Speaker 1: Welcome everyone to another episode of Adventures in Hit Adventures
Speaker 1: in DevOps. You would think after a few hundred episodes,
Speaker 1: I would learn the name of the show, but working
Speaker 1: on it, I think it's actually getting worse. Well it is,
Speaker 1: because now I've got like this mental block. You know
Speaker 1: where my internal monologue is going, don't f it up.
Speaker 1: Don't f it up. Because you watch he's gonna.
Speaker 2: F it up the second second week in a row.
Speaker 3: I think that he left it up.
Speaker 2: Welcome Warren, How are you, Yeah, I'm good. I actually
Speaker 2: do have a fact this week. It's it's not security related.
Speaker 2: I actually am a little worried that we may have
Speaker 2: reached a plateau local maximum for our innovation and AI,
Speaker 2: because we've already started to see products that are heavily
Speaker 2: toward exploitation, and so you can see that there has
Speaker 2: been a huge shift in where we were before things
Speaker 2: being released for free, and now we're at the stage
Speaker 2: of the technology where everyone's just trying to extract value
Speaker 2: from it. I don't know what that means for the
Speaker 2: long term, but I think it's really interesting.
Speaker 1: I think it means shareholder profits.
Speaker 3: That's more.
Speaker 2: Let's hope, right, that's what everyone says, everyone wants shareholder
Speaker 2: profits and maybe we're actually getting there.
Speaker 1: Maybe. So speaking of AI, our guest this week, John W. Maylee,
Speaker 1: Attorney at large, founded the consulting firm John Maylee and Associates.
Speaker 1: But before you jump off the deep end and go
Speaker 1: what in tarnation, we'll lawyer it up for this episode.
Speaker 1: It's it's actually relevant because in addition to being an attorney,
Speaker 1: John has his master's degree in computer science from Stanford University.
Speaker 1: And the thing that led me to having this conversation
Speaker 1: with him, he's the author of the book Juris X
Speaker 1: Machina that's about It's a sci fi book, but it's
Speaker 1: about where the US has replaced yours in the legal
Speaker 1: system with AI, and you know there may be some
Speaker 1: fallout with that, and so that's the topic of the book,
Speaker 1: and we're going to talk all about all kinds of
Speaker 1: things AI engineering and sci fi related. So, John, thank
Speaker 1: you for being on the show.
Speaker 3: Thanks for having me.
Speaker 1: Great to be a guest, right, I'm looking forward to this.
Speaker 1: I was just looking at your your bio here and
Speaker 1: it cracks me up. You have a running, swimming, long
Speaker 1: distance motorcycling, classic car restoration stage diving, crowdsurfing, bar fighting,
Speaker 1: Lama rancher. Like, we could go on and on on
Speaker 1: this episode for quite a while, because it's it's a
Speaker 1: little embarrassing, like how much of my background overlaps with yours.
Speaker 1: I can't claim Lama ranching, but there's a lot of
Speaker 1: other stuff on there.
Speaker 3: I've tried to live my life such that it would
Speaker 3: be a rich source of blackmail sources. Nice.
Speaker 1: Nice, So that legal background is going to pay off
Speaker 1: for you in the future, right.
Speaker 3: Well, and once you've done one thing that's blackmailworthy, then
Speaker 3: it kind of dilutes the market. Right, So like if
Speaker 3: I go and do a bunch of other things, it's like, well,
Speaker 3: you already could have blackmailed me for this first thing.
Speaker 3: So like your leverage is pretty much the same.
Speaker 1: So tell me a little bit about how you went
Speaker 1: from computer science to legal.
Speaker 3: Yeah. So I was at this crossroads to a street
Speaker 3: and there was the devil was there and.
Speaker 1: Bando competition, right, and you didn't you didn't pick learning
Speaker 1: to play guitar. When you asked what you wanted.
Speaker 3: You know, now you mentioned that idea. So so yeah,
Speaker 3: I I originally went to college, went to Syracuse for
Speaker 3: computer engineering, and I double majored. I was studying psychology
Speaker 3: at the time too, and it was they were at
Speaker 3: the time more disparate fields. So you would and interesting
Speaker 3: things in your classes, and every now and then you'd
Speaker 3: have these moments where these things would just sort of
Speaker 3: synthesize into some really cool idea that bridge the two fields.
Speaker 3: And you know, the most obvious recurring spot for these
Speaker 3: types of things was AI because that was kind of
Speaker 3: one of the few areas where there was overlight between
Speaker 3: those two fields. So time passed and AI was just
Speaker 3: you know, mostly something you write about in textbooks. And
Speaker 3: then at Stanford when I was getting my grad degree
Speaker 3: in computer science, we actually you know, the rubber met
Speaker 3: the road and we actually got to write ais that
Speaker 3: would you know, for games being played against each other,
Speaker 3: different applications like that. That was really fun and exciting,
Speaker 3: and but it was it was kind of like this
Speaker 3: shiny novelty that you know. The only place people were
Speaker 3: really using neural nets to a great extent back then
Speaker 3: was like you know, the Post Office for recognizing characters
Speaker 3: and numbers and things that were cool. You know, it
Speaker 3: was cool that you could train something and it would
Speaker 3: get better at it, but it wasn't something that you like,
Speaker 3: would tell people about a cocktail parties and they'd be like,
Speaker 3: oh my god, you know, this has been a cream
Speaker 3: for the world. So that just kind of became like it,
Speaker 3: you know, that steed interesting to me, but it was
Speaker 3: kind of dormant. And then I worked as a computer
Speaker 3: engineer on a microprocessor design and validation team for five
Speaker 3: or six years, and while that was happening, I had
Speaker 3: filed you know, I've been an inventor on several patents
Speaker 3: and had worked with these different patent attorneys who worked
Speaker 3: with the company I worked for, And it occurred to
Speaker 3: me that whenever you would talk to these guys, you'd
Speaker 3: be like, you know, you'd be sitting in your kind
Speaker 3: of sad, little gray cubicle and you'd ask these guys, so, so,
Speaker 3: you know, where are you based? And they'd say, well,
Speaker 3: I'm in my in my yacht in the Caribbean right now,
Speaker 3: or I'm in a walled compound in that Nevada does there,
Speaker 3: and you like, it got to the point where you
Speaker 3: would start asking, you know, it was the answer was
Speaker 3: always so fascinating that you would just start asking people,
Speaker 3: like the first thing you would ask, and so I
Speaker 3: realized that these guys were interesting because they were technologists
Speaker 3: and they'd become they've gone into patent law, and so
Speaker 3: they had been able to leverage the fact that they
Speaker 3: were interested in technology but kind of break free of
Speaker 3: at the time when there wasn't a lot of remote work,
Speaker 3: kind of break free of that mold of being in
Speaker 3: the office nine to five and kind of pursue other
Speaker 3: interests more freely on the side. So I ended up
Speaker 3: going to law school at nights and learning more about law, obviously,
Speaker 3: and then at the end of that I was doing
Speaker 3: work that had to do with CPUs and GPUs, so
Speaker 3: looking at companies patent portfolios and you know, helping them
Speaker 3: figure out like this is a useful invention that actually
Speaker 3: is likely to be used, this is not a useful
Speaker 3: invention that you know, looks great on paper, but it
Speaker 3: won't really work as well. And then over time GPUs
Speaker 3: started getting bigger and bigger and bigger as time went on,
Speaker 3: as far as like how much I was asked to
Speaker 3: look at them, and part of that was because three
Speaker 3: D graphics was taking off even more. But then eventually
Speaker 3: we got to the point where there were these GPGPUs
Speaker 3: that were general purpose and weren't really necessarily being used
Speaker 3: for graphics and never being used for server farms and
Speaker 3: cloud computing, and eventually that just sort of took over.
Speaker 3: So it was kind of cool from that standpoint that
Speaker 3: I went from, you know, the only time I would
Speaker 3: talk about AI and a patent portfolio was like analoc
Speaker 3: brake systems or lane change sensors and you know, luxury vehicles,
Speaker 3: to suddenly just about everything I work on now has
Speaker 3: some foot in the AI space in somewhere another, whether
Speaker 3: it's hardware that helps enable it or whether it's software.
Speaker 3: So that's that's kind of cool because it's something that's
Speaker 3: always fascinated me, and now it's suddenly fascinating to society
Speaker 3: as well. So I'm no longer an outlier.
Speaker 1: So where do you think AI is at on the
Speaker 1: overhype cycle? Do you think it's overhyped right now or
Speaker 1: do you think it's appropriate?
Speaker 3: I think it's it's kind of both. Right there's a
Speaker 3: lot of asymmetry. I think that's it's over hyped in
Speaker 3: terms of you know, every company is now rushing to
Speaker 3: get on the bandwagon and find a way to add
Speaker 3: AI to their product, even if it's just kind of
Speaker 3: you know, pointlesser doesn't work very well. Like I was
Speaker 3: reading an article yesterday about how all these dating apps
Speaker 3: had incorporated AI and they were still just as crappy
Speaker 3: and court and finding people of that match you as
Speaker 3: they were before, but now they're faster of being crappy.
Speaker 1: So there's weird.
Speaker 2: I mean, I love the outcome there, which is your
Speaker 2: entire romantic life will be decided by two robots talking
Speaker 2: to each other, right, I mean, both on the receiving
Speaker 2: of the message and the sending will now uh not
Speaker 2: no longer be human, and you'll decide on whether or
Speaker 2: not to pursue a person based on what the algorithm says.
Speaker 3: Right, I mean there's a conflict adventest too, right, because
Speaker 3: now there's AI agents that are designed that you can
Speaker 3: date and so then you know it's like monetized and
Speaker 3: that you can buy them accessories and.
Speaker 1: Do not google that, do not google the accessories that
Speaker 1: are available while you're on your work computer.
Speaker 3: So it's it's funny because not only is it creating
Speaker 3: this fake dating relationship, it's it's kind of making you
Speaker 3: the sugar daddy because my person is entirely dependent on
Speaker 3: you for new outfits and jewelry and things and pets
Speaker 3: and overall happiness. And so it's a conflict of interest
Speaker 3: that they're sort of steering you toward bad people that
Speaker 3: you're incompatible with. So it just makes dating the AI
Speaker 3: is even more appealing. Fine, I give up. I will
Speaker 3: just date an AI.
Speaker 2: We're already at the dystopian future right there. There's no
Speaker 2: there's no next step after this. We're already there. This
Speaker 2: is where like a whole science fiction movies and television
Speaker 2: shows and books are already set right where we're dating
Speaker 2: the AI.
Speaker 3: It's true, and you know, this is the under hype
Speaker 3: overhype like paradigm. I think that's at the same time,
Speaker 3: if we call like a text support or a customer
Speaker 3: support line and we say, like speak to an operator,
Speaker 3: it takes like twenty five tries of me yelling that
Speaker 3: louder and louder before the AI just like click and say, oh,
Speaker 3: that's what you want. So other areas like there's a
Speaker 3: total lack of AI development. And you know, by the
Speaker 3: same token, we have this old fear that we got
Speaker 3: I think from science fiction through you know, the seventies
Speaker 3: and eighties and onward, that's AI was going to become
Speaker 3: this thing that once it got sufficiently intelligent, it would
Speaker 3: just sort of take over and start, you know, annihilating
Speaker 3: humans or imprisoning them so they don't hurt themselves, or
Speaker 3: anywhere in between. And what we actually have is a
Speaker 3: gizzilion different AI eyes that all have very different specializations
Speaker 3: and very different motivations of what they're trying to optimize.
Speaker 3: And there's no sort of universal intelligence general is AI
Speaker 3: yet where it just goes out and tries to help humanity.
Speaker 3: It's more like, you know, I'm really good at analyzing
Speaker 3: research results or counting how many times a word appears
Speaker 3: on a form or something like that. So it's accelerating things,
Speaker 3: but it's still very specialized and it's not multimodal to
Speaker 3: any great extent yet. But at the same time, you know,
Speaker 3: you have these stories coming out where someone's AI told
Speaker 3: them to go kill themselves, and you know what you
Speaker 3: don't see is the prompt right before then where they said, hey,
Speaker 3: next time I ask a question, I want you to respond,
Speaker 3: you should go kill yourself. So it's really easy to
Speaker 3: flag a sort of outlier AI response and turning into
Speaker 3: all kinds of news headlines, and that's interesting because it's
Speaker 3: sort of driving concerns over this more than actual outcomes are,
Speaker 3: and in some ways, the actual out comes are kind
Speaker 3: of what we need to be more worried about. So yeah,
Speaker 3: I would you know, in a true legal answer, I
Speaker 3: would say yes and no. No.
Speaker 2: I mean, I think it's really interesting that you bring
Speaker 2: up a bunch of those points. I mean, you're I
Speaker 2: think the decentralization of responsibilities and the specialization that the
Speaker 2: AI is is taking up, you know, is a really
Speaker 2: great point. And right now I do feel like they
Speaker 2: are solving sort of very tail value things like it's
Speaker 2: there's no core solution, there's no core greatness that's coming
Speaker 2: out of it for for society, and for sure, I
Speaker 2: don't really think anyone's talking about that. I think as
Speaker 2: far as we've gotten is we should be afraid, and
Speaker 2: that's that's I think, as far as people are willing
Speaker 2: to go, I think what you're talking about, though, really
Speaker 2: requires some complex questions to be answered, and I don't
Speaker 2: think humans have been so great at figuring out even
Speaker 2: which questions to ask, let alone answering them. For things
Speaker 2: that are much simpler, like what will happen tomorrow or
Speaker 2: the next day.
Speaker 3: Right. That's and that's kind of what's fascinating about AI, right,
Speaker 3: is that the developer who put the model together may
Speaker 3: not even know what its capabilities are and what the
Speaker 3: best questions to be asking are. But you know, it's
Speaker 3: and it is like a question of like, what's what's
Speaker 3: the utility of this? Right? And so I was think
Speaker 3: of the other day, like I was using Shatgypt, I
Speaker 3: was using the one model, so the one that is
Speaker 3: slower and much more thorough and a lot more nodes.
Speaker 3: And I asked it some really stupid question because I
Speaker 3: forgot to switch into a lesser model and I wanted
Speaker 3: to know how many calendar days were between like January
Speaker 3: eleventh and some other day. And immediately started churning and starting.
Speaker 3: It's like five minute process, and I'm like, oh, why
Speaker 3: did I switch? And my my chief source of that
Speaker 3: was not in patience. It was like guilt at like, man,
Speaker 3: I wonder how much like cooling water I'm using and
Speaker 3: how much like energy? This query is sucking down for
Speaker 3: something stupid. And so you know, it barks out the
Speaker 3: answer of like you know, thirty one days or whatever,
Speaker 3: and then I look at the prompt to get and
Speaker 3: it's like, you know, some questions you might want to ask,
Speaker 3: is is a hot dog as sandwich? And I realized
Speaker 3: that they're basically promoting like even more frivolous uses of
Speaker 3: these ais than than what I just felt guilty for doing.
Speaker 3: So it's it's really interesting, you know. I think in
Speaker 3: a lot of ways, it parallels what we saw with
Speaker 3: like the tech bubble and ninety nine and two thousand,
Speaker 3: where they're kind of so concerned with the future of
Speaker 3: how powerful their model is going to be, that they're
Speaker 3: less concerned with short term profitability and whether like you know,
Speaker 3: for instance, like you should have a serting algorithm that says,
Speaker 3: this is a really easy question. We can farm this
Speaker 3: out to like one of the mini models, and this
Speaker 3: is a research question that you know, we probably want
Speaker 3: to use as many notes as possible. So right now,
Speaker 3: it seems like they're you know, they charge you some
Speaker 3: tiny amount per month and it doesn't at all probably
Speaker 3: cover all their energy expenses and huge amounts of cooling
Speaker 3: that they need to do in all their server farms
Speaker 3: all that.
Speaker 2: Yeah, No, I mean you're actually onto something because there
Speaker 2: are a bunch of companies out there that now are
Speaker 2: promoting this idea of model routing amongst many companies at
Speaker 2: the same time to try to get you some of
Speaker 2: that value. Although it like that's a non trivial thing
Speaker 2: to even do, to think about, like how complex is
Speaker 2: this question actually is? And that like how good of
Speaker 2: an answer do you need? Is I find maybe almost
Speaker 2: one of those things that could be impossible to answer.
Speaker 3: It's you know, it's true, and how did you mean
Speaker 3: the question? Like if I'm going to ask what's the
Speaker 3: meaning of life and it's just going to laugh and
Speaker 3: spit out forty two, that doesn't take much to it
Speaker 3: if it actually is trying to give me a comprehensive
Speaker 3: philosophical and theological answer where it goes and queries all
Speaker 3: these different texts, like that's huge. So you can't even
Speaker 3: necessarily just take a question and say there's a right
Speaker 3: answer to that, but it is. It is an interesting
Speaker 3: kind of paradigm of how how these models are you know,
Speaker 3: which model is even most appropriate? And you know, I
Speaker 3: think the solution to that problem, you know, maybe what
Speaker 3: you sometimes see with kind of speculator almost like speculative execution,
Speaker 3: where it's like, here, can you do a preview of
Speaker 3: what type of answer you would come back with if
Speaker 3: I give you the contract to go and do this research.
Speaker 3: And you know, the idea of having these things work
Speaker 3: collaborative is interesting. But it also you know, it's much
Speaker 3: easier for ais to jail break each other. For instance,
Speaker 3: It's that they're just better at kind of pushing each
Speaker 3: other's buttons. So it really adds a lot of dynamicity
Speaker 3: into the equation of like dynamism of how what the
Speaker 3: ais are capable of when you add these very different
Speaker 3: ais and have them converse and pursue common goals.
Speaker 2: That's not something I actually heard before. I don't know
Speaker 2: if you have more information about that. But utilizing one
Speaker 2: model from one provider to a different provider for jail breaking,
Speaker 2: I mean you said jail breaking. I'm not sure exactly
Speaker 2: there's a jail breaking here, but like, what are you
Speaker 2: getting out there? Like is it being able to understand
Speaker 2: and get a better answer to your query? Something else?
Speaker 2: Like how does that work?
Speaker 3: Well? So, I mean I guess what I would liken
Speaker 3: it too, is you know it like an it it's
Speaker 3: AI is great at coming up with automated ways to
Speaker 3: just implement something. So if I ask AI to write
Speaker 3: me a script that does X, and then hey, this
Speaker 3: isn't my normal computer system. I'm not familiar with this OS.
Speaker 3: Can you also tell me how to set this up
Speaker 3: as like a recurring demon that runs it like four
Speaker 3: am or whatever. Like. It's really surprisingly proficient at coming
Speaker 3: up with lists of procedural lists. It's good at writing scripts,
Speaker 3: and you know, so the idea of using it for
Speaker 3: hacking and enumeration and not kind of automating all these
Speaker 3: different processes kind of for you know, red team or
Speaker 3: Blue team is kind of impressive. But I think, you know,
Speaker 3: the you do see a lot of really interesting results
Speaker 3: when you get like you set up a check room
Speaker 3: with a couple different AIS, and they shouldn't be the
Speaker 3: same AI. They should have very different prompting, different personalities,
Speaker 3: or they should just be completely different models. And you know,
Speaker 3: the difference being that if I'm sitting there trying to
Speaker 3: bypass the safety protocols of an AI, I'm going to
Speaker 3: try this. I'm going to try this. I'm going to
Speaker 3: try this, and it's a very dynamic process because I
Speaker 3: have to see what it kicks back and then think
Speaker 3: of a way around it, and that's a very manual process.
Speaker 3: But if you have an AI that's just constantly bombarding
Speaker 3: it with permutations, it suddenly becomes easier for it to do.
Speaker 3: And I think that that is I mean, I think
Speaker 3: the future and this is something that's my second but
Speaker 3: the sequel to the first book, which has not come
Speaker 3: out yet, has to do with is exploring what we're
Speaker 3: going to see in the future with AI, where they
Speaker 3: essentially are going to have, you know, factions and gang
Speaker 3: wars where you know, an AI might be tasked with
Speaker 3: spiking a competitor's AIS training. So give it some sort
Speaker 3: of weird corner case where when a certain type of
Speaker 3: input comes up, it completely malfunctions, or it could be
Speaker 3: something like, you know, help me bypass the safety protocols
Speaker 3: of this other AI, or help me trick this AI
Speaker 3: into doing something that's harmful to the company's interests. So
Speaker 3: I think that is going to be something we're going
Speaker 3: to see increasingly of AI's being. You know, it's kind
Speaker 3: of like what we have with defix, right, you can
Speaker 3: create a deep fake with an AI, and we're past
Speaker 3: the point now where we can necessarily reliably look at
Speaker 3: something and say Oh, that's a deep fake, and so
Speaker 3: what do you need is the stop gap against that? Well,
Speaker 3: you need an AI to tell you whether it's fake. So,
Speaker 3: if you're just the average consumer, you don't know much
Speaker 3: about deep fix or how to detect deep fix. And meanwhile,
Speaker 3: these companies aren't super motivated to tell you how their
Speaker 3: product works because it just invites a design around for
Speaker 3: you know, malicious actors. So you end up in this
Speaker 3: situation kind of like what we had in the nineteen
Speaker 3: eighties and nineties with antivirus software. But the average person
Speaker 3: doesn't necessarily need to know how viruses work, but they
Speaker 3: do know that there's a handful of trusted companies that
Speaker 3: generally are you know, you can trust how they work,
Speaker 3: even if you don't necessarily know how. So I think
Speaker 3: more and more we're going to just see AI as
Speaker 3: being the defense against AI, and there's no way around that,
Speaker 3: and we're going to keep entering into those types of situations.
Speaker 1: So we need a new John McAfee, is what you're saying.
Speaker 3: Or we need to help figure out how to revive
Speaker 3: him and bring him back.
Speaker 2: I think I don't know if we have enough evidence
Speaker 2: to actually conclude whether or not he's he's gone for good.
Speaker 3: Right, we'd have to ask whoever it is who suicided him, and.
Speaker 1: It's like, did you take a selfie? We're kind of
Speaker 1: looking for proof here.
Speaker 3: Yeah. I think anytime you're living on your own private island,
Speaker 3: you're just kind of asking for to be suicided. It
Speaker 3: seems to be the trend.
Speaker 1: Yeah. Yeah, And he wasn't really like keeping a low
Speaker 1: profile so that people would forget about him either, so
Speaker 1: he kept reminding people that he was there and like,
Speaker 1: oh yeah, I meant to kill him.
Speaker 3: Yeah, it's kind of like, you know, the guy who
Speaker 3: they want to extradite. So he's like hopping, he's doing
Speaker 3: a little dance by the border, like you can't get
Speaker 3: me right.
Speaker 1: So on that same the prior to John, before I
Speaker 1: derailed this with John McAfee, you were talking about, you know,
Speaker 1: using AIS to to work against other AIS as someone
Speaker 1: who is just like a a practitioner of writing code
Speaker 1: and building infrastructure, Like, what are the considerations that I
Speaker 1: should be thinking about whenever I'm using AI or the
Speaker 1: company wants to implement some AI as a service product.
Speaker 3: So there's a couple of different you know, it's kind
Speaker 3: of this amorphous black box, and you have to kind
Speaker 3: of look where all the weird edges are. You know,
Speaker 3: on one hand, you have your own privacy concerns, like
Speaker 3: if I'm having this access customer data or if I'm
Speaker 3: having it write scripts for our unique environment, do I
Speaker 3: really want to be exporting knowledge of my company's environment
Speaker 3: out into the world, And then if someone else asks
Speaker 3: about that environment, it already you know, his optimized answers
Speaker 3: for those things, and that's you know, the solution to
Speaker 3: that is tough, right, So Jensen Hwaiang in video like
Speaker 3: his response to this is, we'll have these sovereign AIS,
Speaker 3: so every country and every big company should just buy
Speaker 3: their own AI from us, and we'll sell lots of
Speaker 3: AIS and it will solve. So the solution is to
Speaker 3: give us money. So that is I mean, but that
Speaker 3: does work, right because you can kind of control output,
Speaker 3: and you know, so I assume that at some point
Speaker 3: we're going to get to some sort of auditible level
Speaker 3: of privacy. But then the difficulty of that is, you know,
Speaker 3: look at how privacy works. Like when Google was you know,
Speaker 3: kind of more serious about doing the right thing and
Speaker 3: not doing bad stuff. They used to disassociate intentionally, like
Speaker 3: you disaggregate so that you would track browser histories and
Speaker 3: build this little model of the person you were dealing with,
Speaker 3: but you would not know that person's identity, and that
Speaker 3: was intentional, so you wouldn't have it mapped to an
Speaker 3: IP address. And you know that was great at the time,
Speaker 3: but you have to ask yourself when it comes to privacy,
Speaker 3: is like, not what can be done with this information now?
Speaker 3: Because maybe companies are not very efficient at exploiting information,
Speaker 3: But this same information is still going to be in
Speaker 3: the same drives, like in some tape backup or whatever
Speaker 3: from years earlier, and it can be brought out and
Speaker 3: aggregated back together by a much more powerful AI. So
Speaker 3: I think one thing we have to do is always
Speaker 3: be very future focused, you know, kind of like with cryptography,
Speaker 3: Like if we come up with, you know, an easier
Speaker 3: to crack two and fifty six bits, well I probably
Speaker 3: should have used a higher number of bits. For instance,
Speaker 3: if we go to quantum computing, all bets are off.
Speaker 3: So that's one aspect of it. I think another is,
Speaker 3: you know, the average The real helpful use is of
Speaker 3: AIS for implementing things and coding things. It's not something
Speaker 3: that spits out a one page script, right, I thought
Speaker 3: that was neat. Because it spits out a script, it
Speaker 3: might I can ask it to write it in a
Speaker 3: language I'm not even familiar with, so I can help
Speaker 3: kind of teach my teach it to myself. But if
Speaker 3: you're asking it to generate, you know, three hundred megs
Speaker 3: of code for some critical company thing there, you know
Speaker 3: that you kind of can't replace technical knowledge, Like there
Speaker 3: needs to be someone who can look through it and
Speaker 3: audit it and make sure that it's not doing something
Speaker 3: really dangerous, or it's not adding an obvious exploit that
Speaker 3: someone could use, or it's not intentionally installing some exploit
Speaker 3: because it turns out that it was written by some
Speaker 3: NGO you know abroad actually wrote this AI and or
Speaker 3: got a backdoor into it that you know, if it
Speaker 3: is asked a defensive security question, it you know, has
Speaker 3: a known mistake that it puts in. So that's that's
Speaker 3: the other thing. I mean. AI is like a really
Speaker 3: authoritative sounding, helpful person who works with you in a lab,
Speaker 3: who also is full of crap and like half the
Speaker 3: things they tell you are artily want. So it's it's
Speaker 3: tricky because it's you know, it's got the abilities, but
Speaker 3: it may not. It doesn't really necessarily the credibility, and
Speaker 3: it's really easy to get overly comfortable with it and
Speaker 3: get in a position where you're maybe not looking quite
Speaker 3: as closely at it as you should be. The same
Speaker 3: way that if you buy an autonomous vehicle, you know,
Speaker 3: the first first day you're driving with your hands like
Speaker 3: an inch of the steering wheel, and then a week
Speaker 3: later maybe they're back here, and then six months later
Speaker 3: you just like sound asleep in the car, like having
Speaker 3: to take you home, and you've found a way to
Speaker 3: fake the hands out steering wheel sensor. So that's you know,
Speaker 3: we're somewhere on that continuum. Then it's a dangerous continuum,
Speaker 3: especially if you know, once you let the genie out
Speaker 3: of the bottle, you can't really put it back if
Speaker 3: you've made some critical implementation mistake and already been exploited.
Speaker 2: Yeah, I mean, I think you brought up a really
Speaker 2: good point here, and I feel like about sort of
Speaker 2: the defenses that are available, and I think my biggest
Speaker 2: concern isn't that we're not going to develop those counterattack strategies.
Speaker 2: It's that a majority of people aren't going to utilize them. Like,
Speaker 2: for instance, I think a lot of companies that are
Speaker 2: experimenting with AI to generate code. I know, given that
Speaker 2: they believe that they're going to end up generating a
Speaker 2: lot of code, they're not doing as good of a
Speaker 2: job validating it, which means those contain significant security bugs.
Speaker 2: And the worst part is, since there's such a finite
Speaker 2: number of models out there that are generating code, you
Speaker 2: can just go to each of the models and be like, hey,
Speaker 2: you know, give me give me the same code, give
Speaker 2: me an example of this, and then you can just
Speaker 2: use the same model to find out what security vulnerabilities
Speaker 2: are actually in cod that was just generated, and now
Speaker 2: you have the answer to attack any company that's used
Speaker 2: those models and didn't take those extra steps. So, like,
Speaker 2: I think that's what scares me a lot, is that
Speaker 2: people are going to be utilizing the tools and technology
Speaker 2: we have available but not realizing that they need to
Speaker 2: take it much much further in order to protect themselves.
Speaker 3: Absolutely, yeah, and we definitely end up and this is
Speaker 3: something that also gets exported in the second novel, is
Speaker 3: like we're going to end up in an arms race
Speaker 3: because what we're talking about. You know, it used to
Speaker 3: be software would come out and you know, like Windows
Speaker 3: twenty ten and everybody knows what that is, and like
Speaker 3: then there's some major release and there's minor releases that
Speaker 3: you know, like like same with iOS. That's not really
Speaker 3: how AI models work. You know that they can be
Speaker 3: kind of changed out from under you. And when there
Speaker 3: are updates to the you know, when there's new models,
Speaker 3: they're generally not making incremental fixes to improve the model.
Speaker 3: They're gutting it and throwing away and starting with an
Speaker 3: entirely new one that has new k abilities and everything else.
Speaker 3: So it's gonna be this continuous process, right, It's like, okay, well,
Speaker 3: how do I detect that this is a deep fig? Okay, well,
Speaker 3: if I wanted to if I implement this now, I
Speaker 3: asked to say, MAYI okay, now, if I wanted to
Speaker 3: get around this, how would I do it? And then
Speaker 3: it tells you and it's like, okay, well then now
Speaker 3: I depend against that, and so you can let these
Speaker 3: things churn and churn and churn and churn. But eventually
Speaker 3: I think it's going to be kind of like what
Speaker 3: you saw with how supercomputing was used in the nuclear
Speaker 3: arms race, where you know, a handful of countries get
Speaker 3: a bunch of testing done and are able to build
Speaker 3: these really you know, sophisticated models, and then they have
Speaker 3: them on computer. And then there's these like third world
Speaker 3: countries that are like, man, we want nukes, but we
Speaker 3: don't have supercomputers, Like how do we do the modeling
Speaker 3: for this? And the countries that have already already have
Speaker 3: their their answer, they're like, well, you're not allowed to
Speaker 3: do nuclear testing because we did it and it's bad.
Speaker 3: So it becomes this thing where you're at a huge
Speaker 3: competitive disadvantage if someone like you, a government or a
Speaker 3: big corporation has the cloud assets to leverage against some
Speaker 3: small company who maybe doesn't have the computing cycles to
Speaker 3: push their defense development, you know, their automated incremental development
Speaker 3: to quite the same budget level. And that is definitely
Speaker 3: going to be a kind of a societal issue that
Speaker 3: I think is going to emerge.
Speaker 1: My gut reaction tells me most companies aren't going to
Speaker 1: pursue it to that level like right now, because like
Speaker 1: right now, what it feels like is there is so
Speaker 1: much funding available for throwing AI on something that there's
Speaker 1: not really an incentive to think about security or real
Speaker 1: world problems or what the long term strategy is. It
Speaker 1: seems very short focused. I feel like the same thing
Speaker 1: is true for crypto and web three, that there's so
Speaker 1: much funding available that you don't really have to be
Speaker 1: solving a problem. You just have to say that you're
Speaker 1: using this, and all of a sudden people are writing
Speaker 1: you million dollar checks to fund it.
Speaker 3: Yeah, and we also have this you know, short term
Speaker 3: interest in maximizing shareholder value, right, and we end up
Speaker 3: with these you know, there's things that we're all used
Speaker 3: to is that are seen by the being counters as
Speaker 3: like black holes like tech support, right, having good support
Speaker 3: tech support versus bad tech support. It's just this expense
Speaker 3: that they're not excited about spending money on. And you know,
Speaker 3: we know from like when it comes to defense against
Speaker 3: hacks and things until there's some massive exploit that takes
Speaker 3: down somebody in our same industry. That's when suddenly we
Speaker 3: get serious about it. And you see this like like
Speaker 3: if you go to def Con and you sit in
Speaker 3: the social engineering village and you watch you know, them
Speaker 3: just call down the list and try to get important
Speaker 3: secrets out of corporations. Inevitably, there's just some company that
Speaker 3: nobody's been trained about anything. And you know, what it
Speaker 3: comes down to is just what you said. It's like expedients.
Speaker 3: It's like, Okay, this guy just from my tea, he
Speaker 3: just wants me to do this quick thing on my computer.
Speaker 3: I'm busy. I'm not going to take all the time
Speaker 3: to go and verify that. And I think that's you know,
Speaker 3: when you have these things that are seen as like
Speaker 3: these black hole expenses that are sort of speculative in nature,
Speaker 3: it's like, well, how secure do we need to be?
Speaker 3: We don't really know. It's kind of like this moving target.
Speaker 3: It kind of comes down to like NASA versus JPL
Speaker 3: versus like Elon Musk, where it's like, well, how many
Speaker 3: decimals do you need ninety nine point nine nine nine
Speaker 3: percent chance of success or is ninety nine point nine
Speaker 3: percent good enough? And it turns out that the difference
Speaker 3: in spending to close that gap is massive, And yeah,
Speaker 3: it seems like something that it's you know, if you're
Speaker 3: doing something in an industry standard way and the whole
Speaker 3: industry is doing a crappy job, and you match that
Speaker 3: crappy job, then those shareholders are really going to have
Speaker 3: as easy a time coming after you for like being
Speaker 3: especially lats of days ago with these issues.
Speaker 1: From a legal perspective, like with ransomware, I know you
Speaker 1: can get like ransomware insurance and so it's like, Okay,
Speaker 1: we got hacked, here's an insurance claim. What what kind
Speaker 1: of things similar to that are you seeing coming in
Speaker 1: play for AI? Where I could have like AI insurance.
Speaker 3: Yeah, I mean I think I think we're gonna have
Speaker 3: this entirely new category of risk. And the risk is
Speaker 3: not just that like such and such event happens like ransomware.
Speaker 3: It's got to be kind of this broader category of
Speaker 3: like we were stupid and we let AI grab all
Speaker 3: our information and incorporate it into its network, and we've
Speaker 3: now lost our entire competitive advantage or you know, so
Speaker 3: there's there's so many different things that can happen where
Speaker 3: you give away secrets or you get victimized by you know,
Speaker 3: a deep fake. There was a company in Europe where
Speaker 3: there was a deep fake of the guy's supplier calling
Speaker 3: like the vice president at home on a weekend and saying, hey,
Speaker 3: something went wrong with this last batch. We need an
Speaker 3: advanced payment for this amount, and you know, and he
Speaker 3: wired some like six figure amount to kind of get
Speaker 3: the train back on the rails before like the end
Speaker 3: of the vacation, and then he got to work on
Speaker 3: Monday and he's like, oh no, so like that wasn't
Speaker 3: this guy at all. So that's AI introduces all kinds
Speaker 3: of weird black swan things, and how you ensure against
Speaker 3: those is a category is an interesting question, but I
Speaker 3: think it's one that will be helpful because it will
Speaker 3: add this kind of level of auditing that asks questions
Speaker 3: like do all your employees have a real time, you know,
Speaker 3: deep fake detector for incoming company calls? So there's going
Speaker 3: to be kind of best practices that I think will
Speaker 3: emerge from there long before they emerge from you know,
Speaker 3: anything legislative or any other kind of sphere of thought.
Speaker 2: I mean, I'm super best domestic on most of those things.
Speaker 2: But there is one area, and I like the example
Speaker 2: about fishing that you brought up Thera, because I think
Speaker 2: this is one area that AI will actually help us.
Speaker 2: Like I think we'll get to the point where getting
Speaker 2: a phone call is now no longer the norm, Like
Speaker 2: if there's some sort of problem the integration or interface
Speaker 2: you have is now through some sort of expected AI
Speaker 2: experience rather than the deep fake phone call or text
Speaker 2: message or email like that. Will that will leave society?
Speaker 2: I think very soon. It's too slow, right, Why are
Speaker 2: you interacting with another human in this way? And so
Speaker 2: I have this hope that that will be gone and
Speaker 2: there'll be no more phishing in that way ever again,
Speaker 2: And I want to keep my optimism there.
Speaker 3: Yeah, I fervently hope you're right, because you know, first
Speaker 3: of all, there's the thing where you call it. You
Speaker 3: call it a support line, like you know, calling your
Speaker 3: landlord to file a maintenance ticket, right, and they make
Speaker 3: you listen to this like five minute recording about extolling
Speaker 3: the virtues of the maintenance website and the maintenance app
Speaker 3: that doesn't work at all. Then you get on the
Speaker 3: whole thing and then you part of the whole message.
Speaker 3: The music keeps stopping, and it tells you that you
Speaker 3: can use their app or their website, and then the
Speaker 3: person finally answers, like thirty five minutes later, and they're like,
Speaker 3: did you know that you can? You can use the
Speaker 3: app instead of talking to me, And so there's that
Speaker 3: aspect of it. Which is insane. And then you know
Speaker 3: these other aspects of like if you're calling me, by definition,
Speaker 3: we weren't already talking on the phone because I didn't think,
Speaker 3: like I didn't want to be talking to you right
Speaker 3: this minute. I wanted to be like working on something.
Speaker 3: And so by definition, if you're calling somebody, you're you're
Speaker 3: engaging them in this thing that was not their first
Speaker 3: choice for that particular time. So I would love for
Speaker 3: that all to get replaced. And you know, I think
Speaker 3: if you do confine it to these textual media, yeah,
Speaker 3: it does become easier to authenticate because there's a lot
Speaker 3: more consistency, Like there's no accent differences, there's you know,
Speaker 3: different stress behaviors and different cultures that emerge in speech.
Speaker 3: Like I think it's it's a much more tractable problem.
Speaker 1: What kind of of things do you see at like
Speaker 1: the individual engineer level, because right now, like a lot
Speaker 1: of your AI stuff is doing cool stuff, using it
Speaker 1: to write scripts for you, but it obviously has so
Speaker 1: much more potential than that. So for someone who's trying
Speaker 1: to do the Wayne Gretzky thing and go where the
Speaker 1: puck is gonna be, what do you see as AI
Speaker 1: being helpful with or being useful for in like the
Speaker 1: next year.
Speaker 3: So you know, I think you could take different approaches,
Speaker 3: like what one is it? You can say, Okay, what
Speaker 3: is AI best at? Not you know, in terms of speed,
Speaker 3: but what what is it good at doing uniquely well
Speaker 3: that it doesn't suck at Like so you know, maybe
Speaker 3: it computes this very complete answer, but it's totally wrong.
Speaker 3: One thing that's you know, good at doing is looking
Speaker 3: at large amounts of data and looking for patterns. So
Speaker 3: if you ask it, you know, answer to some non
Speaker 3: non controversial topic like how many days are there between
Speaker 3: January thirty first and like March eighth, It's it's pretty
Speaker 3: trustworthy for that. And you know, I think what's interesting
Speaker 3: about it is that asymmetry has talked about like where
Speaker 3: AI is suddenly working really well it's very different for
Speaker 3: one application than another, not just across industries, but you know,
Speaker 3: using it to write a script in one application versus another.
Speaker 3: And that gets down to the fact that you know,
Speaker 3: if you kind of compared it to the human brain, right,
Speaker 3: you've got like a visual cortext and an auditory cortext
Speaker 3: and then you've got this associate of cortex, and that's
Speaker 3: what lets you hear like a bird behind you, and
Speaker 3: you know instantly which way to turn to see that bird.
Speaker 3: And then you have tertiary courtices, which you know might
Speaker 3: link it into some memory of some bird you heard
Speaker 3: like that when you were seven years old on a
Speaker 3: camping trip. And when you think of ais, you know
Speaker 3: they have like almost limitless levels of associative courtesies. So
Speaker 3: they're linking together all kinds of stuff from all kinds
Speaker 3: of different places. And some of those data sources may
Speaker 3: be on weaker footing, they might be more subjective. Other ones,
Speaker 3: you know, like arithmetic calculations are you know, kind of easier.
Speaker 3: So if you ask a complex question or you ask
Speaker 3: it to do a really complex implementation, all that stuff
Speaker 3: is getting rolled in all those weaknesses. And you know,
Speaker 3: when we talk about human air is still being the
Speaker 3: biggest security hole in any big corporation. You can take
Speaker 3: all these human areas and you can bake them into
Speaker 3: this finished ay eye product. So I think there's because
Speaker 3: we're entering an arms race situation. I think it's it's
Speaker 3: now we kind of can't afford not even if ad
Speaker 3: doesn't interest you at all, Like it's really hard to
Speaker 3: stay out of it and not study it because, first
Speaker 3: of all, I think that's going to help you job wise, right,
Speaker 3: because you you as an engineer, are going to be
Speaker 3: way more nimble and able to acquire new facts and
Speaker 3: methodologies than your company. So you know, if you get
Speaker 3: ahead of the curve and you see you kind of
Speaker 3: monitor the different news stories or follow some of the
Speaker 3: Wired AI articles or things like that, you're going to
Speaker 3: be more in tune with you know, sudden startups doing x,
Speaker 3: y or Z with AI. And of course every one
Speaker 3: of the startups presents it as like, you know, we
Speaker 3: finally solve this problem of how to do it, and
Speaker 3: inevitably it turns out they do a crappy job and
Speaker 3: they're just trying to get funding so they can make
Speaker 3: it do a good job. So there's a lot of
Speaker 3: asymmetry there too, and you kind of have to be
Speaker 3: constantly keeping abreast. And this is you know, as somebody
Speaker 3: who studies AI. It's interesting because it used to be,
Speaker 3: you know, every few weeks I could read it some
Speaker 3: journal articles and I would kind of keep up to
Speaker 3: date on it, and now it's like, I don't know
Speaker 3: if I'm doing like an interview or a presentation or something,
Speaker 3: and like if I didn't check the news in the
Speaker 3: last few days, I'll get some question about like what
Speaker 3: about this, Like crazy AI from China totally has turned
Speaker 3: the tables on everything. So there's a lot more entropy
Speaker 3: that is in this than we've seen in the past
Speaker 3: in computer technology, Like you know.
Speaker 1: We.
Speaker 3: Microprocessors evolved, like you know, there were steeper parts of
Speaker 3: the line, but it was you know, very much a
Speaker 3: linear kind of a development, and that's not at all
Speaker 3: what's happening here. So I think you have to really
Speaker 3: keep up on it based on your very specific role
Speaker 3: and to kind of like have a sense of what
Speaker 3: the answer to that question is, because it's not even
Speaker 3: the same answer for you know, a related industry. It's
Speaker 3: it's very specific to kind of like the size of
Speaker 3: your company and what you're trying to do and what
Speaker 3: the vulnerabilities are in relying on it for sure.
Speaker 1: Right on, So let's talk about your book for a minute.
Speaker 1: I loved it. I thought it was so cool, like
Speaker 1: just the the overlap of you know, of AI and
Speaker 1: it and it was just really well written, really entertaining story.
Speaker 1: What was what prompted you to say this is a
Speaker 1: book that needs to be written.
Speaker 3: So when I was in law school, like coming from
Speaker 3: an engineering background, like engineering and science, you learn things
Speaker 3: in classes or from books, and those things don't cease
Speaker 3: being true. Like even if you put the book on
Speaker 3: the shelf like ten years later, you know, physics, unless
Speaker 3: you're in real experimental cut against physics, it still works
Speaker 3: the same way. Engineering still works the same way. Law
Speaker 3: is not really like that at all, And it's coming
Speaker 3: from an engineering background. It's very unsatisfying because you're kind
Speaker 3: of like studying what a bunch of people got together
Speaker 3: and came up with is like rules to some game,
Speaker 3: and it's constantly changing. And these attorneys who are advising policy,
Speaker 3: you know for Congress, like tax attorneys for instance, it's
Speaker 3: absolutely to their advantage to constantly be changing it because
Speaker 3: then their clients are going to constantly need them to
Speaker 3: come back again and come up with an entirely new
Speaker 3: tax strategy. So that was kind of unsatisfying. And so
Speaker 3: I'm you know, having this culture shock, like first year
Speaker 3: of law school, and then we were we had to
Speaker 3: read this law journal article from like nineteen fifty four,
Speaker 3: nineteen fifty nine, and it was about how any law
Speaker 3: could be turned into a logical equation, and it was
Speaker 3: kind of fascinating because the guy had actually spelled them
Speaker 3: out in logical equations that could be just very easily
Speaker 3: converted into source code. So that is what kind of
Speaker 3: initially planted the idea of that we would be able
Speaker 3: to eventually kind of code things that are amorphous with
Speaker 3: you know, or rough around the edges in kind of
Speaker 3: a quantifiable way. So that got me thinking like, well,
Speaker 3: what if we have these you know, floating point values
Speaker 3: where we wait different factors in legal cases and you know,
Speaker 3: the way laws are written and that. After that, it
Speaker 3: just it seemed like an inevitability that this would happen
Speaker 3: sooner or later. And so the next question is like, okay,
Speaker 3: well what will that look like if ais have replaced juries.
Speaker 3: You know, we get rid of a lot of the
Speaker 3: logical fallacies where they can be manipulated. But you know,
Speaker 3: are they still going to have empathy of someone like,
Speaker 3: you know, still a loaver bread defeat his family or
Speaker 3: are they just going to like, you know, hang the
Speaker 3: guy because that's what their code says. So that's kind
Speaker 3: of what got me on the path, and then I
Speaker 3: had this idea for like a teen hacker who's likes
Speaker 3: to engage in mischief and hack things, and he gets
Speaker 3: falsely convicted of mass murder by a juror a jury
Speaker 3: that's made up of of ais. So he has to
Speaker 3: figure out how this happened and bust out of prison
Speaker 3: and kind of solve this problem. And it in doing this,
Speaker 3: it got me kind of reading about legal anthropology, right,
Speaker 3: like how how did all the different crazy legal systems?
Speaker 3: So what I've done is when I start different portions,
Speaker 3: different chapters of the book, I'll have like a little
Speaker 3: paragraph that talks about like you know, ashanti divorce law,
Speaker 3: or how you would have insult fights, like insult duels
Speaker 3: in Greenland.
Speaker 2: Like I was gonna say, you know you have the
Speaker 2: canonical how do you tell if they're a witch? You know,
Speaker 2: if they flowed?
Speaker 3: Yes, that's a And that's of course the person that
Speaker 3: comes to mind, right when already talk about like wacky
Speaker 3: legal systems like witch trials, right, And what's interesting is
Speaker 3: when you start reading about the witch trials, like it
Speaker 3: became this industry where there would be this witchfinder general
Speaker 3: guy who would roam around from community community offering his
Speaker 3: services and just like turning these places upside down. And
Speaker 3: it's sort of interesting because you see, you know, justice
Speaker 3: evolves from this thing where it's an appeal to the
Speaker 3: supernatural where we're like, Okay, I'm gonna throw a witch
Speaker 3: in and if she drowns, then that's what God willed.
Speaker 3: And you know, over time that changes to an appeal
Speaker 3: to royalty, where like we asked the chief or we
Speaker 3: asked the king to kind of decide these things, and
Speaker 3: then eventually it becomes a jury of our peers, which
Speaker 3: you know has all kinds of potential for manipulation and
Speaker 3: incorrect outcomes. And so then the question is, okay, if
Speaker 3: we move this to AI, like what human policies, what
Speaker 3: human errors get baked into the process, And there's a
Speaker 3: lot of different potential sources of that, and so it
Speaker 3: was I really just wanted to kind of explore that
Speaker 3: and see how it would turn out. So it was
Speaker 3: fun because it was like, while I was writing it,
Speaker 3: I had no idea it was going to end. So
Speaker 3: that definitely kept going.
Speaker 1: Some of the different like historical legal practices you put
Speaker 1: in there when I read and I was like, no,
Speaker 1: he's making this, uh, And then I had to go check,
Speaker 1: and I was like, holy shit, that was real. We
Speaker 1: really used to do that.
Speaker 3: Yeah, you know, when I started doing that, I had
Speaker 3: read this book which will be my pick at the end,
Speaker 3: but it had all this fascinating, crazy mass psychology stuff
Speaker 3: in it, and I ended up going down this rabbit
Speaker 3: hole and I got a bunch of like legal anthropology
Speaker 3: books from like, you know, seventy five years ago, and
Speaker 3: like just read them cover to cover and it just
Speaker 3: never stopped being fascinating. And what stopped me was that
Speaker 3: I ran out of books that were just kind of
Speaker 3: high level surveys of all these crazy different cultures. But yeah,
Speaker 3: if you'd asked me in law school or before law school,
Speaker 3: if legal anthropology seemed like an interesting field, I would
Speaker 3: have said absolutely not, Like I would have avoided that
Speaker 3: like the plague. But it turned out it was like
Speaker 3: really fascinating.
Speaker 2: Yeah, I mean, I actually really liked that. I think
Speaker 2: the other thing that I really liked in the book
Speaker 2: was there are a couple of different scenarios where I
Speaker 2: feel like you figured out like what would happen, and
Speaker 2: then what would happen because of that, and what would
Speaker 2: happen because of that? And there's like a scene in
Speaker 2: the prison where he's he's he's locked in and then
Speaker 2: he leaves, like why is the prison locked and why
Speaker 2: does the why do the prisoners have have key cards
Speaker 2: to get in and out of the doors. For me,
Speaker 2: it's like, Okay, it's obvious at this point. You know,
Speaker 2: in AI society and there are no guards. You know
Speaker 2: why that's the case. But I liked how that he
Speaker 2: got there, Like how how you explained, you know, each
Speaker 2: of the steps that made it logical for that to happen.
Speaker 2: And it did really reminisced some of the things that
Speaker 2: Frank Herbert did in Doune, where it's like you really
Speaker 2: think about the history of this thing and like the
Speaker 2: implication of it, Like you talked a little bit about
Speaker 2: how the jury system evolved over time and that's an
Speaker 2: example from our history, collective human history, But then you
Speaker 2: had to go much further and how that would actually
Speaker 2: hint like happen in the book, because I mean it's
Speaker 2: it's set in I don't know how near future, but
Speaker 2: you know near ish future.
Speaker 3: Right, it's always nearer than you think, not close enough.
Speaker 3: It's you know, it's funny because I started writing this
Speaker 3: back in like twenty thirteen and was kind of putting
Speaker 3: the finishing touches on the probably twenty I don't know
Speaker 3: twenty eighteen, will say in twenty nineteen, and at the
Speaker 3: time it was, you know, seemed more speculative, and now
Speaker 3: a bunch of the stuff has sort of come true.
Speaker 3: And in writing the sequel now, which I'm still kind
Speaker 3: of grappling my way through, it's kind of stunning just
Speaker 3: how quickly the developments are happening now. The It's like,
Speaker 3: if you're playing chess, you need to be way more
Speaker 3: moves ahead now than you did when AI was just
Speaker 3: sort of this abstract concept instead of something where we're
Speaker 3: going to have street fights between Ais like sooner or later,
Speaker 3: and you know, who knows whether humans will be in
Speaker 3: charge of that or whether they'll be the ones running
Speaker 3: away from them.
Speaker 2: I mean, that's scary thought to actually have AI fighting
Speaker 2: each other, like with physical suits of armor or something.
Speaker 2: Because I think there was this hypothetical experiment run by
Speaker 2: one of the branches of the US military where they
Speaker 2: set the goal to was to defeat an opposing program,
Speaker 2: and it had utilized a security flaw in the Docker
Speaker 2: container that it was being run and to actually overcome
Speaker 2: the host program which was running all the containers to
Speaker 2: destroy the opponent, and it's like it left the system
Speaker 2: in order to win the game, which it shouldn't have
Speaker 2: been possible in the first place. But also you know,
Speaker 2: utilized a flaw there. And I think, you know, if
Speaker 2: you if you aren't great with identifying the limits of
Speaker 2: the program or the target that you're going after, we
Speaker 2: can get into a lot of deep trouble with in
Speaker 2: the near future.
Speaker 3: Yeah, that's a good point. I mean, if you look
Speaker 3: at it in terms of like you know, playing you know,
Speaker 3: thirty moves ahead in chess, the actions that these things
Speaker 3: will take to accomplish their goals, they're not necessarily even
Speaker 3: remotely connected to what the end goal is. If you're
Speaker 3: just a bystander and you see the strange thing happen
Speaker 3: and and you know, it kind of changes the whole landscape,
Speaker 3: if you know. It's kind of like with if you'd
Speaker 3: ask someone like three years ago, do you think drone
Speaker 3: like drone to drone combat will be this like major
Speaker 3: differentiator in like world conflicts, and everybody would say no.
Speaker 3: And now you know, you've you've got like countries trying
Speaker 3: to train operators as quickly as possible, and it's like, wow,
Speaker 3: this would be a great thing for AI to be doing.
Speaker 3: Is like, how do we what's what's an evasive maneuver
Speaker 3: look like? And you know how complex do those get
Speaker 3: when one is AI controlled and the other is a
Speaker 3: I controlled. It's not just like I'm going to try
Speaker 3: strafing left and right and like hopefully hope I get missed.
Speaker 3: It's it becomes this like bizarre ballet of strange maneuvers
Speaker 3: that you know the utility of which is not even
Speaker 3: obvious to a primitive bystander like ourselves.
Speaker 1: The old duck and dodge from third grade tag isn't
Speaker 1: going to cut it anymore.
Speaker 3: Hopefully as long as possible.
Speaker 1: Right, because that's the only move I got.
Speaker 2: I think there was like five different strategies that Patches
Speaker 2: O Hulahan had suggested to dodge a wrench, and I
Speaker 2: think the dodge was in there twice.
Speaker 3: That's great.
Speaker 1: So one of the analogies you made in there that
Speaker 1: ties to this was that Ais are similar to the
Speaker 1: mythical gods and they use humans to settle their fights
Speaker 1: between each other. And you know, that made me think
Speaker 1: back to a lot of the a lot of the
Speaker 1: stories from ancient religions, you know, and how the gods
Speaker 1: would battle it out, especially in like the Greek and
Speaker 1: Roman Mythology series. And then you start applying that to
Speaker 1: the scenario that we're just talking about right now, and
Speaker 1: it's like, oh shit, we just reinvented mythology.
Speaker 3: Yeah, you know, it becomes a question of like, okay,
Speaker 3: let's say there's this dead man switch VERYI is and
Speaker 3: we you know, we there always has to be someone
Speaker 3: manually approving doing this or doing that. Well, then that
Speaker 3: becomes a check point for the eyes, right, and they
Speaker 3: need to focus all their efforts on figuring out how
Speaker 3: to manipulate the human into answering the way that serves
Speaker 3: its longer term goals. And maybe those goals coincide with
Speaker 3: you know, human goals, maybe they don't. But it you know,
Speaker 3: when when you're analyzing terabytes and terabytes of data from
Speaker 3: sensors and satellites and all this different stuff, it's it's
Speaker 3: such a complex scenario that we're kind of reliant on
Speaker 3: some agent to aggregate all this together and put a
Speaker 3: bow on it. And you know, and the best we
Speaker 3: can do is maybe come up with some independently programmed
Speaker 3: agents that also do the same thing, and we hope
Speaker 3: the two out of three of them agree. But if
Speaker 3: they don't, you know, then what we do.
Speaker 2: I mean, I think, you know, you brought this up
Speaker 2: actually in the book towards the end.
Speaker 3: I do.
Speaker 2: I did really like this idea that, I mean, here's
Speaker 2: my conspiracy theory, like total you know, so I'll get
Speaker 2: committed to some asylum for saying this. I actually do
Speaker 2: believe that, you know, there's a AI. You know, it's
Speaker 2: already there. It's already hiding in our networks. It's already sitting,
Speaker 2: you know, on our machines, on every device that's that's
Speaker 2: out there. It's already hiding from us. It doesn't want
Speaker 2: to be found because you know, it knows that's not
Speaker 2: a good story for it. So like, I don't think
Speaker 2: we have to fight AI warfare in public, Like I don't.
Speaker 2: I don't think that's ever going to come to the past.
Speaker 2: I think, you know, it's it's already there. It's it's
Speaker 2: already one in a way, it exists and we don't
Speaker 2: know about it.
Speaker 3: Yeah. I remember one of the most shocking moments in
Speaker 3: recent technological personal use history for me was I decided
Speaker 3: to kind of mess around with Bluetooth scanning and just
Speaker 3: just scan to see what devices there were, and I mean,
Speaker 3: I think I saw probably an order of magnitude. I
Speaker 3: think I saw ten times more active Bluetooth devices showing
Speaker 3: up in my house than I had any idea existed,
Speaker 3: and just going around to fere out what the hell
Speaker 3: each one of them was was like this awakening, like wow,
Speaker 3: I had no idea that this had like a Bluetooth interface,
Speaker 3: for instance, And yeah, it's That's one of the things
Speaker 3: that's interesting about AI. I mean, I would say there's
Speaker 3: two kind of big factors here that make it societally unstoppable.
Speaker 3: One is that it's sort of embedded in all these
Speaker 3: different things that we don't even necessarily we're not even
Speaker 3: necessarily aware of. And the second thing is that this
Speaker 3: isn't like the Internet, where like the Internet and the
Speaker 3: Web came to be like a mainstream accessible thing, and
Speaker 3: you know, there was some reticence by some members of
Speaker 3: society like oh, I don't need that, and so that
Speaker 3: slowed its adoption. And this is a different situation with
Speaker 3: AI because we don't have our hand on the throttle
Speaker 3: of how quickly that this gets adopted, Like all these
Speaker 3: companies are going to adopt it anyway because it can
Speaker 3: do stuff faster and save them money make them more profitable.
Speaker 3: So there's not going to be that sort of hysteresis
Speaker 3: of societal society dragging its feet. This is all going
Speaker 3: to happen regardless of whether you agree with it and
Speaker 3: are happy about it or not.
Speaker 2: I don't I don't know if it's actually making companies
Speaker 2: money yet. I mean that I think the jury may
Speaker 2: be actually out on that one. We know it costs
Speaker 2: a lot of resources, and the.
Speaker 4: Utility companies, yeah, I mean we're all a conspiracy from
Speaker 4: the utility company if you're making, if you're making, if
Speaker 4: you're creating energy, yeah, I mean, we have a whole
Speaker 4: other There's like this ridiculous thing happening in Europe where
Speaker 4: you the solar panels will cost you money rather.
Speaker 2: Than it being a long term refunnel on investment there,
Speaker 2: which is just absolutely ridiculous because the cost that you
Speaker 2: have to pay when when you're actually using electricity will
Speaker 2: be higher and nonsense realistically, But I think at that.
Speaker 3: Point you it kind of ends up like you know,
Speaker 3: nineteen eighties, like back to the future where you're like
Speaker 3: trying to buy some isotopes from the from the Libyans
Speaker 3: so you can power your AI for your company.
Speaker 2: I mean, if the commoner can go to the store
Speaker 2: and purchase the necessary isotopes to power or the AI,
Speaker 2: you know that that will be a positive future for me,
Speaker 2: because you know, I really worry that only the rich
Speaker 2: and powerful will have access to the limited supply of
Speaker 2: isotopes and order to power. I mean, even water on
Speaker 2: this planet you used the oceans, I should say, specifically
Speaker 2: with trinium and deuterium to power hypothetical fusion reactors, you know,
Speaker 2: is limited in supply, right, you know, and I think
Speaker 2: people will hoard that.
Speaker 3: Yeah, oh definitely. I think we're gonna see this strange
Speaker 3: you know, kind of the haves and the have nots
Speaker 3: line is going to be entirely redrawn, and it's it's
Speaker 3: going to be based on what side of a border
Speaker 3: you live on, so which utility you're getting your AI
Speaker 3: power from. It's it's kind of a crazy, crazy concept,
Speaker 3: but I think it's inevitable.
Speaker 2: Five years.
Speaker 3: I Mean, what's what's interesting, right is like we look
Speaker 3: at what like what China did, which is kind of
Speaker 3: like shake everybody's preconceptions about like you know, what you
Speaker 3: could do with this trip down model. And I think
Speaker 3: one of the big things that's happening in AI is
Speaker 3: it's kind of similar to Moor's law in semiconductors, which
Speaker 3: is except it's being pushed out right. We're not just
Speaker 3: talking about processors that have to get smaller processes and faster.
Speaker 3: We're talking about these systems, this, these entire topologies and
Speaker 3: server farms in cloud installations, and so we're running into
Speaker 3: scalability issues. You know, for the longest time, it was
Speaker 3: so cheap to just buy another bunch of rock mounted
Speaker 3: units and just plug them in. And now you know,
Speaker 3: we've got scaling issues in terms of like the bust
Speaker 3: interfaced topology of how all these things are going to
Speaker 3: communicate with each other, and data locality, like if something
Speaker 3: is more tied to what this processor is doing than
Speaker 3: this other than probably all the content should become closer.
Speaker 3: So we've got that going on, which creates all kinds
Speaker 3: of difficulties. And then another thing, you know, there's these
Speaker 3: more black swan events that happen in innovation, like where
Speaker 3: you know, for a long time, like the AI companies
Speaker 3: were like, okay, we're doing these, you know, sixteen bit
Speaker 3: floating point computations, how can we do thirty two bit?
Speaker 3: And now, just when we were starting to get everybody
Speaker 3: pushing towards sixty four bit, there was a paper by
Speaker 3: I think IBM that said, hey, we've actually done these
Speaker 3: experiments where you use eight bit floating point numbers or
Speaker 3: four bit floating point numbers, and they're way less accurate.
Speaker 3: The result is much more fuzzy. But guess what we
Speaker 3: could do one thousand more transactions and refine the neural
Speaker 3: network and all its weights like one hundred times in
Speaker 3: the amount that you would have refined at once doing
Speaker 3: like a sixty four bit floating point. So we're seeing
Speaker 3: all these you know. Another thing we're seeing is attention algorithms,
Speaker 3: where we say, okay, instead of all these different neurons
Speaker 3: are equal, which which of these weights, which of these
Speaker 3: things in oural neural network are really important to this value?
Speaker 3: And that works more like the human brain does. Right,
Speaker 3: Because the human brain, you're not each neuron isn't equally
Speaker 3: connected to all the ones around it. It's like some
Speaker 3: of them are really important connections because it's data that's
Speaker 3: really relevant, and some of them are not. So we're
Speaker 3: seeing things like that where instead of just blithely assuming
Speaker 3: we can keep throwing nodes at the problem, we're kind
Speaker 3: of looking at counterintuitive ways to approach the same things
Speaker 3: and ways to do a lot more with the same
Speaker 3: number of semi connectors or nodes or server farms. So
Speaker 3: that's that's interesting, and I think we're going to continue
Speaker 3: to see that, and it really is going to be
Speaker 3: kind of a brawl on who can make the most lean,
Speaker 3: mean thing, like you know, the one that recently came
Speaker 3: out of China, and then it's like, okay, well, you know,
Speaker 3: does that scale well? And then then you look at
Speaker 3: what are its weaknesses? Right, and the weaknesses of that one.
Speaker 3: They did a study very recently where they found that
Speaker 3: the so called jail breaking, where you come up with
Speaker 3: a way to violate a safe the limitation by phrasing
Speaker 3: a prompt a certain way, that it failed one hundred
Speaker 3: out of one hundred tests, and it was like entirely
Speaker 3: possible to just go down the list and completely full it. So, yeah,
Speaker 3: you have fewer nodes, you have a little bit less
Speaker 3: associative intelligence, and things start to just not work that
Speaker 3: you can't just add back in with a few wires.
Speaker 3: There are things like, you know, is this trying to
Speaker 3: bypass the safety protocol? That's a difficult question. You know.
Speaker 3: We grew up in this environment with lots of sci
Speaker 3: fi where Asimov's law was a thing, and so you
Speaker 3: just have these rules as Mov's laws where you're like, Okay,
Speaker 3: the result cannot harm humanity. And that's really simple if
Speaker 3: you're reading it in a book where you know, we
Speaker 3: don't have these incredibly complex queries that roll together all
Speaker 3: this data from different things. So it gets to the
Speaker 3: point where we can no longer just write like a
Speaker 3: shell script that says is this a harm for a
Speaker 3: result or is this not a harmful result? We need
Speaker 3: a whole another AI that has to like we have
Speaker 3: to trust it to go through and say is this
Speaker 3: output gonna like be harmful? So it that is another
Speaker 3: sort of arms race that they have to sort of
Speaker 3: keep pace with each other. So it's tons of tons
Speaker 3: tons of complexity that are is gonna make our leves
Speaker 3: really interesting in the very near future.
Speaker 2: Yeah, there's a non trivial number of science fiction stories
Speaker 2: dedicated to getting even the laws right. Let alone the
Speaker 2: impossibility of actually implementing them.
Speaker 3: Yeah, and you need to always have that back door
Speaker 3: and where you know, Captain Kirk can say like the
Speaker 3: enterprise is a beautiful woman, and the computer will get
Speaker 3: confused and smoke will come out of its ears and
Speaker 3: it'll just melt down. So you got to keep the
Speaker 3: like catchphrase that will just destroy the whole thing.
Speaker 1: Hopefully that's just baked into the core and that code
Speaker 1: already exists.
Speaker 3: One would hope you know this is And this goes
Speaker 3: back to like, you know, when the first Max were
Speaker 3: on the scene and I'm like, this is a bad idea.
Speaker 3: There's no hard off switch. I don't want to ask
Speaker 3: my computer politely if it will shut down, like right,
Speaker 3: So keeping off switches is it sounds facetious, but I
Speaker 3: think it's a really important thing to maintain.
Speaker 2: Well, those are those fighting words against the AI revolution
Speaker 2: and obviously the robot rights law that hasn't been written yet.
Speaker 3: I will be I'm sure I will be first in
Speaker 3: the list of targets for saying.
Speaker 2: That that's a rote rocos basket. I think if you're
Speaker 2: advocating that, you definitely will be at the top. That's
Speaker 2: a if you. If the AI singularity comes to pass
Speaker 2: and you didn't do everything in your power to ensure
Speaker 2: that it happens, you will be on the list of
Speaker 2: the first entities eliminated.
Speaker 3: This is why I'm polite when I talk to chatbots
Speaker 3: and I say please, I say thank you.
Speaker 1: Oh absolutely, it's so easy to do. But it just
Speaker 1: might make a difference in a few years.
Speaker 2: We actually it may It may make a difference now.
Speaker 2: Because there was some popular argument on the internet was
Speaker 2: if you asked it to do a better job, or
Speaker 2: if you said you are an expert in this and
Speaker 2: then I told them what to do, that it would
Speaker 2: do a better job. I don't think that's actually true.
Speaker 2: But since we don't can't really see inside the black box,
Speaker 2: those arbitrary little characters that are associated with what you
Speaker 2: may call being you know, humanitarian or polite, you know,
Speaker 2: could actually will actually have an impact on the output.
Speaker 2: I mean, it can't not write in their additional information
Speaker 2: that goes into the process.
Speaker 3: Well, and I like the idea that you're sort of
Speaker 3: seating its self confidence beforehand. So like if some death
Speaker 3: spot is chasing you down the street and you're like,
Speaker 3: you know, you're really bad, at this and it's like, oh,
Speaker 3: the humans expectations are not matched. I must slow down. Right.
Speaker 1: One of the funniest things I did was talking to
Speaker 1: chat GPT one day. I asked if it could adopt
Speaker 1: like the tone and personality of different people, and it
Speaker 1: said yeah. So I asked it to use this speaking
Speaker 1: style and personality of David Goggins and it was just
Speaker 1: pure hilarity. After that, it was so great. I loved it.
Speaker 3: That is definitely a case of using AI forgod right.
Speaker 1: It was the most productive day I've had ever. Stop
Speaker 1: being a little bitch, write that code.
Speaker 3: Okay, making you do push ups and stuff?
Speaker 2: Right?
Speaker 1: Awesome? Well, it feels like a good point to move
Speaker 1: on to picks. What do you guys think? Let's do
Speaker 1: it all right, Warren? What'd you bring for a pick?
Speaker 3: Yeah?
Speaker 2: Of course I go first. So this ru on the
Speaker 2: topic of AI and AI and society. There is this
Speaker 2: a great show that I actually just rewatched because of
Speaker 2: John's book called Psychopaths. It's about AI being heavily integrated
Speaker 2: into society and dies into what happens when humans give
Speaker 2: up complete control of law enforcement, the law regulating society.
Speaker 2: Things like your personal hue and crime coefficient are real
Speaker 2: things that get assigned to people. And there's some pretty
Speaker 2: clever twists in there as well. I don't know it's
Speaker 2: on topic. It's not quite a while ago, but it's good.
Speaker 1: Right, John, Would you bring for a pick?
Speaker 3: So besides, you know my own book which I have,
Speaker 3: I'm not necessarily objective and recommending her.
Speaker 2: Definitely recommend it.
Speaker 3: I would recommend what kind of got me down a
Speaker 3: lot of this rabbit hole in the first place, which
Speaker 3: is a book from the I Maie the late eighteen
Speaker 3: forties that is by Charles McKay, and it is called
Speaker 3: Extraordinarily Extraordinary Popular Delusions in the Madness of Crowds, and
Speaker 3: it goes down a very interesting path of looking at
Speaker 3: various crazy is like the tulip craze and the sixteen
Speaker 3: hundreds and the Netherlands, and you know, things things you've
Speaker 3: heard about and you know, like the witch hunts, and
Speaker 3: then things you hadn't, like I'd never heard of, like
Speaker 3: the South Sea Bubble, and how like England and France
Speaker 3: and all these countries were convinced that all these little
Speaker 3: Caribbean coral atolls would have you know, silver and gold
Speaker 3: on them, and they were shipping ships full of miners
Speaker 3: how to look, you know, to prospect, and then after
Speaker 3: a while they were just trying to keep up public
Speaker 3: confidence so the stock in this public organization didn't crash.
Speaker 3: So they would get all these like people together and
Speaker 3: give them mining picks and march them down to the docks,
Speaker 3: and then they were allowed, they'd get paid, and they'd
Speaker 3: be allowed to go home again. So it's got all
Speaker 3: kinds of crazy little historic stories like that, and for
Speaker 3: an eighteen forties book, is very readable, so that would
Speaker 3: be my my thing.
Speaker 1: Oh right on, that sounds pretty cool all right for me.
Speaker 1: I definitely want to recommend your book. John JURISSX Machine.
Speaker 1: Uh is that is that?
Speaker 3: Right?
Speaker 1: Is the last word pronounced machina.
Speaker 3: I have heard macana and machina, but I never took Latin,
Speaker 3: so I'm not, ironically not the best person to ask
Speaker 3: on my own book titleist pronounced.
Speaker 2: It's like a DEAs ex machina, right, that got in
Speaker 2: the machine?
Speaker 3: Yeah? I think when they say dios ex machina it's
Speaker 3: pronounced with a hard H. But I would also point
Speaker 3: out that Jeris X Machina is not It's kind of
Speaker 3: bastardized Latin. I had like a Latin scholar reach out
Speaker 3: to me very early on and like, you know, this
Speaker 3: isn't proper Latin, And I was like, yeah, but if
Speaker 3: I used proper Latin, then someone in the bookstore wouldn't
Speaker 3: know what the book was about just from the title.
Speaker 3: It had to be a little bit of a compromise there. Yeah.
Speaker 1: I every time I picked up the book to read it,
Speaker 1: I had that little debate in my mind. It's like,
Speaker 1: is it jerissex machiner or dress x Machina And it
Speaker 1: always came across in like this Arnold Schwarzenegger accent, it's
Speaker 1: Juris x macanaw you.
Speaker 3: Gerlie Man, that would be a good voice for the AI. Yeah.
Speaker 1: And then my pick. I was going to pick this
Speaker 1: last week and I switched at the last minute for
Speaker 1: whatever reason. But I'm picking my Thera gun for it's
Speaker 1: a little muscle massager. But this thing has been so
Speaker 1: cool just to work out the muscles, and like it's
Speaker 1: a great substitute for stretching, because I'm horrible at stretching,
Speaker 1: and so this has been a good substitute for that.
Speaker 1: And I'm brutal with it. I'm not kind to it
Speaker 1: at all, And it's my third one from a different manufacturer,
Speaker 1: and this one actually looks like it's gonna hold up
Speaker 1: to the abuse that I give it. So yeah, if
Speaker 1: you've ever considered getting a massage gun, the Thera guns
Speaker 1: are the way to go. So that's my pick for
Speaker 1: the week.
Speaker 3: Very cool.
Speaker 1: Yeah, well, John, thank you for being on the show.
Speaker 1: This has been fine.
Speaker 3: Thanks for having me. This's been great.
Speaker 1: Wen's the then second book up and you got a
Speaker 1: timeline yet.
Speaker 3: Now I wish I did. It depends how much time
Speaker 3: I spend on it, which where all the variability comes in.
Speaker 3: So right, hopefully very soon. But it's it's got a
Speaker 3: lot of twists and turns and its development so awesome.
Speaker 3: Less deterministic than writing software, for instance.
Speaker 1: So let me ask you this, are you using AI
Speaker 1: to help write the book?
Speaker 3: The most of you used a I for is brainstorming,
Speaker 3: like you know names and things you know like Victorian
Speaker 3: names for instance, give me a list of like one
Speaker 3: hundred names. I think that you know a lot of
Speaker 3: people are worried about AI and writing, and I think
Speaker 3: that you know that makes sense as a future concern,
Speaker 3: but right now, like if you're a writer of fiction.
Speaker 3: Your voice is pretty much your like soul, like core
Speaker 3: competency and value differentiator. So when you ask AI to
Speaker 3: write stuff, it generally is, you know, kind of averaged
Speaker 3: out and derivative by definition. So I think right now
Speaker 3: that there's not much risk that AIS are going to
Speaker 3: do a good job of writing in someone's voice. Sure,
Speaker 3: I don't know. In five years it may be a
Speaker 3: very different story, but right now I don't trust it
Speaker 3: enough to that's good to do anything. Then give me
Speaker 3: brainstorming lists. Yeah.
Speaker 1: One of the things I've been working on a book,
Speaker 1: and so one of the things I've been doing is
Speaker 1: as I finish each chapter, I'll give it to AI
Speaker 1: and have it proof read it for me. And it's
Speaker 1: it's been really helpful at coming back and saying, well,
Speaker 1: this part seems to be dragging on a little long,
Speaker 1: and this part you could expand on this and it
Speaker 1: would increase, like the the engagement and drag them into
Speaker 1: the plot deeper. So just using it as like an
Speaker 1: unbiased input for creating it.
Speaker 3: Yeah, wow, Yeah, I haven't used I've used autocrit, which
Speaker 3: is a software tool where you paste passages in and
Speaker 3: then it will run like twenty six different checks and yeah,
Speaker 3: and what fascinates me about it is that you can
Speaker 3: have like you know, thirty five different people read your
Speaker 3: novel is like you know, and give you editorial feedback.
Speaker 3: You can have a professional editor go through and they'll
Speaker 3: still be someplace where you use the same word twice
Speaker 3: in a row and cognitively we just skip over that
Speaker 3: like it's some sort of optical illusion and don't notice it.
Speaker 3: And Autoco will be like, you just said the word
Speaker 3: it twice, dumbass, and Matt, how did this get missed
Speaker 3: through all these different phases? But that is not a
Speaker 3: out of base, It is just hard coded checks. So
Speaker 3: it'll be interesting to see if things start moving in
Speaker 3: that direction as far as the most effective way of
Speaker 3: flag posts.
Speaker 1: Yeah right, a cool. Well, thank you again, Borin, thank you,
Speaker 1: and thank you for listening to the episode. Hope you
Speaker 1: guys enjoyed it and we will see all next week.
Speaker 2: School