S1: Unsupervised Learning is a podcast about trends and ideas in cybersecurity,
S1: national security, AI, technology and society, and how best to
S1: upgrade ourselves to be ready for what's coming. All right,
S1: welcome to unsupervised learning. This is Daniel Miessler. All right.
S1: So absolutely must see conversation. I'm going to open this
S1: up real quick.
S2: One of the top.
S1: Yeah. This these guys here. Unbelievable conversation about AI specifically.
S1: It is extraordinarily good on the aspect of what just
S1: happened with deep seek and actually competition around chips and
S1: AI and Nvidia and all that stuff. And basically how
S1: competition is happening between all the pinnacle makers, open versus
S1: closed source, basically everything. They're just doing so well in
S1: that conversation. So highly, highly recommend that we made the
S1: list of san's top security newsletters and podcasts. That was cool.
S1: Got a quick thing on how to use O1 and
S1: O3 on fabric? It's just a PP is basically the
S1: flag you want to use. You can't use s um,
S1: you have to use R, which is raw, because you
S1: cannot send a temperature to O1 or O3 where you
S1: can with most of the other models. And uh, after
S1: listening to that conversation I just told you about, I
S1: ended up tightening up my definition of AGI, which we're
S1: going to talk about in a second. But it's essentially, uh,
S1: we're going to go into more depth here in a second,
S1: but it's the ability for an AI, and that could
S1: be a model, a product or a system to perform
S1: the work of an average US based knowledge worker from 2022. And, uh, yeah,
S1: I talk about how this is the most debated term,
S1: and I want to spend a lot of time on it.
S1: This is in, um, raid, uh, real world AI definitions.
S1: And I've got two criteria for whether or not something
S1: is a good definition of AGI. Regular people should understand
S1: if we reached it or not. And attaining it should
S1: be significant to society. So who cares if nobody cares
S1: about it? And also, who cares if you can't figure
S1: out like what somebody is talking about? You know, they
S1: start using big words or something. I feel like that's
S1: not going to stick. It's not a useful definition. All right. Security,
S1: deep sky exposed customer data and unprotected database. So basically
S1: as soon as they launched and all this press starts happening,
S1: Nvidia's stock starts falling. Some people a lot of people
S1: and definitely some people, researchers at Wiz found an open
S1: database full of nasty stuff chat logs, API keys. It's
S1: in Chinese, so I'm not sure if there's any. This
S1: is kind of what I was wondering. Does it reveal
S1: any evidence that they used OpenAI for training? because that
S1: has been one of the things that people have been alleging,
S1: that the only reason they got so good is because
S1: they were using OpenAI to actually train on. And I'm
S1: curious if that's talked about in the logs or, um, or,
S1: you know, if there's any evidence of that happening inside
S1: of the thing inside of the leak. Cisa found that
S1: contact patient monitors have been secretly been sending patient data
S1: to China, and you can download and execute files remotely. Evidently,
S1: there's some sort of, uh, yeah, RCI type deal there. Yeah. Evidently,
S1: when you actually fixed it, it still didn't fix it.
S1: It just disabled the network interface, which the backdoor re-enables.
S1: So that was kind of a mess. Sonicwall says there's
S1: a massive exploit campaign against authentication bypass. And, uh, got
S1: some details here. It's 84. 43 is the port about
S1: 2000 of these things vulnerable on showdown right now. Major
S1: hacking forums seized in international operations. Law enforcement took down
S1: some of the biggest hacking forums included Cracked and Nulled,
S1: which had over 10 million users combined. A new startup
S1: called backline raised $9 million to use AI agents that
S1: automatically fix vulnerabilities. This this is interesting, right? Everyone's talking
S1: about using AI to find vulnerabilities. What about fixing the vulnerabilities?
S1: So I'm really looking forward to vulnerability management remediation services
S1: that use AI. I personally think that this is going
S1: to require a whole lot of understanding of the company.
S1: It's going to require a lot of asset management. You're
S1: going to need to know people. You're going to need
S1: to know organizations. You're going to need to know how
S1: different engineering groups actually push code. So there's a lot
S1: of context you're going to have to get from something
S1: like an asset management system to be able to do this.
S1: Tulsi Gabbard is facing a lot of pushback back in
S1: her thing. I'm not sure if she's already been passed through. Uh,
S1: I'm trying not to pay attention to the news right now.
S1: It's a bit depressing, honestly. But, um. Yeah. Want to
S1: make a little bit of a statement here? I think
S1: if you dump top secret documents to the internet, this
S1: is one of the things she was being asked about
S1: was Snowden. Or if you break into the Capitol building
S1: on verification day, on results verification day, because you want
S1: to hopefully change the results, you are an actual criminal.
S1: That's that's what I think. At one point I saw
S1: Snowden as a whistleblower, too. I think he actually technically
S1: is one, and I think he might have been trying
S1: to do some good at some point, but I think
S1: it kind of got all messed up. And I haven't
S1: really been on that boat for many years now. Bedfordshire
S1: Police just became the first UK force to deploy Palantir's
S1: AI system, and they got some good results. 123 at
S1: risk kids were found in just one day argument that
S1: we're getting passkeys all wrong, and that they should be
S1: used alongside magic links, not as a complete replacement for
S1: other auth methods. I've been thinking about this a lot.
S1: I really do love Passkeys, but I do worry that
S1: it's I don't know, it feels like we might be
S1: being a bit sloppy with them. All right. This is
S1: why I think you should care about us reaching AGI.
S1: Wanted to say more about the AGI thing. I think
S1: it's the most important topic in AI. Actually, tons of
S1: very smart people don't know why they should care about it. Like,
S1: who cares if AGI hits this or that benchmark. And
S1: I think there's only one good reason why you should care.
S1: And here's how I talk through that. So think I
S1: coworkers right. Like actual coworkers that you have in person.
S1: But imagine your team at work. You've got like five
S1: coworkers or 20 or 35 whatever, however big your team is.
S1: Now imagine it's like 10,000 co-workers instead. Like overnight. So
S1: it was seven people. You show up and now it's
S1: a zoom call with 150 people or 10,000 people instead
S1: of seven. And you're watching them work. You're seeing them work.
S1: A couple of them, you see physically work you see
S1: them the results of their code, you see it come in.
S1: You get other signals telling you how good of a
S1: job they're doing or whatever, but you find out they're
S1: not perfect. They make mistakes just like anyone else. Someone
S1: still has to review their work. They still get lost.
S1: Sometimes they're like, what are we doing? Like, I'm not sure.
S1: You know, I'm not sure if I should be working
S1: on this project. Does this look correct? Or they mess
S1: up the code base or whatever. In some ways, they're way,
S1: way better than your human counterparts, and in some ways
S1: they're just way more stupid, right? But what they do
S1: is they make steady progress. They show up for video calls.
S1: They can read docs. They can read code. They're talking
S1: to you in slack. They're asking questions. When you ask
S1: them a question, they give you a response about the
S1: work that they're doing, or they help you find a document, whatever.
S1: And they can also readjust based on a coworker or
S1: their manager telling them, hey, that's not the way you
S1: should be doing this, or we got a new directive
S1: from above. You know, the goal is slightly shifted or whatever.
S1: They can change what they're doing. But the key here
S1: is there's actually 10,000 of these people, right? There's 10,000
S1: of these agents instead of ten, or there's 100,000 of
S1: these instead of your team of 100, and they work
S1: 24 over seven. They put in the work, they put
S1: in the effort, and they do not get tired. And
S1: they also constantly improve. So if they start off with
S1: like 105 IQ, which is, you know, decently smart or
S1: two years of knowledge in security or programming or intern
S1: level or whatever. After a year or two, they've had
S1: like 20 updates, which were silent updates or whatever. And
S1: assuming none of those updates went horribly wrong, uh, now
S1: they got 110 IQ. Now they got 120 IQ. Right.
S1: Or whatever. The AGI alternative to uh, or analog to
S1: IQ is. It's not quite IQ, but call it raw
S1: fluid intelligence. Right. So they're getting those constant updates, but
S1: they're also learning to be better coders, not just by
S1: the work that they're doing, but actually by just better
S1: and better models. So now instead of 105 IQ with
S1: two years work experience, now it's 120 IQ with 20
S1: years of experience. So it's like a staff engineer, but
S1: you still have 100,000 of them, right? This is why,
S1: in my opinion, AGI is a big deal. And I
S1: think we're getting really close to this. I've got another
S1: thing to read about. This is what I just posted
S1: earlier on one of the Socials, but I think we're
S1: getting close to this. It's not one component, right? This
S1: is not like one model that's going to release and
S1: do this. It will be a system. It will behave
S1: like one person or one thing or whatever you want
S1: to call it, but it'll be this composite that lets
S1: it behave in a cohesive way, which is actually the
S1: way our brains work as well. My guess was in
S1: 2023 that it was going to be 25 to 28.
S1: That was my thing. And I put that in like
S1: two years ago. Okay. Like March of 23, I think
S1: I made this prediction. 25 to 28 is when we're
S1: going to have this. And my definition for this is
S1: basically somebody who can replace a human knowledge worker, a
S1: decent human knowledge worker working at some remote job. Right.
S1: So I don't think they're going to be able to like,
S1: bring you a coffee because that's robotics. But working a
S1: remote job, pushing code, doing organization, project management. Um, editing documents,
S1: that kind of stuff. 25 to 28. I think that's
S1: what is when we're going to actually hit this. So
S1: my current estimate for this is late 25 or maybe
S1: sometime in 26, and it could actually go into 27.
S1: Because this type of progress that we're seeing in AI,
S1: it's big jumps. But then it kind of plateaus for
S1: weeks or months. But that plateau could actually go for
S1: a year. I don't think it will. I think it's
S1: going to continue to jump kind of violently, but it
S1: could plateau out and then maybe it turns into more 2728.
S1: I think the chances that it happens after 29 are
S1: really low, maybe 5%, which is a lot. It's a lot, right?
S1: So my guarantee for this right now, my high, it's like, uh,
S1: what's the CIA, CIA range? I think the CIA range is, Um,
S1: almost guaranteed or something like that. It's, um, extremely probable.
S1: It's one of those. But it's only like 95%. And
S1: that's where I'm at because I'm not really 100% on anything.
S1: And when this does happen, I think it'll be the
S1: single biggest impact on humanity from tech, by far bigger
S1: than the internet and both in the negative and positive directions.
S1: So that's why I think you should care about AGI.
S1: And in perfect timing, MuleSoft reports that almost all IT
S1: leaders are planning to use autonomous AI agents within two years.
S1: That's 93% and about half are already doing so. Sam
S1: Altman basically says he feels like he was on the
S1: wrong side of history with open source. He didn't say
S1: he's going to do anything about it or what he's
S1: going to do about it. It seems like kind of
S1: obvious that he will do something. My guess is they
S1: will release some nerfed versions of some of their models
S1: just to be like, look, we are playing nice with
S1: the open source model, but I don't think they're going
S1: to completely pivot to like what meta is doing, for example.
S1: And like I said, yeah. OpenAI claims Chinese rival deep
S1: tech stole training data. Yeah, I don't actually think it's
S1: kind of funny to be like, oh, OpenAI is worried
S1: about their stuff being stolen when, you know, they stole
S1: the whole internet to train on. I think. I mean,
S1: anything you put on the internet is just going to
S1: be on the internet, and that's free game. I think,
S1: I think unless you have specific like licenses or something,
S1: and obviously I don't want people to steal like paid
S1: content that I'm producing. Right. But but I don't release
S1: paid content live to the public, on social media or
S1: on a blog post, and then get mad when someone
S1: scrapes it, right? I consider all the stuff that I
S1: put out since like 1999 or whatever, how long I've
S1: been on the internet. You gave it to the world
S1: when you posted it online. That's the way I see it.
S1: I always have R0 or R1 zero Shows AI reasoning
S1: without human training. This is really cool because like I
S1: talk about here, it's like what happened in chess. So
S1: at first they used they built a chess computer. Um,
S1: I think this was Google that was doing this, and
S1: it just got really good at learning the rules, and
S1: it watched human players and it improved on that. And
S1: of course, it could beat previous chess computers like Deep
S1: Blue is nothing compared to these computers. They just crush it.
S1: What the big breakthrough was with AlphaZero and also with
S1: R1 zero is it's learning more actually, or at least
S1: significantly more. It's learning significantly more from the reinforcement learning
S1: from the world. It's learning from the world as opposed
S1: to from the rules or watching other people interact with
S1: the world. So it's just an accelerator because it could
S1: do that by itself, right? Without a lot of effort
S1: from other people and that's really, really powerful. A new
S1: study shows deepfakes AI model refuses to answer the vast
S1: majority of sensitive questions about China. Shocking, shocking. This is
S1: why it matters where you get your models right. Because
S1: according to them, Tiananmen Square didn't happen. It's all a
S1: breakdown of how to properly evaluate ragtag systems and llms
S1: in Practice by Salman Khan and Andrej Karpathy talks about
S1: vibe coding, where you basically get in a flow state
S1: and code like you're playing an instrument. It says the
S1: key is to stop overthinking and just think and respond
S1: and let the AI do most of the work. I
S1: think this is spot on and a lot of people
S1: are really mad. They're like, oh, I can't believe you know,
S1: you're a hard core coder and you're just going to
S1: let AI code for you. And again, he doesn't care.
S1: I don't care. People like us, we don't care because
S1: we could see what's actually coming, which is pretty soon.
S1: You don't fire up an IDE at all. What you
S1: do is you fire up a voice conversation and the
S1: thing starts showing you what the product looks like on
S1: one side, and it's showing you roughly like the code
S1: or the diagram or whatever it is on the left,
S1: and you're having a verbal back and forth like that
S1: is coding within, you know, 1 to 3 years. So
S1: this whole thing of like, oh, you're not doing the
S1: real thing unless you're doing the real coding. Once again,
S1: you have to take that all the way back. Um,
S1: are you writing assembler by hand? No you're not. Okay, well,
S1: you're cheating by using Python or whatever, right? So it
S1: just keeps going farther and farther away, and eventually it's
S1: going to be like, I wish I had a product
S1: that looked like this, blah, blah, blah, and boom, it
S1: just goes into the world. And what did you really contribute?
S1: You contributed the raw base idea. But at some point,
S1: the A's are good enough to contribute that as well.
S1: And now you're competing with them on that. And that's
S1: when it turns a little bit sad. Apple quietly added
S1: Starlink satellite support to iPhones through a software update. And
S1: this is a partnership with SpaceX and T-Mobile. So yeah,
S1: SpaceX is how your iPhone is able to talk to
S1: satellites when you have no signal. Humans massive new survey
S1: revealed that 87% of astrobiologists think that extraterrestrial life exists
S1: somewhere in the universe. 87%. I am one of those people,
S1: not one of the astrobiologists, but one of the people
S1: who agrees with that study. A Montana shows that drones
S1: are way better at keeping grizzlies away from humans, so
S1: they basically fly up to them and chase them and
S1: scare them. Could it be cool to take drones with
S1: you when you go into the forest? So you're worried
S1: about bears? You release like three drones or whatever. That's
S1: part of your panic response. You hit a button. It
S1: makes a really loud sound, like some sort of horn
S1: or something, and you could point the horn at the thing,
S1: but it also launches the drones and the drones go
S1: over and maybe they make noises. Maybe they have sounds, whatever,
S1: but they just start buzzing around the the bear. And maybe, like,
S1: you could sell that as a package is like bear defense.
S1: Maybe they shoot, uh, bear spray as well. That would
S1: be cool. All right. Swerving Broncos I was thinking about well,
S1: I was basically driving on the 101 at like 11:00
S1: at night or something just a couple days ago. And
S1: I am watching the cars on the, on the road
S1: in front of me. And they're just like, drifting, like
S1: right over the line. The second wheel goes over the line.
S1: They're just like drifting. Obvious that they're on their phones
S1: or drunk or high. And then like, I get away
S1: from that thing or it turns off the highway or whatever.
S1: And like, here comes another giant truck, a giant like
S1: Cadillac or a Bronco or something like that comes next
S1: to me, and it just starts drifting into my lane.
S1: And I'm like, what are you doing? And I And
S1: I look at them, they're like looking down. They're not
S1: even paying attention. And I see a third and this
S1: is within like 30 miles or something. I see a
S1: third one. They're just like drifting over. There's no cops around.
S1: I hardly ever see cops anywhere. Like on the on
S1: the highways anymore. There's no cops. There's no one stopping them.
S1: They're just kind of like driving like crazy people. And
S1: I'm thinking, you know what won't do? This is Waymo.
S1: Waymo won't do this either. Will autonomous Tesla or an
S1: autonomous like BYD device from China or whatever. Say what
S1: you want about AI driving. They bicyclists love them because
S1: they never crash into them. I mean, I think we
S1: just have to realize how bad human drivers are before
S1: we start criticizing how bad I drivers are. AI's big jump.
S1: This one is very simple. I think AI from Apple
S1: is like it's been the worst for a while and
S1: it's about to be the best. That's the bottom line.
S1: They are putting out stuff in 18.4 that basically finally
S1: connects Siri to the context inside the phone. So your phone,
S1: you have your exercise, you have your heartbeat, you have
S1: your appointments and your calendar and your email. And it
S1: could see all the different stuff. It could see your life. Right.
S1: But it's never unified that stuff. Well, Apple just got really,
S1: really serious about Siri or they're in the process of
S1: doing that. They got a new leader over it. Like
S1: it's just become super high priority. They're doing that at
S1: the same time that they're doing the cloud secure enclave.
S1: At the same time, they're doing the partnership with ChatGPT
S1: at the same time that they're now giving Siri access
S1: to all that context that they have, and access to
S1: call apps and pass data between the different apps. So
S1: they've been setting up this infrastructure for AI for the
S1: last couple of years. They've built all this stuff. They've
S1: spent all this R&D. And again, I used to work there.
S1: This has nothing to do with anything. I've been gone
S1: for a while. And also, I wouldn't be disclosing anything
S1: if I actually worked on a project like that. But
S1: I've been watching them since, whatever, 2007. And I'm telling
S1: you that they are about to switch this thing on.
S1: And I really do think that they're about to have
S1: the best personal ecosystem integration AI type play. And it's
S1: going to go from being like a are they even
S1: doing anything to Holy crap, this is what they've been
S1: working on. That is my prediction around that. And I
S1: novels are coming. I actually have a YouTube video about this.
S1: But yeah, I'll wait till that comes out. It might
S1: actually be out soon, but yeah. Anyway, I'm going to
S1: wait to talk about this one. Basically, it's going to
S1: be possible to make a complete decent novel using AI.
S1: I think within months or years. I've already got a
S1: got a couple of emails actually, where they're like, what
S1: are you talking about? I've been doing that for a
S1: year and a half. So I think it was possible
S1: to do a year and a half ago, and I've
S1: messed around with some, uh, longer, uh, like, fiction stories,
S1: and it was halfway decent. And that was like a year,
S1: year and a half ago. But it was difficult. What
S1: I'm trying to say is it's going to get easy
S1: and lots of people are going to do it. Um,
S1: ChatGPT new tasks feature. A lot of people are talking
S1: about this. It's basically scheduled and automated, um, agents to
S1: go and do things for you. I have not messed
S1: with this one, but it looks really, really exciting. Really
S1: clever way to use UV which is a fast Python
S1: package manager. Look at this. You put this at the
S1: top of oh man, I haven't messed with this yet.
S1: I have to write this down. I ran it once,
S1: but I haven't put it in like all my main scripts.
S1: This allows you to run anything that has multiple dependencies
S1: and normally breaks, and it will automatically instantiate you a
S1: working environment to make your script run. Isn't that just beautiful?
S1: It's absolutely beautiful. I mean, this is so much better
S1: than regular Pip, because if you have multiple environments like
S1: that's my problem. My regular Python has like 3 or
S1: 4 different environments, different virtual environments, like nested spread all
S1: over the file system. It's like a nightmare. Um, so
S1: that's why I use UV now. And this is just. Yeah,
S1: this is really great. I haven't seen a downside yet,
S1: but I haven't messed with it that much. Uh, cool.
S1: Developer created Napthine's malicious software that traps aggressive AI web
S1: crawlers in infinite loops and feeds them garbage data to
S1: poison their models. Smiling, not smiling OpenAI dropped new ChatGPT
S1: capability called deep Research. Supposedly you only get like 100
S1: a month because they're really expensive and it takes like
S1: takes like ten minutes to finish, but I've heard it's
S1: created some serious, serious research. Really good papers. Haven't checked
S1: if I have access to that API yet. All right.
S1: Justin McGurk explores how William Gibson novels perfectly capture our
S1: obsession with commodifying everything unique and authentic until it loses
S1: all meaning. This is why I'm doing human 3.0. I
S1: do not want to be replaced by AI and technology.
S1: No thank you. I want it to enhance humanity. Found
S1: a really clean python script that lets you download YouTube videos.
S1: This thing works really well, I just worried. I'm just
S1: worried it's going to get blocked soon. Recommendation of the week.
S1: If you ever get overwhelmed by what all this AI
S1: stuff even means, or you want to explain to someone else,
S1: I got a little quote here. So within the next
S1: few years we might have something called AGI where I
S1: can work as a full knowledge worker, like joining the
S1: onboarding cohorts, reading documentation, participating on slack, submitting code, adjusting
S1: their work based on the work of others. But instead
S1: of 2 or 5 of these, imagine hundreds of them
S1: for the cost of one human employee. I think that's
S1: pretty cool way to kind of explain the possible impact
S1: in a way that, like regular people who don't pay
S1: attention to this all the time could understand. And the
S1: aphorism of the week, some books leave us free and
S1: some books make us free, some books leave us free,
S1: and some books make us free. Ralph Waldo Emerson. Unsupervised
S1: learning is produced on Hindenburg Pro using an SM seven
S1: B microphone. A video version of the podcast is available
S1: on the Unsupervised Learning YouTube channel, and the text version
S1: with full links and notes is available at Daniel Miessler newsletter.
S1: We'll see you next time.