:07,960 --> 00:46:09,959
Speaker 2: this like thing where it's like this actually is gonna

751
00:46:10,000 --> 00:46:13,200
Speaker 2: whatever this says is the solution to whatever problem we

752
00:46:13,320 --> 00:46:15,879
Speaker 2: have and like just kind of throwing our hands up

753
00:46:15,920 --> 00:46:19,120
Speaker 2: and all just becoming totally reliant. I mean, i'd imagine

754
00:46:19,160 --> 00:46:22,200
Speaker 2: that's also seems like a place where we could easily

755
00:46:22,280 --> 00:46:25,560
Speaker 2: sort of slip into a problem where it's like, yeah,

756
00:46:25,120 --> 00:46:28,000
Speaker 2: the chatbot may give us this answer.

757
00:46:28,200 --> 00:46:30,919
Speaker 4: But well, or or that people are starting to make

758
00:46:31,080 --> 00:46:35,680
Speaker 4: like very homogeneous decisions and choices right where you would

759
00:46:35,719 --> 00:46:40,800
Speaker 4: make you know, many different choices by because you already

760
00:46:40,800 --> 00:46:43,600
Speaker 4: have a template that's been given to you by you

761
00:46:43,640 --> 00:46:46,799
Speaker 4: know this AI. You're like, oh, okay, well, you know

762
00:46:46,800 --> 00:46:49,920
Speaker 4: I'm just gonna follow you know, this choice or this

763
00:46:50,000 --> 00:46:52,600
Speaker 4: decision that it's going to make for me and not

764
00:46:53,320 --> 00:46:56,799
Speaker 4: you know, do one of the thousand different alternatives right, right,

765
00:46:56,920 --> 00:46:59,759
Speaker 4: and if you do that, I do that. Jack does that, right,

766
00:46:59,800 --> 00:47:02,360
Speaker 4: Like all of a sudden, we would have done vastly

767
00:47:02,400 --> 00:47:05,360
Speaker 4: different choices. But now we have this sort of weird,

768
00:47:05,680 --> 00:47:09,400
Speaker 4: sort of centering effect where all of us are actually

769
00:47:09,520 --> 00:47:14,600
Speaker 4: making much less, you know, very choices in our decision making, right,

770
00:47:15,280 --> 00:47:18,800
Speaker 4: which has I mean it does possess real risk. I

771
00:47:18,840 --> 00:47:24,040
Speaker 4: mean imagine applying this to resume screen right where you

772
00:47:24,040 --> 00:47:26,600
Speaker 4: could imagine the same type of problem, right, and those

773
00:47:26,600 --> 00:47:29,279
Speaker 4: are there's just so many scenarios that you can think

774
00:47:29,320 --> 00:47:34,919
Speaker 4: of where you know, we need to take care, right, Yeah,

775
00:47:34,520 --> 00:47:36,920
Speaker 4: and this is a good time to think about that.

776
00:47:37,400 --> 00:47:40,279
Speaker 1: Right, And we're yeah, we're turning our free will over

777
00:47:40,480 --> 00:47:44,360
Speaker 1: to the care of algorithms and you know, the phones,

778
00:47:44,440 --> 00:47:47,040
Speaker 1: like the skinner boxes in our hands that we're carrying around,

779
00:47:47,040 --> 00:47:49,600
Speaker 1: and like that feels like a thing that's already happening.

780
00:47:49,640 --> 00:47:52,000
Speaker 1: A I might just make it a little bit more

781
00:47:52,000 --> 00:47:57,160
Speaker 1: effective and like quicker to respond and like but yeah,

782
00:47:57,280 --> 00:48:01,719
Speaker 1: it feels like a lot of the concern over AI

783
00:48:01,880 --> 00:48:03,799
Speaker 1: that makes the most sense to me are the ones

784
00:48:03,800 --> 00:48:07,920
Speaker 1: that are already happening. And yeah, just in like to

785
00:48:08,000 --> 00:48:09,759
Speaker 1: kind of tip my head a little bit, like the

786
00:48:10,000 --> 00:48:12,799
Speaker 1: in doing a lot of research on the kind of

787
00:48:13,440 --> 00:48:18,799
Speaker 1: overall general intelligence concerns that we've been talking about, the

788
00:48:18,800 --> 00:48:21,520
Speaker 1: ones where it like takes over and evades human control

789
00:48:21,600 --> 00:48:25,440
Speaker 1: because it wants to you know, wants to defeat humans.

790
00:48:26,080 --> 00:48:30,719
Speaker 1: I was surprised how full of shit those seemed to be. Like,

791
00:48:31,239 --> 00:48:34,920
Speaker 1: for instance, one story that got passed around a lot

792
00:48:35,520 --> 00:48:40,680
Speaker 1: last year where a like during a military exercise and

793
00:48:40,840 --> 00:48:46,160
Speaker 1: AI was being told not to like take out human

794
00:48:46,239 --> 00:48:50,000
Speaker 1: targets by its human operator, and so the AI made

795
00:48:50,080 --> 00:48:54,600
Speaker 1: the decision to kill the operator so that it could

796
00:48:54,680 --> 00:48:57,680
Speaker 1: then just like go rogue and start killing whatever it

797
00:48:57,719 --> 00:49:02,160
Speaker 1: wanted to. And that story, like I passed around a lot.

798
00:49:02,200 --> 00:49:05,520
Speaker 1: I think we've even like referenced it on this show.

799
00:49:05,640 --> 00:49:08,160
Speaker 1: And it's not true. First of all, I think when

800
00:49:08,200 --> 00:49:10,400
Speaker 1: it first got passed around, people were like it actually

801
00:49:10,520 --> 00:49:13,239
Speaker 1: killed someone man, and it was it was just a

802
00:49:13,280 --> 00:49:16,080
Speaker 1: hypothetical exercise first of all, like just in the story

803
00:49:16,160 --> 00:49:18,319
Speaker 1: like as it was being passed around, And second of all,

804
00:49:18,480 --> 00:49:22,759
Speaker 1: it was then debunked, like the person who said that

805
00:49:22,800 --> 00:49:25,560
Speaker 1: it happened in the exercise later came out and was

806
00:49:25,640 --> 00:49:30,080
Speaker 1: like that, actually, like that didn't happen. But it seems

807
00:49:30,200 --> 00:49:34,359
Speaker 1: like there is a real interest and real incentive on

808
00:49:34,480 --> 00:49:36,719
Speaker 1: behalf of the people who are like set up to

809
00:49:36,800 --> 00:49:42,799
Speaker 1: make money off of these ais to like make them

810
00:49:42,880 --> 00:49:46,080
Speaker 1: seem like they have this like godlike reasoning power. There's

811
00:49:46,120 --> 00:49:49,239
Speaker 1: this other story about where like they were testing the

812
00:49:50,080 --> 00:49:54,239
Speaker 1: I think it was GPT four to see like like

813
00:49:54,320 --> 00:49:58,000
Speaker 1: during the alignment testing. Alignment testing is like trying to

814
00:49:58,040 --> 00:50:02,000
Speaker 1: make sure that the AI's goal are aligned with humanities

815
00:50:02,040 --> 00:50:05,160
Speaker 1: and like making humans more happy, and they like ran

816
00:50:05,239 --> 00:50:09,640
Speaker 1: a test to see where the GPT four was basically

817
00:50:09,680 --> 00:50:12,439
Speaker 1: like I can't solve the capture, but what I'm going

818
00:50:12,520 --> 00:50:14,799
Speaker 1: to do is I'm going to reach out to a

819
00:50:14,840 --> 00:50:18,239
Speaker 1: task rabbit and hire a task rabbit to solve the

820
00:50:18,280 --> 00:50:21,920
Speaker 1: capture for me. And the GPT four made up a

821
00:50:21,960 --> 00:50:24,520
Speaker 1: lion said that he was visually impaired and that's why

822
00:50:24,560 --> 00:50:28,200
Speaker 1: he needed the task grabbit to do that. And again

823
00:50:28,239 --> 00:50:31,040
Speaker 1: it's like one of those stories like it feels creepy

824
00:50:31,080 --> 00:50:34,560
Speaker 1: and it gives you goosebumps, and again it's not true.

825
00:50:34,640 --> 00:50:38,239
Speaker 1: It's like they were the GPT was being prompted by

826
00:50:38,280 --> 00:50:41,000
Speaker 1: a human, Like it's very similar to the self driving

827
00:50:41,080 --> 00:50:46,560
Speaker 1: car myth, like that that self driving car viral video

828
00:50:46,560 --> 00:50:49,680
Speaker 1: that Elon Musk put out where it was being prompted

829
00:50:49,719 --> 00:50:52,520
Speaker 1: by a human and like pre programmed, and like that.

830
00:50:52,640 --> 00:50:58,640
Speaker 1: There were all these ways that the human agency involved

831
00:50:58,960 --> 00:51:04,080
Speaker 1: with the kind of clever task that we're worried about

832
00:51:04,080 --> 00:51:06,439
Speaker 1: this thing having done. We're just like taking out. We're

833
00:51:06,480 --> 00:51:09,320
Speaker 1: just edited out so that they could tell a story

834
00:51:09,719 --> 00:51:14,600
Speaker 1: where it seemed like the GPT four, which is like

835
00:51:14,640 --> 00:51:18,919
Speaker 1: what powers chat GPT that like it was doing something evil, right,

836
00:51:19,320 --> 00:51:23,080
Speaker 1: And it's like so it's I get how these stories

837
00:51:23,520 --> 00:51:28,279
Speaker 1: get out there and become like because they're they're the

838
00:51:28,480 --> 00:51:33,319
Speaker 1: version of AI that we've been preparing for because like

839
00:51:33,480 --> 00:51:37,359
Speaker 1: and watching Terminator. But it's surprising to me how much

840
00:51:37,480 --> 00:51:41,799
Speaker 1: like Sam Altman, the head of Open AI, like just

841
00:51:42,120 --> 00:51:45,920
Speaker 1: leans into that shit and is like he like he

842
00:51:46,080 --> 00:51:49,799
Speaker 1: in an interview with like The New Yorker, he was like, yeah,

843
00:51:49,840 --> 00:51:52,880
Speaker 1: I like keep a Sinai capsule on me and like

844
00:51:53,400 --> 00:51:56,200
Speaker 1: all these weapons and like a gas mask from the

845
00:51:56,360 --> 00:52:01,200
Speaker 1: Israeli military in case like an AI takeover happens and

846
00:52:02,000 --> 00:52:06,280
Speaker 1: it's like what, like you of all people should know

847
00:52:06,560 --> 00:52:10,840
Speaker 1: that that is complete bullshit, but it and it totally

848
00:52:10,960 --> 00:52:13,720
Speaker 1: like takes the eye off the ball of like how

849
00:52:13,880 --> 00:52:17,359
Speaker 1: the actual dangers that AI poses, which is that it's

850
00:52:17,440 --> 00:52:20,160
Speaker 1: going to just like flood the zone with shit, like

851
00:52:20,239 --> 00:52:24,440
Speaker 1: it's going to keep making the Internet and phones like

852
00:52:24,560 --> 00:52:27,560
Speaker 1: more and more unusable but also more and more like

853
00:52:28,120 --> 00:52:31,880
Speaker 1: difficult to like tear ourselves away from. Like I feel

854
00:52:31,880 --> 00:52:34,600
Speaker 1: like we've already lost the alignment battle in the sense

855
00:52:34,640 --> 00:52:39,319
Speaker 1: that like we've already lost any way in which our

856
00:52:39,440 --> 00:52:42,279
Speaker 1: technology that is supposed to like serve us and make

857
00:52:42,320 --> 00:52:46,640
Speaker 1: our lives better and enrich our happiness like are doing that,

858
00:52:46,840 --> 00:52:49,200
Speaker 1: Like they they stop doing that a long time ago.

859
00:52:49,600 --> 00:52:52,080
Speaker 1: Like That's why I always like say that the more

860
00:52:52,160 --> 00:52:55,040
Speaker 1: interesting question, like with the questions that were being asked

861
00:52:55,040 --> 00:52:57,640
Speaker 1: in philosophy classes in the early two thousands about like

862
00:52:58,000 --> 00:53:01,120
Speaker 1: singularity and like suddenly this thing spins off and is

863
00:53:01,880 --> 00:53:05,120
Speaker 1: we we don't realize we're no longer being served and

864
00:53:05,239 --> 00:53:07,719
Speaker 1: like we're like twenty steps behind, like that happened with

865
00:53:07,800 --> 00:53:12,040
Speaker 1: capitalism a long time ago, Like that we're like capitalism

866
00:53:12,160 --> 00:53:15,080
Speaker 1: is so far beyond and it's like, you know, We're

867
00:53:15,160 --> 00:53:18,759
Speaker 1: no longer serving ourselves and serving fellow humanity. We're like

868
00:53:18,800 --> 00:53:22,319
Speaker 1: serving this idea of the market that is just you know,

869
00:53:22,520 --> 00:53:27,319
Speaker 1: repeatedly making decisions to make itself more efficient, take the

870
00:53:27,400 --> 00:53:32,360
Speaker 1: friction out of like our consumption behavior. And yeah, I

871
00:53:32,360 --> 00:53:35,799
Speaker 1: think AI is going to definitely be a tool in that.

872
00:53:35,920 --> 00:53:39,280
Speaker 1: But like that is the ultimate battle that we're already losing.

873
00:53:39,640 --> 00:53:43,319
Speaker 4: Yeah, I mean I completely agree with you on the

874
00:53:43,960 --> 00:53:48,840
Speaker 4: on the front of like your phonees are I mean,

875
00:53:49,200 --> 00:53:53,600
Speaker 4: the company is are trying to maximize their profits, which

876
00:53:53,680 --> 00:53:58,839
Speaker 4: mean you know, possibly you being on your phone at

877
00:53:58,880 --> 00:54:04,520
Speaker 4: the disservice doing something else like going outdoors and doing exercise, right,

878
00:54:04,880 --> 00:54:10,360
Speaker 4: or you know, doing something social. I I do see

879
00:54:10,560 --> 00:54:15,000
Speaker 4: on the other hand, that like, you know, maybe I

880
00:54:15,080 --> 00:54:21,160
Speaker 4: can have positive effects right where you know, take tutoring

881
00:54:21,280 --> 00:54:25,920
Speaker 4: for an example, or public health, where we take the

882
00:54:26,000 --> 00:54:30,200
Speaker 4: phone and take the technology and use it for benefit,

883
00:54:30,440 --> 00:54:36,759
Speaker 4: like providing information about public health by helping students who

884
00:54:37,719 --> 00:54:41,560
Speaker 4: don't have the ability to have a tutor have a tutor, right,

885
00:54:41,760 --> 00:54:46,120
Speaker 4: Like all of these kind of things where let's take

886
00:54:46,160 --> 00:54:48,279
Speaker 4: advantage of the fact that you know, the majority of

887
00:54:48,280 --> 00:54:51,319
Speaker 4: the world has phones, right and try to use that

888
00:54:51,480 --> 00:54:57,279
Speaker 4: technology for you know, a good positive societal benefit. But

889
00:54:57,800 --> 00:55:02,600
Speaker 4: like any tool in it has usage that are both

890
00:55:02,640 --> 00:55:05,480
Speaker 4: good and bad. And I do think, like, you know,

891
00:55:05,800 --> 00:55:09,040
Speaker 4: there's going to be a lot of uses of this

892
00:55:09,200 --> 00:55:12,160
Speaker 4: technology that are not necessarily in the best interests of

893
00:55:12,239 --> 00:55:13,960
Speaker 4: humanity or individual people.

894
00:55:14,719 --> 00:55:17,920
Speaker 2: Right, So it's like really like sort of the real

895
00:55:18,080 --> 00:55:21,759
Speaker 2: X factor is how the technology is being deployed and

896
00:55:21,840 --> 00:55:24,279
Speaker 2: for what purpose, not that the technology in and of

897
00:55:24,320 --> 00:55:27,799
Speaker 2: itself is like this runaway train that we're trying to

898
00:55:27,840 --> 00:55:31,040
Speaker 2: rein in. It's that, yeah, if you do phone scams,

899
00:55:31,200 --> 00:55:33,800
Speaker 2: you might come up with better you know, like both

900
00:55:33,960 --> 00:55:37,000
Speaker 2: like voice models to get this Rather than doing one

901
00:55:37,080 --> 00:55:39,200
Speaker 2: call per hour, you can do a thousand calls in

902
00:55:39,239 --> 00:55:42,680
Speaker 2: one hour running the same script and same scam on people.

903
00:55:43,160 --> 00:55:45,959
Speaker 2: Or to your point about how do you persuade Jack

904
00:55:45,960 --> 00:55:49,080
Speaker 2: O'Brien to not vote or to buy X product? Then

905
00:55:49,120 --> 00:55:51,719
Speaker 2: it's all going in that direction versus the things like

906
00:55:52,239 --> 00:55:55,920
Speaker 2: how can we optimize like the ability of a person

907
00:55:55,960 --> 00:55:58,879
Speaker 2: to learn if they're in an environment that typically isn't

908
00:55:58,880 --> 00:56:02,040
Speaker 2: one where people have access information or for the public

909
00:56:02,040 --> 00:56:04,000
Speaker 2: health use. And that's why, like I think in the end,

910
00:56:04,400 --> 00:56:07,560
Speaker 2: because we always see like like, yeah, this thing could

911
00:56:07,560 --> 00:56:10,640
Speaker 2: be used for good, and it's almost every example is like, yeah,

912
00:56:10,640 --> 00:56:13,440
Speaker 2: and it just made two guys three billion dollars in

913
00:56:13,520 --> 00:56:17,440
Speaker 2: two cents, and that's what happened with that, And now

914
00:56:17,480 --> 00:56:20,280
Speaker 2: we all don't know if videos we see on Instagram

915
00:56:20,280 --> 00:56:21,560
Speaker 2: are real anymore. Thanks.

916
00:56:22,080 --> 00:56:26,720
Speaker 1: Yeah, Yeah, it's not. Yeah, it's not inherently a bad technology.

917
00:56:26,800 --> 00:56:32,200
Speaker 1: It's that the system as is currently constituted, favors scams.

918
00:56:32,400 --> 00:56:35,680
Speaker 1: Like that's why we have a scam artist as like

919
00:56:35,880 --> 00:56:41,160
Speaker 1: our most recent president before this one and probably could

920
00:56:41,160 --> 00:56:46,319
Speaker 1: be future president again, like because that is what our

921
00:56:46,560 --> 00:56:50,719
Speaker 1: current system is designed for, and like it is just

922
00:56:51,680 --> 00:56:56,560
Speaker 1: scamming people for money. That's why when when blockchain technology

923
00:56:56,840 --> 00:57:00,800
Speaker 1: like for has its like infant baby steps, imdiately becomes

924
00:57:00,800 --> 00:57:03,960
Speaker 1: a thing that people use to scam one another, because

925
00:57:04,000 --> 00:57:10,200
Speaker 1: that is the software of our current society. So yeah,

926
00:57:10,200 --> 00:57:15,040
Speaker 1: there are like tons of amazing possibilities with AI. I

927
00:57:15,120 --> 00:57:17,960
Speaker 1: don't think we find them in the United States, like

928
00:57:18,400 --> 00:57:22,880
Speaker 1: applying the AI tools to our current software. I would

929
00:57:22,960 --> 00:57:26,040
Speaker 1: love for there to be a version of the future

930
00:57:26,240 --> 00:57:31,360
Speaker 1: where AI is so smart and efficient that it changes

931
00:57:31,920 --> 00:57:36,000
Speaker 1: that paradigm somehow and change it so that our software

932
00:57:35,840 --> 00:57:40,959
Speaker 1: that our society runs on is no longer scams, you know, right, right.

933
00:57:41,080 --> 00:57:44,840
Speaker 4: I mean there is a future where you know, AI

934
00:57:46,280 --> 00:57:51,320
Speaker 4: starts to do a lot of tasks for us. You know,

935
00:57:51,560 --> 00:57:56,240
Speaker 4: I think that there's starting to be you know, depending

936
00:57:57,080 --> 00:58:01,120
Speaker 4: it's mostly in Europe now, but also here talks about

937
00:58:01,160 --> 00:58:06,400
Speaker 4: like universal basic income. It's it's kind of I don't

938
00:58:06,440 --> 00:58:08,240
Speaker 4: think that that's going to be the case. I don't

939
00:58:08,280 --> 00:58:11,760
Speaker 4: think that in the US anybody has an appetite for that.

940
00:58:12,160 --> 00:58:16,440
Speaker 4: Maybe in twenty thirty years. But you know, I do

941
00:58:16,480 --> 00:58:21,200
Speaker 4: think that, you know, the there are certain things about

942
00:58:21,440 --> 00:58:25,600
Speaker 4: my job right as a professor, about like writing and

943
00:58:26,600 --> 00:58:31,280
Speaker 4: certain other components right where you know, here's some things

944
00:58:31,320 --> 00:58:37,000
Speaker 4: where AI can help, right, And you know, today I

945
00:58:37,120 --> 00:58:39,400
Speaker 4: use it as a tool to make me more efficient,

946
00:58:39,920 --> 00:58:44,120
Speaker 4: Like you know, okay, I want to have an editor

947
00:58:44,200 --> 00:58:48,160
Speaker 4: look at this. Okay, Well, you know it's not going

948
00:58:48,200 --> 00:58:50,959
Speaker 4: to do as good as a real editor, but maybe

949
00:58:51,000 --> 00:58:53,800
Speaker 4: as a cop as a you know, bad copy editor.

950
00:58:54,520 --> 00:58:58,440
Speaker 4: Yeah it's good, you know, Like but you know, I

951
00:58:58,440 --> 00:59:01,760
Speaker 4: mean I've been writing papers for a while. You know,

952
00:59:01,960 --> 00:59:05,400
Speaker 4: when the undergraduates write the paper, it does an even

953
00:59:05,440 --> 00:59:09,760
Speaker 4: better job, right because they have more room for growth, right,

954
00:59:10,000 --> 00:59:13,600
Speaker 4: And so I think the you know, yeah, I think

955
00:59:14,080 --> 00:59:18,360
Speaker 4: that on a micro level, these tools are now being used.

956
00:59:18,440 --> 00:59:22,440
Speaker 4: I think the you know, Russian spambots have been most

957
00:59:22,600 --> 00:59:27,880
Speaker 4: likely using large language model technology before chat GPT, right,

958
00:59:28,680 --> 00:59:31,960
Speaker 4: and so you know, I mean there are some ways

959
00:59:31,960 --> 00:59:34,040
Speaker 4: that I think we're going in a positive and good direction.

960
00:59:34,800 --> 00:59:34,960
Speaker 2: Right.

961
00:59:35,080 --> 00:59:35,520
Speaker 4: Yeah.

962
00:59:35,560 --> 00:59:37,479
Speaker 2: Now it's like now that you say that, I'm thinking,

963
00:59:37,480 --> 00:59:40,479
Speaker 2: I'm like, we thought Cambridge Analytico was bad, and it's

964
00:59:40,520 --> 00:59:43,120
Speaker 2: like what happens now when we like turn it up

965
00:59:43,120 --> 00:59:46,040
Speaker 2: to one thousand with this kind of thing where it's like, yeah,

966
00:59:46,080 --> 00:59:49,520
Speaker 2: here are all these voter files, now, like really figure

967
00:59:49,560 --> 00:59:52,600
Speaker 2: out how to suppress a vote or get to sway people.

968
00:59:52,680 --> 00:59:55,040
Speaker 2: And yeah, I again, it does feel like one of

969
00:59:55,080 --> 00:59:57,840
Speaker 2: these things where in the right hands, right, we can

970
00:59:57,960 --> 01:00:02,000
Speaker 2: create a world where people don't have to toil because

971
01:00:02,040 --> 01:00:06,280
Speaker 2: we're able to automate those things. But then the next

972
01:00:06,320 --> 01:00:09,760
Speaker 2: question becomes, how do we off ramp our culture of

973
01:00:09,920 --> 01:00:14,000
Speaker 2: greed and wealth hoarding and concentrating all of our wealth

974
01:00:14,440 --> 01:00:17,120
Speaker 2: into a way to say, like, well, we actually need

975
01:00:17,160 --> 01:00:20,000
Speaker 2: to spread this out that way everyone can have their

976
01:00:20,040 --> 01:00:24,120
Speaker 2: needs met materially, because our economy kind of runs on

977
01:00:24,200 --> 01:00:27,000
Speaker 2: its own in certain sectors. Obviously, other ones need real

978
01:00:27,120 --> 01:00:29,520
Speaker 2: like human labor and things like that. But yeah, that's

979
01:00:29,520 --> 01:00:32,080
Speaker 2: where you begin to see like, Okay, that's the fork

980
01:00:32,120 --> 01:00:35,720
Speaker 2: in the road. Can we make that? Do we take

981
01:00:35,760 --> 01:00:38,920
Speaker 2: the right path or does it turn into you know,

982
01:00:39,120 --> 01:00:41,640
Speaker 2: just like some very half asked version where like only

983
01:00:41,680 --> 01:00:44,640
Speaker 2: a fraction of the people that need like universal basic

984
01:00:44,680 --> 01:00:48,960
Speaker 2: income are receiving it. Well, you know, we read more

985
01:00:49,040 --> 01:00:53,080
Speaker 2: articles about why we don't see Van Vocht in Hollywood anymore.

986
01:00:53,160 --> 01:00:56,640
Speaker 2: As Jack pointed out that yeah, terribly worded thing about

987
01:00:56,720 --> 01:01:00,320
Speaker 2: Vince Vaughan that AI could not get right, couldn't get right.

988
01:01:00,440 --> 01:01:04,440
Speaker 1: So like Van Vought the one. So the sci fi

989
01:01:04,520 --> 01:01:07,800
Speaker 1: dystopia that seems like not not necessarily dystopia, but the

990
01:01:07,840 --> 01:01:10,840
Speaker 1: sci fi future that seems like it's most close at hand,

991
01:01:11,320 --> 01:01:15,680
Speaker 1: and like given you know, doing a weekend's worth research

992
01:01:15,760 --> 01:01:18,440
Speaker 1: on like where where this is, and like where all

993
01:01:18,480 --> 01:01:20,920
Speaker 1: the top thinkers think we're headed, the thing that make

994
01:01:21,360 --> 01:01:24,080
Speaker 1: that seems the closest reality to me is the movie Her,

995
01:01:24,560 --> 01:01:28,720
Speaker 1: Like Her is kind of already here. There's already these

996
01:01:28,760 --> 01:01:33,280
Speaker 1: applications that use chat GPT and you know GPT four

997
01:01:33,960 --> 01:01:37,800
Speaker 1: two again, Like it really seems to me, like and

998
01:01:37,920 --> 01:01:42,160
Speaker 1: I don't necessarily mean this like in a dismissive way.

999
01:01:42,320 --> 01:01:46,480
Speaker 1: This is probably you know, a good description of most jobs.

1000
01:01:46,520 --> 01:01:49,360
Speaker 1: Like that the thing that David Fincher I was like

1001
01:01:49,400 --> 01:01:51,320
Speaker 1: listening to a podcast where they talked about how David

1002
01:01:51,360 --> 01:01:54,320
Speaker 1: Fincher says, like words are only ever spoken in his

1003
01:01:54,440 --> 01:01:58,320
Speaker 1: movies by characters in order to lie, because like that's

1004
01:01:58,560 --> 01:02:01,800
Speaker 1: how humans actually use language, is just to like find

1005
01:02:01,840 --> 01:02:04,120
Speaker 1: different ways to like lie about who they are, what

1006
01:02:04,200 --> 01:02:07,320
Speaker 1: they're you know, approaches to things. And like I think,

1007
01:02:07,880 --> 01:02:11,360
Speaker 1: you know, these language models the thing that they're really

1008
01:02:11,400 --> 01:02:14,000
Speaker 1: good at, like whether it be like up to this

1009
01:02:14,040 --> 01:02:16,200
Speaker 1: point where people are like, holy shit, this thing's alive.

1010
01:02:16,320 --> 01:02:18,680
Speaker 1: That's talking to me, Well it's not, And it's like

1011
01:02:18,840 --> 01:02:21,680
Speaker 1: just doing a really good approximation of that and like

1012
01:02:21,800 --> 01:02:24,760
Speaker 1: kind of fooling you a little bit. Like take that

1013
01:02:24,960 --> 01:02:27,600
Speaker 1: to the ultimate extreme and it's like it makes you

1014
01:02:27,680 --> 01:02:31,720
Speaker 1: think that you're in a like loving relationship with a partner.

1015
01:02:32,160 --> 01:02:35,320
Speaker 1: And like that's already how it's being used in some

1016
01:02:35,600 --> 01:02:38,760
Speaker 1: in some instances to great effect. So like that that

1017
01:02:38,840 --> 01:02:41,080
Speaker 1: seems to be one way that I could see that

1018
01:02:41,160 --> 01:02:44,400
Speaker 1: being becoming more and more a thing where people are like, yeah,

1019
01:02:44,440 --> 01:02:47,160
Speaker 1: I don't have like human relationships anymore. I get like

1020
01:02:47,240 --> 01:02:53,840
Speaker 1: my emotional needs met by like her technology essentially is

1021
01:02:53,880 --> 01:02:57,000
Speaker 1: that what what would you say about that? And what

1022
01:02:57,280 --> 01:03:01,320
Speaker 1: is there a different fictional future sure that you see

1023
01:03:01,360 --> 01:03:01,960
Speaker 1: close at hand.

1024
01:03:03,000 --> 01:03:07,360
Speaker 4: So I think Her is great. I actually, I mean

1025
01:03:07,400 --> 01:03:11,400
Speaker 4: it's really funny because you know, I remember back in

1026
01:03:11,440 --> 01:03:14,800
Speaker 4: twenty seventeen, twenty eighteen where I was like, oh, her

1027
01:03:15,320 --> 01:03:18,920
Speaker 4: is like a great sort of this is where AI

1028
01:03:19,080 --> 01:03:22,160
Speaker 4: is going. And you know, for those of you who

1029
01:03:22,160 --> 01:03:25,720
Speaker 4: haven't seen the movie, it's a funny movie where you know,

1030
01:03:25,880 --> 01:03:29,680
Speaker 4: they have Scarlett Johansson is the voice of an AI

1031
01:03:29,760 --> 01:03:32,840
Speaker 4: agent which is on the phone. And I think that

1032
01:03:32,920 --> 01:03:35,640
Speaker 4: this is actually a pretty accurate description of what we're

1033
01:03:35,640 --> 01:03:38,520
Speaker 4: going to have in the future, not necessarily the relationship part,

1034
01:03:38,600 --> 01:03:41,400
Speaker 4: but the fact that we'll all have this personal assistant,

1035
01:03:42,520 --> 01:03:45,920
Speaker 4: and the personal assistant we'll see so many aspects of

1036
01:03:45,960 --> 01:03:51,120
Speaker 4: our lives right our calendars, our you know, meetings, our

1037
01:03:51,880 --> 01:03:55,600
Speaker 4: phone calls, everything, and so it'll be you know, this

1038
01:03:55,800 --> 01:03:58,840
Speaker 4: assistant that we all have that's helping us to like

1039
01:03:58,960 --> 01:04:03,960
Speaker 4: make things more productive. And as a function of the

1040
01:04:04,080 --> 01:04:09,440
Speaker 4: assistant to be effective, the assistant will probably want to

1041
01:04:09,520 --> 01:04:13,280
Speaker 4: have some connection with you, and that connection will be

1042
01:04:14,320 --> 01:04:19,160
Speaker 4: likely to allow you to trust it. And here's where

1043
01:04:19,200 --> 01:04:23,000
Speaker 4: the slippery slope comes from, you know, where you where

1044
01:04:23,000 --> 01:04:26,160
Speaker 4: you're like, oh, this understands me, and you start getting

1045
01:04:26,200 --> 01:04:29,880
Speaker 4: into a deeper relationship where you know, a lot of

1046
01:04:29,920 --> 01:04:32,680
Speaker 4: the fulfillment of a one on one connection can come

1047
01:04:33,160 --> 01:04:38,120
Speaker 4: with your smart assistant. And I do think that there's

1048
01:04:38,160 --> 01:04:41,240
Speaker 4: a little bit of a danger here. Like you know, again,

1049
01:04:41,600 --> 01:04:47,280
Speaker 4: I'm not a psychologist. I'm someone who studies AI machine learning,

1050
01:04:47,880 --> 01:04:53,360
Speaker 4: but I do, actually, you know, study how well machines

1051
01:04:53,760 --> 01:05:01,600
Speaker 4: can make sense of emotion and empathy. And really, you know,

1052
01:05:02,080 --> 01:05:05,640
Speaker 4: GPT four, which is the current state of the art

1053
01:05:05,720 --> 01:05:10,000
Speaker 4: technology is actually already really good and understanding. I use

1054
01:05:10,080 --> 01:05:15,560
Speaker 4: that phrase with a quote, the phrase understanding, but really

1055
01:05:16,600 --> 01:05:21,120
Speaker 4: what you were feeling, right, how you're feeling under this scenario.

1056
01:05:21,400 --> 01:05:24,840
Speaker 4: And you can imagine that feeling heard is one of

1057
01:05:24,840 --> 01:05:29,280
Speaker 4: the most important parts of relationship, right, And if you're

1058
01:05:29,440 --> 01:05:31,960
Speaker 4: not feeling heard by somebody else and you're feeling heard

1059
01:05:32,040 --> 01:05:36,480
Speaker 4: by you know, your personal assistant, that could shift relationships

1060
01:05:36,520 --> 01:05:40,720
Speaker 4: to to the AI, which you know, could be fundamentally

1061
01:05:40,800 --> 01:05:44,040
Speaker 4: dangerous because it's an AI, not a real person.

1062
01:05:44,640 --> 01:05:48,440
Speaker 1: Right, Yeah, the one I was talking about is replica

1063
01:05:48,840 --> 01:05:52,360
Speaker 1: that app the R E P l I KA. Where

1064
01:05:52,360 --> 01:05:56,600
Speaker 1: they are you know, designing these things explicitly to like

1065
01:05:56,840 --> 01:06:01,720
Speaker 1: fill in as like a romantic partner and at least

1066
01:06:01,760 --> 01:06:03,640
Speaker 1: with some people. And it's always hard to tell, did

1067
01:06:03,640 --> 01:06:06,280
Speaker 1: they find like the three people who are using this

1068
01:06:07,040 --> 01:06:10,960
Speaker 1: to fill an actual hole in their lives or is

1069
01:06:11,000 --> 01:06:14,480
Speaker 1: it actually taking off as a technology. But yeah, the

1070
01:06:14,960 --> 01:06:20,320
Speaker 1: personal assistant thing seems closer at hand than maybe people

1071
01:06:20,400 --> 01:06:23,440
Speaker 1: realized any other like kind of concrete changes that you

1072
01:06:23,480 --> 01:06:29,160
Speaker 1: think are coming to people's lives that they are aren't

1073
01:06:29,200 --> 01:06:31,600
Speaker 1: ready for or haven't haven't really thought about or seen

1074
01:06:31,720 --> 01:06:34,520
Speaker 1: in another sci fi movie.

1075
01:06:36,280 --> 01:06:39,520
Speaker 4: Sci fi movies are remarkably good in predictas.

1076
01:06:39,120 --> 01:06:42,040
Speaker 1: Yeah, or maybe they have seen in a sci fi movie.

1077
01:06:42,440 --> 01:06:46,800
Speaker 4: No, But I think you know another another thing that

1078
01:06:47,960 --> 01:06:52,000
Speaker 4: one potential that I think not enough people are talking

1079
01:06:52,000 --> 01:06:55,200
Speaker 4: about is how much better video games are going to become.

1080
01:06:55,720 --> 01:06:59,959
Speaker 4: M So people are already integrating GPT four into video games,

1081
01:07:00,760 --> 01:07:02,760
Speaker 4: and I think our video games are just going to

1082
01:07:02,840 --> 01:07:07,400
Speaker 4: be so much better because you know, you have this

1083
01:07:07,520 --> 01:07:14,600
Speaker 4: ability to like interact now with a character AI that's

1084
01:07:14,680 --> 01:07:18,040
Speaker 4: not just like very boring and chatting with you, but

1085
01:07:18,040 --> 01:07:21,280
Speaker 4: they can actually be truly entertaining and fully interactive.

1086
01:07:22,280 --> 01:07:22,760
Speaker 2: Interesting.

1087
01:07:22,920 --> 01:07:25,280
Speaker 4: I think you know, computer games are going to be

1088
01:07:26,320 --> 01:07:29,600
Speaker 4: much much better and possibly also more addictive as a

1089
01:07:29,600 --> 01:07:30,760
Speaker 4: function of being much.

1090
01:07:30,600 --> 01:07:34,160
Speaker 2: Better perfect, and then that will dull our appetite for

1091
01:07:34,240 --> 01:07:37,440
Speaker 2: revolution when our jobs are all taken by day. Yes,

1092
01:07:37,520 --> 01:07:38,120
Speaker 2: this is great, This.

1093
01:07:38,080 --> 01:07:41,520
Speaker 1: Is great, all right. I feel much better after having

1094
01:07:41,760 --> 01:07:42,680
Speaker 1: had this conversation.

1095
01:07:42,840 --> 01:07:46,040
Speaker 2: Dude, Grant that auto is way better the kinds of

1096
01:07:46,080 --> 01:07:48,600
Speaker 2: conversations I have with people I would normally bludgeon on

1097
01:07:48,640 --> 01:07:51,600
Speaker 2: the street with my character something else.

1098
01:07:53,640 --> 01:07:56,080
Speaker 1: Well, doctor said, Doc, such a pleasure having you on

1099
01:07:56,120 --> 01:07:58,000
Speaker 1: the dailies. I guess I feel like we could keep

1100
01:07:58,160 --> 01:08:02,600
Speaker 1: talking about this for another two hours, but we've got

1101
01:08:02,600 --> 01:08:04,960
Speaker 1: to let you go. But thank you so much for

1102
01:08:05,040 --> 01:08:07,560
Speaker 1: joining us. We'll have to have you back. Where can

1103
01:08:07,680 --> 01:08:10,400
Speaker 1: people find you? Follow you all that good stuff?

1104
01:08:11,000 --> 01:08:14,800
Speaker 4: Yeah, thank you for having me. I'm on what is

1105
01:08:14,840 --> 01:08:16,800
Speaker 4: now called x at.

1106
01:08:16,920 --> 01:08:22,400
Speaker 2: J O aout on this podcast, we still call it Twitter.

1107
01:08:22,680 --> 01:08:27,920
Speaker 4: Yeah, okay, well Twitter fine, and you know, yeah, that's

1108
01:08:27,960 --> 01:08:31,920
Speaker 4: my that's my currently still my main venue. But yeah,

1109
01:08:32,120 --> 01:08:32,479
Speaker 4: and is.

1110
01:08:32,439 --> 01:08:34,599
Speaker 1: There a work of media you've been enjoying?

1111
01:08:35,600 --> 01:08:40,719
Speaker 4: Good question, Modern dance. So Fall for Dance is starting

1112
01:08:41,320 --> 01:08:46,200
Speaker 4: in New York City. So I've been, you know, moving

1113
01:08:46,240 --> 01:08:50,720
Speaker 4: into watching some modern dance, which is sorry, kind of

1114
01:08:50,760 --> 01:08:53,639
Speaker 4: a fun thing to do modern dance.

1115
01:08:53,680 --> 01:08:59,200
Speaker 2: Okay, Okay, here, I'm gonna have bing Chat recommend some

1116
01:08:59,240 --> 01:09:00,880
Speaker 2: modern dance show for me in New York.

1117
01:09:01,000 --> 01:09:04,880
Speaker 1: So what they say, through work we get access to

1118
01:09:04,920 --> 01:09:09,320
Speaker 1: the being chat butt, so pretty cool.

1119
01:09:09,880 --> 01:09:12,160
Speaker 2: All I do is mess around. I'm like a child

1120
01:09:12,200 --> 01:09:14,639
Speaker 2: with it. I'm like pitch me a movie with Seth

1121
01:09:14,760 --> 01:09:18,240
Speaker 2: Rogan where he's a stoner biker. Did a pretty good job.

1122
01:09:18,400 --> 01:09:21,400
Speaker 2: Pretty good job. Feels a little derivative, but it makes

1123
01:09:21,400 --> 01:09:24,479
Speaker 2: sense because it's only deriving its ideas from existing things

1124
01:09:24,479 --> 01:09:24,840
Speaker 2: out there.

1125
01:09:24,840 --> 01:09:29,080
Speaker 1: But yeah, I asked to summarize the plot of Moby

1126
01:09:29,120 --> 01:09:33,040
Speaker 1: Dick in the formatab wu tang wrap and it was

1127
01:09:34,080 --> 01:09:37,120
Speaker 1: not great, not great, well, not great. It was kind

1128
01:09:37,120 --> 01:09:40,840
Speaker 1: of saying so like more early eighties rap where it

1129
01:09:40,920 --> 01:09:43,920
Speaker 1: was like, hey, my name's Ahab and I'm here to

1130
01:09:43,960 --> 01:09:47,280
Speaker 1: say I hit this whale in a major way.

1131
01:09:50,640 --> 01:09:51,200
Speaker 4: Miles.

1132
01:09:51,520 --> 01:09:53,360
Speaker 1: Where can people find you? What is a work a

1133
01:09:53,439 --> 01:09:55,559
Speaker 1: media you've been enjoying?

1134
01:09:56,280 --> 01:09:58,720
Speaker 2: Now you really got me thinking.

1135
01:09:58,760 --> 01:09:59,840
Speaker 1: Now you've really done it.

1136
01:10:00,200 --> 01:10:03,599
Speaker 2: Wow, Jack sorry, I'm asking it to summarize of mice

1137
01:10:03,640 --> 01:10:05,800
Speaker 2: and men in the form of a ghost face rap verse.

1138
01:10:06,200 --> 01:10:09,240
Speaker 2: You can find me at Miles of Gray on Twitter.

1139
01:10:09,920 --> 01:10:12,719
Speaker 2: I don't even for you know formerly x. I'm calling

1140
01:10:12,760 --> 01:10:16,760
Speaker 2: it twitterformerlyex dot com. Okay, we're inverting the form here,

1141
01:10:17,400 --> 01:10:20,360
Speaker 2: so check me out there pretty much everywhere, Instagram all that,

1142
01:10:21,479 --> 01:10:24,000
Speaker 2: and obviously find us on our basketball podcast, Miles and

1143
01:10:24,040 --> 01:10:26,880
Speaker 2: Jack Got Mad Boosties, where I promise my takes are

1144
01:10:26,920 --> 01:10:30,000
Speaker 2: not written by AI or are they? And also find

1145
01:10:30,040 --> 01:10:32,120
Speaker 2: me on my true crime podcast The Good Thief or

1146
01:10:32,200 --> 01:10:35,160
Speaker 2: We're in Search of the Greek Robinhood, and also for

1147
01:10:35,360 --> 01:10:37,839
Speaker 2: twenty Day Fiance, where I talk about ninety day Fiance.

1148
01:10:38,720 --> 01:10:40,360
Speaker 2: Whoa do you know what? You know?

1149
01:10:40,400 --> 01:10:40,519
Speaker 4: What?

1150
01:10:40,960 --> 01:10:42,680
Speaker 2: You know? They just said when I said, give me

1151
01:10:42,840 --> 01:10:45,360
Speaker 2: of mice and men in the form of ghost face. Yo,

1152
01:10:45,880 --> 01:10:48,400
Speaker 2: let me tell you about a story so true of

1153
01:10:48,479 --> 01:10:55,320
Speaker 2: two migrant workers, George and Lenny, who we're on a

1154
01:10:55,360 --> 01:10:57,880
Speaker 2: mission to own their food and land and live off

1155
01:10:58,000 --> 01:11:00,240
Speaker 2: the fat of the land. That was the plan. George

1156
01:11:00,320 --> 01:11:00,879
Speaker 2: was smallish.

1157
01:11:01,280 --> 01:11:02,479
Speaker 1: Come on, you gotta give me like.

1158
01:11:02,439 --> 01:11:05,599
Speaker 2: A random Italian dish rather mixed with like gore tex

1159
01:11:06,000 --> 01:11:08,840
Speaker 2: or north face. Anyway, it's got it's got some got

1160
01:11:08,840 --> 01:11:14,360
Speaker 2: some learning to do. Let's see any tweets or works

1161
01:11:14,400 --> 01:11:20,160
Speaker 2: of media I'm liking. Not really, not really the thought

1162
01:11:20,160 --> 01:11:23,559
Speaker 2: I thought I had something that I saw recently. Oh,

1163
01:11:23,720 --> 01:11:25,200
Speaker 2: new season A Top Boy. I was watching the new

1164
01:11:25,200 --> 01:11:26,559
Speaker 2: season A Top Boy on Netflix.

1165
01:11:26,560 --> 01:11:29,680
Speaker 1: New Season A Top Boy. Yeah, it's Top Boy a

1166
01:11:30,840 --> 01:11:31,920
Speaker 1: show Top Boy.

1167
01:11:32,080 --> 01:11:37,720
Speaker 2: No, does sound like it would be like The Best Boy? Yeah, No,

1168
01:11:37,800 --> 01:11:41,479
Speaker 2: it's a it's like a London gangster show. Okay, yeah,

1169
01:11:41,560 --> 01:11:42,960
Speaker 2: yeah about Roadman in it?

1170
01:11:43,560 --> 01:11:43,920
Speaker 1: Roadman?

1171
01:11:44,360 --> 01:11:45,080
Speaker 2: Yeah, Roadman.

1172
01:11:45,840 --> 01:11:49,640
Speaker 1: I was a highwayman, all right. You can find me

1173
01:11:49,680 --> 01:11:55,360
Speaker 1: on Twitter at Jack Underscore O'Brien. Uh. Tweet I enjoyed

1174
01:11:55,640 --> 01:11:59,559
Speaker 1: was from Jeff at Used Wigs, tweeted the weekend ending

1175
01:11:59,640 --> 01:12:02,680
Speaker 1: Trump of seeing the sixty minute stop Watch as a

1176
01:12:02,800 --> 01:12:07,559
Speaker 1: kid lives forever and I feel that and it's not

1177
01:12:07,640 --> 01:12:09,960
Speaker 1: just as a kid, not just as a kid.

1178
01:12:10,080 --> 01:12:10,280
Speaker 3: Yeah.

1179
01:12:10,840 --> 01:12:13,240
Speaker 1: You can find us on Twitter at daily Zeitgeist. We're

1180
01:12:13,240 --> 01:12:15,679
Speaker 1: at the Daily Zeikegeist on Instagram. We have a Facebook

1181
01:12:15,680 --> 01:12:18,240
Speaker 1: fan page and our website Daily zeikeuist dot com, where

1182
01:12:18,240 --> 01:12:21,840
Speaker 1: we post our episodes and our footnote where we link

1183
01:12:21,880 --> 01:12:24,519
Speaker 1: off to the information that we talk about in today's episode,

1184
01:12:24,800 --> 01:12:28,000
Speaker 1: so as a song that we think you might enjoy.

1185
01:12:28,240 --> 01:12:30,120
Speaker 1: Miles with song and we think people might enjoy it.

1186
01:12:30,200 --> 01:12:34,560
Speaker 2: Uh. This is from a Thai artist. She's like a drummer, producer,

1187
01:12:34,960 --> 01:12:37,960
Speaker 2: dope drummer and named Salne s A L I N.

1188
01:12:38,000 --> 01:12:42,400
Speaker 2: I'm sorry if I I botched the pronunciations and I'm

1189
01:12:42,400 --> 01:12:45,800
Speaker 2: gonna do it again with the track. It's called see Chompu.

1190
01:12:46,160 --> 01:12:48,280
Speaker 2: The first word si the second where c h O

1191
01:12:48,560 --> 01:12:50,760
Speaker 2: m p h u and I believe that is a

1192
01:12:50,800 --> 01:12:54,439
Speaker 2: region of like northeast Thailand. But she's like this, like

1193
01:12:54,560 --> 01:12:57,200
Speaker 2: it's like a really funky drum track if you like

1194
01:12:57,280 --> 01:13:00,880
Speaker 2: crumbin and kind of sort of like funky like Southeast

1195
01:13:00,880 --> 01:13:03,000
Speaker 2: Asian funk kind of music. This is kind of what

1196
01:13:03,040 --> 01:13:05,800
Speaker 2: that track sounds like, except for drumming. She is so

1197
01:13:05,920 --> 01:13:08,479
Speaker 2: tight on the kit anyway, So check this one out.

1198
01:13:08,600 --> 01:13:10,600
Speaker 2: It's a Salin with see chompoop.

1199
01:13:11,080 --> 01:13:11,439
Speaker 4: All right.

1200
01:13:11,520 --> 01:13:13,080
Speaker 1: We will link off to that in the footnote. The

1201
01:13:13,160 --> 01:13:16,080
Speaker 1: Daily is a production of iHeartRadio Formal podcasts from my

1202
01:13:16,160 --> 01:13:19,160
Speaker 1: heart Radio Visits, the iHeartRadio w ap Apple Podcasts wherever

1203
01:13:19,200 --> 01:13:21,160
Speaker 1: you listen to your favorite shows that it is good

1204
01:13:21,200 --> 01:13:23,520
Speaker 1: to do it for us This morning back this afternoon

1205
01:13:23,880 --> 01:13:26,479
Speaker 1: to tell you what is trending and we'll talk to

1206
01:13:26,479 --> 01:13:30,320
Speaker 1: you then. Bye bye,