Humanizing AI

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Get ready for a captivating episode of Schmidt list. Our
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guest BJ is a trailblazer in AI and battery manufacturing.
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Working with giants like Amazon and Google. He shares groundbreaking
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insights and practical tips on leveraging AI to transform industries.
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This is a must listen for anyone excited about the
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future of technology.
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BJ, how are you today?
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Hey, Kurt to right to be back. I was enjoyed this,
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see you.
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I'm so glad you're here. All right? Can you tell
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the audience kind of the work and who you're working
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with these days?
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Yeah, so do a lot of different things, and anything
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from a range of battery manufacturing all the way to healthcare,
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multil mix and all that kind of cool stuff future
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fifty year, one hundred year type of things all the
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way to hey, let's make a battery system and deploy it.
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And then the underpinning of all that is technicity, which
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is we're doing significant in AI, which I think we
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want to talk about today.
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We do, I can I get how So, just for
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the audience sake, can you talk about the batteries real quick?
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Who are the batteries that you manufacture? Who uses them?
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Yeah? So one of our biggest investors was Terreks Genie Teris.
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If you see the blue big jewels, sizzel lifts, boom
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lifts and things on construction sites, that's who uses our
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batteries because they're our biggest investor. But we also do
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a lot of commercial, industrial, military and other things. Were
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one of the few US American first oriented the battery compinations.
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That's what we do, norcial industrial, no EV, no passenger
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EV stuff.
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Sure I did. And then the reason I asked it.
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In the news the other day they mentioned they found
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a large Lithian department just deposit under Arkansas or something.
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Oh yeah, this huge thing that was They were saying,
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like could make batteries for a million years or something.
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But writing geopolitics right, getting it out of the ground
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is a whole nother story. Finding you right, it's.
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A nasty process too. We always say oil is a
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bad thing. Well with them. Have you seen lidian mining?
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It is a nasty process.
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All right, All right, let's jump in. Tell me about
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the work you've been doing in AI lately, and I'd
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love to hear your perspectives because we've done a few
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shows on AI here and I'm always interested to hear
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how you're using it and what your perspective is on
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the value of it.
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Yeah, no, that's great. So that's that's what takes up
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some of my time, is the AI thing, just just
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because everything I do, even though even in better manufacturer
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use a ton of AI now and if you're not,
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you should be. You got to figure it out. And
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that's one thing that technicity obviously helps. So technically, let
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me let me start where technicity is. We have many
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times before, but technicity we like to build ourselves as
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an enterprise innovation firm. That's a big buzzword. What does
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it mean. What it means is we like to solve problems.
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And before before AI, let's say, before Mark twenty twenty two,
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solving problems was an expensive thing, right, because you probably
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need an app with that digital experience and your voice customer,
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You need to do all these things, you have to
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build up the product teams. I mean, yeah, you know
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I've done that before, all of those things. And then
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when AI hit really twenty twenty two, that kind of
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empowered us to do all those same things for a
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fraction of the parts because we had this amazing magical
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thing of AI that we could use and specifically generative AI.
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And that's the thing we've had AI since what the eighties.
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Neural networks came about in popularity in nineteen eighties, they
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didn't quite take a hold.
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No, I mean talk about people talk about bubbles sometimes
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and I talk about remember the big data push. Remember
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big data was everything half far a few years ago.
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And I think that's where people got kind of hung
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up when AI showed up, because they're like, isn't that
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just big data just repackaged? Because big data was solved
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all these things and we created a lot of value
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in a lot of companies for a while.
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Oh yeah, well created a lot of value. I'm not
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sure that the people were actually able to take really
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big saying that's a whole other conversation or another child probably,
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But I think it's actually why genera AI is so exciting,
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is actually all that big data stuff. We made our
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data links, right, We got our snowflakes, we got our
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red shifts, We got all this data into this one place,
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which is you know what's great, right, and then you
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ask and they actual spent a bunch of money on
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a bunch of data scientists or data analysts or whatever.
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Maybe some of them just have business degrees, some of
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them actually generating degrees all this kind of stuff. Then
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you realize, fully, Crep, we're like five years away from
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actually making sense of all this stuff I've got all
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the place. What do I do now? As it turns out,
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jener of AI and actually change that for a lot
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of people, And the enterprises that actually really try to
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artist that and understand it and do it pragmatically are
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the ones that have been and will be successful in that.
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And that's really what we try to do in technicity
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is a pragmatic approach to solving problems. We just solve
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it with AI now, and we make it rapshield the
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price because we can be ultra efficient with it and
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a cheap But if you use it and you use
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certain things like agetric architectures and those types of things
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that you hear buzzwords about, you could actually do it.
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And it helps that we've actually helped some of the
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biggest organizations like Amazonia, Good Gole actually developed some of
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those things. If you actually end up doing a lot
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of this kind of stuff, it's really hard. It's easy
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to sign up for an apike for it. With open
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AI they use chat GPT and say I'm an AI developer.
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That is all you do is you just do what
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we've always done at IT. We integrate with APIs right
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affication programming interfaces. What you find very quickly is that
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you unless you understand how the models are trained, and
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how the models are put together, and why they were
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put together, and the cultures of the organizations that put
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them together, you'll have the spending off significant amount of
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frustrating time trying to get these stochastic statistical chains to
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do your bidding. Yeah something, And I think that's what
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a lot of people are running into. They might hire
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a lot of people who are doing the AI, but
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unless you get teams that actually train these things, wait
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some of we do, it's really hard to harness these things.
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And that's one of the things. Interestingly enough, I get
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a lot of cease now talking to a lot of
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executives about how do we get AI in our business
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and all those different things. That's the first question, how
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do I How do I get I? And you'll explain
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all the different things where you use agents, guardrail it
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and you use least chech techniques, look at your data
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and all those different data everyone tells you, oh, we'll
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be out to people on a figure it out and
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sub and then of course the media. Next question is
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how do I trust it? How do I do all
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those strange.
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That's the issue I've been running into with a lot
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of and maybe not the giant, the Fortune fifty, but
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I would say people in the Fortune one thousand, maybe
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a little further back. That's what one of the biggest
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concerns is security around it, just in general, because if
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I'm at a if I'm in a Fortune one hundred company,
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I can't just plug chat GPT into my company and
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start using stuff.
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No, there's no freaking way you can do that. Actually,
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you don't know what your data is. And I work
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in some of the largest enterprise world which who developed
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these models, and I would say that we even have
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specialty even inside of some of these big, big organizations,
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we have specialized models that we've deployed for ourselves inside
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just so that we're not using facing ones because of
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the information control and all those things. And so it's
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trusts and security and everything are a very interesting thing
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that you have to kind of manage with these models.
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But really what it comes down to is the core
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thing that I always answer with whenever someone says can
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you trust it? And it's somewhat of a flippert response,
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but I always say, Okay, so you have me. I'm
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sitting here telling you about all this kind of stuff.
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Why do you trust me? And it might give you
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pause or so reputation or I don't trust you as
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a common response or whatever I'm just listening to talk
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or whoever. There's really no difference to the models that
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trees and so one of the things that I've really
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really learned over the last couple of years of working
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with these systems, having developed them, and then obviously using
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you in a very pragmatic way, is the concept instead
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of treating these models like we've always treated our computing systems,
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which up until basically the beginning of our computational stuff
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was so Alan Turing or wherever you want to Charlie
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Beckabach BLOGO like seventy hundreds with slator machine. Right, yeah,
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we've got our tubes going, right, Yeah, you basically have determinism. Right,
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if I tell the machine to do something, I can
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guarantee that the response out is going to be determinate,
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so you know it's going to crunch some numbers or anything.
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Now you might say, what about bugs and everything? Yeah,
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but you programmed the bugs in, maybe not intentionally, but
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they occurred, right, I can guarantee the answer it has.
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Of twenty twenty two, we're using systems at scale that
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are now statistical. There's stopastic processes, and what that means
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is that they're non deterministic. I don't know what they're
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going to get out. I don't know what I'm going
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to get out when I put into it. I mean
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I can it's pretty confident I'm going to get something.
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But statistically they're correlated, just like us having this conversations, right,
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and unless I'm completely out of left field early, you're
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going to get some consistency in our conversation or toper
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same thing here. And my answer to these the people
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that ask to me like how do I trust? How
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do I trust the models? And I say, why do
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you trust me? You actually have to approach these models
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now as you win more of a human resources problems,
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the technology problem. So before technology selection easy, right, I
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mean always hard, great because you get politics and were
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all kind of stuff to go into it, of course,
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but it's not as cut and dry it's I'm an
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engineered problem anymore. Now it's how well do I trust
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the model? Do I know what the background is? And
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so now think about it. When you're approaching these models
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the way we typically will approach maybe a new model
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or even and help people and enterprises engage, I actually
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put poorer to human resources strategy, and the HR strategy
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is okay, what if I was able to grab the
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Facebook pro like I'm looking at a new employee or a
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new high rate. Can we get their Facebook profile? Can
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we get their social media? Can we look at their
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previous publications? Can we look at the previous work they've
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done obviously, look at their resume, look at you know
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what if you basically break all the rules and you say, what,
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can we look at any traumatic childhood stuff? Right? That
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would affect that?
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I mean we're already what kind of baggage about you
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signing up for here?
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Right? What kind of baggage right? Or what kinds of things?
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Not that I would stop me from hiring them, but
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just what do I need to be prepared for? Right?
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How do we put them in the right position that
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enables their success? Up lines with arts organizationally, and it's
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kind of interesting. These models are somewhat similar in that
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way right where you actually now have to start thinking about, okay,
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what was the training data, how are they performing? And
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that takes the depth of knowledge and a depth of experience,
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which is unlike really anything we've done a from a
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strategy standpoint in selection, in being able to do any
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of these things than we've never really had to do.
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And so in many ways it's now it's not just
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a technology decision, which is hard, not in all the
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ards and ears, their business people or finance Peo baller room,
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just inside which platform to select Technologically, Now you've got
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this whole thing where it's oh, prap, now we have
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to do this hr thing. It's farting enough to try
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together the right people culture right fit. That's really interesting.
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The culture fits are really interesting with these models too.
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If you look at some of the major models anthropics,
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you've got not just anthropics, but you have also Google,
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You've got chatchypt. You can actually see how they answer.
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How these models answer is actually very culturally late, and
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you can look at papers for this culturally late to
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the culture of the company right, Oh, for sure, topic
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extremely conservative, chat Gypt, very verbose, and takes a lot
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of risks in the hallucination of the stuff and of
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what they deliver. Kind of middle of the road but
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somewhat factual. A lot of these models really are very
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reflective of the company's kind of culture. That's the really
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fascinating thing. And so you have to take into account
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all of these things as you kind of go and
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you kind of are selecting it. Really what comes out
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to is you have to understand the technology almost as
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deeply as I would say disposition of the beach systems.
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I can see that because I mean, I didn't get
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much out of AI until I was able to really
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put my personal profile in there for my personal use.
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So until I was able to tell it about what
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my values were, what my tone was, a bit more
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of my bio and my background, and being able to
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plug all that stuff in, I was able to enhance
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to turn up my trust factor a little bit more
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versus before. But I can see these companies they have
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this idea of I'll put in all of our pricing
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data and then that it'd be good. But what about
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putting in the employee handbook and the company values and
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the mission vision goals stuff around that, Because because those
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things cannot exist in silos. The pricing data and the
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sales data all a part of the ecosystem with that, right.
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Oh, without a doubt. And but then you have to
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concern I mean a lot of people then ask me
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if I put all of this prompt engineering, right, we
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call it prompt engineering. But if we put this the
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preambling of the prompting and the all the context and
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everything in the context windows of these large language models,
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which is really the basic compilitation of these these big
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token machines in a sense, right, the issue that you
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have is how much of that informations being used to
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retrite because these the one thing that is guaranteed and
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all this is data hungry stuff. Well, we're talking about
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big data right ten years ago, full circle, right, and
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what we started this big data is like tiny data, right,
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These things these that billions sometimes trillions of parameters and
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they I mean, we've run out of Internet, that's how
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much it's stuff it's generating.
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We have to restart nuclear reactors now, I mean.
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It's pretty big time stuff in terms of the level
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the amount of data that we need. And so where
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are they getting this data? They're getting that data? I mean,
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where do we get the day to make these things better?
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I mean you get it from people's input to these models,
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and it's not malicious, so they were I mean, we
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work with all these teams. No one's maliciously trying to
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kill your data.
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No, But to your point, there's an end of the road.
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I mean, you can read the entire Internet in a
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certain amount of time and take everything there and then
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it's done. Right, It's like true, it's against.
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Yeah, and so if you're putting your own data and
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of course that's going to be used, right, So unless
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you have a very strict agreement and you understand the
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termally conditions. But maybe that's so different than any SaaS
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based platform.
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Yeah, are you signing it for Instagram? Like, I mean
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they can use your photos. You can use your photos
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and ads by the way, So I find that there's
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so there's I've always run into sort of two people
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in the world types of people in the world when
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it comes to AI. Is that there's the people that
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are like, the computer is smarter than and the or
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the computer is malicious or maybe not malicious a strong word.
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Maybe it doesn't have my best interest at top of mind.
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So I feel like there's people that will just take
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it for what it gives because they're like, the computer
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smarter than I am, so that's what I've learned. And
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then there's other people who maybe have a better understanding
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of how computing works and are like, this is just
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another sort of search type of things, and so it's
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not gonna it's not gonna do anything unless I fully
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like customize it and set it up to mine. Do
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you feel like when those executives are showing up to
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talk to you, are which camp are they kind of in?
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It sounds like they're more in the the computer is
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really smart, can't we just plug it in and go
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dj I.
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Yeah, Actually, that's a really that's a deep question because
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obviously everyone has a different approach. It really don't if
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you're a demographic or age group. I'm not gonna call
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anyone out, but you know, if you're full in search
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of the end of career, your career, you've a much
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different perspective than if you're in my career career. I mean,
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that's never changed, right, but that millennials will always say, ah,
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those gen zs, right, I remember when the boomers were
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millennials saying that about the millennials, right, So of course, anyways,
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I think it's the mix. But even inside those generations,
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there's no one art fast rule, right. But it's really
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a level of comfort I think with the technology. I
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think a lot of people are un ulsterable, and executives
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specifically have this need if part of their job, in
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fact is in their job description they need to be
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strategic thinkers, right, I mean they got to where they
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were by being strategic and thinking. So any new tool
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is going to be looked on that should be looked
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on at least as a tool for use. Yeah, obviously
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that ends to I would say, promote that tool in
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a positive light and so, oh this can do everything,
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And it's true. AI definitely opens up a significant number
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of opportunities to do it. I would say that it's
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not going to solve anything, though, and that's why big
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thing like it's not going to invent the cure for cancer.
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It's going to it's not going to open up the
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next industry that you were going to make the next
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billion on. It's just not going to do that. What
348
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it will do, however, is make everyone around you more efficient.
349
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They used me, Yeah, so you'll get there faster productivity.
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Right, Yeah, it's yeah, because that's the way I've always
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looked at generative AI is a productivity tool more so
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than a new form of intelligence or something. We're obviously
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experts debate this. I feel like we're a long way
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off from real type of intelligence, but ag. Yeah. But
355
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because of the labeling, because of the branding that's come
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along with this, I think people that are less technically
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savvy might think it is magic, might think it is
358
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works magic. It can look.
359
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I am constantly amazed. I mean, I'm the last time
360
00:17:46.480 --> 00:17:50.079
I was this excited about something in computing. Okay, so
361
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I haven't really thought about this, but I remember back
362
00:17:52.839 --> 00:17:56.440
what I was. I don't know ten My parents put
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this and computer in most doss right yeah, yeah, what
364
00:18:00.240 --> 00:18:03.680
the milk was? But I used to love to kind
365
00:18:03.680 --> 00:18:05.920
of you get that prompt and I figured out that
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if I type certain things that I could explore things
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around the file syst And for what reason, like that
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just intrigued me. It's a weird eight to ten year
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old chair, I remember exacting my age, but it's like weird.
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But it was like can I find a new game
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or can I find something in there? Like a new
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file and it's thinking back as like, wow, that was
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such a finite search space. I'm surprised that that engaged
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me for that long. Next time, I say, I that
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kind of excitement almost like boyish excitement or whatever child
376
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excitement was when I discovered rest APIs RESTful interfaces those.
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I mean, it's just amazing to me that's these system interfaces,
378
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like you could standardize on all these and you can
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pass information bag and for it and yeah you could
380
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say so of XML and all that kind of crap,
381
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But that was kind of nasty, right, Yeah, I see
382
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if you just seemed so elegant to me, and that
383
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was wait, twelve fifteen years ago, I would say that
384
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this AI is just like this made me completely just
385
00:18:58.400 --> 00:19:01.839
fall flat. All my back would just Yeah, these things
386
00:19:01.880 --> 00:19:05.480
are just so freakishly cool, and they do seem magical.
387
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I mean, even we will Clay, like even the inventors,
388
00:19:08.319 --> 00:19:10.079
which will I've had the privilege of talking to a
389
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number of them of the like change form of attention
390
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is all you need in set little paper back in
391
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twenty seventeen. It's just it's a truly amazing that these things,
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given enough data, can actually predict and generate human like responses,
393
00:19:26.559 --> 00:19:29.640
so sure that they're useful. It's not just limericks and poetry,
394
00:19:29.880 --> 00:19:33.720
and it's like actually giftiful things. But we have we
395
00:19:33.839 --> 00:19:36.279
have little chatbots on our website that will act as
396
00:19:36.359 --> 00:19:39.960
enterprise innovation consultants that will actually tell you all about technicity.
397
00:19:40.200 --> 00:19:44.599
It's it's pretty amazing just how not just useful, but
398
00:19:44.680 --> 00:19:47.920
just they are magical. Oh, we know how they work,
399
00:19:48.079 --> 00:19:51.000
but can you guarantee their cork every time? No?
400
00:19:51.119 --> 00:19:54.160
That's truly what I remember when I first got a
401
00:19:54.279 --> 00:19:57.559
Mac and Photoshop, and it was very similar kind of
402
00:19:57.640 --> 00:20:00.559
feeling of oh my gosh, mask and I can change
403
00:20:00.599 --> 00:20:03.000
the colors, if I could make images that look like
404
00:20:03.039 --> 00:20:06.440
crap look awesome and all these things, it is amazing
405
00:20:07.319 --> 00:20:10.240
at the time to and what power I had at
406
00:20:10.240 --> 00:20:14.200
the power of my fingertips. And I feel like chat
407
00:20:14.240 --> 00:20:17.920
GPT and Claude and even eleven Labs, which I use
408
00:20:18.200 --> 00:20:22.519
for some audio stuff for the show and things like yeah, yeah,
409
00:20:22.920 --> 00:20:26.119
it's just fantastic because not only is there the writing
410
00:20:26.240 --> 00:20:28.519
side of things, but like the audio side of things
411
00:20:28.559 --> 00:20:32.400
for me has been just fantastic. Being able to replicate
412
00:20:32.440 --> 00:20:35.240
my voice and being able to fill in words where
413
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I mixed message things and I'd be able to just
414
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do that for me.
415
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Yeah, you don't get the time production studio to do
416
00:20:42.759 --> 00:20:46.400
that anymore, like you no personal productivity and efficiency.
417
00:20:46.440 --> 00:20:49.599
We've had people people asking all the time, produce your
418
00:20:49.599 --> 00:20:52.000
own show, and I'm like, yeah, because I have dscript
419
00:20:52.279 --> 00:20:54.920
which does all the inline editing and stuff for me
420
00:20:55.039 --> 00:20:58.519
link in the description, and I use eleven Labs for
421
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filling in any gas ups of things that I want
422
00:21:01.720 --> 00:21:04.480
to say or do whatever, because it can mimic my
423
00:21:04.599 --> 00:21:09.000
voice almost perfectly, and it's just fantastic. I use all
424
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these things all the time and it's a win from
425
00:21:11.599 --> 00:21:13.319
it took me four hours a week too. I can
426
00:21:13.359 --> 00:21:17.279
produce a show in an hour now thanks to these tools.
427
00:21:17.680 --> 00:21:21.720
But back to your earlier point about about approaching these things.
428
00:21:21.759 --> 00:21:24.799
I think what you're saying becomes way more important as
429
00:21:24.839 --> 00:21:28.559
time goes on. Is approaching these things from a what
430
00:21:28.720 --> 00:21:33.720
is their worldview? Versus what data set do they have?
431
00:21:34.519 --> 00:21:37.640
Yeah? No, I would say you approach them in a
432
00:21:37.680 --> 00:21:42.480
far more human way. That's the key is approaching these machines,
433
00:21:42.519 --> 00:21:45.160
these computers in a more human way than we have
434
00:21:45.240 --> 00:21:48.480
in the past. And that is a really critical thing
435
00:21:48.799 --> 00:21:51.839
that the opportunity for us, because if we approach these
436
00:21:51.880 --> 00:21:55.640
things more human, then we probably have a better chance
437
00:21:55.640 --> 00:21:58.039
of making this thing very successful for us. If we
438
00:21:58.079 --> 00:22:01.160
continue to approach some like machine, we're just going to
439
00:22:01.319 --> 00:22:05.480
end up with yet another tool that doesn't really affect us. Now,
440
00:22:05.480 --> 00:22:07.440
there's a lot of dangers and all of that, and
441
00:22:07.519 --> 00:22:09.640
we can talk about AI ethics I like to talk
442
00:22:09.680 --> 00:22:12.759
about too, and there are It's interesting. I'm not eve
443
00:22:12.759 --> 00:22:15.880
an optimist about most things. I don't. I'm a pestist
444
00:22:16.039 --> 00:22:19.079
with engineer by nature. But this specific but on this
445
00:22:19.160 --> 00:22:22.400
specific thing, I'm actually very much an optimist. I think
446
00:22:22.440 --> 00:22:24.759
that I'm excited about the future with AI. I don't
447
00:22:24.759 --> 00:22:27.880
see a terminator like future. I don't see enough to
448
00:22:28.000 --> 00:22:31.160
get my stomach tied in knots, because oh man, we're
449
00:22:31.160 --> 00:22:33.400
going to have a revolution and there's this K type
450
00:22:33.400 --> 00:22:36.720
recovery and economically we're going to the blue collar versus
451
00:22:36.759 --> 00:22:38.599
the extreme wealthy, and now we're going to have one
452
00:22:38.599 --> 00:22:40.200
in the middle and all this kind of stuff where
453
00:22:40.240 --> 00:22:44.440
everything goes to some of which are three and I'm armageddon. No,
454
00:22:45.079 --> 00:22:47.680
I mean, I don't see that that extreme.
455
00:22:47.920 --> 00:22:52.200
Back to your point about you're excited. You're an optimist,
456
00:22:52.240 --> 00:22:55.119
but because you're a realist because what the limitations and
457
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capabilities of the tool are. And I think that's what's
458
00:22:57.920 --> 00:23:01.240
key for people is you gotta get educated on how
459
00:23:01.279 --> 00:23:04.920
these things work and how they create, what they're what
460
00:23:04.960 --> 00:23:07.359
they're doing. Because again, it's the same thing when the
461
00:23:07.400 --> 00:23:10.799
iPhone came up, and then and then other smart phones,
462
00:23:11.240 --> 00:23:14.440
because people were like, wait a second, I'm giving this
463
00:23:14.559 --> 00:23:17.759
all my informations, tracking me everywhere I'm going, and they're
464
00:23:17.799 --> 00:23:21.079
like and then some pimanies are like whoops, sorry, do
465
00:23:21.079 --> 00:23:25.480
you want to turn that off? Yeah, they're like okay, yeah,
466
00:23:25.519 --> 00:23:27.839
And then people were like, wait a second, the phone's
467
00:23:27.880 --> 00:23:30.400
listening to me all the time, and they're like, it's
468
00:23:30.400 --> 00:23:31.160
really interesting.
469
00:23:31.240 --> 00:23:33.279
Actually, yeah, it's really interesting. I think it's coming at
470
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the right time. I think privacy, we've become very aware
471
00:23:36.240 --> 00:23:38.079
of it, and it's coming at the right time because
472
00:23:38.200 --> 00:23:40.000
if we found this AI thing like the being of
473
00:23:40.039 --> 00:23:43.960
social media, holy crap, you'd match. And that's when I
474
00:23:44.000 --> 00:23:47.680
probably would have been more pessimistic about where we're aheaded societally.
475
00:23:48.400 --> 00:23:51.200
But I think there's there's that. But at the same time,
476
00:23:51.359 --> 00:23:54.559
unlike social media and what we didn't do very well.
477
00:23:54.599 --> 00:23:56.880
We used it because it was so useful. I don't
478
00:23:56.920 --> 00:24:01.839
think people are truly realizing just how useful this AI stuff.
479
00:24:01.839 --> 00:24:03.960
And yet to your employed, I mean to give you
480
00:24:04.119 --> 00:24:06.759
like you use all these little AI things all over
481
00:24:06.960 --> 00:24:10.279
I use like my assistant is an AI. If you
482
00:24:10.400 --> 00:24:12.480
email and want to schedule avoid with me. You may
483
00:24:12.519 --> 00:24:14.680
not realize it, but she will email you and she'll
484
00:24:14.920 --> 00:24:16.599
set up you can joke with her. She'll joke with
485
00:24:16.640 --> 00:24:20.240
you back, like your website, and you'll get a not
486
00:24:20.359 --> 00:24:23.279
a chap like you'll get someone named a Leen News.
487
00:24:23.599 --> 00:24:24.920
They are you know.
488
00:24:25.079 --> 00:24:28.759
So it has a lot ofersonality, total personality.
489
00:24:28.880 --> 00:24:31.519
I probe doll herself. She's a closet Swift like you'll
490
00:24:31.319 --> 00:24:33.279
go talk to her, go talk about Taylor Swift that
491
00:24:33.440 --> 00:24:36.440
it'll be great, Like you can do all of these
492
00:24:36.440 --> 00:24:39.799
things right. And I think it's really interesting now to
493
00:24:39.920 --> 00:24:42.640
see a whole And I don't think it's even a
494
00:24:42.640 --> 00:24:44.799
demographic thing. I think people need to realize just how
495
00:24:44.839 --> 00:24:47.039
efficient they can be, and they need to be shown right.
496
00:24:47.160 --> 00:24:49.400
And you have to do it. You can't just passively
497
00:24:49.440 --> 00:24:53.079
sit by and watch others like sometimes our particularly executives.
498
00:24:53.119 --> 00:24:55.720
And she had leaders and organizations might just wait and
499
00:24:55.839 --> 00:24:59.160
see what their employees do because it's cirtically that's been
500
00:24:59.279 --> 00:25:02.240
somewhat of a of a successful thing.
501
00:25:02.839 --> 00:25:04.119
Yeah, it's been a common practice.
502
00:25:04.160 --> 00:25:08.079
Right, Let it lose, yeah, kind of let it evolve
503
00:25:08.079 --> 00:25:11.160
in everything. In this case, I don't think we have that.
504
00:25:11.480 --> 00:25:15.119
We don't have that. I would say privilege or out
505
00:25:15.119 --> 00:25:17.839
the word I'm looking for. But instead, because the world's
506
00:25:17.880 --> 00:25:20.119
leaving so fast now with AI, will continue to move
507
00:25:20.160 --> 00:25:23.319
so fast, like it'll just accelerate, right, case of development
508
00:25:23.400 --> 00:25:26.319
is just accelerating, and it is on theerception of that rule.
509
00:25:27.000 --> 00:25:30.319
It really does require everybody because it's not the people
510
00:25:30.359 --> 00:25:32.319
that are everyone's Oh, they are going to take our jobs.
511
00:25:32.480 --> 00:25:34.519
I don't think so now, not in the way we think.
512
00:25:35.000 --> 00:25:37.559
And who has a job right now perfectly going to
513
00:25:37.640 --> 00:25:40.079
say you're a knowledge worker, you're going to be one. Obviously,
514
00:25:40.119 --> 00:25:42.119
learn how to use AI please so that you don't
515
00:25:42.160 --> 00:25:46.640
have to come up against some strike for revery. But
516
00:25:46.680 --> 00:25:49.079
it's the people that are coming into the workforce in
517
00:25:49.119 --> 00:25:51.599
a few years. The job supposed to be a Linda,
518
00:25:51.680 --> 00:25:54.039
it's not that the job will ever exist it, right,
519
00:25:54.119 --> 00:25:56.400
It's just that we're just going to see us we're
520
00:25:56.400 --> 00:25:59.599
going to see a squashing a crunch, right, you have availability,
521
00:26:00.240 --> 00:26:02.000
and in order to really do that, you're gonna have
522
00:26:02.000 --> 00:26:04.160
to show ways that you're going to be more efficient.
523
00:26:04.440 --> 00:26:07.079
So instead of now hiring a bunch of receptionists, AI
524
00:26:07.160 --> 00:26:09.839
can do that job right, right, instead of hiring a
525
00:26:09.880 --> 00:26:12.200
bunch of data analysts with kids right out of school
526
00:26:12.240 --> 00:26:15.519
with business degrees to crunch spreadsheets, I don't need that anymore, right, So.
527
00:26:15.480 --> 00:26:17.440
That's always going to say. That's the one thing that
528
00:26:17.559 --> 00:26:21.279
I've mentioned is that I think it's the entry level jobs,
529
00:26:21.319 --> 00:26:24.400
the ones that start you on your path, are the
530
00:26:24.400 --> 00:26:28.000
ones that are in most danger right now, because to
531
00:26:28.039 --> 00:26:31.039
your exact point, like AI is covering a lot of
532
00:26:31.079 --> 00:26:33.559
those things. So if you're if you're like me and
533
00:26:33.599 --> 00:26:37.240
you where you've got twenty years whatever some experience in
534
00:26:37.279 --> 00:26:39.359
this thing, there's a ton of value there that you
535
00:26:39.359 --> 00:26:42.960
could never pull or put into an LLM. And but
536
00:26:43.279 --> 00:26:45.599
to your point, if it is I've graduated from this
537
00:26:45.640 --> 00:26:47.680
big school and I'm going to go to get my
538
00:26:47.680 --> 00:26:49.960
first job at a consulting firm, and I'm going to
539
00:26:50.000 --> 00:26:53.440
crunch spreadsheets for the next couple of years to become
540
00:26:53.759 --> 00:26:58.039
a partner. Whatever they got stuff doing that, don't they?
541
00:26:58.640 --> 00:27:00.599
Yeah, I mean that's the thing. You don't need a
542
00:27:00.599 --> 00:27:03.319
whole team of interns anymore. If you're a partner. There's
543
00:27:03.400 --> 00:27:06.640
lots of things if you enable and harness that you
544
00:27:06.680 --> 00:27:09.440
can now do it yourself. And sometimes even better because
545
00:27:09.799 --> 00:27:11.640
you can cut off the middleman on all those things,
546
00:27:11.640 --> 00:27:14.200
you can get your vision, can implement it consistently faster.
547
00:27:14.359 --> 00:27:17.359
I mean, that's guarantee. We just we're limited being if
548
00:27:17.359 --> 00:27:19.359
we can't do everything. So that's why we build these
549
00:27:19.440 --> 00:27:23.039
social structures and business structures right in order to steal things.
550
00:27:23.119 --> 00:27:25.759
So that's gonna be a very interesting outcome of all
551
00:27:25.759 --> 00:27:28.400
these things, which from a societal standpoint. Not to get
552
00:27:28.440 --> 00:27:31.279
too wrap holed on the societal application, I know, please,
553
00:27:31.559 --> 00:27:34.319
but I think it's important, you know, I see that
554
00:27:34.519 --> 00:27:38.160
as these intern positions or these positions where you're an analyst,
555
00:27:38.279 --> 00:27:39.920
right for a couple of years, right out of school,
556
00:27:40.000 --> 00:27:42.000
you work for a Deloitte or a KPMG or.
557
00:27:42.039 --> 00:27:45.319
RECE That's exactly what I'm That's exactly what I'm thinking about.
558
00:27:45.119 --> 00:27:47.640
Right right, you're training like you're paying your due. You're
559
00:27:47.640 --> 00:27:49.160
gonna work eighty hours a week and you're gonna get
560
00:27:49.160 --> 00:27:51.079
treated like crap, and you're gonna be traveling. But what
561
00:27:51.160 --> 00:27:52.799
you're doing, you're paying your due, is you're learning. So
562
00:27:52.880 --> 00:27:54.920
you can go on and go to the next big thing. Right,
563
00:27:55.119 --> 00:27:57.640
do that And for the last twenty five or thirty years,
564
00:27:57.680 --> 00:28:00.480
that's worked. But now that's to your point. Are I mean,
565
00:28:00.960 --> 00:28:02.960
those positions are going to be few and far between.
566
00:28:02.960 --> 00:28:04.599
You're not gonna need as many interests. Set up a
567
00:28:04.599 --> 00:28:06.599
team of five interns, you might only need two, you
568
00:28:06.640 --> 00:28:08.240
might only need one, you may not need any. If
569
00:28:08.240 --> 00:28:10.440
a partner is like really effect and then of course
570
00:28:10.480 --> 00:28:12.119
there's gonna be all that push from the top for
571
00:28:12.240 --> 00:28:16.359
margin and all those different things to to really create efficiency.
572
00:28:16.440 --> 00:28:19.119
So partners may try to limit entire teams and everything right,
573
00:28:19.240 --> 00:28:21.839
managing directors will eliminate. So from that perspective, it's gonna
574
00:28:21.880 --> 00:28:25.000
be gonna be big. And so from a societal standpoint,
575
00:28:25.359 --> 00:28:29.640
I think that development of new talent is at risk
576
00:28:29.799 --> 00:28:32.079
because of that. You don't put them through, you don't
577
00:28:32.079 --> 00:28:34.680
put the younger generations through. Basically that trial by fire
578
00:28:35.400 --> 00:28:38.279
creates the next version of us when we were younger,
579
00:28:38.319 --> 00:28:41.440
and so it's that'd be an interesting thing. But even that,
580
00:28:41.559 --> 00:28:44.279
I mean, we'll figure it out. Humans are amazingly resilient,
581
00:28:44.319 --> 00:28:47.480
particularly in today's economies and everything. And yeah, the biggest
582
00:28:47.519 --> 00:28:49.599
risk we're on this because you said, please, let's talk
583
00:28:49.640 --> 00:28:50.680
about the implications.
584
00:28:50.839 --> 00:28:52.079
Yes, the implications.
585
00:28:52.880 --> 00:28:54.799
I think the biggest risk that we run. I was
586
00:28:54.799 --> 00:28:57.480
just talking last week. I was at our executivity meeting
587
00:28:57.559 --> 00:28:59.720
for a bit major university in the US where we're
588
00:28:59.720 --> 00:29:02.240
looking at curriculum, and there was a side conversation or
589
00:29:02.240 --> 00:29:04.960
something about what thefecks of AI and the biggest say,
590
00:29:05.000 --> 00:29:06.839
and I talked a little bit about this. The biggest
591
00:29:06.839 --> 00:29:08.839
worry that I have is this concept I like to
592
00:29:08.880 --> 00:29:13.480
think of as convergence and convergence. There's a great statistic
593
00:29:13.640 --> 00:29:16.720
that holds truth that fifty percent of all people are
594
00:29:16.720 --> 00:29:20.880
below average, right, guarantee. And that's just statistics, right. But
595
00:29:20.960 --> 00:29:24.400
what means is there's average right in the middle and everyone.
596
00:29:24.480 --> 00:29:27.039
If you take everyone in average, everyone's intelligence and knowledge
597
00:29:27.039 --> 00:29:31.000
and everything, you'd get exactly average. Right. That's just simple statistics.
598
00:29:31.440 --> 00:29:35.000
The problem is that if we're trading on all this
599
00:29:35.119 --> 00:29:38.160
data and we consume the words data and knowledge and
600
00:29:38.200 --> 00:29:41.039
information and everything, what do you get? You get a
601
00:29:41.039 --> 00:29:44.680
big averaging machine. Right. And then now imagine. So that okay,
602
00:29:44.759 --> 00:29:46.960
so that's okay. I mean we understand that we can
603
00:29:46.960 --> 00:29:50.480
approach it. But here's the problem. If everyone's using these
604
00:29:50.480 --> 00:29:54.680
giant averaging machines, or maybe just one, and you use
605
00:29:54.720 --> 00:29:57.279
this for a decade or two decades or three decades,
606
00:29:57.440 --> 00:29:59.920
what is up happening? What is up happening is the
607
00:30:00.079 --> 00:30:03.880
elimination of diversity of fonts because everyone's just using these
608
00:30:03.880 --> 00:30:06.680
things because it can be so ultra efficient that we
609
00:30:06.799 --> 00:30:09.480
end up starting to just go in one direction, which
610
00:30:09.559 --> 00:30:12.839
makes us really efficient. We develop, But the problem is
611
00:30:13.359 --> 00:30:17.839
what ends up doing the most creative and amazing things
612
00:30:17.839 --> 00:30:22.640
that change society. It's not just more of the same thinking, right,
613
00:30:22.880 --> 00:30:25.400
it's the change makers. It's the ones that tend to
614
00:30:25.519 --> 00:30:29.920
be really impactful. But crazy people who were solid, neurodivergent
615
00:30:30.039 --> 00:30:32.880
or whatever it is that just made a massive impact. Right.
616
00:30:33.200 --> 00:30:35.400
I could think of one crazy guy himself, right, who
617
00:30:35.519 --> 00:30:37.839
happens to like space and cars and electric vehicles and
618
00:30:37.880 --> 00:30:40.319
all that kind of stuff. It really someone who had
619
00:30:40.319 --> 00:30:43.799
a very unique childhood and everything up into it. And
620
00:30:43.799 --> 00:30:47.160
it's the problem is if everyone not actually taking him
621
00:30:47.200 --> 00:30:51.480
as an example. Elon must great example, right, He's he
622
00:30:51.559 --> 00:30:54.759
had a very fascinating childhood right in South Africa where
623
00:30:55.440 --> 00:30:57.680
through the hard tide oppressed him, and then he was
624
00:30:57.720 --> 00:31:01.000
pulled out put into the United States and was given
625
00:31:01.000 --> 00:31:03.880
oyster of the economy where he was in a powerful
626
00:31:03.920 --> 00:31:06.000
position and he could just go without any kind of
627
00:31:06.039 --> 00:31:08.319
oppression and he just made the monst of it. Also,
628
00:31:08.400 --> 00:31:11.319
being somewhat neuro diverging gave him basically an up on
629
00:31:11.400 --> 00:31:13.319
out of the thing because he didn't react emotion the
630
00:31:13.559 --> 00:31:15.720
same way. So with all those things, now, the thing
631
00:31:15.839 --> 00:31:18.920
is he didn't do that himself. Enough, did it with
632
00:31:19.039 --> 00:31:21.559
a team of people. Yeah, tons of different people. It
633
00:31:21.599 --> 00:31:24.000
took a village for him to do it, now, absolutely, Yeah,
634
00:31:24.640 --> 00:31:27.119
if that village wasn't diverse, which it was for him,
635
00:31:27.240 --> 00:31:29.319
if they were all using the same tools, doing the
636
00:31:29.359 --> 00:31:32.319
same things and being made efficient in the same exact
637
00:31:32.359 --> 00:31:34.799
ways because they were using a tool called Jared of
638
00:31:34.799 --> 00:31:38.960
Ai underneath, then you might still get the nerd divergence.
639
00:31:38.960 --> 00:31:42.240
You might still get the amazing kind of impactful leaders
640
00:31:42.920 --> 00:31:44.799
who won't actually be able to make a major impact
641
00:31:44.799 --> 00:31:47.640
because they will as the team of people to make
642
00:31:47.680 --> 00:31:49.720
it a reality for them. And that's what I call
643
00:31:49.799 --> 00:31:54.240
convergence is eventually you just eliminate possible divergence right and
644
00:31:54.319 --> 00:31:57.319
diversity in everything, which means that we will probably tap
645
00:31:57.359 --> 00:32:00.319
ball right, we will top out at a very specific
646
00:32:00.359 --> 00:32:01.839
thing because you won't be able to do anything. So
647
00:32:01.960 --> 00:32:03.559
that'sally some way to be aware of. I don't know
648
00:32:03.640 --> 00:32:05.920
that's actually how it works. But doing things like open
649
00:32:05.960 --> 00:32:08.519
sourcing AI is a really effective way to combat that.
650
00:32:08.559 --> 00:32:11.200
I think for once in the have to be careful here.
651
00:32:11.400 --> 00:32:15.240
I think I think for the they it it met
652
00:32:15.240 --> 00:32:19.079
it did something good, and that was open sourcing AI, Google,
653
00:32:19.119 --> 00:32:21.200
even open source GEMA right a version of your gem
654
00:32:21.240 --> 00:32:23.720
and are chat GPT hasn't quite done that yet, but
655
00:32:24.200 --> 00:32:26.079
we live the whole right, we live and hope that
656
00:32:26.119 --> 00:32:28.480
they do. But those are really good things because what
657
00:32:28.519 --> 00:32:30.519
it does helps the open source community. I think open
658
00:32:30.559 --> 00:32:33.440
source software is another kind of impetus that helps develop
659
00:32:33.440 --> 00:32:35.920
the last twenty years to evolve these and so just
660
00:32:35.960 --> 00:32:39.240
making sure that we foster all these various different types
661
00:32:39.279 --> 00:32:42.519
of tools, we foster diversity and not the way that
662
00:32:42.559 --> 00:32:46.039
we typically talk about it in quality conclusion all the same,
663
00:32:46.039 --> 00:32:48.079
I'm just talking about just never see it thought, never
664
00:32:48.119 --> 00:32:50.319
see it tools, the things that make great well, things
665
00:32:50.359 --> 00:32:53.079
that make our country great, right, But all those things,
666
00:32:53.160 --> 00:32:56.960
I think that's the key, and it's that to me
667
00:32:57.440 --> 00:32:59.400
is what we have to be careful and moving forward
668
00:32:59.440 --> 00:33:01.720
with AI. And we really only know how to do
669
00:33:01.759 --> 00:33:04.519
that if everyone really starts to understand it. And it's
670
00:33:04.559 --> 00:33:07.599
such a low barrier to get into these things, right,
671
00:33:07.880 --> 00:33:10.480
play with it beyond just using it as a search engine.
672
00:33:10.839 --> 00:33:13.279
I have to do something like, don't just ask to
673
00:33:13.279 --> 00:33:15.519
write a poem because you know your kid told you right,
674
00:33:15.799 --> 00:33:18.400
you'll get on and pick a problem. I have a
675
00:33:18.440 --> 00:33:20.559
good personal example out of sidon the AI guys. So
676
00:33:20.799 --> 00:33:24.119
that's but I had a furnish problem over a couple
677
00:33:24.160 --> 00:33:26.759
of weeks ago. In the furnace, I actually had some
678
00:33:26.799 --> 00:33:28.400
guy look at at her new star. At first, I
679
00:33:28.440 --> 00:33:31.200
don't know, I was flashing light whatever, so I was like, okay,
680
00:33:31.319 --> 00:33:35.079
Gemini took a picture of pulled the panel off, said hey,
681
00:33:35.079 --> 00:33:37.240
what's this light doing? And I used the live so
682
00:33:37.279 --> 00:33:39.400
I was able to talk it through and it said, hey,
683
00:33:39.799 --> 00:33:41.640
I think there's something wrong with the exhaust van and
684
00:33:41.640 --> 00:33:43.200
all this kind of stuff. And I was actually able
685
00:33:43.279 --> 00:33:45.359
to talk me all the way through it. I was
686
00:33:45.400 --> 00:33:48.039
able to say, and I mean, that's a pretty amazing thing.
687
00:33:48.799 --> 00:33:51.160
So I guess that story is to say, look, if
688
00:33:51.160 --> 00:33:52.640
you have a problem, then you're like, man, I really
689
00:33:52.680 --> 00:33:54.960
wish I could talk to someone who knew something instead
690
00:33:55.000 --> 00:33:56.519
of picking up a phone call and just talking on
691
00:33:56.519 --> 00:33:58.839
a service tech as you really should. Don't do anything
692
00:33:58.880 --> 00:34:02.079
unsafe by doing it first, just to understand the problem,
693
00:34:02.119 --> 00:34:04.279
don't actually try to fix this. Still call that, so
694
00:34:04.359 --> 00:34:07.240
still call the experts, but understand so you can talk
695
00:34:07.279 --> 00:34:08.079
to the expert better.
696
00:34:08.159 --> 00:34:08.639
I love it.
697
00:34:08.840 --> 00:34:11.000
So that's what you can do, and then you'll see
698
00:34:11.039 --> 00:34:14.119
the true power of jarative AI. I think, yeah, you will.
699
00:34:14.400 --> 00:34:17.000
You don't. We won't see it until you actually have
700
00:34:17.079 --> 00:34:18.559
it help you, And it may not work on the
701
00:34:18.639 --> 00:34:20.280
very first time because you have to plant it right
702
00:34:20.320 --> 00:34:22.719
all those things. Yeah, try sure times, try to chill.
703
00:34:22.719 --> 00:34:25.800
It helps you. I guarantee it. I guarantee you you
704
00:34:25.800 --> 00:34:27.079
will find that it helps you.
705
00:34:27.119 --> 00:34:29.440
And I think that's I think that's a fantastic piece
706
00:34:29.480 --> 00:34:32.320
of advice, because not only will you be amazed by
707
00:34:32.320 --> 00:34:34.599
what it can do, you'll also get a real sense
708
00:34:34.599 --> 00:34:38.519
of the limitations and where the guardrails are, where we'll
709
00:34:39.039 --> 00:34:41.360
where we'll push back and say I'm sorry, I don't
710
00:34:41.440 --> 00:34:44.079
I don't know, or its response will be so obviously
711
00:34:44.159 --> 00:34:46.559
wrong that you'll be like, Okay, I've gotten over your
712
00:34:46.559 --> 00:34:49.800
head here. And but that's your point to the people
713
00:34:49.840 --> 00:34:53.719
that look at that as an opportunity to leap from
714
00:34:53.760 --> 00:34:57.480
that area versus recoil from that, right, is that, oh,
715
00:34:57.519 --> 00:34:59.880
this is this and that's what I've always found. AI
716
00:35:00.119 --> 00:35:02.719
is a jumping off point for me, and that's how
717
00:35:02.760 --> 00:35:05.599
I've used it is and that's how I've always approached
718
00:35:06.119 --> 00:35:08.519
this new type of technology is not kind of the
719
00:35:08.559 --> 00:35:11.559
alpha and omega, just it's the jumping off point for
720
00:35:11.679 --> 00:35:14.559
the next exactly. And if you treat it that way,
721
00:35:14.800 --> 00:35:16.639
I think you're gonna get a ton of value out
722
00:35:16.639 --> 00:35:18.760
of it. To your point, but yeah, if we don't
723
00:35:18.760 --> 00:35:22.679
have the Elon Musk, Steve Jobs and those types of
724
00:35:22.679 --> 00:35:25.280
people in the in the world, none of this is
725
00:35:25.320 --> 00:35:28.800
gonna all this technology is for nothing in the end, actually,
726
00:35:29.679 --> 00:35:32.519
because people are trying to the real problems right. US
727
00:35:32.920 --> 00:35:37.440
wants to be multiplanetary species. Steve Jobs was a computer
728
00:35:37.480 --> 00:35:39.719
as a tool to change the world. That's it. Like,
729
00:35:39.760 --> 00:35:43.920
it's not. He didn't care about microprocessors and all those things.
730
00:35:43.920 --> 00:35:46.519
He was like, we're gonna change the world. We're gonna
731
00:35:46.559 --> 00:35:49.039
make kids smarter than they've ever been. We're gonna have
732
00:35:49.079 --> 00:35:52.880
people access to better information than they've ever had. He
733
00:35:52.960 --> 00:35:57.280
wasn't like, ooh, can we get more processing power out
734
00:35:57.280 --> 00:35:59.480
of this GPUs? Like he's just making it work.
735
00:36:00.440 --> 00:36:03.159
Yeah, it takes a village, right, and it does. That's easy.
736
00:36:03.360 --> 00:36:05.719
Like you would have just been stuck with vacuum tubes,
737
00:36:05.800 --> 00:36:07.679
right if everyone would if l A, they just said,
738
00:36:07.679 --> 00:36:10.679
we always use vacuum tubes. Statistically, that's all I've ever seen.
739
00:36:10.719 --> 00:36:13.360
So therefore that's all I'm gonna say. We're always gonna
740
00:36:13.360 --> 00:36:16.679
be stuck with vacuum tubes. Wouldn't necessarily say, oh, I
741
00:36:16.719 --> 00:36:19.159
need that whole team to think of this microprocessor thing.
742
00:36:19.320 --> 00:36:21.599
So yeah, no, that's exactly. So a few a few
743
00:36:21.599 --> 00:36:24.440
tools and tips and tricks, because so far, you know,
744
00:36:24.519 --> 00:36:26.800
I've just I just basically said, oh, you should try
745
00:36:26.840 --> 00:36:28.960
to do the problem. A few prop techniques that I've
746
00:36:28.960 --> 00:36:30.559
found useful for people.
747
00:36:30.639 --> 00:36:31.840
So it's your first time.
748
00:36:32.480 --> 00:36:35.519
I'm sure everyone's tried chat GPT. I also recommend Gemini.
749
00:36:35.599 --> 00:36:37.320
I might be a little biased there, but I also
750
00:36:37.360 --> 00:36:40.599
recommend Gemini to try some things. Anthropics good too. Clonde
751
00:36:41.239 --> 00:36:44.119
is a relatively good one to try out in your
752
00:36:44.159 --> 00:36:48.440
prompt always append like anything. So if it's like, hey,
753
00:36:48.519 --> 00:36:51.840
I have this kind of thus a Lennox whatever, explain
754
00:36:51.840 --> 00:36:55.079
it to me like I'm six, and it's that preamble, right,
755
00:36:55.119 --> 00:36:57.559
Explain it to me like I'm this now, I might
756
00:36:57.719 --> 00:36:59.599
what I sometimes will do if I'm like, I know
757
00:36:59.639 --> 00:37:01.559
exactly what responsible going to get because I train this thing,
758
00:37:01.599 --> 00:37:02.960
so I kind of know why it's going to respond
759
00:37:03.039 --> 00:37:04.280
this way. So what I'm gonna do is I'm going
760
00:37:04.360 --> 00:37:08.159
to frame it, write a preamble, some context, and it's hey,
761
00:37:08.599 --> 00:37:10.000
treat me like I'm an engineer, like I do a
762
00:37:10.000 --> 00:37:12.679
lot of biology kind of stuff, right, And I always
763
00:37:12.760 --> 00:37:16.440
preface if I'm asking about a molecule and an interaction, Oh,
764
00:37:16.519 --> 00:37:18.199
how does this? How do your key tones interact? And
765
00:37:18.239 --> 00:37:20.480
the liver produces a key tone, and the key tone
766
00:37:20.559 --> 00:37:22.079
is a signal or for fat burning, you know all
767
00:37:22.119 --> 00:37:24.559
those different things. The way I learned that was through
768
00:37:24.559 --> 00:37:26.880
a large language model. But what I said is explain
769
00:37:26.880 --> 00:37:28.760
it to me like I'm an engineer, and it has
770
00:37:28.760 --> 00:37:31.039
an amazing way to relate it to me. So if
771
00:37:31.079 --> 00:37:34.039
you are considering yourself as let's say that, I'm going
772
00:37:34.119 --> 00:37:36.280
to account you, right, and I want to understand something
773
00:37:36.400 --> 00:37:39.119
about my car because I'm having a trouble. I hear something,
774
00:37:39.119 --> 00:37:42.079
it's like, hey, i'm doing this far is this time
775
00:37:42.400 --> 00:37:44.760
explains to me like I'm an accountant, and it will
776
00:37:44.760 --> 00:37:47.599
actually relate it to your domain and your terms. And
777
00:37:47.639 --> 00:37:49.760
that's a big thing that I've been finding is the
778
00:37:49.800 --> 00:37:52.880
ability to interact with systems and if you have different systems,
779
00:37:52.880 --> 00:37:56.800
generate AI is an amazing translator of domains and domain
780
00:37:56.800 --> 00:37:58.760
knowledge because I can do that because it will actually
781
00:37:58.760 --> 00:38:00.800
do it really well. So that's one trick is a
782
00:38:00.880 --> 00:38:03.320
prompt and just say explain to me. If you're like, look,
783
00:38:03.360 --> 00:38:05.000
I'm nos far than a five year old, let's say
784
00:38:05.039 --> 00:38:06.079
explain to me like I'm five.
785
00:38:06.440 --> 00:38:08.639
I do it all the time, especially for like my
786
00:38:08.760 --> 00:38:11.679
LinkedIn bio. I will write out stuff and then I'll
787
00:38:11.679 --> 00:38:13.840
go and I'll to chat GPT and I'll say this
788
00:38:13.880 --> 00:38:15.920
to me like I have an eighth grade reading level,
789
00:38:16.519 --> 00:38:19.519
and it's amazing. It works so great to help me
790
00:38:20.239 --> 00:38:22.800
make sure because not because people are dumb and they
791
00:38:22.840 --> 00:38:25.239
only have eighth grade, but that when people are scanning,
792
00:38:25.280 --> 00:38:28.639
they're usually scanning at an eighth grade yeah sort of
793
00:38:28.719 --> 00:38:31.519
reading level. And if you know the area that you're
794
00:38:31.679 --> 00:38:35.079
presenting the information, like in a presentation deck. You don't
795
00:38:35.079 --> 00:38:37.920
want it to be something people are reading. They should
796
00:38:37.920 --> 00:38:40.000
be able to scan it quickly. And so if you're
797
00:38:40.039 --> 00:38:43.559
doing a presentation deck, I'll do all I'll do all
798
00:38:43.559 --> 00:38:45.519
the work and then I'll be like, tell me this
799
00:38:45.679 --> 00:38:47.800
like in an eight thrade reading level. It's been amazing.
800
00:38:48.239 --> 00:38:51.000
Yeah, that's true. Second second thing I would do.
801
00:38:51.239 --> 00:38:52.159
A lot of times.
802
00:38:52.280 --> 00:38:54.840
If you look at Google search, right, it's actually the
803
00:38:54.840 --> 00:38:56.800
opposite of Google search. If you get really good at
804
00:38:56.800 --> 00:39:00.159
Google searching, you would five ways of fragments sent and
805
00:39:00.159 --> 00:39:03.440
saying things right. But as it turns out, you need
806
00:39:03.440 --> 00:39:06.280
to reverse your feeling on that. Right. So if you've
807
00:39:06.280 --> 00:39:08.400
gotten really good at Google searching, instead of writing out
808
00:39:08.400 --> 00:39:10.280
the whole sentence into the Google search bar, which you
809
00:39:10.280 --> 00:39:12.000
don't do anymore, right, you just search and you do
810
00:39:12.039 --> 00:39:14.599
your specific keywords and that of what you're searching for,
811
00:39:14.679 --> 00:39:16.800
and then your copy and paste it. Don't do that
812
00:39:16.840 --> 00:39:18.840
to a large language model. Talk to it like it's
813
00:39:18.840 --> 00:39:21.400
a human. And the reason is what talk to it
814
00:39:21.519 --> 00:39:25.000
like you are talking to basically a high school senior
815
00:39:25.119 --> 00:39:28.679
or undergraduate, first year undergraduate. That's typically how I will
816
00:39:28.679 --> 00:39:30.800
think of it when I'm relating to a large language
817
00:39:30.800 --> 00:39:34.320
model of AI. A generator bay is if you're if
818
00:39:34.360 --> 00:39:38.039
you actually communicate with it like it's a human, it's
819
00:39:38.079 --> 00:39:40.440
going to behave better and it's going to give you
820
00:39:40.519 --> 00:39:43.760
the response that you want better. Why because it was
821
00:39:43.800 --> 00:39:46.880
trained on human language? Yeah right, it wasn't trade on
822
00:39:47.000 --> 00:39:49.880
fragmented search keywords that we use in Google.
823
00:39:50.039 --> 00:39:52.599
I'm so glad you brought this up because this actually
824
00:39:52.719 --> 00:39:56.119
changed my experience working with chat TPT and Claude and
825
00:39:56.159 --> 00:39:58.679
some other ones when I gave it feedback, like hey,
826
00:39:58.760 --> 00:40:01.239
that was a really good response. But I real like
827
00:40:01.280 --> 00:40:03.719
it a little bit like this, I'm telling you, like
828
00:40:03.800 --> 00:40:07.039
every time I am working with it, like i'm working
829
00:40:07.039 --> 00:40:09.719
with another person, I get way better results.
830
00:40:09.880 --> 00:40:12.760
It's true. Yeah, And don't you expect it to be
831
00:40:12.880 --> 00:40:15.239
right the first time, just like you wouldn't expect a
832
00:40:15.280 --> 00:40:17.480
high school senior to be right the first time. When
833
00:40:17.599 --> 00:40:20.159
you're trying to help it to help you solve a problem,
834
00:40:20.199 --> 00:40:22.440
asking someone to help you solve a problem, it's just
835
00:40:22.480 --> 00:40:25.039
not gonna work that way. Commit a little bit, say
836
00:40:25.320 --> 00:40:27.519
hey I'm not sure I understand, or hey, can you
837
00:40:27.559 --> 00:40:29.800
explain a different way? Or Hey, you were wrong about this.
838
00:40:29.960 --> 00:40:32.159
Maybe your perspective needs to be changed a little bit.
839
00:40:32.199 --> 00:40:34.119
Here can I explain it a little better this way?
840
00:40:34.199 --> 00:40:36.280
So treat it like it's a human At least it's
841
00:40:36.280 --> 00:40:39.000
more natural for you to do speech to text, do
842
00:40:39.079 --> 00:40:41.599
that sometimes, like I found myself not even wanting to
843
00:40:41.599 --> 00:40:44.519
type anymore, Jude Jemini to I do to say, I
844
00:40:44.559 --> 00:40:45.960
talk to it a lot. I just talked to it
845
00:40:46.000 --> 00:40:48.320
now because I actually get a better response because it's
846
00:40:48.360 --> 00:40:50.400
been trained on these human like interactions.
847
00:40:50.440 --> 00:40:52.480
I think it's great. I think those are excellent tips.
848
00:40:52.519 --> 00:40:54.360
All right, Bjay, I could talk to you all day,
849
00:40:54.519 --> 00:40:56.920
but I got to go. So if I want to
850
00:40:56.920 --> 00:40:58.440
get in touch with you, if I want to learn
851
00:40:58.440 --> 00:41:00.719
more about technicity, where do I go or to find
852
00:41:00.719 --> 00:41:01.039
out more?
853
00:41:01.039 --> 00:41:04.360
Imf bj dot Yrkovich Technicality to io. We've got a
854
00:41:04.400 --> 00:41:06.159
new website and everything, so check it out. You can
855
00:41:06.159 --> 00:41:08.480
talk to others. You'll just reach out directly to me.
856
00:41:08.760 --> 00:41:10.280
You can find me on LinkedIn. And I have to
857
00:41:10.280 --> 00:41:12.400
say this every time I do not accept connection requests.
858
00:41:12.440 --> 00:41:14.119
I'm still not at the social media. I'm still one
859
00:41:14.159 --> 00:41:16.360
of those kids from a long time ago. I'm totally
860
00:41:16.360 --> 00:41:18.920
in the AI out, but it's okay. Message me on LinkedIn.
861
00:41:18.960 --> 00:41:20.519
I will get back to you. I promise. You can
862
00:41:20.519 --> 00:41:22.320
also find me at aq on dot com, do on
863
00:41:22.400 --> 00:41:24.559
energy dot com. You can also find me at final
864
00:41:24.639 --> 00:41:27.039
health dot org. And you can find me on some
865
00:41:27.119 --> 00:41:30.519
of my my my investing problem line capitals and all
866
00:41:30.559 --> 00:41:33.800
of those different places, and obviously just anywhere on the
867
00:41:33.800 --> 00:41:36.639
Internet you can find AI. You'll probably run into something
868
00:41:36.679 --> 00:41:38.360
that's impacting.
869
00:41:38.280 --> 00:41:39.880
Something that's got your fingerprints on it.
870
00:41:39.960 --> 00:41:42.719
Up. Yeah, yeah, for a better or work, it's probably worse.
871
00:41:42.880 --> 00:41:45.119
Again, thank you so much for taking the time. A
872
00:41:45.119 --> 00:41:48.800
busy guy, I really appreciate you your flexibility being able
873
00:41:48.840 --> 00:41:50.559
to come out on the show. And there were the
874
00:41:50.599 --> 00:41:52.199
links below for everybody if you want to get in
875
00:41:52.280 --> 00:41:53.679
touch or if you want to view some of his
876
00:41:53.760 --> 00:41:56.920
projects he's working on. So thanks AGAINBJ I really appreciate it.
877
00:41:57.280 --> 00:41:59.159
I really appreciate it. Kirk always a good time.
878
00:42:02.239 --> 00:42:04.639
We appreciate you taking the time to listen to this
879
00:42:04.760 --> 00:42:08.400
episode of Schmidt List. The stories shared by our guests
880
00:42:08.519 --> 00:42:13.519
are genuinely inspiring and offer insightful knowledge. It's important to
881
00:42:13.559 --> 00:42:18.360
remember that success doesn't happen overnight and requires collaboration, learning,
882
00:42:18.519 --> 00:42:22.159
and perseverance. If you want to broaden your professional connections,
883
00:42:22.280 --> 00:42:25.119
check out Kurt's book The Little Book of Networking, How
884
00:42:25.159 --> 00:42:28.559
to Build your Career one Conversation at a Time on Amazon.
885
00:42:29.360 --> 00:42:32.760
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886
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887
00:42:36.000 --> 00:42:39.880
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888
00:42:40.360 --> 00:42:47.039
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