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Speaker 1: Eighth five Here at fifty five KRCD talk station, Brian

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Speaker 1: Thomas wishing everyone a very happy Tuesday and always looking

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Speaker 1: forward to this time a week. We have appointment listening

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Speaker 1: here in the morning show. One of them is right now.

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Speaker 1: We call it the breit Bart Inside Scoop, and today

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Speaker 1: happy to welcome back to the bright Bart Inside Scoop

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Speaker 1: in the fifty five krc Morning Show Breitbart's tech editor

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Speaker 1: Colin May nine. And of course, as I always say

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Speaker 1: before we start talking, book mark this site b R

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Speaker 1: E I T b a art dot com. Welcome back, Colin.

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Speaker 1: It's always great having you on the show. Happy to

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Speaker 1: be here, Brian, and sort of welcome to the city

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Speaker 1: of Since then, I know, it's a virtual class. Anybody

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Speaker 1: in America or wherever, whoever you're listening, whoever you're listening

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Speaker 1: to my voice, including Germany, Steve, Good morning again. Empower

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Speaker 1: you America dot org. A virtual class. Just register ahead

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Speaker 1: of time, log in at seven pm because Colinen, Collin's

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Speaker 1: got to be talking about technology and artificial intelligence and

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Speaker 1: what's coming our way with a question mark after that,

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Speaker 1: what is coming our way?

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Speaker 2: Colin?

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Speaker 3: Well, Brian, you know they kind of they kind of

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Speaker 3: put me under the microscope with that headline, because anyone

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Speaker 3: who says with complete confidence they knows what's coming is

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Speaker 3: lying to you, you know, right. I like to say,

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Speaker 3: I don't even really write a speech because if you

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Speaker 3: write a speech about AI, it's going to be out

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Speaker 3: of date by the time you give it. Yeah, no question.

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Speaker 1: That's like technology generally speaking. Man, I'm old enough to

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Speaker 1: remember having to learn MS DOS to deal with, you know,

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Speaker 1: with computers, and I learned basic when I was in

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Speaker 1: K through twelve education. And you know, fast forward to today.

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Speaker 1: We have moved at light speed and every day you

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Speaker 1: wake up something has dramatically changed. So the world is

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Speaker 1: crazy with technology. So I certainly get your point on that.

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Speaker 1: And artificial intelligence is going to be the most moving

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Speaker 1: target out there. I mean, I read articles where we're

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Speaker 1: all going to be unemployed. AI is putting everybody out

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Speaker 1: of a job. To no, no, no, the human element

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Speaker 1: will never be replaced. And of course, the only thing

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Speaker 1: I think it is immune from some impact by AI.

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Speaker 1: And you can feel free to chime in on this generalized,

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Speaker 1: generalized statement trades. AI can't install plumbing or electric or

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Speaker 1: along those lines, So maybe that's the one area of

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Speaker 1: refuge from AI. But your response to that, Colin, well,

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Speaker 1: in large.

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Speaker 3: In part, I agree with that. You know, AI cannot

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Speaker 3: do the trades. They can't fix your plumbing, et cetera.

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Speaker 3: They can't lay you know, they can't do the electrical

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Speaker 3: wiring that they need for their data centers.

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Speaker 2: So that would be the view of Nvidia CEO.

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Speaker 3: Johnson Wong that you know there's going to be an

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Speaker 3: explosion of jobs in the trades.

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Speaker 2: And related to the trades.

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Speaker 3: You know, certainly the powers that be want AI to

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Speaker 3: do those things. They envision the future, and this would

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Speaker 3: be elon mush for example, where robots do everything. So

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Speaker 3: you know it's not AI necessarily that turning the wrench

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Speaker 3: on that pipe, but it's a robot controlled by AI.

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Speaker 3: That's directly you get determinators and you know our party.

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Speaker 1: Colin, I literally just got done writing Fighting Wars for

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Speaker 1: us when you said that. It's the first thing that

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Speaker 1: came to my mind anyhow, Right.

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Speaker 3: So you know that's there's certainly some of these people

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Speaker 3: working towards that. I'm a big believer in the human

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Speaker 3: element because I see a lot of problems with AI,

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Speaker 3: and I don't you know, I don't believe that the

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Speaker 3: proper application is trying to replace humanity and have us all,

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Speaker 3: you know, sitting on virtual reality, eating crickets and smoking wheed.

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Speaker 3: You know, that's frankly what Silicon Valley wants for our

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Speaker 3: children and grandchildren. You and I don't want that. For

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Speaker 3: our chance. Our children and grandchildren.

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Speaker 1: Get are stipend from government for buying food. Otherwise it's

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Speaker 1: naval contemplation and poetry writing for all of you. Interesting,

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Speaker 1: But you mentioned problems, and you know, depending on people's

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Speaker 1: perceptions of what AI represents in terms of problems, I

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Speaker 1: think a lot of it springs largely from at least

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Speaker 1: from you know, my listeners standpoint, a lot of people

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Speaker 1: who are concerned about AI. You know, the whole garbage in,

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Speaker 1: garbage out philosophy that who's programming it has sort of

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Speaker 1: pre programmed an implicit bias towards things that might not

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Speaker 1: sit well politically, ethically, or morally with a lot of

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Speaker 1: people out there that might use AI. How do we

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Speaker 1: address that component.

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Speaker 3: That's a tremendous problem. And there's two forms of bias

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Speaker 3: for one, you know, as if one wasn't bad enough. Brian, So,

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Speaker 3: first of all, you know, we have the bias of

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Speaker 3: the people making the AI. You know, pro tip there's

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Speaker 3: not a lot of red hats in sitting in open

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Speaker 3: AI and meta right.

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Speaker 2: And right shocking. He gets not the HR for doing that.

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Speaker 3: So how that impacts things is AI almost works in

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Speaker 3: two stages. It formulates answers and then it sort of

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Speaker 3: checks those answers against almost.

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Speaker 2: The rules for behavior. Here's how I'm supposed to act.

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Speaker 3: Okay, And so those very biased progressive leftists create those

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Speaker 3: rules for how you're supposed to act. That causes AI

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Speaker 3: to do a lot of wacky things, like when Google.

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Speaker 3: You know, one of the infamous moments in recent AI

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Speaker 3: history was Google launched the Gemini Gemini AI image creator.

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Speaker 3: One of the rules that Google set for that tool.

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Speaker 2: Was make everything diverse.

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Speaker 3: You know, it doesn't matter.

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Speaker 2: What they asked for, make it diverse. To hope.

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Speaker 3: People said, you know, make a picture of founding fathers,

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Speaker 3: and you got Asian founding fathers, Black founding fathers.

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Speaker 1: Black George Washington, if I recall correctly, it generated that one.

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Speaker 3: Yeah, and you know, make a picture of Nazis and

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Speaker 3: you got Indian Nazis. You know, like it was doing

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Speaker 3: what it was programmed to do the other portion. And

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Speaker 3: this is another form of bias is AI operates off

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Speaker 3: training data. You know, you know, imagine your hard drive

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Speaker 3: at home times ten million. Okay, that's the amount of

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Speaker 3: training data these things work with. It's not much more.

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Speaker 3: And that training data is not sort of clean factual data.

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Speaker 3: For example, they love to use Reddit to train oh no,

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Speaker 3: to train these things, and you I did from your

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Speaker 3: action you know who's on Reddit?

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Speaker 1: Oh my god.

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Speaker 3: Well, we don't want the generation, the next generation to

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Speaker 3: become since all day on Reddit, that's being sucked into

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Speaker 3: AI as factual, good info and it's operating on that.

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Speaker 3: So you now see there's sort of two forms of

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Speaker 3: bias in one.

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Speaker 1: Well that kind of goes back to my garbage in

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Speaker 1: garbage out, because one of the other components is erroneous

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Speaker 1: information that not only would you have biased information if

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Speaker 1: you're just looking at Reddit. Not only there's random musings,

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Speaker 1: people with sphincters we all got one, and offering their

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Speaker 1: thoughts and comments on any given topic. But you know,

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Speaker 1: when it comes to generating what we hope are is

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Speaker 1: legitimate content, like for example, the many times stupid lawyers

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Speaker 1: relied on AI generated briefs and then come to find

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Speaker 1: out when they go to court to judge, you know,

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Speaker 1: fund them in contempt because the cases that are cited

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Speaker 1: in the AI generated brief are fake. They are whole

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Speaker 1: cloth made up. How can that even be?

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Speaker 3: There's actually, you know, I love talking about this with you, Brian,

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Speaker 3: because you're a lot background. There's actually now a worst

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Speaker 3: case where a person on appeal not only did they

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Speaker 3: have made up cases and stuff, but they made up

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Speaker 3: from whole cloth. The AI made up from whole cloth

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Speaker 3: quotes and statements that the prosecutor as supposed to know,

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Speaker 3: and they're defamatory. They basically said, look how evil and

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Speaker 3: wrong this prosecutor is when they said quote unquote and

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Speaker 3: it was not in the record.

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Speaker 1: It's just oh my god. So yeah, there's other lawyers

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Speaker 1: out they're reacting an equal shock on that one that

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Speaker 1: I have Geese Louise making up the lower court record.

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Speaker 2: Wow.

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Speaker 3: So, Brian, you know one of the things I want

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Speaker 3: to do tonight with empower you and I think this

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Speaker 3: is really valuable for people is I'm in the front

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Speaker 3: line to this right.

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Speaker 2: I'm dealing with the.

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Speaker 3: News every day and what that's enabled me to do

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Speaker 3: is kind of create some baseline concepts that can help

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Speaker 3: you understand the news about AI. So I'll just share

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Speaker 3: one right now because it goes to what you're talking about.

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Speaker 3: It's very very clear that when people use AI to

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Speaker 3: replace themselves or human activity in a final product like

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Speaker 3: a legal filing, a letter, anything like that, people don't

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Speaker 3: even read that before they turn it in. Oh my lord,

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Speaker 3: it's good, and they turn it in. So that lawyer,

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Speaker 3: it would never you know, no matter how many lawyers

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Speaker 3: get busted for this, it never occurs to the lawyer, Hey,

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Speaker 3: I ought to have my in your associate check, you know,

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Speaker 3: Brian Thomas v. Collin, and make sure that case actually exists. Right,

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Speaker 3: They just they just say the machine said it. The

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Speaker 3: machine is God. I'm turning it in. And so that

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Speaker 3: happens with kids, that happens with lawyers, that happens with

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Speaker 3: everyone in between.

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Speaker 1: But you know, the mind blowing thing about that. I

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Speaker 1: hate to do one of the legal aspects of this,

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Speaker 1: and maybe a little bit weed dwelling, but a case

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Speaker 1: cited in anything generated for me, I'm going to have

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Speaker 1: to look at it anyway, just to find out if

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Speaker 1: it's still valid case law. Not that I would even

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Speaker 1: think first off, that they how could something that's supposed

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Speaker 1: to be intelligent whole cloth create a case that never

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Speaker 1: even existed in law books. Now, that seems to be

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Speaker 1: a glitch that should be easily solvable by our you know,

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Speaker 1: intellectual lords and masters putting this together. But I would

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Speaker 1: first go and grab that case and want to run

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Speaker 1: through shepherds to find out whether it's still case law

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Speaker 1: that's valid or has been overturned by some other decision,

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Speaker 1: because there's no guarantee that they've run it through that

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Speaker 1: process and generating the original brief.

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Speaker 2: Brian, you're so last center.

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Speaker 3: I just can't deal with.

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Speaker 2: People like you.

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Speaker 3: So let me add one guy a little more feel

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Speaker 3: to the fire story is we're not when when we're

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Speaker 3: talking about this happening, Brian, we're almost never talking about like,

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Speaker 3: you know, just out.

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Speaker 2: Of law school of people, and it's like a say, like.

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Speaker 3: A you know, a minor traffic accident kind of case,

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Speaker 3: right right, This is happening on gigantic stages. This is happening.

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Speaker 3: One of the classic cases this has happened is a

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Speaker 3: major mega law firm doing Walmart and you know, the

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Speaker 3: judge called them out on it. They had to remove

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Speaker 3: the one of their partners from the case, and I

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Speaker 3: think they lost the case against Walmart eventually in part

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Speaker 3: because you know, they had this stain on them of

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Speaker 3: doing these fake filings. And so you know, that's not

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Speaker 3: a one man firm, that's not Matt Locke messing up

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Speaker 3: a case or something. Right, this is you have a

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Speaker 3: million people in suits and no one can check that

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Speaker 3: that citation.

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Speaker 1: And billing the out of fifteen hundred dollars or more

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Speaker 1: an hour, good gig.

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Speaker 2: If you can get it well.

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Speaker 1: And then pivoting over to something even more profound, and

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Speaker 1: I think probably more people can connect with the idea

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Speaker 1: that AI is certainly going to be used in the

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Speaker 1: field of medicine, and the idea that a erroneous diagnosis,

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Speaker 1: an erroneous treatment protocol, or something is relied upon, maybe

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Speaker 1: by a physician who's looking for a little help trying

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Speaker 1: to figure out his patient's disease state, might rely on

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Speaker 1: something that could ultimately prove to be I don't know,

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Speaker 1: even fatal to a patient.

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Speaker 3: Right. So, you know, this is a concern I've actually

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Speaker 3: talked to with my son about. So my son is

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Speaker 3: graduating college, he's going to medical school to no, you know,

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Speaker 3: neither of us have any concern that you know, by

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Speaker 3: the time he's out there will be no doctors.

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Speaker 2: She'll just get a doctor. AI.

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Speaker 3: We don't lose a minute of sleep about that, right, right,

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Speaker 3: So what we do, what we have talked about is

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Speaker 3: you know, how will that be used? And there's no

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Speaker 3: question AI has some great applications clearly in medicine, so

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Speaker 3: ranging from drug research because it can sit there all

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Speaker 3: day and all night running numbers and checking things and

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Speaker 3: flagging the humans.

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Speaker 2: Hey, this might work. It can.

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Speaker 3: Also it has a lot of applications and things like

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Speaker 3: reading you know, X rays and.

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Speaker 1: I agree, CT scans, MRIs. They could probably identify tumorous

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Speaker 1: things or little things that may be overlook by a

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Speaker 1: human being. I get that, and of course what positions to.

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Speaker 3: Brian, there's actually it's funny because when you look at

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Speaker 3: test results scans, AI is good at catching certain things,

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Speaker 3: but humans.

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Speaker 2: Are better at catching very experienced.

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Speaker 3: Right, So that's why they should work together. AI should

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Speaker 3: always help us, not replace us. But like to your point,

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Speaker 3: one of the problems with AI, and it goes to

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Speaker 3: the same thing with the lawyers, is people assume the

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Speaker 3: AI is right when the AI is basically a moron

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Speaker 3: that has a huge database and can google that right,

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Speaker 3: So we doctors are going to have to be trained

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Speaker 3: how to use it and how not to say, well

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Speaker 3: the computer said this, so it's this exactly. Well, that

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Speaker 3: will not be true no matter how much the technology

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Speaker 3: advances learning.

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Speaker 1: As a learning prager, we'll have to.

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Speaker 3: Use it as a friend and a support, not a

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Speaker 3: not a replacement.

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Speaker 1: Seven PM log in only empower your America Dot or

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Speaker 1: just register ahead of time and enjoy what Colin has

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Speaker 1: to say and learn a lot. And finally, before we've

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Speaker 1: part company here real quickly, I note that from the

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Speaker 1: notes that you're going to addressing the ideas for the

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Speaker 1: conservative movement to master AI for its own purposes. Briefly,

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Speaker 1: where's that going, Colin?

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Speaker 3: Well, what that's all about is we're behind on the

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Speaker 3: left because the left made this stuff. So we have

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Speaker 3: to quickly put together, you know, some framework of how

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Speaker 3: we're going to use AI and not just you know,

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Speaker 3: the answer is not to just be a latite who

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Speaker 3: doesn't use it, because you're using it today even if

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Speaker 3: you don't know it, if you call your credit card company.

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Speaker 1: I know.

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Speaker 3: But but you know, we can use it effectively for

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Speaker 3: our ends, but it's mostly not falling into the traps,

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Speaker 3: you knows, not falling, not falling into the danger zones

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Speaker 3: where we brought our brains, especially for young people.

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Speaker 1: That's why we have you. Collum Adine, Tech editor, Breitbart

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Speaker 1: Learn tonight seven pm. Empower you America dot org. Don't

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Speaker 1: follow in the traps, listen to what Colin has to say. Colin,

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Speaker 1: you'll be back on the Morning Show at some point

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Speaker 1: in the future. I'll look forward to that. In the meantime,

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Speaker 1: thank you for everything that you and the team at

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Speaker 1: Breitbart does. I appreciate your efforts and I certainly rely

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Speaker 1: upon you in preparation for the Morning Show. Outstanding reporting there, sir.

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Speaker 3: Thank you, Brian, and I hope everyone joins us tonight

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Speaker 3: and power you.

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Speaker 1: Amen to that. Empower you America dot or you'll be

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Speaker 1: linked on my blog page fifty five KC dot com.

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Speaker 2: Have great