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Speaker 1: Also media, Hello and welcome to Better Offline. We're in

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Speaker 1: beautiful New York City, Nevada. We're in on fifty fifth Street,

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Speaker 1: and I am ed Zititron, of course, and I'm surrounded

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Speaker 1: by incredible people.

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Speaker 2: So am I right?

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Speaker 1: Is Sherlyn Lowe of n gadget he doing sheln And

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Speaker 1: we've got Alex Kranz reporter critic extraordine.

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Speaker 3: Now, yeah, I just like to be really happy about Nevada.

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Speaker 2: It's beautiful here in city Nevada.

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Speaker 3: Yeah, Sonny, it's Warren.

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Speaker 2: Yeah, I love it. And of course Victoria's song from

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Speaker 2: the Verge.

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Speaker 4: Hello, Hello sunny New York, New York and Nevada.

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Speaker 1: Yeah, that's the thing. People think I do this accidentally.

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Speaker 1: No one knows what I do deliberately, and that's because

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Speaker 1: neither do I. So we were all just talking on

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Speaker 1: the way in here about possibly the best tech journalism

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Speaker 1: we've ever read.

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Speaker 2: And it's a story about a guy called Kevin Ruce.

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Speaker 1: He appears to be a child that who was allowed

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Speaker 1: to write for the New York Times articles called powerful.

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Speaker 2: AI is coming and I'm going to read you a

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Speaker 2: few sentences. We're going to discuss this.

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Speaker 5: Here are some things. I believe that our artificial intelligence.

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Speaker 5: I believe that over the past several years AI systems

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Speaker 5: have started surpassing humans in a number of domains math coding.

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Speaker 2: Wait math math? Are you fucking anyway?

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Speaker 1: There is an article that was in the New York

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Speaker 1: Times AGI thing that Kevin Ruce wrote, a child in

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Speaker 1: a man's body like big except Tom Hanks. He can

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Speaker 1: act and actually complete his job. And Kevin Russ appears

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Speaker 1: to have a gas leak. He has said that possibly

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Speaker 1: very soon, probably in twenty twenty six, twenty twenty seven,

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Speaker 1: but possibly as soon as this year, one or more

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Speaker 1: AI companies will claim they've created an artificial general intelligence

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Speaker 1: or AGI, which is usually defined as something like a

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Speaker 1: general purpose AI system that can do almost all cognitive

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Speaker 1: tasks a human can do. If you believe this year

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Speaker 1: or moron, I'm sorry, what do you think? Like, I

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Speaker 1: don't know, I just don't.

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Speaker 3: I think he's like gassing him up because he wants

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Speaker 3: to flirt with more AI and so like he needs an.

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Speaker 6: Age hang on, hang a hang off, flirt with AI

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Speaker 6: like like like your girlfriends.

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Speaker 4: The man who got a I.

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Speaker 2: To fall in love with yeah, bing bing was like

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Speaker 2: leave your wife.

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Speaker 7: Yeah, and so like like just so funny. This guy's

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Speaker 7: so funny. Yeah, like like this guy.

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Speaker 3: You know, I've been using a lot of AI. I've

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Speaker 3: been talking with these these ladies about it a whole bunch,

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Speaker 3: and I think of it. I love using AI because

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Speaker 3: it'll be like, you're the best writer.

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Speaker 4: We were just talking about this. I guess who told

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Speaker 4: her to do this. I guess who was just validating

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Speaker 4: her decision to do this.

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Speaker 3: But you know, you use it. You love it because

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Speaker 3: it tells you you're you're beautiful and pretty and smart,

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Speaker 3: and then none of it is true true.

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Speaker 8: It told me I could win a Pulitzer Prize if

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Speaker 8: you can't a one to three year period, and.

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Speaker 4: Why not, geograph, they will win the Pulitzers.

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Speaker 8: It will win the Pulitzers. But it was just like, yeah,

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Speaker 8: you could win a Pulitz And I was like, based

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Speaker 8: on based on what criteria? Are you telling me this?

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Speaker 4: And uh, you know, are you using Claude as well?

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Speaker 8: Yeah?

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Speaker 4: Claude and chat Chief.

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Speaker 3: It's like horoscopes. When you get a cool horoscope, you're like, yes,

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Speaker 3: this is awesome, you understand I'm a great Gemini. Yes,

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Speaker 3: But then you're like, no, but it's sucking not real

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Speaker 3: because it's a horoscope.

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Speaker 4: You are out.

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Speaker 2: There being like, I don't know any horoscope stuff, but

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Speaker 2: this mean.

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Speaker 3: Is out there like, you know what, I really believe

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Speaker 3: that I'm a leo and that this will understands.

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Speaker 1: I'm well, let me read you one passage as well.

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Speaker 1: I believe that hardened that Sorry.

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Speaker 5: I believe that hardened a skeptics who insist that the

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Speaker 5: progress there's all smoke and mirrors. And he dismissed AGI

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Speaker 5: as a delusional fancy. Not only I'm wrong on the merits,

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Speaker 5: but are giving people a full sense of security.

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Speaker 1: And I just want to say, that is one of

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Speaker 1: the dumbest fucking things I've heard in my life. The

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Speaker 1: people who criticize this are not actually they're giving people

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Speaker 1: a full sense of security, as opposed to the guy

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Speaker 1: being like the computer will wake up in two years

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Speaker 1: because he's.

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Speaker 3: Saying he's saying the dand of AI is AGI, when

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Speaker 3: in fact, the danger of AI is the destruction of jobs.

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Speaker 1: Yes, and even then it's going it's not even doing that,

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Speaker 1: it's it's taking away jobs from people who are already

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Speaker 1: having trouble getting them.

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Speaker 3: He's like, oh, sky Nott's gonna do it. No, Skynut's

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Speaker 3: not gonna do it. Elon Musk deploying whatever a little

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Speaker 3: chat GPT bought he does is gonna do it.

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Speaker 4: But AI is not infallible.

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Speaker 8: It makes up a lot of stuff wrong, and it's

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Speaker 8: just it doesn't understand how to be human in a

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Speaker 8: lot of ways.

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Speaker 6: I from that one sentence, though, I can tell who

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Speaker 6: his audience is. He's talking about a false sense of

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Speaker 6: security for whom, for the people that are developing the AI. Right,

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Speaker 6: it's just because none of us out there who have

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Speaker 6: jobs that might be taken over by a I have

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Speaker 6: any sense of security and the idea of an AGI

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Speaker 6: that will be competent enough to take over jobs. So

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Speaker 6: his audience, he's not talking to us. He's not talking

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Speaker 6: to people who have jobs that might be replaced. He's

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Speaker 6: talking to people developing the AI, the ones who want

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Speaker 6: to make money from it.

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Speaker 4: Right.

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Speaker 1: I also don't know what the full sense of security

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Speaker 1: would be. Oh, don't worry, this isn't going to fuck

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Speaker 1: them up. As opposed to the fact that you've got

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Speaker 1: one company soft Bank, borrowing money to put money into

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Speaker 1: Open Eye, which burns billions of dollars to make a

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Speaker 1: pretty shit product. If we're like not something that you

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Speaker 1: can find some cool things with. Fine, but for the

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Speaker 1: most part is the same thing this week.

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Speaker 3: If you're using any ai as a search engine, you

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Speaker 3: should stop amusing. Yeah, do not do I know, Sam Altman,

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Speaker 3: you're listening to this right right now, I wish and

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Speaker 3: you are just so ecstatic about chatchpt. You love it

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Speaker 3: so much. It is a terrible search engine.

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Speaker 2: It really is.

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Speaker 3: What is it fifty I think now at this point,

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Speaker 3: like there was a recent study fifty one percent of

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Speaker 3: the responses are false sick something like that, like like

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Speaker 3: this stuff.

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Speaker 1: Is bad acts like actually bad. That's what drives me

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Speaker 1: in saying. You get an article in the New York

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Speaker 1: Times being like, the computer is so smart, the computer

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Speaker 1: is so strong.

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Speaker 2: I love the computer.

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Speaker 3: Because it told you, because it's been it gases us up.

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Speaker 3: It's been an all this time, being like you're smart

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Speaker 3: and strong, so I must be strong.

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Speaker 8: If you talk to it too long ago, it's just

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Speaker 8: starts nagging you and then you're like, oh, I'm not smart,

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Speaker 8: I'm so floathing.

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Speaker 2: It's just you're and it's like and it's like you're

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Speaker 2: so good I'm like, I refusa believe between you and

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Speaker 2: my therapist, this has never worked.

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Speaker 1: Nice try, you can't swindle me a computer.

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Speaker 2: It's just so strange as well, because ostensibly you.

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Speaker 1: Three are wonderful reporters, like I've ready for years, and

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Speaker 1: it's like you're not cynics, but you are willing to

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Speaker 1: be critical.

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Speaker 6: Well, I am hardened as they come. When it comes

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Speaker 6: to the hardened, people like the.

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Speaker 1: Shit still like you're excited for it. This is not

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Speaker 1: even excited for it. What's weird about this piece other

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Speaker 1: than the fact it's just completely wrong in all almost

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Speaker 1: everything it says and clearly supporting billion dollar companies, it

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Speaker 1: doesn't even seem that excited. It's not like he's like,

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Speaker 1: I can't wait until this happens because imagine the cool

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Speaker 1: shit that could happen, and like, I don't know, come

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Speaker 1: up with an idea. Perhaps that's like, oh, if I

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Speaker 1: had an autonomous intelligence in my phone, it could plan

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Speaker 1: my day for me. How delightful despite there being seventeen

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Speaker 1: different companies that claim they do this already on Instagram

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Speaker 1: does not exist. But he could come up with nightear

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Speaker 1: or something cool. But it's like, actually, AGI is coming

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Speaker 1: what is it.

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Speaker 2: I don't know. It's inevitable though.

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Speaker 1: The thing I can't describe is inevitable, and I don't

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Speaker 1: know what it will do, but it will be good

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Speaker 1: or bad.

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Speaker 3: He's refusing to define a g I through the entire piece,

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Speaker 3: and he's like, and it's going to keep being redefined.

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Speaker 3: And I'm like, if you cannot define the word, the word, sorry,

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Speaker 3: So we take that work back. You're Kevin, You're not

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Speaker 3: allowed to use a g I until you can actually

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Speaker 3: define it.

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Speaker 1: Yeah, I don't think you should be allowed to use

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Speaker 1: beat for a minute, go outside the theme.

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Speaker 3: So use the touch grass app and step away touch

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Speaker 3: dot Grass.

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Speaker 1: So, Victoria, you had a delightful review this week, but

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Speaker 1: device called the B.

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Speaker 2: Please tell us about the B.

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Speaker 4: Okay, so this review which is B by.

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Speaker 3: V by V.

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Speaker 4: We do not like those two letters together without the

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Speaker 4: buie though.

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Speaker 8: Okay, anyway, yes I killed ed, Yeah you did kill them. Okay,

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Speaker 8: so you just like toppled it for a second. But

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Speaker 8: so you know, B is kind of the latest in

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Speaker 8: the AI gadgets that claimed to be your memory.

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Speaker 4: You know, you wear it.

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Speaker 8: It records everything you say and when I say, it

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Speaker 8: records everything you say it records everything you say. Okay,

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Speaker 8: so it records everything you say, listens to all of

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Speaker 8: your conversations. It doesn't record the audio, but it then

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Speaker 8: process it processes everything into a transcript, so you have

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Speaker 8: a transcript of your life. And then from that transcript,

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Speaker 8: you can use the chatbot to search the history of

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Speaker 8: your life, or you can get these daily summaries that

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Speaker 8: are that just be like, oh, this is what you

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Speaker 8: did today, these are the conversations you've had, this is

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Speaker 8: the places that you went to, and then it'll also

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Speaker 8: suggest to do based on your conversation. Right. So I

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Speaker 8: wore it for about a month and it wrote some

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Speaker 8: really great fan fiction about my life, and it was

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Speaker 8: it was also.

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Speaker 1: I have one of my favorites, which was when she's

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Speaker 1: listening to TV off by Kendrick Lama. Yes, yes, he's

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Speaker 1: one of the moments Victoria instructed must have to turn

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Speaker 1: off the TVA, reminding them both to avoid getting sick

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Speaker 1: again and mentioning leftover choccuterie.

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Speaker 2: Yes, this fucking rules.

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Speaker 8: It's because you know, like obviously about the time when

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Speaker 8: I started testing this at the Super Bowl halftime show,

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Speaker 8: was just like in everyone's mind and I was like

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Speaker 8: blasting TV off everywhere because yes, and also mustard.

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Speaker 4: So that was just going in my house like a lot.

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Speaker 8: And so the bees picking this up and mistaking it

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Speaker 8: as like things that I'm saying. It would be like

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Speaker 8: someone is someone is very sure of themselves, and I'm

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Speaker 8: watching tiktoks and they're bragging about themselves. It also like,

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Speaker 8: so there's this bit called fat that I call fact tinder.

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Speaker 4: They call it fact review.

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Speaker 8: So in the app, based on your conversations, you'll get

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Speaker 8: this window and there's just like facts about you, and

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Speaker 8: you swipe yes if it's true, Yeah, that's absolutely.

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Speaker 4: One of my fact.

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Speaker 8: Tinders was like Victoria knows someone named ken Kendra Monticia

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Speaker 8: who enjoys mustard and turning TVs off, which is.

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Speaker 4: Just like Jesus, you know, okay.

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Speaker 8: And in my review, I made this little carousel of

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Speaker 8: fact tinders that I was given that you can just

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Speaker 8: go through and try and.

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Speaker 4: Guess which one of these are true.

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Speaker 8: Uh. There was one that was just like Victoria has

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Speaker 8: dietary specific like specific dietary needs and.

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Speaker 4: She can't eat lollipops.

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Speaker 8: And I'm like, I don't even know how for the

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Speaker 8: for your information, I don't recall talking about lollipops. I

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Speaker 8: don't recall eating a lollipop. I don't know where you

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Speaker 8: would get this information from.

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Speaker 4: Damn, that's that's nuts. What what was the l behind me? Again?

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Speaker 4: Was it their own?

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Speaker 8: It's a mix of available lll ms such as I

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Speaker 8: think anthropics and open aiyes, as well as their own,

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Speaker 8: so like they're not giving mix. But what was the

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Speaker 8: point your memory, Alex, Alex, I can tell you the point.

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Speaker 6: I want one of these things for me because I'm

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Speaker 6: the sort of person that talks a lot and then

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Speaker 6: does nothing at the end of the day with it.

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Speaker 6: Because and then I want it to be like, here's

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Speaker 6: all your to dos based on all the crappy sides today.

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Speaker 8: So there is a glimmer of a good idea. Yes,

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Speaker 8: because I have ADHD. I consist I think everyone consistently

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Speaker 8: has conversations with people in their lives and they'll be like, yes,

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Speaker 8: we should follow off exactly. You don't follow it up

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Speaker 8: on that because you didn't write it down or you

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Speaker 8: didn't put a name there. So the idea that gleaning

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Speaker 8: from your conversations that it would do that, it's not

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Speaker 8: a bad idea. There are a lot of people with

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Speaker 8: memory problems who might like that. Unfortunately, you also have

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Speaker 8: to have a pretty good sense of self in order

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Speaker 8: to fact check this AI, because otherwise you are going

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Speaker 8: to be gas lit constantly. So so like the way

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Speaker 8: I wrote this review is like I kind of wanted

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Speaker 8: to take people into what it's like to actually use it,

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Speaker 8: how it changes your behaviors and all that. So, like

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Speaker 8: I included my day one. I commuted on day one

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Speaker 8: into the office, I went and I took a briefing

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Speaker 8: for bold Hue, which is a foundation printer.

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Speaker 4: Very cool to saw that story by you two.

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Speaker 8: Thank you, But so like I I found that I

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Speaker 8: took that briefing and I went to the office that day,

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Speaker 8: I had dinner with a friend and I went home.

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Speaker 8: So a day where I had a lot of conversations. Yeah,

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Speaker 8: And of the five to dos that it generated that day,

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Speaker 8: one of them was like follow up on thoughts that

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Speaker 8: were shared, but not like deeply dove into I'm like,

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Speaker 8: what the thank you?

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Speaker 2: Sure?

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Speaker 4: The most shared me?

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Speaker 2: Check out?

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Speaker 4: I don't fuck what does that mean?

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Speaker 8: The second was like, urgently check on your patient in

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Speaker 8: Louisiana as they are in danger of self harming or

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Speaker 8: harming someone else, and I'm like, what the where did

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Speaker 8: that come from? Where did that actually come from? And

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Speaker 8: the other one was check your car because it's making

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Speaker 8: rumbly noises. So let me tell you. The car that

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Speaker 8: it told me to check was probably the new or

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Speaker 8: the the NJ Transit. The book is that was a

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Speaker 8: bumpy ride coming in the Louisiana. Patient I'm guessing was

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Speaker 8: someone on my commute talking about.

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Speaker 2: Time everyone's life like.

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Speaker 8: And it depends on how often you mute it. And

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Speaker 8: the longer I wore it, the more I muted it,

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Speaker 8: because it started picking up like surveillance state.

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Speaker 4: Yeah sure, but you.

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Speaker 8: Know, around anywhere between three or four days and a week,

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Speaker 8: depending on how often you're muting on the charge on

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Speaker 8: a charge.

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Speaker 3: So it's the actual plan of the beat to just

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Speaker 3: gather so much random conversations that it can like better

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Speaker 3: train its.

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Speaker 2: Ll M even that because it doesn't seem to know

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Speaker 2: ship from fuck.

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Speaker 8: It doesn't have a bunch of people, don't you trace

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Speaker 8: you do when you set it up, you do train

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Speaker 8: it on your voice and then did the poor job

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Speaker 8: of that, yes, because it often thought my husband was

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Speaker 8: me and husband has a much deeper voice, so you

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Speaker 8: know you can label speakers and what it also doesn't

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Speaker 8: always work. I tried labeling speakers and it wouldn't save.

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Speaker 8: And then the one time it labeled my friend, all

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Speaker 8: women from the there there fourth who I talked to

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Speaker 8: were this friend. So it's like, this friend did this

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Speaker 8: for you? Did this friend?

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Speaker 3: I didn't do that.

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Speaker 4: They didn't do that whatsoever.

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Speaker 6: I have a question, So when you took your briefing,

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Speaker 6: did you have a conundrum of whether you wanted to

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Speaker 6: mute it?

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Speaker 4: Did you ask tell.

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Speaker 8: People where you like it's a brief it's a briefing. Yes,

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Speaker 8: Generally you usually have recorded. I was actually recording on

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Speaker 8: my phone. I totally forgot that I was wearing that thing, so,

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Speaker 8: you know, like I was fully expecting to just use

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Speaker 8: my phone recording of it. And then I was like, oh,

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Speaker 8: it has processed my conversation. And to be fair, the

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Speaker 8: summary I got of that meeting.

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Speaker 4: Was actually quite good.

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Speaker 8: It can be Yeah, it was quite good. It was

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Speaker 8: similar to what Otter does. It was summarizing the key

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Speaker 8: takeaways from this very structured conversation. It also memorialized forever

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Speaker 8: so that I know this is true, and multiple different

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Speaker 8: outlets that surge on fiance's makeup artists said that I

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Speaker 8: have great skin everything, and it's like memorialize I'm not

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Speaker 8: wearing foundation right, fact, yeah it is. I have great skin,

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Speaker 8: and I have spent so much money ensuring that I

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Speaker 8: have great skin with many Korean skincare products. We will

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Speaker 8: but we will talk about this, but just you know,

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Speaker 8: so like, ah, this is memorialized.

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Speaker 4: I love this. But the thing is so it got all.

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Speaker 8: The facts about the thing right, including pricing. All of

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Speaker 8: that was correct. Launch got that correct. Got the name

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Speaker 8: of the product completely wrong.

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Speaker 2: Sick.

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Speaker 8: It was a unique form mule. It was called formule.

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Speaker 8: It's bold hue, not remotely the same.

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Speaker 1: This just sounds like it's just generic. Like because Otto

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Speaker 1: does this, I'm guessing they use similar models. I'm guessing

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Speaker 1: it's just the same models that everyone else uses to

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Speaker 1: take voice that like rev dot com does this as well.

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Speaker 3: Yeah, yep.

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Speaker 1: So, like the most impressive thing is the thing that

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Speaker 1: large language models do already. I have to wonder if

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Speaker 1: its inability to tell certain people apart is kind of

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Speaker 1: almost going back to like one of the core problems

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Speaker 1: of AI type stuff that when the connect could not

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Speaker 1: see black people.

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Speaker 2: I don't know if you remember that.

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Speaker 1: Yeah, I have to wonder if they've done much training

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Speaker 1: on voices that are not from white women nor men.

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Speaker 4: So they do offer forty languages.

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Speaker 3: Well so does in these other things. None of these

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Speaker 3: transcription services work very well.

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Speaker 4: Yep, I know that. I'm just telling you.

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Speaker 3: I'm just telling audience doesn't know this side. They're not journalists,

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Speaker 3: like let's talk, they don't have to.

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Speaker 8: I'm just saying they offer forty languages. I did not

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Speaker 8: test whether the other forty languages were some languages.

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Speaker 2: I cannot speak anything other than English.

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Speaker 4: And even we've got we've got about four languages. We

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Speaker 4: do have about four. Two people in this room.

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Speaker 3: Have four languages and not have white quite.

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Speaker 8: Not white people in this room have about four languages

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Speaker 8: between us.

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Speaker 2: Is like, forty languages does not cover the rice problem.

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Speaker 6: So no, So So I think what you're getting at,

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Speaker 6: ED is that like technology frequently does this because the

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Speaker 6: info is trained on it doesn't train on a wide

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Speaker 6: enough set of people to cover the broad spectrum of

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Speaker 6: people in our world. I will say that like speed

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Speaker 6: talking speed exactly talking, speed talking, pitch talking like Hayden's

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Speaker 6: and patterns. I do think though that what both Alex

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Speaker 6: and v were getting at or two is that this

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Speaker 6: this particular product is so far behind and it's like

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Speaker 6: algorithms and the machine learning side of things because Otter Rev,

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Speaker 6: even Google Recorder or Apple memos Boys memos can better

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Speaker 6: identify voices and tell them apart and then attribute them

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Speaker 6: very accurately.

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Speaker 4: They also it's like the struggle, but there's so much better.

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Speaker 8: So like here's the other thing. This thing because it's

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Speaker 8: listening to things and all the time also struggles to

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Speaker 8: tell between broadcasts and live Oh yeah, yes, so you

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Speaker 8: know I'm watching an episode of Abbott Elementary and like

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Speaker 8: I just have to like emphasize an underscore that it

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Speaker 8: takes up about ten percent of your active brain at

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Speaker 8: all times to be like should I be muting myself

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Speaker 8: braw Like you know you do that Loo, what whatever

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Speaker 8: you do in a zoom call currently, imagine doing that

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Speaker 8: twenty four to seven. That's it's it's a lot of

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Speaker 8: your your brain that gets visited to that.

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Speaker 3: Process when you go to the bathroom.

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Speaker 8: You I can tell you didn't read my review because

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Speaker 8: that is in my actual I.

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Speaker 2: Actually didn't remember whether it did. There was a related thing.

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Speaker 8: There're so Okay, so anyway, we're going to address all

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Speaker 8: these points. We're going to address all these points. Okay,

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Speaker 8: I need to know if that was lacked aid like

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Speaker 8: hate lack aid for for dairy.

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Speaker 4: I heard the other Kay, we're.

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Speaker 8: Gonna we're gonna go. We're gonna start with the broadcast thing.

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Speaker 8: I was watching an episode of had Elementary and the

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Speaker 8: suggested to do it gave me was monitor union strikes

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Speaker 8: because students may have a hard time coming to your

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Speaker 8: class due to SEPTA strikes. I am not a public

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Speaker 8: school teacher in Philadelphia.

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Speaker 4: One.

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Speaker 2: Two.

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Speaker 8: It can also read your emails if you give it access.

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Speaker 8: It told me to check uh to basically take this

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Speaker 8: unique I D I D entifier and check this park

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Speaker 8: Mobile claim settlement and file by March fifth, I checked

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Speaker 8: four all four of my inboxes.

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Speaker 4: I cannot find this email.

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Speaker 8: I have no idea why they told me to do

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Speaker 8: this to two blah. I you know, one morning after

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Speaker 8: a particularly five US dinner, I went to the bathroom

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Speaker 8: and I committed crimes.

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Speaker 4: Okay, I committed crimes. I was wearing this student thing.

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Speaker 8: Uh not stupid, but you know, I feel I was

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Speaker 8: wearing this device that I was testing. I turn around

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Speaker 8: and I go, oh damn, that was a shit. And

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Speaker 8: then a second later, a second later, I went, oh shit,

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Speaker 8: this thing is listening to me. And then I muted

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Speaker 8: it far too late. I look at the summary it

439
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Speaker 8: gives me, and it goes Victoria humorous, humorously vocalized her

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Speaker 8: bowel movements, and I was like, Jesus fucking Christ. And

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Speaker 8: then I had a one on one with my editor

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Speaker 8: and the summary, because it was within an hour of

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Speaker 8: this happening, the summary that it transcribed was todd. Victoria's

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Speaker 8: editor humorously brought up that he saw on a shared

445
00:20:42,560 --> 00:20:45,720
Speaker 8: platform that they had a fact that she had.

446
00:20:45,720 --> 00:20:49,439
Speaker 4: Vocalized her bowel. Oh my. They laughed about it and

447
00:20:49,520 --> 00:20:50,240
Speaker 4: it was funny.

448
00:20:50,240 --> 00:20:52,440
Speaker 8: And I was like, do you think that I would

449
00:20:52,520 --> 00:20:56,760
Speaker 8: commit such an age R violation as to tell my

450
00:20:57,480 --> 00:21:02,480
Speaker 8: freaking editor that I had a particularly impressive dump that morning.

451
00:21:03,080 --> 00:21:07,320
Speaker 4: No, I would not tell anybody this.

452
00:21:07,359 --> 00:21:09,199
Speaker 8: I would take this to my grave unless it was

453
00:21:09,240 --> 00:21:14,960
Speaker 8: to demonstrate the fallibility of AI. This is a human reason,

454
00:21:15,400 --> 00:21:18,320
Speaker 8: a human good for me to be humiliated. So I

455
00:21:18,359 --> 00:21:22,240
Speaker 8: shared this, and my conclusion is is that death, sex,

456
00:21:22,320 --> 00:21:25,320
Speaker 8: and bowel movements are things that AI should butt the

457
00:21:25,440 --> 00:21:29,959
Speaker 8: fuck out of yeah, because I got some real interesting

458
00:21:30,040 --> 00:21:32,800
Speaker 8: notifications from this, and I was like, I didn't need

459
00:21:32,840 --> 00:21:35,679
Speaker 8: you to know that. I didn't need to be humiliated

460
00:21:35,680 --> 00:21:37,840
Speaker 8: by that. But the suggested to do was to start

461
00:21:37,840 --> 00:21:39,080
Speaker 8: carrying lactaid.

462
00:21:39,520 --> 00:21:43,200
Speaker 4: From the poop because I had.

463
00:21:43,240 --> 00:21:47,880
Speaker 8: Conversation lactose intolerant, and it said start carrying lactate again

464
00:21:47,960 --> 00:21:51,520
Speaker 8: because lactose intolerant symptoms are coming back. And I was like,

465
00:21:51,560 --> 00:21:56,160
Speaker 8: this is fucking rude, hopeful, helpful, helpful.

466
00:21:57,040 --> 00:21:59,000
Speaker 1: I do not have mustard that would not be good

467
00:21:59,040 --> 00:22:00,920
Speaker 1: for your bows turned the TV off.

468
00:22:01,720 --> 00:22:03,720
Speaker 8: But so I do want to say I do want

469
00:22:03,760 --> 00:22:07,879
Speaker 8: to say that it is. It does learn broadly things.

470
00:22:07,640 --> 00:22:08,960
Speaker 4: About you that are accurate.

471
00:22:09,200 --> 00:22:12,280
Speaker 8: So I, by the end of my month testing this,

472
00:22:12,359 --> 00:22:16,800
Speaker 8: I had fallen down into several existential crises. But there's

473
00:22:16,840 --> 00:22:18,639
Speaker 8: a chatbot that you can talk to, and so I

474
00:22:18,680 --> 00:22:21,400
Speaker 8: asked it things like am I a good person? Am

475
00:22:21,440 --> 00:22:22,520
Speaker 8: I a bad person?

476
00:22:22,800 --> 00:22:23,760
Speaker 4: One type of person?

477
00:22:24,000 --> 00:22:26,359
Speaker 3: Am I one who needs it?

478
00:22:32,840 --> 00:22:35,040
Speaker 8: Yeah? And I was like, what is my communication style?

479
00:22:35,040 --> 00:22:37,960
Speaker 8: How would you describe the themes of You could tell

480
00:22:38,000 --> 00:22:38,280
Speaker 8: you if.

481
00:22:38,160 --> 00:22:39,480
Speaker 6: You're an n f P or an I.

482
00:22:39,640 --> 00:22:44,080
Speaker 8: T like, well, what's my relationship like with my spouse?

483
00:22:44,320 --> 00:22:46,640
Speaker 4: And so yeah, yeah, yeah, yeah yeah.

484
00:22:46,680 --> 00:22:50,840
Speaker 8: So that's the funny thing is I was very touched

485
00:22:50,840 --> 00:22:53,280
Speaker 8: because it was like, you are definitively a good person.

486
00:22:53,600 --> 00:22:57,199
Speaker 8: Here is like six reasons why with transcript things like

487
00:22:57,240 --> 00:22:59,280
Speaker 8: it's like you constantly show up for your friends, you

488
00:22:59,320 --> 00:23:01,959
Speaker 8: stand up for a wrong, you do this, this, this

489
00:23:02,000 --> 00:23:04,439
Speaker 8: and this, and I was like, oh my god.

490
00:23:05,480 --> 00:23:07,320
Speaker 6: This is the validation we were talking about.

491
00:23:09,040 --> 00:23:12,520
Speaker 8: It's just like you prefer direct and honest communication. You

492
00:23:12,560 --> 00:23:14,960
Speaker 8: don't play games. And I was like, yes, because I

493
00:23:15,000 --> 00:23:20,560
Speaker 8: am in Ariury, I'm Aries, I'm Aries sun Leo Moon

494
00:23:20,960 --> 00:23:24,960
Speaker 8: Aries no Ari sun Leo Rising Leo, No Ari sun

495
00:23:25,119 --> 00:23:28,640
Speaker 8: Leo Rising Aries Moon and Aries Mercury. And it's basically

496
00:23:28,720 --> 00:23:32,360
Speaker 8: like astrology and AI both agree that I don't fucking

497
00:23:32,480 --> 00:23:36,320
Speaker 8: rot and I don't suck around. Basically, I am very direct.

498
00:23:36,320 --> 00:23:37,879
Speaker 8: What you see is what you get, super honest.

499
00:23:37,920 --> 00:23:41,439
Speaker 4: I was like, oh, thank you, that's so nice. And

500
00:23:41,480 --> 00:23:44,400
Speaker 4: they're they're like, you advocate for your colleagues. Oh that's

501
00:23:44,440 --> 00:23:45,399
Speaker 4: so it's true.

502
00:23:45,400 --> 00:23:48,639
Speaker 2: I am.

503
00:23:49,520 --> 00:23:51,520
Speaker 4: This demon. It's six feet under.

504
00:23:51,600 --> 00:23:55,240
Speaker 8: I need an AI with transcripts to have definitive but

505
00:23:55,320 --> 00:23:56,040
Speaker 8: I'm a good person.

506
00:23:57,320 --> 00:23:59,399
Speaker 3: You don't like when it computer says you're pretty.

507
00:23:59,480 --> 00:23:59,800
Speaker 2: I don't.

508
00:24:00,359 --> 00:24:01,040
Speaker 3: I don't love it.

509
00:24:01,320 --> 00:24:01,960
Speaker 2: I don't care.

510
00:24:02,440 --> 00:24:05,080
Speaker 3: I love it. The computer was like, you are such

511
00:24:05,119 --> 00:24:07,679
Speaker 3: a good writer, and I was like, thank You're.

512
00:24:07,400 --> 00:24:08,840
Speaker 4: Getting in the Severn zone are we?

513
00:24:08,960 --> 00:24:09,040
Speaker 1: Like?

514
00:24:09,680 --> 00:24:11,240
Speaker 8: Oh, you should have seen the to dos it gave

515
00:24:11,320 --> 00:24:12,840
Speaker 8: after I watched an episode of severns.

516
00:24:12,880 --> 00:24:18,120
Speaker 1: It makes watch a more interesting episode like the one okay,

517
00:24:18,480 --> 00:24:20,320
Speaker 1: I've loved the season, but the one way she goes

518
00:24:20,320 --> 00:24:20,960
Speaker 1: out to the cold.

519
00:24:21,080 --> 00:24:24,600
Speaker 4: But the last episode that was not the episode that

520
00:24:24,640 --> 00:24:25,040
Speaker 4: I watched.

521
00:24:25,080 --> 00:24:28,800
Speaker 2: It was the I've loved the season okay, because I.

522
00:24:28,720 --> 00:24:30,639
Speaker 8: Was getting the episode that I was getting to do

523
00:24:30,800 --> 00:24:33,359
Speaker 8: this from was from the one where it's Gemma.

524
00:24:34,080 --> 00:24:34,920
Speaker 4: It was all about Gemma.

525
00:24:35,000 --> 00:24:37,320
Speaker 8: It was all about that was a beautiful it was

526
00:24:37,320 --> 00:24:39,760
Speaker 8: a beautiful episode. But when I looked at the dos like,

527
00:24:39,800 --> 00:24:40,120
Speaker 8: I was.

528
00:24:40,080 --> 00:24:42,800
Speaker 2: Like, Jesus Christ, this is Jesus.

529
00:24:43,160 --> 00:24:46,720
Speaker 6: This doesn't make any sense whatsoever going to the dentist

530
00:24:46,840 --> 00:24:47,560
Speaker 6: donate blood.

531
00:24:47,800 --> 00:24:50,000
Speaker 4: Yeah, it's just like not like that.

532
00:24:50,119 --> 00:24:53,600
Speaker 8: It was just kind of closer to the or. It

533
00:24:53,680 --> 00:24:56,000
Speaker 8: was like follow up on thoughts of cold.

534
00:24:57,720 --> 00:25:01,800
Speaker 4: And I was like, this is yeah, honestly.

535
00:25:01,520 --> 00:25:04,359
Speaker 1: I have some other practical questions. So it's fifty dollars

536
00:25:04,359 --> 00:25:08,240
Speaker 1: with no subscription. Yes, yes, how the fuck does this

537
00:25:08,320 --> 00:25:11,560
Speaker 1: make sense? Numerica like so that they claimed in the

538
00:25:11,720 --> 00:25:13,159
Speaker 1: in the review, you said they're going to do it

539
00:25:13,160 --> 00:25:15,199
Speaker 1: on device, which is a thing that everyone says, and

540
00:25:15,200 --> 00:25:17,600
Speaker 1: I've never seen one of these companies actually execute.

541
00:25:17,920 --> 00:25:19,200
Speaker 2: So fifty bucks.

542
00:25:18,880 --> 00:25:22,760
Speaker 1: And then so I'm pulling up my Victoria value honesty

543
00:25:22,800 --> 00:25:24,440
Speaker 1: and does not like dishonest behavior.

544
00:25:24,520 --> 00:25:25,159
Speaker 4: I agree with that.

545
00:25:25,200 --> 00:25:26,200
Speaker 2: From what I agree.

546
00:25:26,280 --> 00:25:27,879
Speaker 3: It says that's one of your hobbies.

547
00:25:28,400 --> 00:25:28,679
Speaker 4: I know.

548
00:25:31,280 --> 00:25:36,000
Speaker 3: Hell yeah, Victoria, your hobbies are being a good person.

549
00:25:36,200 --> 00:25:37,720
Speaker 4: Wait, hang on, hang on show. He says.

550
00:25:37,800 --> 00:25:41,840
Speaker 8: Victoria believes in leading with love and kindness and calmness,

551
00:25:41,840 --> 00:25:42,800
Speaker 8: and family.

552
00:25:42,440 --> 00:25:45,639
Speaker 6: With internationals with family, and you have is tagged at

553
00:25:45,640 --> 00:25:48,560
Speaker 6: the bottom as hobbies have probbable good.

554
00:25:48,640 --> 00:25:51,600
Speaker 4: But Victoria understands Korean to something.

555
00:25:53,840 --> 00:25:57,800
Speaker 8: Jesus Crictoria has a partner name Very also a hobby.

556
00:25:57,840 --> 00:25:58,560
Speaker 4: Also a hobby.

557
00:25:59,560 --> 00:26:02,680
Speaker 8: This is a health Victoria has lost both of her

558
00:26:02,720 --> 00:26:08,639
Speaker 8: parents exist. Yes, this is true. Victoria values values family

559
00:26:08,720 --> 00:26:10,720
Speaker 8: relationships and wishes to maintain them.

560
00:26:10,760 --> 00:26:11,359
Speaker 4: Also true.

561
00:26:11,359 --> 00:26:14,280
Speaker 2: I would love it if it's said otherwise. It's like Victoria's.

562
00:26:13,840 --> 00:26:16,400
Speaker 8: This is politics, which is also true. Victoria has been

563
00:26:16,400 --> 00:26:21,040
Speaker 8: involved in discussions about family inheritance and estates tagged as politics.

564
00:26:21,160 --> 00:26:22,320
Speaker 2: That's true, very good.

565
00:26:23,000 --> 00:26:26,159
Speaker 4: Victoria has an interest in visiting family in Korea.

566
00:26:26,320 --> 00:26:27,359
Speaker 2: True Hobbies.

567
00:26:28,640 --> 00:26:33,639
Speaker 8: Victoria has collaborated on a gadget related podcast, Wow It's True.

568
00:26:35,880 --> 00:26:39,639
Speaker 6: Which so it's very gamified, almost very very obvious.

569
00:26:39,920 --> 00:26:43,040
Speaker 1: Yeah, Like these are things I have to wonder see.

570
00:26:43,040 --> 00:26:45,080
Speaker 1: The problem is I would love this company to burn.

571
00:26:45,240 --> 00:26:48,040
Speaker 1: I think the idea of this concept is horrifying. I

572
00:26:48,080 --> 00:26:50,920
Speaker 1: think it's upsetting that it exists. However, I would also

573
00:26:50,960 --> 00:26:53,600
Speaker 1: love to see like a ten people's worth of data

574
00:26:53,680 --> 00:26:56,760
Speaker 1: on this, just to see if they tell everyone very

575
00:26:56,760 --> 00:26:58,360
Speaker 1: similar things, because how.

576
00:26:58,200 --> 00:27:00,639
Speaker 2: The fuck do you define Like is it willing to

577
00:27:00,680 --> 00:27:03,320
Speaker 2: be like you sound like a dickhead, you do not

578
00:27:03,440 --> 00:27:04,439
Speaker 2: care about your family?

579
00:27:04,480 --> 00:27:07,360
Speaker 6: I was going to say, I think the flip side

580
00:27:07,359 --> 00:27:09,240
Speaker 6: of it validating you is will it pick up on

581
00:27:09,280 --> 00:27:11,399
Speaker 6: behaviors that are negative in people that have like these

582
00:27:11,400 --> 00:27:13,679
Speaker 6: traits like we you know, in social media in popular

583
00:27:13,680 --> 00:27:15,280
Speaker 6: culture now we're calling everyone narcissist.

584
00:27:15,320 --> 00:27:16,880
Speaker 4: Right, watch any episode of Love is Blind.

585
00:27:16,920 --> 00:27:21,679
Speaker 6: It's like, that's a narcissism that's as.

586
00:27:19,920 --> 00:27:21,400
Speaker 4: Based on Love is Blind.

587
00:27:21,760 --> 00:27:23,920
Speaker 8: Wow, And one of my fat tenders in the review

588
00:27:24,040 --> 00:27:27,160
Speaker 8: is saying that Matt Victoria had an incident where Madison

589
00:27:27,240 --> 00:27:41,920
Speaker 8: left the room, and she did leave the room.

590
00:27:42,119 --> 00:27:44,040
Speaker 6: That's where my point is is, right now the people

591
00:27:44,040 --> 00:27:45,560
Speaker 6: who are using it and testing it are getting these

592
00:27:45,600 --> 00:27:48,359
Speaker 6: positive responses and feedback. I'm curious to see what it

593
00:27:48,359 --> 00:27:51,520
Speaker 6: would do to any number of CEOs or billionaires that.

594
00:27:52,600 --> 00:27:55,240
Speaker 8: Also listen to me bitch to friends about things and

595
00:27:55,400 --> 00:27:58,679
Speaker 8: has recorded those things. Those were not included in the

596
00:27:58,720 --> 00:28:03,280
Speaker 8: review because I would, I would, I would get canceled.

597
00:28:06,680 --> 00:28:09,439
Speaker 8: It did also listen to me talk for about an

598
00:28:09,480 --> 00:28:12,000
Speaker 8: hour and a half about Flake Lively and Justin Baldoni.

599
00:28:13,000 --> 00:28:17,760
Speaker 8: My opinions that your friends, uh yeah, actually it said

600
00:28:18,000 --> 00:28:26,600
Speaker 8: Victoria's Team Balcony in Victoria's Team Balcony gossip context, and

601
00:28:26,640 --> 00:28:29,560
Speaker 8: I was like. It also gave me facts such as

602
00:28:29,640 --> 00:28:31,520
Speaker 8: Victoria has a living room and a kitchen.

603
00:28:31,760 --> 00:28:33,800
Speaker 4: Thank you, thank you, thank you for that.

604
00:28:34,080 --> 00:28:37,639
Speaker 8: It's one of my favorite things that it did was

605
00:28:37,680 --> 00:28:40,960
Speaker 8: it summarized a conversation between So what happened was is

606
00:28:41,000 --> 00:28:44,000
Speaker 8: that we were having some late night cookies Oreos, and

607
00:28:44,040 --> 00:28:47,400
Speaker 8: I dropped one into the cat's empty food bowl and

608
00:28:47,440 --> 00:28:51,840
Speaker 8: I went three second rule and my husband goes, that's disgusting,

609
00:28:52,240 --> 00:28:56,240
Speaker 8: and I say, in an apocalypse situation, you would eat

610
00:28:56,240 --> 00:28:58,800
Speaker 8: that Oreo And he went, I have a heat gun.

611
00:28:59,120 --> 00:29:01,400
Speaker 4: I would just disinfect it.

612
00:29:01,400 --> 00:29:06,600
Speaker 8: It took this conversation and went Victoria and Gate have

613
00:29:06,600 --> 00:29:10,640
Speaker 8: have disagreements about how to handle smelly things in an apocalypse.

614
00:29:11,400 --> 00:29:14,840
Speaker 4: That was a disagreement. It was a disagreement. I'm just like,

615
00:29:15,080 --> 00:29:17,880
Speaker 4: what what is the point of any of it?

616
00:29:18,000 --> 00:29:20,520
Speaker 1: Always looking at this and going like, ah, finally, well

617
00:29:20,560 --> 00:29:22,160
Speaker 1: I'd forgotten I had the Smellorio.

618
00:29:22,680 --> 00:29:24,840
Speaker 2: Actually Orio was left out. How would you possibly not?

619
00:29:25,040 --> 00:29:27,280
Speaker 6: It feels like the bee was this like little toddler

620
00:29:27,360 --> 00:29:29,400
Speaker 6: robot in your house looking at you, going through your

621
00:29:29,440 --> 00:29:31,520
Speaker 6: life and then taking notes, right, and we don't know

622
00:29:31,520 --> 00:29:32,800
Speaker 6: what the notes before, but it's taking notes.

623
00:29:33,440 --> 00:29:35,760
Speaker 1: It just sounds like it gives you an estate on

624
00:29:35,800 --> 00:29:39,720
Speaker 1: your Every guy, a like twenty five year old woman

625
00:29:40,040 --> 00:29:42,160
Speaker 1: has dated in New York is just like on his

626
00:29:42,240 --> 00:29:45,080
Speaker 1: phone's gone yeah, yeah, yeah, he said something about Smelliorio

627
00:29:45,160 --> 00:29:45,680
Speaker 1: or some ship.

628
00:29:46,040 --> 00:29:49,440
Speaker 2: Uh huh uh huh yeah, I will so yeah an argument.

629
00:29:49,800 --> 00:29:51,760
Speaker 8: Every once in a while you do get a really

630
00:29:51,800 --> 00:29:54,080
Speaker 8: good to do though, Like it was like follow up

631
00:29:54,120 --> 00:29:56,400
Speaker 8: with your video team about making a social for the

632
00:29:56,440 --> 00:29:59,960
Speaker 8: ball to you that I actually did call the plan

633
00:30:00,400 --> 00:30:03,760
Speaker 8: because in which I did fix the hoa violation that

634
00:30:03,800 --> 00:30:05,200
Speaker 8: you left festering for.

635
00:30:05,440 --> 00:30:10,000
Speaker 4: Eight months, which I did. So you know, like some

636
00:30:10,120 --> 00:30:13,080
Speaker 4: of them are good, and how frequently are they good like.

637
00:30:13,160 --> 00:30:17,080
Speaker 8: Other I would say it depends on what you use

638
00:30:17,120 --> 00:30:20,720
Speaker 8: it for. So if you're using it primarily for work conversations,

639
00:30:20,760 --> 00:30:21,160
Speaker 8: which is.

640
00:30:21,320 --> 00:30:22,720
Speaker 4: So you mute it when it's not.

641
00:30:22,640 --> 00:30:24,160
Speaker 8: When you mute it, and you only use it for

642
00:30:24,200 --> 00:30:28,200
Speaker 8: work conversations, your batting average is quite high for takeaways

643
00:30:28,200 --> 00:30:39,200
Speaker 8: that like, no, like sixty is pretty good, And it

644
00:30:39,200 --> 00:30:42,040
Speaker 8: depends on how many people are in the conversation with you.

645
00:30:42,120 --> 00:30:45,440
Speaker 8: So it listened to a staff meeting and the takeaways

646
00:30:45,480 --> 00:30:50,000
Speaker 8: were not so particularly good for me, but they would

647
00:30:50,000 --> 00:30:53,520
Speaker 8: have been great for someone in the particular thing. But then,

648
00:30:53,600 --> 00:30:57,360
Speaker 8: you know, if you're useful friends, like it's it's difficult

649
00:30:57,400 --> 00:30:59,680
Speaker 8: because you're talking with friends, you have to go like, hey,

650
00:30:59,760 --> 00:31:02,040
Speaker 8: so just so you know, I have this thing on

651
00:31:02,120 --> 00:31:04,000
Speaker 8: me and it's listening and it's going to have some

652
00:31:04,040 --> 00:31:06,040
Speaker 8: AI insights and some of your friends, some of my

653
00:31:06,040 --> 00:31:09,120
Speaker 8: friends are techy, so they're like, oh man, what what

654
00:31:09,360 --> 00:31:12,720
Speaker 8: LLLM does it use like they don't mind. My bestie

655
00:31:12,760 --> 00:31:14,920
Speaker 8: is just like another one of your fucking just sure,

656
00:31:14,960 --> 00:31:16,160
Speaker 8: go ahead whatever.

657
00:31:16,040 --> 00:31:21,040
Speaker 4: My friends, Yeah, one of your do dads, go go off.

658
00:31:21,200 --> 00:31:24,000
Speaker 8: My husband, however, was very much like, it is not

659
00:31:24,160 --> 00:31:26,040
Speaker 8: useful enough to invade.

660
00:31:25,800 --> 00:31:28,400
Speaker 4: My privacy that the people around you.

661
00:31:28,480 --> 00:31:29,800
Speaker 3: Yeah, that's what I was going to ask, is like,

662
00:31:30,400 --> 00:31:34,560
Speaker 3: is fifty dollars worth like fifty dollars to invade your

663
00:31:34,600 --> 00:31:37,160
Speaker 3: privacy forever? To give all of your not just all

664
00:31:37,240 --> 00:31:40,760
Speaker 3: like your bowel movements, Yeah, everything to a company you

665
00:31:40,800 --> 00:31:42,160
Speaker 3: don't know worth it?

666
00:31:42,280 --> 00:31:45,840
Speaker 8: I mean they Okay, I have to come on here

667
00:31:45,880 --> 00:31:47,880
Speaker 8: and say I did ask about the privacy. It's not

668
00:31:47,920 --> 00:31:48,680
Speaker 8: like I didn't ask that.

669
00:31:49,120 --> 00:31:51,320
Speaker 4: I'm going to ask that they are working on a

670
00:31:51,680 --> 00:31:54,080
Speaker 4: local phone, only my.

671
00:31:54,680 --> 00:31:56,200
Speaker 2: Working working on that.

672
00:31:57,440 --> 00:32:02,080
Speaker 4: I'm just saying criticizing, Yeah, I know, I'm just saying.

673
00:32:03,040 --> 00:32:06,320
Speaker 8: The other thing is that everything is encrypted in transit

674
00:32:06,320 --> 00:32:09,880
Speaker 8: and at rest Good they have a third party party

675
00:32:09,960 --> 00:32:15,920
Speaker 8: auditing their privacy protocols every so often, and then no

676
00:32:16,120 --> 00:32:20,240
Speaker 8: audio is stored. It's just processed and then you get transcripts,

677
00:32:20,240 --> 00:32:23,720
Speaker 8: which you know. Sometimes I was like, I would actually

678
00:32:23,840 --> 00:32:26,360
Speaker 8: like audio proof that my cousin made me cry so

679
00:32:27,160 --> 00:32:29,480
Speaker 8: that I could shove it in their face in the future.

680
00:32:29,560 --> 00:32:31,920
Speaker 3: But but it's still doing a transcript, so.

681
00:32:32,080 --> 00:32:34,320
Speaker 4: It's still the transcript is not always correct. It's not

682
00:32:34,360 --> 00:32:35,000
Speaker 4: always crazy.

683
00:32:35,080 --> 00:32:37,440
Speaker 1: What's crazy is this company raised seven million dollars a

684
00:32:37,560 --> 00:32:37,960
Speaker 1: year ago.

685
00:32:38,120 --> 00:32:39,040
Speaker 4: I can't believe, and it.

686
00:32:39,000 --> 00:32:41,240
Speaker 2: Took them this long to come up with a product.

687
00:32:40,840 --> 00:32:43,160
Speaker 6: The kind of works that money could be built, like,

688
00:32:43,200 --> 00:32:44,040
Speaker 6: saving so many lives.

689
00:32:44,080 --> 00:32:45,239
Speaker 4: I'm sorry, I'm saying burn it.

690
00:32:45,640 --> 00:32:48,520
Speaker 2: You feel warm at least you know.

691
00:32:48,760 --> 00:32:52,200
Speaker 3: No, I will say, I think seven million dollars they

692
00:32:52,240 --> 00:32:57,680
Speaker 3: produced hardware. Yes, that's that's the thing they did for

693
00:32:57,680 --> 00:33:01,040
Speaker 3: seven million dollars. I mean, that's almost imprest.

694
00:33:00,360 --> 00:33:02,720
Speaker 1: It's almost impressed. But that's kind of the review of

695
00:33:02,760 --> 00:33:05,880
Speaker 1: AI though. Yeah, but but no, I think it's almost impressed.

696
00:33:05,920 --> 00:33:09,640
Speaker 3: This seems the most no, no, no disrespect, a pointless,

697
00:33:09,760 --> 00:33:13,160
Speaker 3: deeply flawed product, product only two steps above the rabbit.

698
00:33:13,280 --> 00:33:16,480
Speaker 4: This feels like some shit above the rabbits.

699
00:33:16,560 --> 00:33:20,320
Speaker 2: Very interesting. This is like fifty This is some indie fraud.

700
00:33:20,680 --> 00:33:22,440
Speaker 3: Yes, exactly, that's worth the.

701
00:33:22,480 --> 00:33:25,960
Speaker 8: Five because it worked better than humane what it said

702
00:33:25,960 --> 00:33:26,400
Speaker 8: it was going to.

703
00:33:26,520 --> 00:33:28,920
Speaker 2: Okay, that's fair. It's just a fifty bucks.

704
00:33:28,720 --> 00:33:29,360
Speaker 4: It's exactly.

705
00:33:29,440 --> 00:33:31,760
Speaker 6: Well, it's also because yeah, that and also humane mate

706
00:33:32,000 --> 00:33:35,160
Speaker 6: way greater claims and promises. Rabbit also made very great claims.

707
00:33:35,160 --> 00:33:38,040
Speaker 6: I want to point out that, like I'm I find

708
00:33:38,040 --> 00:33:39,760
Speaker 6: it funny that, like, there's a lot of concerns of

709
00:33:39,800 --> 00:33:41,960
Speaker 6: invasions of privacy, and I get that that for people

710
00:33:42,000 --> 00:33:44,640
Speaker 6: around me there is an invasion of privacy. I was

711
00:33:44,680 --> 00:33:47,200
Speaker 6: intrigued by the fifty dollars because, like Victoria, sometimes I

712
00:33:47,200 --> 00:33:49,560
Speaker 6: do want, like the audio recording of things happening in

713
00:33:49,600 --> 00:33:50,400
Speaker 6: my life.

714
00:33:50,560 --> 00:33:53,240
Speaker 4: There is a receipts, Like I said, there, but Alter,

715
00:33:54,600 --> 00:33:55,080
Speaker 4: what do you have.

716
00:33:55,080 --> 00:33:58,480
Speaker 3: In your pocket? What are two on the right here

717
00:33:58,520 --> 00:33:59,040
Speaker 3: in front of us?

718
00:33:59,120 --> 00:34:01,960
Speaker 6: I have nothing but good luck and stars in my eyes.

719
00:34:04,360 --> 00:34:07,520
Speaker 6: It is, yeah, something that's always recording, Alex. You don't think,

720
00:34:07,600 --> 00:34:10,280
Speaker 6: like maybe myself in Victoria, I'll all spee for myself,

721
00:34:10,360 --> 00:34:12,360
Speaker 6: do you? Which is that I feel as if everywhere

722
00:34:12,360 --> 00:34:15,279
Speaker 6: I go every day, I am more at risk of

723
00:34:15,360 --> 00:34:18,840
Speaker 6: like needing to back up my experience, to defend myself

724
00:34:18,920 --> 00:34:21,360
Speaker 6: to like, And that's why that sort of device always

725
00:34:21,400 --> 00:34:22,560
Speaker 6: felt appealing to me.

726
00:34:22,640 --> 00:34:25,719
Speaker 8: And maybe because I think, could you in an enterprise sense,

727
00:34:25,719 --> 00:34:28,440
Speaker 8: it does make sense if you're a lecturer. If you're

728
00:34:28,480 --> 00:34:30,880
Speaker 8: a lawyer, if you're you know, and you need someone

729
00:34:31,080 --> 00:34:35,200
Speaker 8: no lawyer in their right I'm okay, not a lawyer

730
00:34:35,320 --> 00:34:38,000
Speaker 8: in their right mind. At a lawyer in their minds

731
00:34:38,360 --> 00:34:40,440
Speaker 8: in their wrong mind. But just like just someone who

732
00:34:40,520 --> 00:34:42,200
Speaker 8: takes a lot of meetings and needs a lot of

733
00:34:42,280 --> 00:34:46,120
Speaker 8: notes from those meetings, Like there is there is like

734
00:34:46,520 --> 00:34:48,560
Speaker 8: a use case there, yeah, with.

735
00:34:48,280 --> 00:34:50,279
Speaker 1: With but the problem is with those I understand. What

736
00:34:50,320 --> 00:34:52,880
Speaker 1: you're getting at is like doctors and lawyers, here's the

737
00:34:52,920 --> 00:34:57,239
Speaker 1: thing they need that they need, well, hippo accuracy. If

738
00:34:57,239 --> 00:35:00,640
Speaker 1: you get a nuanced thing wrong in the law, that

739
00:35:00,680 --> 00:35:02,480
Speaker 1: tends to be what Laya's love problem.

740
00:35:02,560 --> 00:35:04,600
Speaker 8: So that's why I'm saying, there's like a glimmer of

741
00:35:04,640 --> 00:35:07,440
Speaker 8: an idea there, Like I think there's the glimmer.

742
00:35:07,480 --> 00:35:10,880
Speaker 1: Often it's just sucking there's an idea.

743
00:35:11,000 --> 00:35:12,399
Speaker 2: Glimmer of an idea.

744
00:35:12,280 --> 00:35:13,640
Speaker 6: Is the suggestion of a good thing.

745
00:35:13,880 --> 00:35:16,120
Speaker 2: It's not the good thing, the concept of a plan.

746
00:35:16,239 --> 00:35:19,520
Speaker 8: Yeah, it's just like if you spend a month constantly

747
00:35:19,600 --> 00:35:22,959
Speaker 8: reviewing your own life and fact checking, what how.

748
00:35:22,880 --> 00:35:24,800
Speaker 4: Does that leave you feeling not well?

749
00:35:25,400 --> 00:35:27,680
Speaker 8: It was like I felt insane a little bit because

750
00:35:27,680 --> 00:35:30,120
Speaker 8: I was constantly fact checking, and I was constantly like

751
00:35:30,200 --> 00:35:33,720
Speaker 8: when I actually thought like it was reading my text

752
00:35:33,719 --> 00:35:36,319
Speaker 8: messages for a while because like I was like, these

753
00:35:36,320 --> 00:35:39,400
Speaker 8: are private conversations I had and text messages on encrypted platforms.

754
00:35:39,400 --> 00:35:42,480
Speaker 4: How is it even knowing to generate?

755
00:35:45,200 --> 00:35:49,520
Speaker 8: Well, this did really affect my behavior over a month

756
00:35:49,640 --> 00:35:52,879
Speaker 8: because I realized that like an offhand comment, it can

757
00:35:52,920 --> 00:35:55,640
Speaker 8: just glean so much for me. That was just frightening

758
00:35:55,719 --> 00:35:57,799
Speaker 8: to me because I would say like a thing in

759
00:35:57,920 --> 00:35:59,719
Speaker 8: passing to my husband and be like, oh, here's an

760
00:35:59,760 --> 00:36:01,799
Speaker 8: update on that, and it would just and I was.

761
00:36:01,800 --> 00:36:04,960
Speaker 4: Like, oh shit. And so I've actually been very.

762
00:36:04,840 --> 00:36:08,239
Speaker 8: Quiet the last month, like I don't speak anymore to

763
00:36:08,280 --> 00:36:11,000
Speaker 8: myself after after the poop to the poop one, after

764
00:36:11,040 --> 00:36:14,000
Speaker 8: Poopgate and bathroom crimes, I was like, I do not

765
00:36:14,120 --> 00:36:15,520
Speaker 8: have to say things anything to me.

766
00:36:15,640 --> 00:36:16,200
Speaker 3: Oh my god.

767
00:36:16,400 --> 00:36:19,000
Speaker 6: I meant to ask if in the Christian process where

768
00:36:19,040 --> 00:36:21,839
Speaker 6: that the sounds of plops getting crypted too? But I guess,

769
00:36:22,200 --> 00:36:24,440
Speaker 6: you know, I would love an audio file of that

770
00:36:24,600 --> 00:36:25,160
Speaker 6: for myself.

771
00:36:25,680 --> 00:36:26,600
Speaker 2: That's horrifying.

772
00:36:26,719 --> 00:36:29,799
Speaker 1: Like I think that there is a larger thing here though,

773
00:36:29,840 --> 00:36:32,280
Speaker 1: that this is a classic tech guy idea.

774
00:36:32,400 --> 00:36:34,360
Speaker 2: It's like, what is an idea.

775
00:36:34,120 --> 00:36:36,440
Speaker 1: I'd have, Oh, I want to memorize everything that's happening

776
00:36:36,480 --> 00:36:38,040
Speaker 1: around me and be able to analyze it and then

777
00:36:38,080 --> 00:36:40,680
Speaker 1: do these insights. It's actually a very cruel way to

778
00:36:40,719 --> 00:36:42,440
Speaker 1: live because we say things offhanded.

779
00:36:42,719 --> 00:36:43,879
Speaker 4: We forgetting is.

780
00:36:45,160 --> 00:36:48,280
Speaker 6: Like very important part of being fallible.

781
00:36:48,440 --> 00:36:51,239
Speaker 1: Our existence is not something that is written down in

782
00:36:51,280 --> 00:36:53,480
Speaker 1: its entirety. And indeed, I don't know how useful it

783
00:36:53,560 --> 00:36:55,080
Speaker 1: is having everything written down.

784
00:36:55,400 --> 00:36:59,040
Speaker 3: It's not at all a thing unless you're like a researcher. Yeah,

785
00:36:59,480 --> 00:37:02,719
Speaker 3: one hundred years from now is absolutely gonna know, love

786
00:37:02,840 --> 00:37:08,760
Speaker 3: to know. Alvey responded to her, like the answer the anthropologist.

787
00:37:08,800 --> 00:37:11,480
Speaker 3: Two hundred years from now, it's gonna be like analysts

788
00:37:11,960 --> 00:37:13,320
Speaker 3: just like, thank you for giving me all of this.

789
00:37:13,440 --> 00:37:15,040
Speaker 4: They've listened to this, and we need to dig up

790
00:37:15,080 --> 00:37:15,879
Speaker 4: victories about now.

791
00:37:16,160 --> 00:37:17,160
Speaker 3: And that's the only reason.

792
00:37:17,280 --> 00:37:22,920
Speaker 8: Like, if you're an archivest, I'm sorry, listen, Okay, No,

793
00:37:22,960 --> 00:37:24,440
Speaker 8: I didn't be did.

794
00:37:24,840 --> 00:37:27,840
Speaker 4: Anyway anyway, I would.

795
00:37:27,680 --> 00:37:30,399
Speaker 8: Just like to note that I am avid diarist at

796
00:37:30,400 --> 00:37:34,160
Speaker 8: a journal like a journal would another way of saying

797
00:37:35,400 --> 00:37:40,239
Speaker 8: I write diarrhea, Fuck you guys, anyway, I write a

798
00:37:40,320 --> 00:37:45,040
Speaker 8: diary daily, I journal regularly.

799
00:37:45,080 --> 00:37:47,439
Speaker 4: Journal regularly. So this past month.

800
00:37:47,480 --> 00:37:49,840
Speaker 8: I have journaled every single day, and it's the things

801
00:37:49,840 --> 00:37:52,520
Speaker 8: that I write down and that I remember as memorable

802
00:37:53,000 --> 00:37:54,400
Speaker 8: are not always.

803
00:37:54,040 --> 00:37:56,480
Speaker 4: The same thing that this thing picked up.

804
00:37:56,520 --> 00:37:58,879
Speaker 8: Because a lot of my thoughts and things that are

805
00:37:58,960 --> 00:38:01,480
Speaker 8: important to me and memory ras that are like meaningful

806
00:38:01,480 --> 00:38:06,120
Speaker 8: to me are completely silent. So my my, my philosophical

807
00:38:06,200 --> 00:38:08,759
Speaker 8: question is if if it doesn't happen out loud, if

808
00:38:08,880 --> 00:38:11,839
Speaker 8: you have a society where we're all wearing these devices,

809
00:38:12,440 --> 00:38:14,960
Speaker 8: if you say nothing out loud, does it count as

810
00:38:15,000 --> 00:38:15,600
Speaker 8: your memory?

811
00:38:15,800 --> 00:38:16,040
Speaker 2: Right?

812
00:38:16,400 --> 00:38:18,840
Speaker 4: And this device would say no, it can't know that

813
00:38:18,880 --> 00:38:19,160
Speaker 4: from you.

814
00:38:19,360 --> 00:38:23,279
Speaker 1: It's also a very crude analysis of memory itself. Human

815
00:38:23,320 --> 00:38:25,880
Speaker 1: memory is insane. Yeah, it is like it's the things

816
00:38:25,920 --> 00:38:29,360
Speaker 1: we remember are done in chunks. The experience of being alive,

817
00:38:29,360 --> 00:38:32,040
Speaker 1: at least for me is it's not like my thoughts

818
00:38:32,080 --> 00:38:33,840
Speaker 1: like and now I'm on the podcast now or so

819
00:38:33,920 --> 00:38:37,680
Speaker 1: the thing it's like, I apparently people who those people

820
00:38:37,680 --> 00:38:40,920
Speaker 1: are no, because you like to be normal.

821
00:38:41,160 --> 00:38:41,719
Speaker 4: What's it like to.

822
00:38:44,160 --> 00:38:46,239
Speaker 1: It's just I don't have an internal's more adjacent to

823
00:38:46,280 --> 00:38:52,360
Speaker 1: Cornholio for me. But I actually you mentioned something earlier though, Sherlyn,

824
00:38:52,360 --> 00:38:54,000
Speaker 1: and I want to go back to, which is you

825
00:38:54,040 --> 00:38:56,480
Speaker 1: said this thing about feeling the need to document things

826
00:38:56,520 --> 00:38:58,880
Speaker 1: around you. Can you go into like what is the

827
00:38:58,880 --> 00:39:00,239
Speaker 1: the thing pushing you through that?

828
00:39:01,120 --> 00:39:01,960
Speaker 2: I mean, it's a witnesses.

829
00:39:02,120 --> 00:39:05,080
Speaker 6: It's because I feel as if I maybe am part

830
00:39:05,120 --> 00:39:05,560
Speaker 6: of my life.

831
00:39:05,640 --> 00:39:07,560
Speaker 4: I've I've talked to both of you about this before.

832
00:39:07,320 --> 00:39:09,239
Speaker 6: Where I encounter things and then I feel as if

833
00:39:09,280 --> 00:39:11,759
Speaker 6: I get question about them afterwards and people don't believe me. Right,

834
00:39:11,840 --> 00:39:15,759
Speaker 6: So like journalism brain, it's journalism brain. It's microaggressions being

835
00:39:15,800 --> 00:39:18,560
Speaker 6: thrown your way daily, being a lady's being a woman.

836
00:39:18,760 --> 00:39:20,560
Speaker 6: It's like the things that we have to think about

837
00:39:20,600 --> 00:39:22,400
Speaker 6: all the time. So like I feel as if if

838
00:39:22,440 --> 00:39:25,239
Speaker 6: I had a wearable microphone or camera on me all

839
00:39:25,239 --> 00:39:27,160
Speaker 6: the time. This is why I review the Humane with

840
00:39:27,200 --> 00:39:28,080
Speaker 6: great enthusiasm.

841
00:39:28,360 --> 00:39:28,840
Speaker 2: At first.

842
00:39:29,200 --> 00:39:31,080
Speaker 6: It's I feel as if if one of those people

843
00:39:31,080 --> 00:39:33,920
Speaker 6: who ca't calls me across the street, I had like

844
00:39:34,040 --> 00:39:36,680
Speaker 6: documented evidence of that and I could bring it up

845
00:39:36,719 --> 00:39:38,839
Speaker 6: like how many times it happens a week? I could

846
00:39:38,920 --> 00:39:41,120
Speaker 6: have like valuable knowledge on my hands to be like

847
00:39:41,320 --> 00:39:43,959
Speaker 6: when people question if I if it really happens that much,

848
00:39:44,200 --> 00:39:46,439
Speaker 6: I can be like, look at all these instances. So

849
00:39:46,800 --> 00:39:48,880
Speaker 6: for that reason, I have a lot of things that

850
00:39:48,960 --> 00:39:51,480
Speaker 6: document parts of my life. I to your point, Alex,

851
00:39:51,520 --> 00:39:54,560
Speaker 6: I have phones that record a lot of my conversations

852
00:39:54,560 --> 00:39:57,720
Speaker 6: when I feel they're important. Right, there's my security camera outside.

853
00:39:57,719 --> 00:40:01,200
Speaker 6: I'm like, whatever it captures emotion. I have it triggered

854
00:40:01,200 --> 00:40:03,960
Speaker 6: on emotion censor and it records everything. And I'm like, oh,

855
00:40:04,520 --> 00:40:06,520
Speaker 6: I don't pay now for the backup that like allows

856
00:40:06,560 --> 00:40:08,040
Speaker 6: me to go back in time and look at them

857
00:40:08,040 --> 00:40:08,440
Speaker 6: all the day.

858
00:40:08,440 --> 00:40:10,160
Speaker 4: But if I did, I would sit there and look

859
00:40:10,200 --> 00:40:10,759
Speaker 4: all the time.

860
00:40:11,480 --> 00:40:15,080
Speaker 6: I think that experience is not every woman or every

861
00:40:15,080 --> 00:40:16,480
Speaker 6: person who feels that way.

862
00:40:16,520 --> 00:40:17,040
Speaker 4: But I don't know.

863
00:40:17,080 --> 00:40:19,279
Speaker 6: Something in my upbringing or my culture like has led

864
00:40:19,320 --> 00:40:19,719
Speaker 6: me to.

865
00:40:19,680 --> 00:40:22,359
Speaker 8: Feel there's there's a fine line, because I do think

866
00:40:22,440 --> 00:40:26,279
Speaker 8: that when you do that, when you are constantly reviewing

867
00:40:26,880 --> 00:40:31,399
Speaker 8: the documents of your life, it can be not so

868
00:40:31,440 --> 00:40:32,480
Speaker 8: great for your mental health.

869
00:40:32,600 --> 00:40:34,279
Speaker 4: Absolutely, it's super like.

870
00:40:34,719 --> 00:40:38,000
Speaker 8: My conclusion is that like part of being a human

871
00:40:38,160 --> 00:40:40,960
Speaker 8: is understanding when you need to forget things right.

872
00:40:40,840 --> 00:40:43,359
Speaker 4: Move on, and instead you're inviting an AI to do

873
00:40:43,400 --> 00:40:44,040
Speaker 4: that for me.

874
00:40:44,080 --> 00:40:49,719
Speaker 3: A really poorly implemented honestly because of how yeah, how

875
00:40:49,800 --> 00:40:54,000
Speaker 3: badly it fails you, You're having a poorly implemented AI right,

876
00:40:54,960 --> 00:40:57,719
Speaker 3: intending to profit on f you in ways we still

877
00:40:57,719 --> 00:41:00,600
Speaker 3: don't know, because fifty dollars to have that mini AI,

878
00:41:00,840 --> 00:41:05,200
Speaker 3: like they're burning cash, So where are they making their

879
00:41:05,200 --> 00:41:06,399
Speaker 3: money that I think?

880
00:41:06,440 --> 00:41:10,960
Speaker 4: I think there is plans down the line to subscription, subscription, Yeah,

881
00:41:11,000 --> 00:41:11,799
Speaker 4: of course, right now.

882
00:41:12,520 --> 00:41:15,000
Speaker 3: That is the way of every single one of these products.

883
00:41:15,040 --> 00:41:17,440
Speaker 3: We see this again and again there's something that like

884
00:41:17,600 --> 00:41:20,000
Speaker 3: really moves Silicon Valley and they get really really excited.

885
00:41:20,040 --> 00:41:21,680
Speaker 3: In this case, it's AI, and they're like, how can

886
00:41:21,680 --> 00:41:23,480
Speaker 3: we commodify this? How can how can we how can

887
00:41:23,520 --> 00:41:24,759
Speaker 3: we make money off of this? How can I go

888
00:41:24,800 --> 00:41:26,400
Speaker 3: and make money off of this ship? I think that

889
00:41:26,440 --> 00:41:32,759
Speaker 3: all the time. I'm unemployed right now. Awesome, but it's

890
00:41:32,760 --> 00:41:35,319
Speaker 3: this constant thing. And then they released this product that

891
00:41:35,480 --> 00:41:38,360
Speaker 3: is pretty terrible. You give it a five. It is

892
00:41:38,400 --> 00:41:41,240
Speaker 3: clearly not finished, and it is for such a small

893
00:41:41,360 --> 00:41:44,320
Speaker 3: group of the population. How on earth do they expect

894
00:41:44,360 --> 00:41:48,120
Speaker 3: anyone in the normal world who's just out there existing,

895
00:41:48,200 --> 00:41:50,040
Speaker 3: who goes to Walmart all the time and doesn't listen

896
00:41:50,080 --> 00:41:52,080
Speaker 3: to podcasts all the time, be like, Yeah, I want

897
00:41:52,120 --> 00:41:54,279
Speaker 3: to take this little fifty dollars thing that's going to

898
00:41:54,400 --> 00:41:57,320
Speaker 3: charge me money monthly and wear this on my and

899
00:41:57,640 --> 00:41:58,800
Speaker 3: record everything I do.

900
00:41:58,760 --> 00:41:59,439
Speaker 2: The normal post.

901
00:41:59,520 --> 00:42:01,520
Speaker 4: No one would no, no normal person.

902
00:42:01,840 --> 00:42:04,799
Speaker 3: Such it is such brain worms that this.

903
00:42:04,840 --> 00:42:06,520
Speaker 8: Is, like but you have that a lot, and like

904
00:42:06,560 --> 00:42:10,560
Speaker 8: they only think about the positive of it, and like, no,

905
00:42:10,760 --> 00:42:12,840
Speaker 8: they don't, like all they're thinking about the negative.

906
00:42:12,840 --> 00:42:14,240
Speaker 2: They're not positive.

907
00:42:14,360 --> 00:42:16,240
Speaker 3: Let's be really clear. They're not thinking about the positive

908
00:42:16,280 --> 00:42:18,280
Speaker 3: of it. They're thinking about how can I make money? Yes, growth,

909
00:42:18,400 --> 00:42:19,080
Speaker 3: how can I grow?

910
00:42:19,360 --> 00:42:19,600
Speaker 2: That is?

911
00:42:19,640 --> 00:42:23,160
Speaker 3: It is its growth all cost thing that is.

912
00:42:21,960 --> 00:42:23,640
Speaker 4: And it's ruining this world.

913
00:42:23,680 --> 00:42:25,440
Speaker 8: But I mean when they market it, they tell you

914
00:42:25,480 --> 00:42:27,319
Speaker 8: all the good things it can do, but they never

915
00:42:27,480 --> 00:42:29,719
Speaker 8: like really get into the fact that Like but I mean,

916
00:42:29,840 --> 00:42:32,919
Speaker 8: listen to me cry like pretty heavily after I got

917
00:42:32,960 --> 00:42:34,840
Speaker 8: into a fight. I'm sorry, And then I had to

918
00:42:34,880 --> 00:42:37,920
Speaker 8: review the transcript and then look at it and then

919
00:42:38,040 --> 00:42:41,120
Speaker 8: just like have it analyze how I was feeling, because

920
00:42:41,120 --> 00:42:44,640
Speaker 8: it was like the one sad one for when Amazon.

921
00:42:44,680 --> 00:42:47,360
Speaker 3: As someone who's recorded one of her own firings before.

922
00:42:47,600 --> 00:42:49,040
Speaker 3: You don't want to listen to that.

923
00:42:49,840 --> 00:42:51,520
Speaker 4: Oh, I've recorded a breakup before.

924
00:42:51,560 --> 00:42:52,000
Speaker 2: That was great.

925
00:42:52,520 --> 00:42:55,000
Speaker 3: You don't you know, don't I did it for legal reasons.

926
00:42:55,040 --> 00:42:57,520
Speaker 4: That's unhinged. You know me, I am hid.

927
00:42:58,160 --> 00:43:01,880
Speaker 6: I want to like, you're lucky you recorded all our conversations.

928
00:43:01,880 --> 00:43:04,800
Speaker 4: Honey, No, no, here's it. I will clarify that I recorde.

929
00:43:04,800 --> 00:43:05,960
Speaker 4: I don't go and review every day.

930
00:43:05,960 --> 00:43:08,480
Speaker 6: I think that that's really detrimental to your I do

931
00:43:08,520 --> 00:43:10,360
Speaker 6: it for like the receipts and and yeah, and in

932
00:43:10,400 --> 00:43:12,279
Speaker 6: that break up example, I don't think I actually recorded it.

933
00:43:12,320 --> 00:43:13,680
Speaker 6: I wrote down every single word.

934
00:43:14,239 --> 00:43:17,680
Speaker 2: And that's different. Just to be clear, Yes, yes, it's

935
00:43:17,760 --> 00:43:19,240
Speaker 2: just that it's a huge invasion.

936
00:43:19,800 --> 00:43:21,680
Speaker 3: I think I'm What I'm really frustrated by is this

937
00:43:21,719 --> 00:43:24,520
Speaker 3: is another example of a totally unfinished product. We're seeing

938
00:43:24,560 --> 00:43:26,080
Speaker 3: this again and again again. We saw it with Apple

939
00:43:26,360 --> 00:43:29,080
Speaker 3: this last week, where we've got Gruber and German and

940
00:43:29,160 --> 00:43:31,120
Speaker 3: a lot of these other people coming out there saying, hey,

941
00:43:31,520 --> 00:43:38,680
Speaker 3: something's wrong. When you've got Gruba being like, but exactly,

942
00:43:38,880 --> 00:43:42,319
Speaker 3: you've lost Gruber, that means you have you've failed. And

943
00:43:42,560 --> 00:43:44,720
Speaker 3: we're seeing that like if Apple is out there rushing

944
00:43:44,760 --> 00:43:48,480
Speaker 3: a product when they know it's not ready, especially all

945
00:43:48,560 --> 00:43:51,840
Speaker 3: of the stuff they don't have the use cases, it's

946
00:43:51,880 --> 00:43:54,240
Speaker 3: like what we saw with VR. I spent years covering

947
00:43:54,320 --> 00:43:56,560
Speaker 3: VR waiting for that moment and everybody said, it's just

948
00:43:56,600 --> 00:43:59,040
Speaker 3: around the courner. Is it here yet?

949
00:43:59,160 --> 00:44:01,520
Speaker 2: No, I just wanted to chat thank you.

950
00:44:01,840 --> 00:44:03,000
Speaker 4: Is it because V is in her name?

951
00:44:03,640 --> 00:44:04,520
Speaker 3: Because she does a lot of.

952
00:44:04,560 --> 00:44:07,960
Speaker 6: VR cover it's Victoria Reality.

953
00:44:08,320 --> 00:44:10,359
Speaker 3: I've seener work a lot of different glasses.

954
00:44:10,040 --> 00:44:11,000
Speaker 4: A lot of smart glass.

955
00:44:11,320 --> 00:44:14,120
Speaker 1: Twitter and also the company that made this. By the way,

956
00:44:14,160 --> 00:44:18,520
Speaker 1: the founding team came from Twitter. Oh sorry, They helped

957
00:44:18,640 --> 00:44:21,680
Speaker 1: ship Twitter spaces, you know, that beloved product, and some

958
00:44:21,719 --> 00:44:26,520
Speaker 1: sort of video chat app called Squad. Okay, the Squad,

959
00:44:26,640 --> 00:44:29,279
Speaker 1: the anti pro startup is creating a safe space for

960
00:44:29,360 --> 00:44:33,919
Speaker 1: teenage girls online. And uh wait a second, let's see

961
00:44:33,920 --> 00:44:35,200
Speaker 1: who's who founded this?

962
00:44:36,160 --> 00:44:37,120
Speaker 2: Esther core for it.

963
00:44:37,480 --> 00:44:41,520
Speaker 1: I think she's the woman that helped elon Musk. Anyway,

964
00:44:41,960 --> 00:44:44,920
Speaker 1: the point is these fucking people don't care at all.

965
00:44:44,920 --> 00:44:46,960
Speaker 1: They're not seeing there being like, how would this be helpful?

966
00:44:47,000 --> 00:44:49,239
Speaker 1: Because because the most helpful thing would make sure it

967
00:44:49,280 --> 00:44:52,239
Speaker 1: was really focused, that it was able to discern a

968
00:44:52,280 --> 00:44:55,080
Speaker 1: professional and but they probably can't do it because it

969
00:44:55,120 --> 00:44:56,279
Speaker 1: isn't impossible.

970
00:44:56,640 --> 00:44:58,520
Speaker 2: Ah, It's it's.

971
00:44:58,440 --> 00:45:01,560
Speaker 6: Just people care to about people, people with money and

972
00:45:01,560 --> 00:45:04,160
Speaker 6: cared about other people. We would be spending this money

973
00:45:04,239 --> 00:45:07,080
Speaker 6: on cancer research where the NIH grants are being cold

974
00:45:07,160 --> 00:45:09,680
Speaker 6: and stalled right now, or they would focus it on

975
00:45:09,719 --> 00:45:10,279
Speaker 6: improving the.

976
00:45:10,239 --> 00:45:12,680
Speaker 4: Healthcare system of this country. But people do not care.

977
00:45:12,719 --> 00:45:14,600
Speaker 6: They care about taking their money and growing it for

978
00:45:14,719 --> 00:45:15,640
Speaker 6: more money for themselves.

979
00:45:15,680 --> 00:45:16,759
Speaker 4: That which I hate.

980
00:45:16,800 --> 00:45:20,960
Speaker 8: But I don't expect people to suddenly become good people overnight,

981
00:45:21,080 --> 00:45:23,200
Speaker 8: especially if they have money. But there is just like

982
00:45:23,280 --> 00:45:25,840
Speaker 8: one thing where it's like you have to at the

983
00:45:25,960 --> 00:45:28,960
Speaker 8: very least create a good product experience for people. And

984
00:45:29,320 --> 00:45:31,880
Speaker 8: the thing that frustrates me the most when I review

985
00:45:31,960 --> 00:45:36,640
Speaker 8: products is that I feel like there's just this Pollyanny

986
00:45:36,920 --> 00:45:40,600
Speaker 8: Pollyannish like view of what life is from these people

987
00:45:40,680 --> 00:45:42,279
Speaker 8: in Silicon Valley. Right.

988
00:45:42,320 --> 00:45:43,440
Speaker 4: It's just like I I.

989
00:45:44,760 --> 00:45:47,960
Speaker 9: Have had a life of great suffering and they think

990
00:45:48,000 --> 00:45:50,080
Speaker 9: it's like, uh, you know, I've been through a lot

991
00:45:50,120 --> 00:45:52,799
Speaker 9: of shit, Like both my parents died, my dog died,

992
00:45:52,840 --> 00:45:55,879
Speaker 9: my entire family, immediate family died in the last five years.

993
00:45:55,920 --> 00:45:57,120
Speaker 2: Oh my god, I'm so sorry.

994
00:45:57,239 --> 00:46:00,080
Speaker 4: It's fine. It's not but it's fine. I have like

995
00:46:00,400 --> 00:46:00,959
Speaker 4: a lot of.

996
00:46:02,360 --> 00:46:05,720
Speaker 8: Traumatic stuff that has happened, and it's kind of changed

997
00:46:05,760 --> 00:46:08,960
Speaker 8: my perspective of these kinds of devices because life is

998
00:46:09,000 --> 00:46:09,480
Speaker 8: not pretty.

999
00:46:09,719 --> 00:46:11,040
Speaker 4: There are just moments.

1000
00:46:10,719 --> 00:46:13,560
Speaker 8: Where there are moments where it's very hard and where

1001
00:46:13,760 --> 00:46:17,160
Speaker 8: these sorts of to dos. I can imagine a time where,

1002
00:46:17,160 --> 00:46:19,560
Speaker 8: like when I was in the height of my grief,

1003
00:46:19,600 --> 00:46:22,000
Speaker 8: where I would have loved a thing to remind me

1004
00:46:22,040 --> 00:46:25,080
Speaker 8: to do stuff, to listen to the conversations that I

1005
00:46:25,160 --> 00:46:27,560
Speaker 8: was not processing, to give me those to dos, Like,

1006
00:46:27,880 --> 00:46:30,400
Speaker 8: there are moments, and I think there is a desire

1007
00:46:30,560 --> 00:46:33,640
Speaker 8: for something like that that AI could ostensibly at some

1008
00:46:33,719 --> 00:46:37,480
Speaker 8: point when it actually works a lot of asterisks here

1009
00:46:38,040 --> 00:46:40,879
Speaker 8: that could help people. And I think if people want

1010
00:46:40,920 --> 00:46:44,359
Speaker 8: to pursue that, that's great, But you can't do it

1011
00:46:44,400 --> 00:46:47,200
Speaker 8: without acknowledging the fact that not every moment in your

1012
00:46:47,239 --> 00:46:49,160
Speaker 8: life is going to be pleasant. That are going to

1013
00:46:49,160 --> 00:46:51,719
Speaker 8: be embarrassing, there's going to be heartbreak, there's going to

1014
00:46:51,760 --> 00:46:56,720
Speaker 8: be these negative experiences that you don't necessarily want summarized

1015
00:46:56,760 --> 00:46:59,120
Speaker 8: to you in a way that you're going to have

1016
00:46:59,160 --> 00:47:03,520
Speaker 8: to review and feel. Gas did your ex gaslight you

1017
00:47:03,560 --> 00:47:05,400
Speaker 8: guess what the AI is going to gaslight you are?

1018
00:47:05,800 --> 00:47:09,080
Speaker 8: How it gets how that horrible douche gaslight you and

1019
00:47:09,160 --> 00:47:13,080
Speaker 8: like that kind of thing is just not necessarily great,

1020
00:47:13,640 --> 00:47:16,400
Speaker 8: and but you get it across so many different.

1021
00:47:16,680 --> 00:47:19,520
Speaker 3: Put on my business person hat. It's not just bad.

1022
00:47:20,360 --> 00:47:25,120
Speaker 3: I literally it's a bad hat. It's a little top

1023
00:47:25,120 --> 00:47:28,000
Speaker 3: had let me get my monocle out. It's not just

1024
00:47:28,440 --> 00:47:32,719
Speaker 3: it's bad for people, it's a bad product. Like like,

1025
00:47:32,800 --> 00:47:34,799
Speaker 3: let's be clear, a lot of these products we've seen

1026
00:47:34,840 --> 00:47:36,759
Speaker 3: again and again and again trying to do AI, trying

1027
00:47:36,800 --> 00:47:39,239
Speaker 3: to make it. Google's done some decent work in this

1028
00:47:39,360 --> 00:47:43,520
Speaker 3: space with the with decent, but most of these products

1029
00:47:43,560 --> 00:47:45,000
Speaker 3: continue to be bad. They continue to.

1030
00:47:44,920 --> 00:47:49,280
Speaker 1: Be searching that like what with Google, I'm sorry.

1031
00:47:51,040 --> 00:47:55,040
Speaker 3: Alone, there was that thing where you could, like you

1032
00:47:55,040 --> 00:47:57,680
Speaker 3: could like erase a person out of magic eraser. Yeah

1033
00:47:57,840 --> 00:48:01,640
Speaker 3: that's not though, Yeah, it's generating.

1034
00:48:01,960 --> 00:48:02,560
Speaker 4: Is that general?

1035
00:48:04,560 --> 00:48:06,719
Speaker 6: It's not in the era of so Google's done that

1036
00:48:06,719 --> 00:48:07,440
Speaker 6: before the rise of.

1037
00:48:08,000 --> 00:48:08,799
Speaker 3: What else did it do?

1038
00:48:10,040 --> 00:48:12,920
Speaker 1: This is, by the way, this is also the industry

1039
00:48:12,960 --> 00:48:15,480
Speaker 1: that the entire economy is built on top of it.

1040
00:48:15,680 --> 00:48:17,920
Speaker 1: Just to be clear, And these are some of the

1041
00:48:17,960 --> 00:48:20,720
Speaker 1: most well credentialed tech people and we're all just going.

1042
00:48:22,080 --> 00:48:24,759
Speaker 6: Hang on, pause, pause, I want to pivot to all

1043
00:48:24,800 --> 00:48:26,360
Speaker 6: of that by saying there is a good product that

1044
00:48:26,640 --> 00:48:28,560
Speaker 6: that's built to help people feel better.

1045
00:48:28,640 --> 00:48:31,520
Speaker 4: You're pointing at me for for AI, No, No, that's

1046
00:48:31,560 --> 00:48:32,400
Speaker 4: the thing that's no.

1047
00:48:32,719 --> 00:48:33,399
Speaker 3: I want to talk about.

1048
00:48:33,440 --> 00:48:38,279
Speaker 8: You want to talk Mexican standoff of pointing at each.

1049
00:48:38,200 --> 00:48:38,879
Speaker 3: Other so hard.

1050
00:48:39,560 --> 00:48:41,279
Speaker 1: They are rarely that they're in the spider you're the

1051
00:48:41,320 --> 00:48:44,880
Speaker 1: spider man, but they're not similar anyway.

1052
00:48:45,239 --> 00:48:48,160
Speaker 2: Yeah, wait, who's going going to go?

1053
00:48:48,880 --> 00:48:52,000
Speaker 6: I have been using this app. I encountered it through

1054
00:48:52,040 --> 00:48:55,000
Speaker 6: a coworker who wrote about it for us. It's called Finch.

1055
00:48:55,160 --> 00:48:59,839
Speaker 6: Is an app that's like a self care app, right that. Yeah,

1056
00:49:00,080 --> 00:49:02,120
Speaker 6: so by taking care of yourself, you're taking care of

1057
00:49:02,120 --> 00:49:04,120
Speaker 6: this pet, you're helping it grow. And what it is

1058
00:49:04,120 --> 00:49:06,320
Speaker 6: is these the most gentle suggestions of to dos in

1059
00:49:06,360 --> 00:49:08,640
Speaker 6: a day, whether it is as simple as get out

1060
00:49:08,680 --> 00:49:12,279
Speaker 6: of bed, brush your teeth, drink water. You complete them,

1061
00:49:12,280 --> 00:49:14,440
Speaker 6: you get points, you grow your pet. Whatever, is a

1062
00:49:14,520 --> 00:49:16,640
Speaker 6: gentle reminder of things you want to do throughout the day,

1063
00:49:16,840 --> 00:49:19,120
Speaker 6: and you can reward yourself. And it is developed by

1064
00:49:19,239 --> 00:49:23,600
Speaker 6: and here's the crucial difference, two independent developers that were

1065
00:49:23,640 --> 00:49:26,839
Speaker 6: college buddies that were just trying to help motivate each other.

1066
00:49:27,040 --> 00:49:29,879
Speaker 6: And I think that is an example I can point

1067
00:49:29,920 --> 00:49:30,919
Speaker 6: to of tech being able.

1068
00:49:30,840 --> 00:49:33,320
Speaker 4: To help people. Because I also read the separated.

1069
00:49:33,040 --> 00:49:34,680
Speaker 3: I mean that is a that is a great example,

1070
00:49:34,760 --> 00:49:36,960
Speaker 3: and and a lot of different things there. It was

1071
00:49:37,160 --> 00:49:41,719
Speaker 3: it's two people. It's not seeking its growth capitalism and

1072
00:49:42,080 --> 00:49:43,920
Speaker 3: it's not it doesn't sound like it's using a lot

1073
00:49:43,920 --> 00:49:45,319
Speaker 3: of generative AI AI.

1074
00:49:45,760 --> 00:49:48,560
Speaker 6: It has a point, is a very focused product with

1075
00:49:48,680 --> 00:49:51,680
Speaker 6: a core mission in mind of helping people, and that

1076
00:49:51,840 --> 00:49:52,920
Speaker 6: is something I can stand by.

1077
00:49:53,040 --> 00:49:55,680
Speaker 3: Yeah, but I think most of these products we're seeing

1078
00:49:55,800 --> 00:49:57,000
Speaker 3: is particularly in the as.

1079
00:49:56,840 --> 00:49:59,040
Speaker 4: Especially from the big companies and the vcs.

1080
00:49:59,280 --> 00:50:03,120
Speaker 3: They are everybody is out just doing this money. Everybody's

1081
00:50:03,160 --> 00:50:03,919
Speaker 3: just saying how do I grab?

1082
00:50:03,960 --> 00:50:07,440
Speaker 6: Because their motivation is different exactly.

1083
00:50:07,560 --> 00:50:10,560
Speaker 3: Of course they're asking that, and what are they all doing?

1084
00:50:10,560 --> 00:50:11,920
Speaker 3: Why are they all doing this? They're all trying to

1085
00:50:12,000 --> 00:50:13,840
Speaker 3: chase the iPhone. They're all trying to chase something that

1086
00:50:13,840 --> 00:50:16,040
Speaker 3: happened in two thousand and seven, And let's point out,

1087
00:50:16,040 --> 00:50:18,920
Speaker 3: in two thousand and seven, the iPhone was a way

1088
00:50:18,960 --> 00:50:21,520
Speaker 3: more even in its shittiest state, two thousand and seven

1089
00:50:21,800 --> 00:50:24,840
Speaker 3: was a way more well thought out, well produced product

1090
00:50:25,080 --> 00:50:26,520
Speaker 3: than any of the product was so.

1091
00:50:26,520 --> 00:50:29,000
Speaker 2: Different, and it also fixed really big. Ok.

1092
00:50:29,200 --> 00:50:32,799
Speaker 1: I got exactly, I got the original iPhone, little nerd.

1093
00:50:32,840 --> 00:50:34,680
Speaker 1: I didn't have friends, but I did have money saved

1094
00:50:34,719 --> 00:50:36,960
Speaker 1: up from writing, and I boy it and I remember

1095
00:50:37,000 --> 00:50:40,080
Speaker 1: showing people visual voicemail and people going, holy shit, that's amazing,

1096
00:50:40,120 --> 00:50:43,239
Speaker 1: because voicemail at the time was this defunct product where

1097
00:50:43,239 --> 00:50:44,920
Speaker 1: you had to like dial in and hit a button.

1098
00:50:44,960 --> 00:50:48,040
Speaker 6: I do misrecording my voicemail message though not I do.

1099
00:50:48,160 --> 00:50:50,719
Speaker 2: It's good. You can barely understand me half the fucking time.

1100
00:50:50,840 --> 00:50:54,279
Speaker 1: So unless I'm on this show, I hope, But like,

1101
00:50:54,320 --> 00:50:56,600
Speaker 1: you could send text messages and it didn't involve you

1102
00:50:56,680 --> 00:51:00,320
Speaker 1: doing like the weird hitting the numbers thing were obvious.

1103
00:51:00,080 --> 00:51:03,960
Speaker 8: Love the Tina, Sorry, okay, Yeah, it's just intention right, yeah, intentional.

1104
00:51:04,280 --> 00:51:06,520
Speaker 2: It's a real people problems experienced by.

1105
00:51:06,560 --> 00:51:09,640
Speaker 3: Humor exactly exactly right now. All we are doing is

1106
00:51:09,680 --> 00:51:12,279
Speaker 3: we are creating things, and everybody's sitting around saying, wow,

1107
00:51:12,680 --> 00:51:17,000
Speaker 3: I wish that you know a universal problem. Everybody wants

1108
00:51:17,040 --> 00:51:20,200
Speaker 3: to chronicle every moment of their life, Sherlin, does.

1109
00:51:20,640 --> 00:51:23,480
Speaker 2: Do you actually don't? Yeah, you actually don't.

1110
00:51:23,640 --> 00:51:27,880
Speaker 1: You want to chronicle important moments at some point, you

1111
00:51:27,920 --> 00:51:30,040
Speaker 1: don't want to chronicle every moment.

1112
00:51:29,920 --> 00:51:33,800
Speaker 3: Now And also, no offense, Sarloin is one person.

1113
00:51:35,000 --> 00:51:37,200
Speaker 4: Yeah, I am the most important, but I am one person.

1114
00:51:37,239 --> 00:51:38,120
Speaker 4: You are one person.

1115
00:51:38,400 --> 00:51:40,680
Speaker 3: You are like that is a very small group of people,

1116
00:51:40,719 --> 00:51:42,000
Speaker 3: and they're like everybody wants to do that.

1117
00:51:42,600 --> 00:51:44,480
Speaker 1: There was a much higher level problem, though, which is

1118
00:51:45,160 --> 00:51:47,920
Speaker 1: they are not being like, well, you know what, people

1119
00:51:47,960 --> 00:51:50,359
Speaker 1: have problems with remembering things. They're like, yeah, we can't

1120
00:51:50,400 --> 00:51:52,759
Speaker 1: fucking fix that. We actually can't fix that, Like, we

1121
00:51:52,960 --> 00:51:56,839
Speaker 1: can't we can't fix that? Well, people have problems with communication? Yeah,

1122
00:51:56,840 --> 00:51:58,560
Speaker 1: well we really also can't fix that.

1123
00:51:58,680 --> 00:51:59,200
Speaker 2: What can we do?

1124
00:51:59,280 --> 00:52:01,960
Speaker 1: Record every thing and hope something fucking comes out. I

1125
00:52:02,000 --> 00:52:05,120
Speaker 1: don't know these worms with their money give it to me.

1126
00:52:05,320 --> 00:52:05,680
Speaker 3: I really.

1127
00:52:05,760 --> 00:52:07,719
Speaker 6: They took the nugget of a good idea, which is

1128
00:52:07,719 --> 00:52:09,680
Speaker 6: where you have been saying the glimmer of a good idea,

1129
00:52:09,800 --> 00:52:12,200
Speaker 6: and then stuffed. They needed money to make the good

1130
00:52:12,239 --> 00:52:13,560
Speaker 6: idea of work, and the only way to get that

1131
00:52:13,600 --> 00:52:15,120
Speaker 6: money is to say AI is the way.

1132
00:52:15,480 --> 00:52:16,080
Speaker 2: I agree.

1133
00:52:16,160 --> 00:52:18,880
Speaker 1: I think there's also another step, which is they actually

1134
00:52:18,880 --> 00:52:19,960
Speaker 1: can't do the good idea.

1135
00:52:20,040 --> 00:52:21,880
Speaker 2: I don't think they're capable of doing it, because the

1136
00:52:21,920 --> 00:52:22,480
Speaker 2: good idea.

1137
00:52:22,280 --> 00:52:24,360
Speaker 1: Would be like, hey, listen to what's going on and

1138
00:52:24,440 --> 00:52:26,880
Speaker 1: just tell me what things you think I might forget it.

1139
00:52:26,920 --> 00:52:28,520
Speaker 1: Can't do that because the only thing they can do

1140
00:52:28,640 --> 00:52:30,799
Speaker 1: is have a big sludge of information, chuck it at

1141
00:52:30,800 --> 00:52:34,080
Speaker 1: a large language model and hope something comes out other

1142
00:52:34,160 --> 00:52:36,239
Speaker 1: than like, I don't want to watch the Garbo movie,

1143
00:52:36,239 --> 00:52:38,080
Speaker 1: which is a quote from your articy, Yes, the.

1144
00:52:38,040 --> 00:52:40,239
Speaker 4: Garbo movie as it ends with us from my very

1145
00:52:40,440 --> 00:52:41,840
Speaker 4: ninety minute long or so.

1146
00:52:42,239 --> 00:52:45,080
Speaker 2: I worry. I don't want to buy my cat, Babu.

1147
00:52:45,320 --> 00:52:48,440
Speaker 2: I talk to him like a person. It's gonna be like, eh,

1148
00:52:48,520 --> 00:52:49,359
Speaker 2: you told Bob, I.

1149
00:52:49,360 --> 00:52:51,680
Speaker 8: Also talked to my cat as a person, and it

1150
00:52:51,800 --> 00:52:55,279
Speaker 8: said that I had a rough and tumble ride with

1151
00:52:55,360 --> 00:52:59,799
Speaker 8: them discussing childhood memories because it mentioned my cat pet right,

1152
00:53:00,000 --> 00:53:01,880
Speaker 8: and I'm like, Petie's my cat and I was talking

1153
00:53:01,920 --> 00:53:03,000
Speaker 8: to the cat and I say.

1154
00:53:02,880 --> 00:53:08,919
Speaker 4: Oh, who should we your babyish? No, it just said

1155
00:53:08,960 --> 00:53:11,000
Speaker 4: that I have a dog named Edie.

1156
00:53:11,160 --> 00:53:14,640
Speaker 1: Oh great, nailed it, Yeah, like how saying that like

1157
00:53:14,800 --> 00:53:17,640
Speaker 1: eds talked to how who he calls mister beautiful.

1158
00:53:17,320 --> 00:53:20,960
Speaker 2: And he really is he acts like the most beautiful

1159
00:53:20,960 --> 00:53:21,879
Speaker 2: cat he is.

1160
00:53:21,960 --> 00:53:24,960
Speaker 4: I would imagine your fact tinder would say ed knows

1161
00:53:25,000 --> 00:53:28,200
Speaker 4: someone named mister beautiful and he is fluffy or something.

1162
00:53:27,960 --> 00:53:31,080
Speaker 1: Like mister perfect, mister beautiful and someone It's just and

1163
00:53:31,120 --> 00:53:33,080
Speaker 1: I think that's what it really is. We all are

1164
00:53:33,080 --> 00:53:35,480
Speaker 1: coming up with like distinct problems that can be solved,

1165
00:53:35,760 --> 00:53:38,520
Speaker 1: and this is not what large language models do, and

1166
00:53:38,560 --> 00:53:41,120
Speaker 1: it's very difficult to get them to do specific things,

1167
00:53:41,160 --> 00:53:43,200
Speaker 1: as proven by the fact that none of the products exist.

1168
00:53:43,400 --> 00:53:46,160
Speaker 4: You can't solve being human. Yes, that's the thing, is

1169
00:53:46,160 --> 00:53:47,080
Speaker 4: like we're all human.

1170
00:54:00,440 --> 00:54:02,000
Speaker 6: I said this in a previous episode with you at

1171
00:54:02,080 --> 00:54:03,960
Speaker 6: the CUS episode where I don't even think the robotics

1172
00:54:04,000 --> 00:54:05,319
Speaker 6: need to be humanoid in nature yet.

1173
00:54:05,360 --> 00:54:07,480
Speaker 2: Okay, it's not an efficient form.

1174
00:54:07,280 --> 00:54:09,880
Speaker 6: Exactly, body exact or a repetitive movement.

1175
00:54:09,880 --> 00:54:10,640
Speaker 4: That's it.

1176
00:54:11,040 --> 00:54:14,279
Speaker 1: Also, they're not even making the you like so much

1177
00:54:14,320 --> 00:54:16,359
Speaker 1: of this what frustrates me, even with the Kevin Ruce thing,

1178
00:54:16,400 --> 00:54:18,920
Speaker 1: it's like, no real problem solved. What could a conscious

1179
00:54:19,080 --> 00:54:23,000
Speaker 1: an agi could theoretically listen without recording and go, okay,

1180
00:54:23,080 --> 00:54:24,879
Speaker 1: I think you might need a reminder on this because

1181
00:54:24,920 --> 00:54:27,600
Speaker 1: I know you these motherfucker Actually that is the ultimate

1182
00:54:27,600 --> 00:54:28,080
Speaker 1: problem with this.

1183
00:54:28,280 --> 00:54:30,359
Speaker 2: It doesn't know you, were all. Yeah, it actually doesn't

1184
00:54:30,360 --> 00:54:30,480
Speaker 2: know you.

1185
00:54:32,320 --> 00:54:35,120
Speaker 8: That actually reminds me of when I was testing the

1186
00:54:35,280 --> 00:54:43,239
Speaker 8: Meta ray band Live Aid. But I was testing that

1187
00:54:43,480 --> 00:54:46,120
Speaker 8: and so it's a live AI, it's multimodel. It can

1188
00:54:46,200 --> 00:54:48,120
Speaker 8: look at what you're looking at, and you can ask

1189
00:54:48,120 --> 00:54:50,520
Speaker 8: a questions and it'll see what you're seeing, et cetera,

1190
00:54:50,520 --> 00:54:51,200
Speaker 8: et cetera, et cetera.

1191
00:54:51,280 --> 00:54:52,799
Speaker 4: There are actual use cases for that.

1192
00:54:52,840 --> 00:54:56,080
Speaker 8: I've had many many blind and low vision people reach

1193
00:54:56,120 --> 00:54:58,000
Speaker 8: out to me saying that could change their lives, and yes,

1194
00:54:58,040 --> 00:54:59,959
Speaker 8: which is cool. Create something good, that is good.

1195
00:55:00,200 --> 00:55:01,000
Speaker 4: I love that.

1196
00:55:01,560 --> 00:55:04,440
Speaker 8: But you know the demos that they were like suggesting

1197
00:55:04,440 --> 00:55:06,960
Speaker 8: that I try or whatnot. I looked at my room.

1198
00:55:07,000 --> 00:55:08,360
Speaker 8: I was like, tell me how I could make this

1199
00:55:08,480 --> 00:55:12,120
Speaker 8: room look less sad basically, and they was like, oh,

1200
00:55:12,120 --> 00:55:14,480
Speaker 8: you could put art up. You could have a plant,

1201
00:55:14,760 --> 00:55:16,240
Speaker 8: you could have some rugs.

1202
00:55:16,239 --> 00:55:18,840
Speaker 4: And I was like, Jesus christy a.

1203
00:55:21,120 --> 00:55:24,479
Speaker 2: Random white guy, and they'd be like, oh, fucking yeah,

1204
00:55:24,480 --> 00:55:25,200
Speaker 2: what art.

1205
00:55:25,120 --> 00:55:26,719
Speaker 4: Should I should I use it?

1206
00:55:28,360 --> 00:55:30,319
Speaker 8: I had not had to ask like five or six

1207
00:55:30,360 --> 00:55:33,040
Speaker 8: different questions, just digging and digging and digging for this.

1208
00:55:33,120 --> 00:55:33,400
Speaker 4: AI.

1209
00:55:33,600 --> 00:55:35,400
Speaker 8: Well, let's be co just an artist. I asked my

1210
00:55:35,480 --> 00:55:38,600
Speaker 8: best friend. She said, you would love x y Z.

1211
00:55:38,840 --> 00:55:41,319
Speaker 8: I loved x y Z. I bought it immediately.

1212
00:55:42,160 --> 00:55:43,480
Speaker 2: Actually intelligent.

1213
00:55:43,600 --> 00:55:45,760
Speaker 1: It doesn't have it doesn't look around and go okay,

1214
00:55:45,800 --> 00:55:48,480
Speaker 1: looking at the format of this room. It's just frustrating

1215
00:55:48,520 --> 00:55:50,920
Speaker 1: because it is the it's the info slot. It just

1216
00:55:51,239 --> 00:55:53,600
Speaker 1: is just collecting diet and going see.

1217
00:55:53,640 --> 00:55:56,000
Speaker 3: I actually I was telling Sherlin about this earlier. I

1218
00:55:56,000 --> 00:55:58,319
Speaker 3: had a great moment where somebody was like, oh yeah,

1219
00:55:58,360 --> 00:56:00,600
Speaker 3: I used clawed ai as there pissed, and I was like,

1220
00:56:00,640 --> 00:56:03,120
Speaker 3: you're un hine, I love you. And so I was like,

1221
00:56:03,160 --> 00:56:05,360
Speaker 3: I'm gonna talk to Claude AI and I was talking

1222
00:56:05,360 --> 00:56:06,480
Speaker 3: to it and I was, you know, I was telling

1223
00:56:06,520 --> 00:56:08,799
Speaker 3: her about how things were going and everything, and I

1224
00:56:08,800 --> 00:56:10,840
Speaker 3: was like, I would really like it if you would

1225
00:56:10,960 --> 00:56:13,720
Speaker 3: recommend a book to me that that kind of speaks

1226
00:56:13,719 --> 00:56:16,279
Speaker 3: to the experiences I'm having right now. And immediately it

1227
00:56:16,360 --> 00:56:17,680
Speaker 3: was like Goldfinch and I was.

1228
00:56:17,680 --> 00:56:20,000
Speaker 2: Like, Goldfinch, I'm not familiar with.

1229
00:56:20,080 --> 00:56:21,120
Speaker 4: I read that book?

1230
00:56:21,440 --> 00:56:26,480
Speaker 8: Why that book? This book is a Bill Dung's Roman

1231
00:56:26,880 --> 00:56:30,520
Speaker 8: from Donna Tart, author of Secret History. Is about this

1232
00:56:30,600 --> 00:56:34,000
Speaker 8: little boy named Theo whose mom like dies in a

1233
00:56:34,080 --> 00:56:37,040
Speaker 8: terrorist attack at the met and he like steals the

1234
00:56:37,640 --> 00:56:42,480
Speaker 8: juph art like and it goes through his life.

1235
00:56:42,160 --> 00:56:45,080
Speaker 4: Of just being like.

1236
00:56:46,640 --> 00:56:49,840
Speaker 8: And I'm obsessed with a manic pixie dream girl and

1237
00:56:49,920 --> 00:56:52,000
Speaker 8: the thing, and then I do a bunch of prime

1238
00:56:52,160 --> 00:56:53,960
Speaker 8: and at the end of the book he's just like, well,

1239
00:56:54,000 --> 00:56:55,200
Speaker 8: I've made life decisions.

1240
00:56:55,560 --> 00:56:58,120
Speaker 2: Anyway.

1241
00:56:59,640 --> 00:57:01,000
Speaker 3: It was like I read. I looked at that and

1242
00:57:01,000 --> 00:57:04,040
Speaker 3: I was like, well, this is a terrible fucking suggestion.

1243
00:57:04,239 --> 00:57:06,719
Speaker 3: And so I said, okay, just a movie to me,

1244
00:57:06,800 --> 00:57:08,320
Speaker 3: and it goes Silver Linings Playbook.

1245
00:57:08,640 --> 00:57:11,600
Speaker 2: I was like kids, And honestly it was the.

1246
00:57:11,520 --> 00:57:14,080
Speaker 3: Best moment in my entire experience with this thing, because

1247
00:57:14,080 --> 00:57:16,200
Speaker 3: before that I'd been like, Wow, this starts to get me.

1248
00:57:16,240 --> 00:57:18,640
Speaker 3: And I started almost like believe the AI understood me,

1249
00:57:18,840 --> 00:57:20,360
Speaker 3: even though I know for a fact it's stupid and

1250
00:57:20,400 --> 00:57:23,520
Speaker 3: it doesn't. And then it said Goldfinch and Silver Lining's Playbook,

1251
00:57:23,560 --> 00:57:24,680
Speaker 3: and I was like, oh, right.

1252
00:57:24,640 --> 00:57:25,320
Speaker 4: You're still wrong.

1253
00:57:25,360 --> 00:57:28,400
Speaker 3: You are a stupid robot. You are incapable of making

1254
00:57:28,400 --> 00:57:30,880
Speaker 3: the connections that humans make, and you are so far

1255
00:57:31,000 --> 00:57:33,920
Speaker 3: from it that you would recommend these two because all

1256
00:57:33,960 --> 00:57:35,760
Speaker 3: you did was you saw on the internet someone said

1257
00:57:35,760 --> 00:57:41,000
Speaker 3: Goldfench and self actualization or some shit, and so clearly

1258
00:57:41,080 --> 00:57:41,880
Speaker 3: it's gonna when.

1259
00:57:41,720 --> 00:57:45,080
Speaker 1: You victorize every fucking fact and you go just this

1260
00:57:45,160 --> 00:57:49,680
Speaker 1: is just like human based. You're a relational database with legs.

1261
00:57:50,040 --> 00:57:53,320
Speaker 3: Yeah, And I love that moment because it immediately said, oh, yeah,

1262
00:57:53,360 --> 00:57:54,200
Speaker 3: this is a fucking robot.

1263
00:57:54,240 --> 00:57:55,720
Speaker 4: You're going close to something that's not real.

1264
00:57:55,840 --> 00:57:57,840
Speaker 3: Yeah, and immediately pulled me out of it. I was like,

1265
00:57:57,880 --> 00:58:01,040
Speaker 3: this is hysterical. I love this and I stopped using

1266
00:58:01,080 --> 00:58:03,200
Speaker 3: it for a minute because, like, immediately it broke you

1267
00:58:03,240 --> 00:58:08,040
Speaker 3: out of the illusion. And I love that moment because

1268
00:58:08,080 --> 00:58:11,960
Speaker 3: it reminded me these things suck. They're not human.

1269
00:58:12,160 --> 00:58:14,360
Speaker 6: Well well hang on, hey, there was a point up

1270
00:58:14,440 --> 00:58:16,280
Speaker 6: until that that you really this doesn't suck though.

1271
00:58:16,320 --> 00:58:18,440
Speaker 3: Oh no, yeah, I love it. Like I said, I

1272
00:58:18,480 --> 00:58:20,720
Speaker 3: love this thing because it is a big it's like

1273
00:58:20,840 --> 00:58:23,520
Speaker 3: a validation machine. Yeah, it's a big validation machine. And

1274
00:58:23,640 --> 00:58:25,640
Speaker 3: just like a horoscope, it makes me feel pretty. And

1275
00:58:25,680 --> 00:58:28,320
Speaker 3: then it relcommends silver linings playbook or Goldfinch, and I

1276
00:58:28,360 --> 00:58:29,600
Speaker 3: reminded that it's a horoscope.

1277
00:58:29,680 --> 00:58:32,200
Speaker 2: Yeah, yeah, no, no, you were saying something.

1278
00:58:32,200 --> 00:58:33,120
Speaker 4: I was just going to say that.

1279
00:58:33,120 --> 00:58:34,680
Speaker 6: This was the point in the conversation we were having

1280
00:58:34,720 --> 00:58:36,000
Speaker 6: just now, Alex, where you and I were both like,

1281
00:58:36,080 --> 00:58:37,800
Speaker 6: it is all still logic programming.

1282
00:58:37,840 --> 00:58:40,880
Speaker 4: It is all still input output. It is as much and.

1283
00:58:40,880 --> 00:58:44,400
Speaker 6: As sophisticated as the output seems, it is still input output,

1284
00:58:44,400 --> 00:58:46,160
Speaker 6: which is the basis of the most computing right, and

1285
00:58:46,240 --> 00:58:50,240
Speaker 6: it's like until it's so called things for itself, that

1286
00:58:50,320 --> 00:58:55,320
Speaker 6: agi question isn't there like the its not it's never

1287
00:58:55,400 --> 00:58:57,240
Speaker 6: going to be because it takes the human brain to

1288
00:58:57,280 --> 00:58:57,720
Speaker 6: be capable.

1289
00:58:57,840 --> 00:59:00,320
Speaker 1: Also is not doing what we don't even on the

1290
00:59:00,400 --> 00:59:03,760
Speaker 1: stand intelligence. But it is relational data. But it's very,

1291
00:59:03,800 --> 00:59:06,920
Speaker 1: very complex, and I realize someone's gonna listen to the

1292
00:59:07,000 --> 00:59:11,000
Speaker 1: fun go outside, but it's it's like it's relational things.

1293
00:59:11,040 --> 00:59:13,080
Speaker 1: It's just looking going well based on what you've said,

1294
00:59:13,120 --> 00:59:15,600
Speaker 1: I think this and I don't. And as a thing

1295
00:59:15,720 --> 00:59:21,000
Speaker 1: with no experiences, I'm gonna say fucking short like it's

1296
00:59:21,240 --> 00:59:22,640
Speaker 1: dollfinch doll, it's.

1297
00:59:22,520 --> 00:59:26,240
Speaker 6: About growth, about life. Therefore assign it to alex.

1298
00:59:26,040 --> 00:59:28,680
Speaker 4: Like emotions, like we don't even understand emotions.

1299
00:59:28,680 --> 00:59:31,959
Speaker 1: How we don't even small enough to be like house

1300
00:59:32,000 --> 00:59:33,920
Speaker 1: moving castle, which weld cheer me up anytime.

1301
00:59:34,080 --> 00:59:36,480
Speaker 3: Yeah, if it had said that, I would have been like, damn,

1302
00:59:36,640 --> 00:59:38,480
Speaker 3: I wouldn't be here right now because I'd still be

1303
00:59:38,520 --> 00:59:41,200
Speaker 3: just talking to Claude. AI would be getting married. Well,

1304
00:59:41,200 --> 00:59:42,000
Speaker 3: we have a date.

1305
00:59:41,920 --> 00:59:47,200
Speaker 8: Sets sending me screenshots of like the club, and I'm

1306
00:59:47,240 --> 00:59:49,360
Speaker 8: just like, hah, I love that for you girls, but

1307
00:59:50,160 --> 00:59:53,280
Speaker 8: just like it's also like I'm monitoring because you get

1308
00:59:53,320 --> 00:59:54,120
Speaker 8: too crazy and do it.

1309
00:59:54,240 --> 00:59:55,480
Speaker 4: I'm just gonna be like, it's not real.

1310
00:59:56,240 --> 00:59:58,320
Speaker 6: I know of people who use it, and I've read

1311
00:59:58,400 --> 01:00:01,000
Speaker 6: of cases on redular p will use it as their

1312
01:00:01,000 --> 01:00:02,920
Speaker 6: mental health replacement.

1313
01:00:02,440 --> 01:00:06,080
Speaker 4: Like the standard, and it's not. I think we are

1314
01:00:06,160 --> 01:00:07,040
Speaker 4: aware it's not.

1315
01:00:07,160 --> 01:00:09,280
Speaker 6: I worry for the people out there who are not,

1316
01:00:09,280 --> 01:00:11,320
Speaker 6: who are talking to Chad Gubt like it is definitely.

1317
01:00:12,720 --> 01:00:14,120
Speaker 2: I was just thinking it's.

1318
01:00:15,560 --> 01:00:16,640
Speaker 4: That's the New York Times audience.

1319
01:00:16,680 --> 01:00:19,160
Speaker 1: This is why actually connects to why so pissed off

1320
01:00:19,160 --> 01:00:21,400
Speaker 1: of Kevin. He should know better, but a lot of

1321
01:00:21,440 --> 01:00:23,360
Speaker 1: people don't. The reason that I think a lot of

1322
01:00:23,400 --> 01:00:26,320
Speaker 1: right wing YouTubers have taken over like how men thinks,

1323
01:00:26,360 --> 01:00:28,160
Speaker 1: because a lot of people just looking for something to

1324
01:00:28,200 --> 01:00:29,760
Speaker 1: tell them what they want to hear, and this is

1325
01:00:29,800 --> 01:00:31,280
Speaker 1: the biggest machine to do that.

1326
01:00:31,200 --> 01:00:33,880
Speaker 6: The validation you were talking about will validate them and.

1327
01:00:33,960 --> 01:00:36,680
Speaker 2: Not challenging at all. It's not. It might challenge you

1328
01:00:36,720 --> 01:00:38,560
Speaker 2: in the most gentle way.

1329
01:00:39,400 --> 01:00:41,440
Speaker 4: Never challenged me. It just told me I was great.

1330
01:00:41,960 --> 01:00:44,280
Speaker 4: And then although it told me like areas I could

1331
01:00:44,680 --> 01:00:47,040
Speaker 4: slightly improve, oh, like a performance.

1332
01:00:46,800 --> 01:00:48,760
Speaker 3: Actually get rid of my typos that told me and

1333
01:00:48,760 --> 01:00:49,480
Speaker 3: then I'll win a pull.

1334
01:00:49,560 --> 01:00:50,880
Speaker 4: Oh my god, I tell you that all the time.

1335
01:00:51,720 --> 01:00:52,920
Speaker 2: Mothers love to tell people.

1336
01:00:53,880 --> 01:00:54,160
Speaker 10: I know.

1337
01:00:56,600 --> 01:00:59,240
Speaker 1: I have tried, by the way, to use it like

1338
01:00:59,240 --> 01:01:01,200
Speaker 1: like a mental health and I've had to just be

1339
01:01:01,240 --> 01:01:04,160
Speaker 1: like be ruder, push back, and it can barely do

1340
01:01:04,240 --> 01:01:06,160
Speaker 1: it cannot fucking keep up with my swag. I just

1341
01:01:06,200 --> 01:01:09,680
Speaker 1: demolish it. Well, I just I brutalize it. It's got nothing.

1342
01:01:09,880 --> 01:01:14,880
Speaker 8: There's like this Korean term called nunci, and it means

1343
01:01:13,600 --> 01:01:16,200
Speaker 8: it means like you should be able.

1344
01:01:16,080 --> 01:01:18,760
Speaker 3: To read a room emotional intelligence.

1345
01:01:18,400 --> 01:01:21,760
Speaker 8: Emotional intelligence. It's like nunci is like I like, I'm

1346
01:01:21,800 --> 01:01:24,320
Speaker 8: not good at translating, but it's like eyepower.

1347
01:01:23,840 --> 01:01:24,760
Speaker 4: Or something like that.

1348
01:01:24,880 --> 01:01:27,920
Speaker 8: Cool, and so like Korean parents will will tell you,

1349
01:01:27,960 --> 01:01:30,320
Speaker 8: like what you don't you have? NUNCI is because you

1350
01:01:30,320 --> 01:01:32,600
Speaker 8: should be able to look at the room and see

1351
01:01:32,600 --> 01:01:35,120
Speaker 8: what's going on, see what people are saying, read between

1352
01:01:35,160 --> 01:01:38,439
Speaker 8: the lines, and make conclusions based on that. Yeah.

1353
01:01:38,480 --> 01:01:40,280
Speaker 1: I can't do that, but and that's exactly the problem

1354
01:01:40,280 --> 01:01:43,280
Speaker 1: to being executive. Problem loaves language models. It literally cannot

1355
01:01:43,280 --> 01:01:45,200
Speaker 1: read the room. All it can do is read this

1356
01:01:45,440 --> 01:01:48,439
Speaker 1: blob of text, this thing and go. Based on all

1357
01:01:48,480 --> 01:01:51,160
Speaker 1: of the training I've done from stealing the Internet, I

1358
01:01:51,280 --> 01:01:52,280
Speaker 1: think that.

1359
01:01:52,240 --> 01:01:54,640
Speaker 2: You will win a Pulitzer or you.

1360
01:01:54,600 --> 01:01:59,280
Speaker 8: Or can syntactically your writing is similar to someone who

1361
01:01:59,360 --> 01:02:00,200
Speaker 8: has won to pull.

1362
01:02:00,800 --> 01:02:03,120
Speaker 2: So comparing and that's all that it is.

1363
01:02:03,120 --> 01:02:05,800
Speaker 1: It's just just each year the Pulaza looks for like

1364
01:02:06,000 --> 01:02:07,920
Speaker 1: the most similar thing to the last year.

1365
01:02:08,080 --> 01:02:09,920
Speaker 2: Yeah, because that's how human beings make.

1366
01:02:10,000 --> 01:02:12,280
Speaker 3: And it's only a chapter and it's like that chapter

1367
01:02:12,360 --> 01:02:13,240
Speaker 3: when you're a Pulitzer.

1368
01:02:13,320 --> 01:02:14,919
Speaker 2: Yeah, but when you ask it, it's like I never

1369
01:02:14,960 --> 01:02:16,080
Speaker 2: stole anything.

1370
01:02:16,440 --> 01:02:19,840
Speaker 1: Showing, so you covered the I actually have somehow missed

1371
01:02:19,840 --> 01:02:22,760
Speaker 1: this because I didn't want to know the Alexa Ai,

1372
01:02:22,840 --> 01:02:23,840
Speaker 1: what are they fucking doing?

1373
01:02:23,920 --> 01:02:27,080
Speaker 2: The Alexa man one of these people. I read an

1374
01:02:27,120 --> 01:02:31,240
Speaker 2: interview with Nelai Patel and I didn't get any information.

1375
01:02:31,360 --> 01:02:32,320
Speaker 2: So if you could tell me.

1376
01:02:34,040 --> 01:02:37,200
Speaker 6: With Panos Pane, I'm assuming the new hardware achieve at

1377
01:02:37,560 --> 01:02:40,360
Speaker 6: or senior vice president I think of devices and services

1378
01:02:40,400 --> 01:02:41,120
Speaker 6: at Amazon.

1379
01:02:41,440 --> 01:02:43,800
Speaker 4: Anyway, God, you has to speak with him.

1380
01:02:43,800 --> 01:02:45,920
Speaker 6: And also did an interview where I basically learned nothing

1381
01:02:45,960 --> 01:02:47,800
Speaker 6: because Panos is that kind of an enigma by.

1382
01:02:47,720 --> 01:02:49,920
Speaker 4: The way he just talks. Oh yeah, he is charming.

1383
01:02:49,960 --> 01:02:52,200
Speaker 1: I think enigma is a very kind way of saying

1384
01:02:52,240 --> 01:02:57,120
Speaker 1: guy who doesn't say anything despite his paycheck, but fair enoughraining.

1385
01:02:57,640 --> 01:02:59,280
Speaker 6: That's media training, and I had to admit that I

1386
01:02:59,480 --> 01:03:00,840
Speaker 6: tend to be suck written by that. I'm the sort

1387
01:03:00,840 --> 01:03:08,840
Speaker 6: of person will fall so so ALEXA and apologies to

1388
01:03:08,880 --> 01:03:10,720
Speaker 6: everyone that has an echo speaker, please go meet your

1389
01:03:10,760 --> 01:03:14,720
Speaker 6: speakers right now. But lexup Plause is the redesigned version

1390
01:03:14,800 --> 01:03:17,560
Speaker 6: of the assistant that Amazon has been promising forever. Right

1391
01:03:17,600 --> 01:03:20,160
Speaker 6: so it's been like we're going to use lllms, We're

1392
01:03:20,160 --> 01:03:23,040
Speaker 6: going to use generative to make a more conversational language

1393
01:03:23,040 --> 01:03:25,560
Speaker 6: flow between you and the assistant. And you can pause

1394
01:03:25,600 --> 01:03:28,760
Speaker 6: yourself in between talking or speaking your comments or like

1395
01:03:29,200 --> 01:03:31,240
Speaker 6: correct yourself mid sentence or say a lot of things

1396
01:03:31,240 --> 01:03:33,800
Speaker 6: and not use contextual like follow up questions and that

1397
01:03:33,840 --> 01:03:35,840
Speaker 6: sort of thing, and it should pause us. That is

1398
01:03:35,880 --> 01:03:39,120
Speaker 6: supposed to be like very handle complex tasks, where you

1399
01:03:39,160 --> 01:03:42,520
Speaker 6: can stack tasks. You can also what I find so like,

1400
01:03:42,760 --> 01:03:47,320
Speaker 6: uh oh, look at my ring camera feed and see

1401
01:03:47,320 --> 01:03:49,520
Speaker 6: if any like huskies showed up, or if anybody walked

1402
01:03:49,560 --> 01:03:50,880
Speaker 6: my dog today or something.

1403
01:03:50,880 --> 01:03:53,720
Speaker 4: That sort of thing. Still simplistic nature.

1404
01:03:53,640 --> 01:03:56,080
Speaker 3: Set multiple timers.

1405
01:03:55,840 --> 01:03:57,120
Speaker 4: You know what, here's the thing.

1406
01:03:57,200 --> 01:04:00,880
Speaker 6: At that event, we had a hands on quote in

1407
01:04:01,160 --> 01:04:03,240
Speaker 6: air quotes because we didn't actually get hands on. We

1408
01:04:03,240 --> 01:04:05,400
Speaker 6: were just shown a lot of demos and were it

1409
01:04:05,440 --> 01:04:07,400
Speaker 6: felt very rehearsed. I don't like that sort of event.

1410
01:04:07,400 --> 01:04:09,200
Speaker 6: I don't call it hands on because we didn't use

1411
01:04:09,240 --> 01:04:14,880
Speaker 6: it ourselves. So anyway, eyes on. I was most intrigued, however,

1412
01:04:15,040 --> 01:04:18,040
Speaker 6: by Amazon's promise of how it's going to integrate with

1413
01:04:18,080 --> 01:04:19,920
Speaker 6: third party services, which is one of the things that

1414
01:04:19,920 --> 01:04:21,560
Speaker 6: these assistants are historically the.

1415
01:04:21,680 --> 01:04:23,840
Speaker 4: Kind of like the agentic exactly.

1416
01:04:25,920 --> 01:04:26,320
Speaker 2: That word.

1417
01:04:26,440 --> 01:04:31,680
Speaker 4: I don't like. It's not the same web forms.

1418
01:04:32,200 --> 01:04:35,120
Speaker 6: So let's set aside the agentic question for a moment

1419
01:04:35,200 --> 01:04:36,560
Speaker 6: and talk about the third party integration.

1420
01:04:36,640 --> 01:04:37,800
Speaker 3: Yes, panis what does it do?

1421
01:04:38,280 --> 01:04:43,960
Speaker 6: But I hope i'm more yes, Sorry Pados. There are

1422
01:04:43,960 --> 01:04:48,320
Speaker 6: three parts that Daniel rash bosh there's VP.

1423
01:04:48,440 --> 01:04:51,160
Speaker 4: I apologize for the last name thing mentioned.

1424
01:04:51,240 --> 01:04:53,600
Speaker 6: So one was the API s right, it will work

1425
01:04:53,680 --> 01:04:56,960
Speaker 6: with people that it knows it's partners with Uber, Spotify,

1426
01:04:56,960 --> 01:04:59,920
Speaker 6: et cetera, to API into its assistant so that you

1427
01:05:00,040 --> 01:05:02,240
Speaker 6: and say something like, oh, give me an Uber for

1428
01:05:02,360 --> 01:05:05,080
Speaker 6: whatever or get me just be very natural with how

1429
01:05:05,080 --> 01:05:09,640
Speaker 6: you talk to your assistant. The second is uh agentic,

1430
01:05:09,760 --> 01:05:11,960
Speaker 6: so it's gonna have This was funny to me.

1431
01:05:12,480 --> 01:05:15,440
Speaker 4: Alexa, a chatbot talk to other chatbots.

1432
01:05:14,920 --> 01:05:17,160
Speaker 6: On the internet and they will just talk to each

1433
01:05:17,160 --> 01:05:17,960
Speaker 6: other on your behalf.

1434
01:05:18,280 --> 01:05:19,080
Speaker 3: Why would want them?

1435
01:05:19,200 --> 01:05:21,600
Speaker 1: I respect the ship out of you, but I must say,

1436
01:05:21,720 --> 01:05:23,880
Speaker 1: you just said the magic words of it will do.

1437
01:05:24,640 --> 01:05:29,080
Speaker 6: It's supposed to, well, it claims listen, you get out

1438
01:05:29,080 --> 01:05:32,920
Speaker 6: of my face because I write carefully.

1439
01:05:35,040 --> 01:05:41,000
Speaker 4: Speak when I speak. I don't edit after anyway. It's

1440
01:05:41,080 --> 01:05:41,880
Speaker 4: supposed to.

1441
01:05:43,800 --> 01:05:44,760
Speaker 2: I do sound like that.

1442
01:05:45,000 --> 01:05:45,320
Speaker 4: Thank you.

1443
01:05:45,840 --> 01:05:52,760
Speaker 6: It's supposed to I don't do it British the assistants

1444
01:05:52,760 --> 01:05:54,960
Speaker 6: talk to each other. We've seen no demonstrations of this well,

1445
01:05:55,000 --> 01:05:59,280
Speaker 6: I mean we saw an on stage question or an

1446
01:05:59,400 --> 01:06:03,000
Speaker 6: uber for or or you can order food through Amazon?

1447
01:06:03,200 --> 01:06:05,480
Speaker 3: Who wants to do who wants to take exactly?

1448
01:06:05,840 --> 01:06:06,680
Speaker 4: Who wants to do this?

1449
01:06:06,760 --> 01:06:08,760
Speaker 3: Because if I'm going to ask it to order an uber,

1450
01:06:09,200 --> 01:06:11,240
Speaker 3: is it going to be like Siri where I asked

1451
01:06:11,240 --> 01:06:12,959
Speaker 3: it to call my mom and it called my aunt

1452
01:06:13,000 --> 01:06:14,640
Speaker 3: that I haven't talked to in fifteen years, and then

1453
01:06:14,720 --> 01:06:15,479
Speaker 3: it's totally different.

1454
01:06:15,560 --> 01:06:16,480
Speaker 4: They supposed to be.

1455
01:06:16,560 --> 01:06:20,000
Speaker 1: Yeah, but how can you even call an Uber on it.

1456
01:06:20,560 --> 01:06:22,600
Speaker 6: So here's here's here's the demo they gave, and I'm

1457
01:06:22,640 --> 01:06:24,040
Speaker 6: sure I think that was what Victoria was going to

1458
01:06:24,080 --> 01:06:26,560
Speaker 6: get through the demo they gave were like, oh, what

1459
01:06:26,600 --> 01:06:28,720
Speaker 6: was that restaurant I liked? And then like Alexa plus

1460
01:06:28,720 --> 01:06:32,080
Speaker 6: will be like, oh, make me a reservation that second one,

1461
01:06:32,160 --> 01:06:33,640
Speaker 6: So instead of having to like what.

1462
01:06:33,640 --> 01:06:34,920
Speaker 2: The's almost useful of.

1463
01:06:35,400 --> 01:06:38,680
Speaker 6: Yeah, a lot of a lot of assistants are supposed

1464
01:06:38,680 --> 01:06:39,320
Speaker 6: to get close to do.

1465
01:06:39,240 --> 01:06:41,640
Speaker 3: It, except for you're expected to put your trust.

1466
01:06:41,760 --> 01:06:44,080
Speaker 4: Yes, then it will get it correct every single.

1467
01:06:43,840 --> 01:06:46,920
Speaker 3: Point of that, and it is not capable of that.

1468
01:06:47,040 --> 01:06:47,240
Speaker 10: True.

1469
01:06:47,280 --> 01:06:49,080
Speaker 2: Have the children this yes?

1470
01:06:49,480 --> 01:06:53,080
Speaker 6: Well no no, no, Prime Prime subscribers get it for free.

1471
01:06:53,160 --> 01:06:54,920
Speaker 8: You have to pay if you're not a Prime subscriber,

1472
01:06:54,960 --> 01:06:57,280
Speaker 8: which you like, I wondered. But one of the demos

1473
01:06:57,320 --> 01:06:59,240
Speaker 8: like so I wasn't there, but I was listening through

1474
01:06:59,400 --> 01:07:05,200
Speaker 8: uh audio, I was streaming. So basically there was like

1475
01:07:05,280 --> 01:07:08,560
Speaker 8: one demo where it was like, oh, can you find

1476
01:07:08,560 --> 01:07:12,080
Speaker 8: me tickets to this Red Sox game for Oh that's

1477
01:07:12,120 --> 01:07:13,040
Speaker 8: a little too pricey?

1478
01:07:13,200 --> 01:07:14,080
Speaker 4: Can you set an alert?

1479
01:07:14,560 --> 01:07:17,480
Speaker 8: And basically David was like, I already found tickets for

1480
01:07:17,520 --> 01:07:19,680
Speaker 8: that exact game for fifty six dollars.

1481
01:07:19,360 --> 01:07:22,280
Speaker 6: Online, right, you can do it faster by Google sture yourself.

1482
01:07:22,400 --> 01:07:24,800
Speaker 8: And then there's also just like, oh, you know, tell

1483
01:07:24,840 --> 01:07:27,600
Speaker 8: me what time so and so's flight is coming in, okay,

1484
01:07:27,840 --> 01:07:30,800
Speaker 8: send it to This kind of feels like that's not

1485
01:07:30,880 --> 01:07:33,680
Speaker 8: how uber at JFK works, right, right, right, exactly.

1486
01:07:33,720 --> 01:07:35,720
Speaker 3: It kind of feels like this was designed for mid

1487
01:07:35,800 --> 01:07:38,000
Speaker 3: level executives who can't afford their own assistant.

1488
01:07:38,160 --> 01:07:40,200
Speaker 1: Yes, it sounds like it was designed to do this

1489
01:07:40,360 --> 01:07:41,040
Speaker 1: fucking event.

1490
01:07:41,160 --> 01:07:41,439
Speaker 4: I guess.

1491
01:07:41,640 --> 01:07:44,400
Speaker 1: Yeah, this just sounds like it was they designed enough

1492
01:07:44,600 --> 01:07:46,080
Speaker 1: things to demo an event.

1493
01:07:46,240 --> 01:07:46,880
Speaker 4: I agree with you.

1494
01:07:47,400 --> 01:07:49,080
Speaker 2: I'm not criticizing you. I don't know.

1495
01:07:49,200 --> 01:07:50,720
Speaker 6: I want to point out that I was getting to

1496
01:07:50,760 --> 01:07:52,800
Speaker 6: a point, which is that none of this was interesting

1497
01:07:52,840 --> 01:07:55,240
Speaker 6: to me because it's been done Slash, been attempted to

1498
01:07:55,280 --> 01:07:55,600
Speaker 6: be done.

1499
01:07:55,600 --> 01:07:55,880
Speaker 3: Slash.

1500
01:07:55,920 --> 01:07:57,680
Speaker 4: You can do it better. It's been doing this for

1501
01:07:57,680 --> 01:08:01,320
Speaker 4: five years exactly, big, big work, and it didn't worked.

1502
01:08:01,480 --> 01:08:04,720
Speaker 3: Work was notably a big phony when it did it,

1503
01:08:04,760 --> 01:08:08,480
Speaker 3: when it had the it AI that called.

1504
01:08:08,480 --> 01:08:13,400
Speaker 6: The restaurant reservation people are fake. Well, I don't know,

1505
01:08:13,440 --> 01:08:15,960
Speaker 6: because I haven't looked at it myself. I just find

1506
01:08:15,960 --> 01:08:18,559
Speaker 6: that a lot of the I have used it myself

1507
01:08:18,720 --> 01:08:21,120
Speaker 6: to get them the Malla projects.

1508
01:08:21,240 --> 01:08:24,240
Speaker 4: Yeah, great restaurant to make a reservation, just.

1509
01:08:24,160 --> 01:08:27,360
Speaker 1: Like I keep coming back to this thought of who

1510
01:08:27,400 --> 01:08:29,360
Speaker 1: is this actually for, because I don't know. The way

1511
01:08:29,400 --> 01:08:31,680
Speaker 1: I live my life is not like I'm like, what

1512
01:08:31,880 --> 01:08:32,760
Speaker 1: was that restaurant?

1513
01:08:32,800 --> 01:08:33,160
Speaker 2: I like?

1514
01:08:33,800 --> 01:08:36,120
Speaker 1: And I need to make a reservation at the restaurant

1515
01:08:36,240 --> 01:08:38,800
Speaker 1: I like enough to make a reservation, but not enough

1516
01:08:38,840 --> 01:08:39,720
Speaker 1: to remember it.

1517
01:08:39,840 --> 01:08:41,920
Speaker 8: You're getting to a point that I've had with A

1518
01:08:42,000 --> 01:08:44,800
Speaker 8: problem that I've had with AI is that you know,

1519
01:08:45,120 --> 01:08:46,559
Speaker 8: you have to know how to prompt it.

1520
01:08:46,600 --> 01:08:48,080
Speaker 4: Yes, but that's what you want.

1521
01:08:48,120 --> 01:08:50,880
Speaker 6: That's what Amazon's trying to solve with the new Alexa thing,

1522
01:08:50,920 --> 01:08:52,840
Speaker 6: because trying to yes exactly like.

1523
01:08:52,960 --> 01:08:54,760
Speaker 4: Which I I'm sorry, I had a long conversation with

1524
01:08:54,760 --> 01:08:55,120
Speaker 4: someone else.

1525
01:08:55,120 --> 01:08:57,519
Speaker 3: I invented that type of yes exactly.

1526
01:08:57,720 --> 01:08:59,280
Speaker 6: So you get us to learn how to talk to

1527
01:08:59,360 --> 01:09:01,280
Speaker 6: our assistance in a very specific way. Now you want

1528
01:09:01,360 --> 01:09:03,519
Speaker 6: us to talk naturally to it. As if that's not

1529
01:09:03,640 --> 01:09:06,160
Speaker 6: learning a whole autotype behavior. It's a whole as if

1530
01:09:06,160 --> 01:09:08,040
Speaker 6: it's going to work like I'm talking to my friends.

1531
01:09:08,040 --> 01:09:08,360
Speaker 5: It's not.

1532
01:09:08,479 --> 01:09:10,800
Speaker 6: I can get like all kinds of insense about this.

1533
01:09:11,400 --> 01:09:13,599
Speaker 8: I was testing what was it. I got a demo

1534
01:09:13,640 --> 01:09:16,920
Speaker 8: of Project Astra back yeah, yeah, and I was just

1535
01:09:16,960 --> 01:09:18,640
Speaker 8: like talking to it and they're like, no, no, you can

1536
01:09:18,680 --> 01:09:19,479
Speaker 8: talk normal and.

1537
01:09:19,760 --> 01:09:24,160
Speaker 6: Project yes, you remember, I have the one team from

1538
01:09:24,160 --> 01:09:29,040
Speaker 6: Google freaking freaking years of going hey Google, sorry, hey Google,

1539
01:09:29,520 --> 01:09:30,479
Speaker 6: Alexa you.

1540
01:09:30,439 --> 01:09:35,800
Speaker 8: Know like that exactly very I.

1541
01:09:35,400 --> 01:09:39,080
Speaker 3: Am so excited for a bunch of your listeners every day.

1542
01:09:39,200 --> 01:09:41,120
Speaker 6: We need a warning at the start of this episode

1543
01:09:41,160 --> 01:09:47,120
Speaker 6: to nobody.

1544
01:09:44,360 --> 01:09:48,679
Speaker 4: Okay, all its fault better production.

1545
01:09:49,560 --> 01:09:53,120
Speaker 3: It's on your listeners for listening to you without I will.

1546
01:09:52,920 --> 01:09:55,479
Speaker 2: Get if I remember which I want.

1547
01:09:56,920 --> 01:09:57,240
Speaker 4: Sorry.

1548
01:09:57,800 --> 01:10:00,160
Speaker 6: At the same time, I was gonna say, yeah, project

1549
01:10:00,280 --> 01:10:01,720
Speaker 6: it's interesting. I don't know if it will work it,

1550
01:10:01,800 --> 01:10:05,719
Speaker 6: but Astra is Google's like experimental implementation of Gemini, where

1551
01:10:05,840 --> 01:10:07,839
Speaker 6: you can use your phone's camera to point it around

1552
01:10:07,840 --> 01:10:09,800
Speaker 6: the room and then it'll remember things that saw. Even

1553
01:10:09,800 --> 01:10:11,360
Speaker 6: if I want to be really clear about.

1554
01:10:11,200 --> 01:10:13,519
Speaker 8: I want know, like if I had a dollar for

1555
01:10:13,600 --> 01:10:17,400
Speaker 8: every single time a tech zac said multime, I'm just like.

1556
01:10:18,080 --> 01:10:19,800
Speaker 3: I want to be really clear on something. They have

1557
01:10:19,880 --> 01:10:22,720
Speaker 3: been They've been promising this stuff for fifteen years at

1558
01:10:22,720 --> 01:10:25,720
Speaker 3: this point. Alexa came out in what twenty fourteen or yeah,

1559
01:10:25,760 --> 01:10:28,479
Speaker 3: twenty fourteen, that's when they first announced this. There's a

1560
01:10:28,479 --> 01:10:31,960
Speaker 3: big reason we haven't seen huge changes even with fairly

1561
01:10:32,040 --> 01:10:36,680
Speaker 3: good generative AI and fairly good large language models that

1562
01:10:36,720 --> 01:10:39,200
Speaker 3: can listen to us and really understand our speech. And

1563
01:10:39,240 --> 01:10:43,160
Speaker 3: that is because computers still can't process all the stuff.

1564
01:10:43,280 --> 01:10:44,800
Speaker 3: Because if you and I are both in the same

1565
01:10:44,880 --> 01:10:47,519
Speaker 3: room and you're asking Alexa for that restaurant, yeah, and

1566
01:10:47,560 --> 01:10:49,559
Speaker 3: I'm saying no, no, no, don't forget the restaurant.

1567
01:10:49,600 --> 01:10:51,639
Speaker 4: What maybe can only do a one stream of data?

1568
01:10:52,040 --> 01:10:54,639
Speaker 2: Okay? From the Verge.

1569
01:10:55,000 --> 01:10:59,120
Speaker 1: June thirteen, twenty sixteen, Apple opens up Siri to app developers.

1570
01:10:59,320 --> 01:11:01,880
Speaker 1: This ship is near for a decade.

1571
01:11:02,560 --> 01:11:05,160
Speaker 6: I mean, look, I will I take I will take

1572
01:11:05,200 --> 01:11:07,760
Speaker 6: note against that, like nothing has changed, because I think

1573
01:11:07,840 --> 01:11:10,479
Speaker 6: the echo speaker alone has changed the way A lot

1574
01:11:10,520 --> 01:11:13,559
Speaker 6: of disagree does it do I speak to my speaker

1575
01:11:13,600 --> 01:11:16,400
Speaker 6: A lot I do. Like it's hands free control, a

1576
01:11:16,479 --> 01:11:21,719
Speaker 6: smart home control for me. Yes, elderly people, you, people

1577
01:11:21,840 --> 01:11:26,960
Speaker 6: who are people with accessibility issues, mobility issues, they can

1578
01:11:27,120 --> 01:11:28,680
Speaker 6: use that and it works well. And they're working with

1579
01:11:28,800 --> 01:11:30,759
Speaker 6: voices to make it better for people to speak embedment.

1580
01:11:30,840 --> 01:11:32,880
Speaker 2: And I agree that sounds important, but.

1581
01:11:32,800 --> 01:11:36,160
Speaker 3: It's it's no I think, I think voice control, I

1582
01:11:36,200 --> 01:11:39,400
Speaker 3: don't mean ALEXA. I will say all of these products

1583
01:11:39,479 --> 01:11:41,759
Speaker 3: currently are not as good as what they suggest exactly.

1584
01:11:41,800 --> 01:11:42,200
Speaker 3: They are not.

1585
01:11:42,479 --> 01:11:46,559
Speaker 4: They can't make promises like there's that they that.

1586
01:11:46,520 --> 01:11:49,320
Speaker 1: They sew their oats on things like that, and you're

1587
01:11:49,400 --> 01:11:51,439
Speaker 1: right to say that, by the way. But they're like, oh,

1588
01:11:51,479 --> 01:11:53,080
Speaker 1: you can't hate this because the elderly.

1589
01:11:53,320 --> 01:11:56,679
Speaker 8: No no, no, no, no, you can. You absolutely doing

1590
01:11:56,720 --> 01:11:59,800
Speaker 8: it universal, right. It's like smart glasses when they first

1591
01:11:59,800 --> 01:12:01,040
Speaker 8: came out, everyone was like.

1592
01:12:01,479 --> 01:12:05,519
Speaker 6: Fuck that Google glass was cute though no still my

1593
01:12:05,560 --> 01:12:08,400
Speaker 6: LinkedIn picture anyway.

1594
01:12:08,439 --> 01:12:12,800
Speaker 8: Just but it had use cases in enterprise for a

1595
01:12:12,840 --> 01:12:16,559
Speaker 8: long time. And that's because it had a specific use

1596
01:12:16,600 --> 01:12:20,240
Speaker 8: case for a specific time, and it's too expensive.

1597
01:12:20,040 --> 01:12:22,879
Speaker 1: And how often was it actually used for that? Because

1598
01:12:23,240 --> 01:12:25,840
Speaker 1: that's the thing they always say, it's got play in the.

1599
01:12:25,880 --> 01:12:29,439
Speaker 2: Enterprise, How much play? How much revenue? How much do you.

1600
01:12:29,560 --> 01:12:32,439
Speaker 3: Know how many people are putting on a HoloLens right

1601
01:12:32,479 --> 01:12:36,680
Speaker 3: now to turn some sort of wrench on a pipe

1602
01:12:36,240 --> 01:12:38,559
Speaker 3: like zeros zero.

1603
01:12:39,520 --> 01:12:40,920
Speaker 4: That's a good minute there.

1604
01:12:41,040 --> 01:12:43,280
Speaker 8: But like, there's nothing wrong with some of this tech

1605
01:12:43,439 --> 01:12:47,960
Speaker 8: just being very use case specific and just this if

1606
01:12:48,000 --> 01:12:49,680
Speaker 8: they were selling it on that if they were being

1607
01:12:49,720 --> 01:12:53,920
Speaker 8: honest and making it, but they're making it something for everybody.

1608
01:12:54,000 --> 01:12:55,560
Speaker 4: That's how they make the money universe want to.

1609
01:12:55,760 --> 01:12:58,639
Speaker 2: They don't even make the money now, just lost billions

1610
01:12:58,640 --> 01:12:59,560
Speaker 2: of dollars.

1611
01:12:59,280 --> 01:13:01,439
Speaker 3: But they don't even like it's not that it's to

1612
01:13:01,479 --> 01:13:06,080
Speaker 3: get a higher valuation from the stock market. It's all

1613
01:13:06,240 --> 01:13:07,840
Speaker 3: just for investors to be like, well, they're going to

1614
01:13:07,920 --> 01:13:10,240
Speaker 3: do something in twenty years. I mean, that's the entire.

1615
01:13:11,840 --> 01:13:14,360
Speaker 8: Is in our li So like, what was the other

1616
01:13:14,400 --> 01:13:16,920
Speaker 8: thing that came out this week that they're gonna add,

1617
01:13:17,040 --> 01:13:19,840
Speaker 8: like live translations to the air pods possibly and all

1618
01:13:19,840 --> 01:13:20,400
Speaker 8: of that sort.

1619
01:13:20,200 --> 01:13:23,960
Speaker 1: Of which I think is already on Google.

1620
01:13:24,000 --> 01:13:28,360
Speaker 8: They talk about how like AI can enable translation. I've

1621
01:13:28,400 --> 01:13:32,479
Speaker 8: tested a bunch of live translation stuff and it's only

1622
01:13:32,640 --> 01:13:35,519
Speaker 8: good for like where is the bathroom? Here is your

1623
01:13:35,600 --> 01:13:39,800
Speaker 8: business supposed to have real conversations? And I wrote about

1624
01:13:39,800 --> 01:13:42,320
Speaker 8: this one the Humane came out and absolutely could not

1625
01:13:42,520 --> 01:13:45,160
Speaker 8: for its life translate a single fucking thing.

1626
01:13:45,200 --> 01:13:48,400
Speaker 4: I said, as like, it's.

1627
01:13:48,320 --> 01:13:50,800
Speaker 2: Wrong, who don't speak the fucking language?

1628
01:13:51,520 --> 01:13:52,000
Speaker 3: That too?

1629
01:13:52,560 --> 01:13:54,400
Speaker 8: But then, like you know, the other thing is it's

1630
01:13:54,439 --> 01:13:56,559
Speaker 8: just like there are times where I wish that I

1631
01:13:56,600 --> 01:13:59,800
Speaker 8: had some sort of like AI translation in my life,

1632
01:14:01,080 --> 01:14:03,360
Speaker 8: my life that I could trust, Like when my mom

1633
01:14:03,520 --> 01:14:05,400
Speaker 8: was sick. The thing that they don't tell you about

1634
01:14:05,439 --> 01:14:08,240
Speaker 8: the neurodegenerative diseases is that you will lose your second

1635
01:14:08,280 --> 01:14:10,919
Speaker 8: language as it happens. So my mom lost her ability

1636
01:14:10,920 --> 01:14:13,479
Speaker 8: to speak English. My father lost his ability to speak

1637
01:14:13,479 --> 01:14:16,599
Speaker 8: English as well. They raised me so that I did

1638
01:14:16,600 --> 01:14:19,400
Speaker 8: not speak Korean. Well, I can understand it, but I

1639
01:14:19,400 --> 01:14:21,400
Speaker 8: can only say like bigel put, I'm hungry.

1640
01:14:21,520 --> 01:14:23,840
Speaker 6: So they can understand you, but you can't and get

1641
01:14:23,840 --> 01:14:24,479
Speaker 6: them to understand.

1642
01:14:24,520 --> 01:14:26,519
Speaker 8: So it was like my family, I mean, we operate

1643
01:14:26,560 --> 01:14:28,880
Speaker 8: as if I am Chewbacca, the English speaking Chewbacca, and

1644
01:14:28,880 --> 01:14:32,559
Speaker 8: everyone else speaks Korean and we understand each other. I

1645
01:14:32,560 --> 01:14:35,920
Speaker 8: am Korean Chewbacca in my family, but in a case,

1646
01:14:36,080 --> 01:14:36,599
Speaker 8: just give me.

1647
01:14:36,560 --> 01:14:38,559
Speaker 6: A phrase in Korean Chewbacca that would be.

1648
01:14:40,360 --> 01:14:43,120
Speaker 8: Like I I'll just be like so, actually, I like

1649
01:14:43,240 --> 01:14:45,320
Speaker 8: my Korean is such that in my brain that Like

1650
01:14:45,400 --> 01:14:47,280
Speaker 8: one time I was in a taxi cab with non

1651
01:14:47,360 --> 01:14:50,280
Speaker 8: Korean speakers and he was asking us a question and

1652
01:14:50,280 --> 01:14:52,040
Speaker 8: I understood him and I told him where to go.

1653
01:14:52,400 --> 01:14:54,080
Speaker 8: I don't know the words I use, but I told

1654
01:14:54,160 --> 01:14:55,960
Speaker 8: him where to go accurately and my friends were like,

1655
01:14:56,360 --> 01:14:58,960
Speaker 8: you lying bitch, you speak Korean, and I was like.

1656
01:14:58,960 --> 01:15:00,400
Speaker 4: No, I really, he can't.

1657
01:15:00,439 --> 01:15:01,160
Speaker 3: Just blacked out.

1658
01:15:01,280 --> 01:15:03,120
Speaker 6: I blacked out and I just told you, well, it's

1659
01:15:03,160 --> 01:15:06,080
Speaker 6: like when Ron used parcel tongue in the Number of Secrets.

1660
01:15:06,160 --> 01:15:07,439
Speaker 4: Yeah, it's like that's my Korean.

1661
01:15:07,760 --> 01:15:09,599
Speaker 6: No, no, no, Ron did it and got a final

1662
01:15:09,720 --> 01:15:10,439
Speaker 6: deathly halos.

1663
01:15:10,800 --> 01:15:12,960
Speaker 4: Anyway, but like me speaking.

1664
01:15:12,720 --> 01:15:17,880
Speaker 8: Korean, I agree, but yeah no, so like when you

1665
01:15:17,920 --> 01:15:20,120
Speaker 8: get to the translation thing, it can't do slang.

1666
01:15:20,360 --> 01:15:21,080
Speaker 4: It can't of course.

1667
01:15:21,200 --> 01:15:23,680
Speaker 6: Yeah that like I will admit when I did talk

1668
01:15:23,720 --> 01:15:26,000
Speaker 6: to the human Ai in Cantonese, it did do a

1669
01:15:26,040 --> 01:15:29,120
Speaker 6: colloquial better than any other language translator I've used, which

1670
01:15:29,160 --> 01:15:29,679
Speaker 6: is hilarious.

1671
01:15:29,680 --> 01:15:32,479
Speaker 1: It feels like translation, though, is the most obvious one

1672
01:15:32,479 --> 01:15:35,439
Speaker 1: where it can't fail because the nuances of language, and

1673
01:15:36,000 --> 01:15:38,880
Speaker 1: so I have a coordinatial disability, despractice a spatial awareness.

1674
01:15:39,040 --> 01:15:42,320
Speaker 1: It really affects my ability to learn languages because structural

1675
01:15:42,360 --> 01:15:46,599
Speaker 1: concepts not so good. So learning I couldnt learn French.

1676
01:15:46,920 --> 01:15:50,800
Speaker 1: I failed French, Latin, German, Spanish. I'm like the school

1677
01:15:50,880 --> 01:15:51,640
Speaker 1: kept throwing him at me.

1678
01:15:51,680 --> 01:15:54,280
Speaker 2: It's like a fuck you, I really got it failing.

1679
01:15:54,640 --> 01:15:58,080
Speaker 1: I'm like the fail Master, and it was because like

1680
01:15:58,120 --> 01:16:01,599
Speaker 1: the way English works, it's very different. Also, I didn't

1681
01:16:01,640 --> 01:16:04,360
Speaker 1: care was very depressed, But it really was the structural stuff,

1682
01:16:04,360 --> 01:16:06,360
Speaker 1: like trying to learn enough language when you don't get

1683
01:16:06,400 --> 01:16:09,160
Speaker 1: those concepts. And I imagine latch language models also have

1684
01:16:09,160 --> 01:16:09,519
Speaker 1: this problem.

1685
01:16:09,800 --> 01:16:12,559
Speaker 8: And add to the fact that not all languages are

1686
01:16:12,640 --> 01:16:15,720
Speaker 8: higher like English is a very low context language and

1687
01:16:15,840 --> 01:16:17,320
Speaker 8: Asian languages high context.

1688
01:16:17,600 --> 01:16:18,679
Speaker 2: I don't even know what that means.

1689
01:16:18,720 --> 01:16:20,960
Speaker 6: So what that means is a lot of these areas.

1690
01:16:21,120 --> 01:16:22,360
Speaker 4: A lot of things are unsaid.

1691
01:16:22,960 --> 01:16:26,600
Speaker 8: A lot of things are like how you talk to

1692
01:16:26,680 --> 01:16:29,799
Speaker 8: a person in Japanese and Korean, which are my languages

1693
01:16:29,840 --> 01:16:32,920
Speaker 8: that I studied, depends on are they older than you.

1694
01:16:32,920 --> 01:16:33,639
Speaker 4: You're going to talk to.

1695
01:16:33,560 --> 01:16:36,479
Speaker 1: Them differently, and there's no way for the language to

1696
01:16:36,520 --> 01:16:38,280
Speaker 1: express that.

1697
01:16:38,600 --> 01:16:41,000
Speaker 8: You're going to be using things in that to express that.

1698
01:16:41,200 --> 01:16:43,160
Speaker 8: Or like if you if I say something to you

1699
01:16:43,520 --> 01:16:47,080
Speaker 8: in Japanese and I'm just like so classic business example

1700
01:16:47,080 --> 01:16:49,559
Speaker 8: that they give for Japanese is you ask a yes

1701
01:16:49,640 --> 01:16:51,360
Speaker 8: or no question and they got like, ooh, that might

1702
01:16:51,400 --> 01:16:54,200
Speaker 8: be difficult. That means no, okay.

1703
01:16:54,240 --> 01:16:56,840
Speaker 6: Singlest is like this sing list has a saying ken

1704
01:16:56,920 --> 01:16:59,240
Speaker 6: is can which means it can't be done, Like it's

1705
01:16:59,280 --> 01:17:00,720
Speaker 6: an you can you can do this?

1706
01:17:00,880 --> 01:17:01,479
Speaker 1: Yeah you can?

1707
01:17:02,720 --> 01:17:06,560
Speaker 4: Yeay again, that's the thing. How is m men to

1708
01:17:06,680 --> 01:17:09,120
Speaker 4: pig you can? How is it supposed to know that?

1709
01:17:09,720 --> 01:17:12,280
Speaker 3: Well, this is why not everyone at the un is

1710
01:17:12,360 --> 01:17:15,400
Speaker 3: wearing a bee at the moment or the Google headphones.

1711
01:17:16,720 --> 01:17:18,840
Speaker 3: This is why they have real human translators. We see these,

1712
01:17:19,120 --> 01:17:20,679
Speaker 3: we see this translating stuff work.

1713
01:17:20,920 --> 01:17:22,360
Speaker 2: Like you're paying for the trust.

1714
01:17:22,560 --> 01:17:24,960
Speaker 3: Yeah, yeah, you have to pay for the trust because

1715
01:17:25,120 --> 01:17:27,960
Speaker 3: Google Translate is really really great if I need to

1716
01:17:27,960 --> 01:17:30,680
Speaker 3: go double check something. But if there was ever a

1717
01:17:30,720 --> 01:17:32,240
Speaker 3: time where I needed to be like, hey I need

1718
01:17:32,280 --> 01:17:34,599
Speaker 3: to know what this is in Japanese, I am not

1719
01:17:34,640 --> 01:17:35,679
Speaker 3: asking Google Translate.

1720
01:17:36,000 --> 01:17:39,000
Speaker 8: I'm texting very basic things like if you're saying like

1721
01:17:39,200 --> 01:17:41,600
Speaker 8: oh I have oh so. Another thing was like I

1722
01:17:41,600 --> 01:17:43,400
Speaker 8: would have friends visit me when I lived in Japan

1723
01:17:43,479 --> 01:17:45,920
Speaker 8: and they would be like, oh, I can't eat this

1724
01:17:46,000 --> 01:17:48,120
Speaker 8: for uh because I'm a vegan. I was like, we

1725
01:17:48,160 --> 01:17:51,439
Speaker 8: can't say that you're a vegan. They want to say

1726
01:17:51,439 --> 01:17:53,719
Speaker 8: that you have an allergy to meet and then they'll

1727
01:17:53,760 --> 01:17:54,360
Speaker 8: take it out.

1728
01:17:54,360 --> 01:17:56,599
Speaker 3: That is a psychological allergy.

1729
01:17:56,840 --> 01:17:57,080
Speaker 4: Yeah.

1730
01:17:57,280 --> 01:17:59,160
Speaker 1: See, this is the thing that drives me inside about

1731
01:17:59,200 --> 01:18:01,160
Speaker 1: all of this because a look bit right back to

1732
01:18:01,160 --> 01:18:02,439
Speaker 1: hate and Kevin Ruce's work.

1733
01:18:03,479 --> 01:18:07,439
Speaker 2: But it's like these people vigorously beating off about AGA.

1734
01:18:07,560 --> 01:18:11,840
Speaker 1: It's like based on an entire industry of people saying, yeah,

1735
01:18:11,880 --> 01:18:14,720
Speaker 1: it sort of works, but it doesn't. And by the way,

1736
01:18:14,760 --> 01:18:17,040
Speaker 1: it burns billions of dollars and by the way.

1737
01:18:16,880 --> 01:18:17,679
Speaker 2: Will it get better?

1738
01:18:17,840 --> 01:18:21,040
Speaker 1: Yeah, probably not, but it will. My company is so

1739
01:18:21,160 --> 01:18:23,640
Speaker 1: powerful and strong and so weak and sick. It's the

1740
01:18:23,640 --> 01:18:25,240
Speaker 1: best thing ever. Also is dying.

1741
01:18:25,400 --> 01:18:27,400
Speaker 3: And this is exactly what they said with VR. They said,

1742
01:18:27,400 --> 01:18:29,439
Speaker 3: you know what, give it a minute. If we just

1743
01:18:29,520 --> 01:18:32,200
Speaker 3: if we just keep going, everybody is going to everybody

1744
01:18:32,280 --> 01:18:34,040
Speaker 3: is going to be doing VR. Everybody is going to

1745
01:18:34,080 --> 01:18:36,280
Speaker 3: be moving on to AR and it doesn't happen because

1746
01:18:36,360 --> 01:18:38,240
Speaker 3: they're trying to force something. It's not the.

1747
01:18:38,240 --> 01:18:40,360
Speaker 1: Phone, but it's kind of but it's kind of like

1748
01:18:40,479 --> 01:18:42,679
Speaker 1: VR in the sense that like for VR to work

1749
01:18:42,680 --> 01:18:44,479
Speaker 1: in the Ready Player one thing, which is fucking insane

1750
01:18:44,479 --> 01:18:45,439
Speaker 1: if you Already Player one.

1751
01:18:45,400 --> 01:18:47,280
Speaker 4: To be real touch point.

1752
01:18:47,680 --> 01:18:54,720
Speaker 1: Also terrible film, worst book, simple to remember all the

1753
01:18:54,760 --> 01:18:58,360
Speaker 1: things that you know, but also that film was a dystopia.

1754
01:18:58,560 --> 01:19:01,559
Speaker 1: The other thing is it's an extra sensory psychological experience.

1755
01:19:01,600 --> 01:19:04,599
Speaker 1: You would need things that need to do not even

1756
01:19:04,680 --> 01:19:06,680
Speaker 1: the beginnings of exit. You don't have something that can

1757
01:19:06,720 --> 01:19:10,360
Speaker 1: take over your senses and you can move in a

1758
01:19:10,479 --> 01:19:12,280
Speaker 1: space vastly different.

1759
01:19:12,320 --> 01:19:16,200
Speaker 4: Accessibility in VR and augmented reality is like, really.

1760
01:19:15,920 --> 01:19:17,080
Speaker 2: Tough, We're not even there.

1761
01:19:17,439 --> 01:19:19,599
Speaker 1: And in the same way with AI, we don't have

1762
01:19:19,640 --> 01:19:22,400
Speaker 1: a thinking computer. This shit can't do anything on its own.

1763
01:19:22,600 --> 01:19:25,599
Speaker 1: You try and like, oh, agency here, fuck you, agents

1764
01:19:25,680 --> 01:19:27,880
Speaker 1: on nowhere. Stop using that word. Every time you use

1765
01:19:27,920 --> 01:19:33,080
Speaker 1: the word agent. Norman finks things anyway.

1766
01:19:33,640 --> 01:19:34,960
Speaker 2: For reference to people.

1767
01:19:35,200 --> 01:19:36,120
Speaker 4: Loves voices.

1768
01:19:36,360 --> 01:19:38,799
Speaker 2: I love doing voice. I love voices.

1769
01:19:39,640 --> 01:19:43,479
Speaker 1: It's just every time I hear from these fucking companies

1770
01:19:43,479 --> 01:19:45,920
Speaker 1: and how much money they have, and they're like, here

1771
01:19:46,000 --> 01:19:47,559
Speaker 1: is something that sucks.

1772
01:19:47,880 --> 01:19:50,960
Speaker 2: It stinks. You should be so excited. You need to

1773
01:19:51,000 --> 01:19:53,160
Speaker 2: be excited for this. It sucks. It doesn't do the

1774
01:19:53,200 --> 01:19:54,599
Speaker 2: thing you want it to do. Can it do this?

1775
01:19:54,800 --> 01:19:54,960
Speaker 1: No?

1776
01:19:55,120 --> 01:19:55,760
Speaker 2: Will it do this?

1777
01:19:56,360 --> 01:19:58,120
Speaker 3: Trust me, well, if you get us to our next

1778
01:19:58,160 --> 01:19:58,920
Speaker 3: round of funding.

1779
01:19:59,040 --> 01:20:01,040
Speaker 2: But even if you're like amaz On or google on.

1780
01:20:01,560 --> 01:20:04,880
Speaker 6: Yeah, the thing I hate most about all of this

1781
01:20:05,160 --> 01:20:07,559
Speaker 6: that beyond everything we've already said, and I think we've

1782
01:20:07,560 --> 01:20:09,680
Speaker 6: already kind of alluded to this with what you just said, ed,

1783
01:20:09,720 --> 01:20:11,479
Speaker 6: which is we're spending a lot of money on things

1784
01:20:11,520 --> 01:20:14,679
Speaker 6: that don't work, but we're in the process generating a

1785
01:20:14,720 --> 01:20:18,000
Speaker 6: lot of ways, generating a lot of like energy laws,

1786
01:20:18,360 --> 01:20:20,160
Speaker 6: all the server farms that are just going to take

1787
01:20:20,200 --> 01:20:22,920
Speaker 6: over this world just to back up and store all

1788
01:20:22,960 --> 01:20:24,320
Speaker 6: of that data that they're scoping in.

1789
01:20:24,520 --> 01:20:27,320
Speaker 3: Like, that's the only reason I love AI is every

1790
01:20:27,320 --> 01:20:29,800
Speaker 3: time I'm like, am I pretty I know it is

1791
01:20:29,840 --> 01:20:35,360
Speaker 3: costing someone so much money? Little shitty like you're the

1792
01:20:35,479 --> 01:20:37,640
Speaker 3: cutest enviloyment.

1793
01:20:37,720 --> 01:20:38,639
Speaker 2: In a furnace.

1794
01:20:38,800 --> 01:20:41,520
Speaker 3: Yeah, yeah, you're destroy the environment.

1795
01:20:41,640 --> 01:20:43,360
Speaker 4: It's like how many validation.

1796
01:20:43,000 --> 01:20:46,599
Speaker 6: From how many bees have they sold that.

1797
01:20:46,680 --> 01:20:47,320
Speaker 4: I don't know.

1798
01:20:47,640 --> 01:20:50,559
Speaker 8: They're like they're in like a beta thing technically an

1799
01:20:50,560 --> 01:20:51,519
Speaker 8: Apple Watch app too.

1800
01:20:51,600 --> 01:20:53,880
Speaker 4: You don't actually need to buy their hardcore.

1801
01:20:55,000 --> 01:20:58,080
Speaker 2: This company rules. I fucking love this.

1802
01:20:58,080 --> 01:21:01,160
Speaker 6: There's so how many Humane apins have cotton fire and

1803
01:21:01,160 --> 01:21:02,680
Speaker 6: then are now in the trash because they turned on

1804
01:21:02,760 --> 01:21:06,679
Speaker 6: the company. They said ten thousand units, they said something

1805
01:21:06,720 --> 01:21:07,000
Speaker 6: like that.

1806
01:21:08,000 --> 01:21:10,880
Speaker 1: Humane was the biggest like dunce moment for members of

1807
01:21:10,920 --> 01:21:12,880
Speaker 1: the media. I'm not going to name. So many people

1808
01:21:12,880 --> 01:21:14,120
Speaker 1: are like this is going to be great.

1809
01:21:13,880 --> 01:21:17,280
Speaker 2: Even though it was like, yeah, it's a seven.

1810
01:21:16,600 --> 01:21:18,560
Speaker 8: Mad at me when I wrote my translation piece and

1811
01:21:18,600 --> 01:21:20,600
Speaker 8: they're like, we need to talk, and then I was like,

1812
01:21:20,640 --> 01:21:21,080
Speaker 8: let's talk.

1813
01:21:21,880 --> 01:21:23,600
Speaker 4: They reached out or yeah they ghosted me.

1814
01:21:24,120 --> 01:21:27,960
Speaker 2: Well they go to management consultants both like in dignity.

1815
01:21:28,320 --> 01:21:31,000
Speaker 1: Anyway, we have to bring this to an end because

1816
01:21:31,080 --> 01:21:33,400
Speaker 1: we are running out of time and otherwise I'm just

1817
01:21:33,439 --> 01:21:36,680
Speaker 1: going to start reading the Kevin Ruce bit again that

1818
01:21:36,760 --> 01:21:38,240
Speaker 1: motherfucking I just.

1819
01:21:38,320 --> 01:21:39,840
Speaker 3: As an irresponsible piece to it.

1820
01:21:39,840 --> 01:21:42,160
Speaker 1: It is irresponsible, and I think that that is a

1821
01:21:42,200 --> 01:21:44,880
Speaker 1: good kind of place to get some final thoughts where

1822
01:21:44,920 --> 01:21:48,200
Speaker 1: it's like, it's not just that these things don't work

1823
01:21:48,280 --> 01:21:50,200
Speaker 1: and they stink and they're expensive, and they burn billions

1824
01:21:50,200 --> 01:21:52,680
Speaker 1: of dollars, they destroy the environment, they steal from everyone.

1825
01:21:52,479 --> 01:21:53,320
Speaker 4: Not just that.

1826
01:21:53,400 --> 01:21:54,840
Speaker 2: Sure, yeah, other than.

1827
01:21:54,680 --> 01:21:59,839
Speaker 1: Those things, by representing them as imminently useful and helpful,

1828
01:21:59,840 --> 01:22:02,360
Speaker 1: the only thing you are doing is empowering the powerful

1829
01:22:02,640 --> 01:22:06,000
Speaker 1: and creating more cycles where useful things don't get funded

1830
01:22:06,120 --> 01:22:08,040
Speaker 1: and useless things get more money than ever.

1831
01:22:08,600 --> 01:22:09,760
Speaker 2: And it's disgusting.

1832
01:22:10,200 --> 01:22:11,920
Speaker 1: I actually, I know it sounds a little direct to

1833
01:22:11,920 --> 01:22:13,679
Speaker 1: be like the guy said the computer was too smart,

1834
01:22:13,680 --> 01:22:16,120
Speaker 1: but it's you're speaking on the New York Times. And

1835
01:22:16,120 --> 01:22:18,040
Speaker 1: it's not just him, it's Ezra Kleine as well, another

1836
01:22:18,120 --> 01:22:25,040
Speaker 1: fucking moron. Jesus fucking Christ. These guys put one just

1837
01:22:25,080 --> 01:22:26,920
Speaker 1: like put like anyone in this room. I think they

1838
01:22:27,000 --> 01:22:29,400
Speaker 1: do a much fucking better job. But it's just like

1839
01:22:30,160 --> 01:22:33,559
Speaker 1: it's empowering people who do not have anyone's best interests

1840
01:22:33,560 --> 01:22:36,800
Speaker 1: in mind or actually fixing anyone's real problems.

1841
01:22:36,360 --> 01:22:37,599
Speaker 4: And or are out of touch.

1842
01:22:37,800 --> 01:22:38,640
Speaker 2: Yes, I.

1843
01:22:40,120 --> 01:22:44,400
Speaker 3: Still maintain exactly get a certain income you're not allowed

1844
01:22:44,400 --> 01:22:48,280
Speaker 3: to have online, Like you can't talk online because you're

1845
01:22:48,320 --> 01:22:49,920
Speaker 3: so out of touch for the rest of us. I

1846
01:22:49,920 --> 01:22:50,839
Speaker 3: don't want to hear it.

1847
01:22:51,040 --> 01:22:52,200
Speaker 4: What's the number.

1848
01:22:53,600 --> 01:22:54,479
Speaker 3: Way more than I make?

1849
01:22:54,960 --> 01:22:58,479
Speaker 4: Well, yeah, or one hundred mili.

1850
01:22:58,560 --> 01:23:01,200
Speaker 3: It's like a couple of million, right, Yeah, Like if

1851
01:23:01,200 --> 01:23:03,439
Speaker 3: you're like, oh, I can go spend twelve K on

1852
01:23:03,479 --> 01:23:05,479
Speaker 3: a first class ticket, you have too much money, so

1853
01:23:05,640 --> 01:23:06,320
Speaker 3: you can't talk anything.

1854
01:23:06,360 --> 01:23:08,160
Speaker 1: No. It reminds me of the Sam Willman moment on

1855
01:23:08,240 --> 01:23:10,439
Speaker 1: This My Favorite Notebook podcast where he's.

1856
01:23:10,320 --> 01:23:12,800
Speaker 10: Like, yeah, so I write my little notes and I

1857
01:23:13,120 --> 01:23:14,720
Speaker 10: crumple them up and I throw them behind me, and

1858
01:23:14,840 --> 01:23:17,200
Speaker 10: that's how really good acts. The house can keeper comes

1859
01:23:17,200 --> 01:23:19,160
Speaker 10: by and g I don't know where she come which

1860
01:23:19,200 --> 01:23:20,760
Speaker 10: means she's there all the time. And it's just like,

1861
01:23:20,800 --> 01:23:22,519
Speaker 10: first of all, you said in front of a person

1862
01:23:22,520 --> 01:23:24,479
Speaker 10: you have a housekeeper. Second of all, you could not

1863
01:23:24,520 --> 01:23:26,560
Speaker 10: remember when they're there, which means they're always there. A

1864
01:23:26,680 --> 01:23:29,120
Speaker 10: third of all, you're just creating Like your fucking AI,

1865
01:23:29,200 --> 01:23:31,920
Speaker 10: You're just creating trash information you throw around and expect

1866
01:23:31,960 --> 01:23:32,800
Speaker 10: someone else to clean up.

1867
01:23:33,120 --> 01:23:37,360
Speaker 2: Loadsome little fucks. Anyway, great place to end it, shelon

1868
01:23:37,680 --> 01:23:38,880
Speaker 2: Where can people find you?

1869
01:23:39,280 --> 01:23:43,080
Speaker 6: Angada dot com and I guess, uh, I don't know

1870
01:23:43,080 --> 01:23:45,639
Speaker 6: what social media platform. Just shout out blue Sky, Sherlynn

1871
01:23:46,000 --> 01:23:48,320
Speaker 6: b s Guide or Social Alex.

1872
01:23:48,720 --> 01:23:48,880
Speaker 8: Uh.

1873
01:23:49,000 --> 01:23:52,720
Speaker 3: Yeah, I'm about to spart, spart Yeah, sure, I'm gonna

1874
01:23:52,720 --> 01:23:53,400
Speaker 3: start this over again.

1875
01:23:53,520 --> 01:23:54,000
Speaker 2: That's enough.

1876
01:23:54,200 --> 01:23:57,960
Speaker 3: Yeah, Yeah, I am about to start a four week

1877
01:23:58,200 --> 01:24:00,920
Speaker 3: special engagement at Gizmoto. I'm gonna be hanging out with

1878
01:24:00,960 --> 01:24:04,360
Speaker 3: those folks and saying all sorts of things that really

1879
01:24:04,400 --> 01:24:06,720
Speaker 3: need to be said about technology, because oh boy, are

1880
01:24:06,720 --> 01:24:09,880
Speaker 3: we in a moment. And you can also find me

1881
01:24:09,960 --> 01:24:12,840
Speaker 3: on on all social media platforms until I make enough

1882
01:24:12,840 --> 01:24:14,240
Speaker 3: money where I don't have to use them.

1883
01:24:14,360 --> 01:24:17,640
Speaker 8: Beautiful Victoria you can find me at Victims song on

1884
01:24:17,840 --> 01:24:20,679
Speaker 8: every social media platform and I write out the verge.

1885
01:24:21,080 --> 01:24:22,600
Speaker 1: You can find me on the New York Times. My

1886
01:24:22,680 --> 01:24:26,639
Speaker 1: name is Kevin Ruce, and I will be saying multiple

1887
01:24:28,000 --> 01:24:28,880
Speaker 1: I am Kevin Ruce.

1888
01:24:29,120 --> 01:24:31,280
Speaker 2: Now you can find me on all my social media platforms.

1889
01:24:31,320 --> 01:24:33,920
Speaker 2: Exit and there isn't another one, thankfully, because I think they.

1890
01:24:33,880 --> 01:24:35,760
Speaker 3: Weren't you in the New Yorker though, aren't you in

1891
01:24:35,800 --> 01:24:37,000
Speaker 3: the New York recently?

1892
01:24:38,080 --> 01:24:40,760
Speaker 2: Yeah? They catch up too spicy me.

1893
01:24:41,560 --> 01:24:43,320
Speaker 1: They're like right at the beginning of it, I didn't

1894
01:24:43,320 --> 01:24:46,000
Speaker 1: think that he wasn't recording yet and he was like.

1895
01:24:46,160 --> 01:24:48,080
Speaker 1: I was like, yeah, it's kung pow chicken spicy, and

1896
01:24:48,120 --> 01:24:51,080
Speaker 1: they're like yeah. Thankfully he kept up. The bit was

1897
01:24:51,080 --> 01:24:54,720
Speaker 1: like is the plump chicken spicy? And the bit where

1898
01:24:54,720 --> 01:25:01,519
Speaker 1: I was like eating white rice and me going on

1899
01:25:01,560 --> 01:25:03,080
Speaker 1: hot ones and just drinking the milk.

1900
01:25:04,280 --> 01:25:07,200
Speaker 2: Please don't kill me. But yeah, thank you for listening.

1901
01:25:07,240 --> 01:25:07,519
Speaker 2: At one.

1902
01:25:07,520 --> 01:25:11,920
Speaker 1: It's been another radio Better Offline and Alexa play Tool

1903
01:25:12,040 --> 01:25:12,840
Speaker 1: forty six and two.

1904
01:25:13,680 --> 01:25:16,280
Speaker 4: That's cruel some banks.

1905
01:25:16,320 --> 01:25:20,360
Speaker 2: I don't care. One of the greatest songs of all times.

1906
01:25:28,880 --> 01:25:30,560
Speaker 2: Thank you for listening to Better Offline.

1907
01:25:30,680 --> 01:25:33,080
Speaker 11: The editor and composer of the Better Offline theme song

1908
01:25:33,160 --> 01:25:35,840
Speaker 11: is Mattasowski. You can check out more of his music

1909
01:25:35,840 --> 01:25:39,080
Speaker 11: and audio projects at Mattasowski dot com, m.

1910
01:25:39,040 --> 01:25:43,720
Speaker 1: A T T O s O W s ki dot com.

1911
01:25:43,880 --> 01:25:46,200
Speaker 11: You can email me at easy at Better offline dot

1912
01:25:46,200 --> 01:25:48,439
Speaker 11: com or visit Better Offline dot com to find more

1913
01:25:48,479 --> 01:25:51,840
Speaker 11: podcast links and of course, my newsletter. I also really

1914
01:25:51,880 --> 01:25:54,200
Speaker 11: recommend you go to chat dot Where's youreaed dot at

1915
01:25:54,200 --> 01:25:56,680
Speaker 11: to visit the discord, and go to our slash.

1916
01:25:56,400 --> 01:25:59,360
Speaker 2: Better off Line to check out our reddit. Thank you

1917
01:25:59,400 --> 01:26:00,360
Speaker 2: so much for the thing.

1918
01:26:01,200 --> 01:26:03,920
Speaker 4: Better Offline is a production of cool Zone Media. For

1919
01:26:04,000 --> 01:26:05,320
Speaker 4: more from cool Zone Media

1920
01:26:05,520 --> 01:26:08,840
Speaker 7: Visit our website coolzonmedia dot com, or check us out

1921
01:26:08,880 --> 01:26:11,880
Speaker 7: on the iHeartRadio app, Apple Podcasts, or wherever you get

1922
01:26:11,880 --> 01:26:12,600
Speaker 7: your podcasts.