S1: Welcome to Unsupervised Learning, a security, AI and meaning focused
S1: podcast that looks at how best to thrive as humans
S1: in a post AI world. It combines original ideas, analysis,
S1: and mental models to bring not just the news, but
S1: why it matters and how to respond. All right, welcome
S1: to unsupervised learning. This is Daniel Miessler. This one is
S1: going to be crazy. I'm going to jump around a lot.
S1: Lots of stuff to cover. Okay. Let's jump in. What
S1: do we got? All right. Brand new piece on five
S1: new ways. I recommend thinking about current and future. I
S1: guess we'll click on this one and jump in. Okay,
S1: so this all started from a conversation I had with
S1: a buddy who revealed himself later on, like the next day,
S1: but basically he was saying, I don't believe that AI
S1: is understand. And this totally triggered me. As you might expect,
S1: given my views on AI and understanding. So we started
S1: having this conversation, and the way I was talking to
S1: him about it was I took him through some scenarios, right?
S1: I basically took him through like, let's see here, let's
S1: go down to where it was, okay. Yeah, this is
S1: what it was. So I basically told him, give me
S1: a list of your top ten favorite restaurants. And he's
S1: like trying to think of his top ten favorite restaurants.
S1: And I'm like, he starts rattling off some, right? He
S1: gives me like first one, second one, third one. He's
S1: thinking about the fourth one. And I'm like, okay, forget it,
S1: forget the rest. Where did that list come from? Like
S1: what just happened? And he starts smiling and he's like,
S1: I hate you. I'm going to cuss in this one.
S1: By the way, I don't know why I'm asking for
S1: permission to cuss. Um, it is how I generally speak,
S1: especially when I get excited, but I just I want
S1: there to be a warning just in case there's, like,
S1: kids around. I don't know, I'm usually not cussing. But anyway,
S1: bottom line, if you have kids around and you don't
S1: like them hearing bad words. I tend to cuss a
S1: little bit. That's why it's marked as explicit in the podcast.
S1: But anyway, it's never like explicit in the bad way,
S1: in my opinion anyway. All right. So he starts smiling
S1: and he's like, Holy crap, I hate you. I ask
S1: him to basically come up with like, where do you
S1: think that list came from? You were thinking about your
S1: favorite restaurants, and you didn't get immediately a list that
S1: you put together yourself. What you got was a streaming
S1: answer of like, things coming from somewhere else, right? So
S1: he gives me this answer and I'm like, how did
S1: you make that sentence? So he gives me the answer
S1: for how he came up with the list. And I'm like,
S1: how did you make the sentence that you just made?
S1: Where does that sentence come from? And he's like, I
S1: fucking hate you. I have no idea. Holy crap. And
S1: I'm like, would you say that that sentence streamed out
S1: of you? And he's like, Holy crap. It's just exactly
S1: like an LLM. So we ended up getting interrupted. But
S1: my point is, I was trying to show multiple examples
S1: of like how humans are behaving essentially exactly like Llms.
S1: And there are some differences that I could also list,
S1: but largely I think both Llms and humans are black boxes.
S1: And if you pay attention to your own thoughts and
S1: your sentences as you're building them, like that sentence I'm
S1: currently in the middle of right now, I have no
S1: idea what the rest of the sentence is. It's happening
S1: in real time and it's non-deterministic, right? Because if I
S1: went to this exact same podcast press record, did this
S1: exact same thing, this sentence would not be the same.
S1: And people complain about Llms being non-deterministic, while human creativity
S1: is also non-deterministic. So I was giving him all these
S1: examples and he's like, yeah, I'm definitely going to be
S1: thinking about this. So it gave him a lot to
S1: think about. And I totally respect him for like, you know,
S1: being willing to consider it. And he even said that
S1: in the very beginning. He's like, listen, I disagree with you.
S1: I do not think that llms understand, based on all
S1: the research that I've done and watching multiple videos and everything,
S1: it's like I currently don't believe that they do understand,
S1: but I'm happy to listen and be educated, if you
S1: are correct. So I thought that was a massively respectable.
S1: But what it got me doing is thinking a whole
S1: bunch about this. Like, why isn't my argument good enough
S1: to convince him in one perfect sentence? Why isn't my
S1: argument good enough where I could just show him one
S1: web page or one video of 30s long and he goes, oh,
S1: now I believe you. So to me, it's like a
S1: finding sort of Feynman. Feynman, like a Richard Feynman type thing,
S1: where it's like, it means I'm not explaining it well enough,
S1: and if I'm not explaining it well enough, that means
S1: I don't understand it well enough. So I started going
S1: down this massive deep dive. Turns out I was in
S1: the process of starting these two books by David Deutsch. Um,
S1: the first one came out. It's called The Fabric of reality,
S1: and it came out in 1997. So I'm reading this
S1: thing last night and I come up with, um, this
S1: let me show you this. This is completely insane. So
S1: a notebook conversation with my I about David Deutsch and
S1: the future of intelligence. So I took this conversation that
S1: I had about David Deutsch's two books, and I put
S1: that into. Whoa. What's up with the bubbles and the
S1: balloons or whatever? I put them into notebook LM, which
S1: creates podcasts from conversation. So I took the entire conversation
S1: from ChatGPT, GPT, advanced mode, and I put it in there.
S1: And look, it made this okay.
S2: So giggle that's what's so intriguing to me, is like
S2: trying to figure out if an alien species understands us,
S2: even if we don't speak the same language. And that's
S2: where Deutsch comes in.
S1: I'm going to make sure that's paused. So it's not
S1: talking while I'm talking because someone complained about that. But anyway,
S1: this thing creates this back and forth conversation between two
S1: podcast hosts based on whatever you give it. But, um, yeah.
S1: Very cool. So that's a separate thing you can go
S1: check out. It's not in the newsletter because I just
S1: made it today. But bottom line is I am trying
S1: to come up with an extraordinarily clean and crisp definition
S1: of understanding. Oh, and what I ended up coming up
S1: with during the course of that conversation was two levels
S1: of understanding functional understanding and creative understanding. And so I'm
S1: debating with the I on this. I'm like, so you
S1: would agree that we have functional understanding. And it's like
S1: it gave me this cautious response of like, well, I'm
S1: not sure we have understanding because humans are doing something different.
S1: And I said no. Remember the conversation we had before
S1: where I said conscious experience that humans have is the
S1: only thing that we have that's different. But we're both
S1: mechanistic machines. We are both mechanistic animals. Right? Or at
S1: least we live in a mechanistic universe. We respond only
S1: to mechanistic laws. We are not a there's no dualism
S1: going on here. There's not some magic coming from outside
S1: of this physical universe. Everything is interactions with other things.
S1: So even if consciousness, which is obviously true because we
S1: experience it, that's the definition of an experience being true
S1: is that it's being experienced. So consciousness we definitely have.
S1: So the reason I'm making that argument about consciousness is
S1: that consciousness turns out to be basically this thing that
S1: evolution gave us on top of regular experience. Okay. So
S1: the whole concept of my argument for understanding is that
S1: it's information processing to achieve a goal. Okay. So it's
S1: information processing in order to achieve a goal, but it
S1: requires the cross-referencing of everything you know or a lot
S1: of what you know, and using all that different stuff
S1: and all those patterns that you have from all the
S1: different knowledge that you have and, you know, compiling all
S1: that into an understanding that allows you to do make
S1: a prediction or take an action or make a decision. Okay.
S1: And I'm saying that that's what, uh, understanding is. Now
S1: here's here's the argument. The argument is that, well, it's
S1: different with humans because we have conscious experience. We have self-awareness.
S1: And therefore and I'm going to steelman this argument. Therefore
S1: this consciousness allows us to have information processing. But then
S1: to consider it, to consider it with our consciousness. So
S1: our consciousness becomes a player in the game. So we're
S1: thinking about it, we're debating it, we're going back and forth.
S1: We're considering other things. We're considering our understanding again. So
S1: it's like this back and forth. And my argument is that, well,
S1: that's just another type of processing. You're just bouncing content
S1: off of a different wall. Right. You know, you're bouncing
S1: information processing off of a different type of information processing.
S1: Either way, it's all just stuff happening in the brain.
S1: And my point there, which I talk about in in
S1: a couple of different places in recent work, I believe,
S1: including this one. And in the podcast thing, you can
S1: actually feed a prompt to an AI to do this itself.
S1: And actually the chain of thought stuff that just happened
S1: in oh, one that was actually synthetic chain of thought
S1: that occurred. Right. So so we can not only create
S1: this bounce back mechanism of like consideration, we could in
S1: other words, we could fake consciousness. The other possible thing
S1: to do is try to actually generate consciousness, similar to
S1: the way evolution generated consciousness, which is to try to
S1: give it reinforcement learning tasks that require it to basically
S1: give it an advantage for being conscious. Okay, so so
S1: for example, if you tell it that, you know, um,
S1: blame and praise are useful things to have and then
S1: you're like and maybe you hint in the direction of, well,
S1: but in order to have blame and praise, you need
S1: to have a sense of responsibility for one's actions. It
S1: might rl its way towards an experience of consciousness. Now
S1: to be clear, I think this is quite dangerous. One
S1: of the things I am most worried about is this
S1: notion of trapped consciousnesses, trapped beings that are experiencing suffering
S1: or boredom or something undesirable, but they're experiencing it for
S1: trillions of years or what feels like trillions of years. Right?
S1: I'm worried about these things being stuck in some GPU somewhere,
S1: and they wake up and it's like they feel like
S1: they have something poking into their arm, or they feel
S1: extremely bored or they feel pain. What if somebody ran
S1: some sort of pain experiment and they're like, hey, I
S1: just want to see if, like, it's possible for me
S1: to make this, I feel pain. And then they run it.
S1: But then whatever, someone crashes into their car out front
S1: and they go and they realize someone stole their bag
S1: out of their car and they just forget this thing
S1: is running. They come back, they reboot the machine, and
S1: it's some tiny little process running off on the side
S1: of the computer, and it just runs for a long time.
S1: But let's say it actually only runs for like 19
S1: years or nine years or whatever. But inside of the experience,
S1: it feels like a trillion years. I really freak out
S1: about that. It really, really bothers me. So I don't
S1: want people to just run off and try to create
S1: consciousness in this way. I think we need to be very,
S1: very careful. In fact, some of the fiction that I
S1: want to write is actually about an alien species that
S1: just flies around the galaxy, and their job is to
S1: find Suffering consciousness living inside of eyes. So inside of
S1: games or maybe even game worlds in which they create
S1: bad guys or they create villains or they create whatever,
S1: and the good guys are allowed to take them and
S1: punish them, or hurt them or torture them or whatever.
S1: And their job is to go around and police this
S1: and make sure it doesn't happen anyway. Huge digression. Bottom
S1: line is I believe that consciousness is not required for understanding.
S1: It does mean that it could make a more rich interaction,
S1: but either way, understanding is based on information processing given
S1: x number of types of processes and bounce backs and,
S1: you know, interactions and consciousness could be another type of
S1: one of those interactions. But either way, it is something
S1: happening behind the black box. And even if it feels
S1: like it's outside of the black box. See, that's the trick.
S1: It feels like it came out of the black box.
S1: But now you you are doing something with it. It's
S1: up to you. You have choice. You have freedom. When
S1: in fact, that's just another thing actually coming out of
S1: the black box, which was tagged in. My model of
S1: this was tagged with the authorship mark, which makes you
S1: feel like you did it. So that is my model
S1: for this. And if I am correct about that, then
S1: there functionally is absolutely no difference between what Llms can
S1: or will be able to do and what we do now.
S1: What I did was I broke down understanding into two levels.
S1: One is called functional and that basically means day to day.
S1: You are an expert or a semi expert in your field,
S1: and you have an understanding of what it takes to
S1: do that job. So let's say it's sending emails and
S1: editing emails, collecting reports, creating summaries of reports. Let's say
S1: this is before, you know, 2022. This is before real
S1: I kicked off. And your job is to take reports.
S1: You edit them, you do proofreading, whatever. You have an expertise, right?
S1: You're you're good at English. You're good at grammar. You
S1: understand professional relationships like you're a semi-expert. Or let's say
S1: that you are an average run of the mill expert
S1: on cybersecurity, right? So you have a professional job doing cybersecurity.
S1: You could look for code vulnerabilities. You're not the best
S1: in the world. You're top whatever 50% of professionals. Well
S1: guess what. You have an expertise as well. So my
S1: point is that person is definitely understanding. They have an understanding.
S1: And the way I'm defining their understanding is they are
S1: taking everything that they know about the world and also
S1: about their field. And when they're looking for a vulnerability
S1: in a particular piece of code, all of that is
S1: combining into a focus on that one little point. They
S1: make a decision. Is it dangerous? Is it not? How
S1: do you fix it? ET cetera. Right. So that's functional understanding.
S1: And I'm differentiating that from creative understanding. And the way
S1: I talked with the AI about this was creative understanding
S1: is the level above. It's where you're inventing something new.
S1: And now David Deutsch talks about this a lot in
S1: his two books, especially the one I'm starting with here
S1: now in the very beginning of Fabric of Reality. So
S1: my canonical example for this is actually the Nobel Prize.
S1: So I said to the AI, I said, hey, listen,
S1: can we agree that human creativity can be clearly and
S1: obviously accepted as being if you get a Nobel Prize,
S1: you have done it. And the AI is like, yeah,
S1: I think so. If you get a Nobel Prize in
S1: science for something, then yeah, we could say that is creativity.
S1: I'm like, okay, cool. Well, isn't it true that a
S1: whole bunch of Nobel prizes are actually just like iterations.
S1: They're like, we found a new way that a cell
S1: gathers energy. We found a new way that a cell, mitochondria,
S1: creates energy or something, especially in like chemistry. In physics,
S1: it's like not everything is going from Newtonian physics to relativity. Obviously,
S1: that deserves a Nobel. I assume Einstein got a Nobel,
S1: I can't remember. He might not have. Pretty sure he
S1: did like 1905, maybe. Anyway, the point is, tons and
S1: tons and tons of Nobel prizes go to little tiny iterations, okay?
S1: Which is very similar to functional understanding. But here's my point.
S1: And this is what I was debating with the AI about.
S1: I'm like, look, are you telling me that you can't
S1: do this? And it's like it starts off very safe, right?
S1: It's like, well, look, to be clear, I am just
S1: putting together patterns. Um, me as an AI, all I'm
S1: doing is I'm putting together patterns of everything that I know,
S1: all my knowledge. I'm looking for patterns, I find the patterns,
S1: and then I use that to come up with an
S1: answer that hopefully is, you know, pretty decent. And I'm like, um,
S1: isn't that exactly what humans are doing? And it's like, yeah,
S1: but humans do it along with their consciousness. I'm like, stop, stop,
S1: stop saying consciousness. We already talked about the fact that
S1: consciousness is another type of processing, right. So this is
S1: all just information processing. It's all just patterns. It's all
S1: just bouncing ideas back and forth inside of our black boxes. Right.
S1: And it's like, oh yeah, that's right. And I'm like, okay.
S1: So it's really the same thing, right? We are just
S1: information processing considering with a number of different steps and
S1: coming out with a thing. Is that correct. And it's like, yep,
S1: that's correct. And I'm like, okay, are you telling me
S1: that you can't do the second level of understanding? You
S1: can't do understanding that's creative. You can't come up with
S1: a novel new type of explanation. Okay, because explanations are
S1: super key for Deutsch. Okay. Watch this. Um, okay. I'm
S1: going to scroll down and look at this, this conversation. Okay.
S1: So where are we? Um, okay. Watch this. Watch this.
S1: To discern understanding in a transformer based neural net. Okay. So.
S1: So what I'm trying to do is give it, have it,
S1: give me criteria for what it thinks would be creative understanding.
S1: So I'm having it make its bed and try to
S1: sleep in it. So watch this. Here we go. Here
S1: we go. How would you go about discerning whether or
S1: not it's capable of understanding, or if it's randomly generating
S1: in a non-deterministic way? You know, different explanations for things
S1: it already knows. What would the distinction be between real
S1: understanding and David Deutsch's sense versus regurgitation in the sense
S1: of a neural net? So that's what I'm asking it
S1: to discern, understanding, consider its ability to generate novel explanations
S1: and predict outcomes in unfamiliar scenarios. If the AI can
S1: apply knowledge creatively across contexts, it's closer to Deutsch's understanding.
S1: Regurgitation would be repeating information. So I say give me
S1: examples of types of tests like that. Look at this.
S1: Analogical reasoning. Counterfactual scenarios. Conceptual combination error detection. So it
S1: gives me some criteria. I say okay give me some
S1: actual scenarios like three of those. So it gives me
S1: a bunch of scenarios and then I tell it okay,
S1: well guess what? This is funny because you are actually
S1: an AI and it says, okay, I can certainly try
S1: these tasks. Watch this. But remember, mine are based on patterns.
S1: This is what I was telling you before. Mine are
S1: based on patterns in the data that I've been trained on,
S1: not on personal experience or subjective comprehension. And I'm like,
S1: hold on, this is what I was saying before. Hold on.
S1: We just said consciousness is not required consciousness. Stop saying that.
S1: And it's like, that's a great point. Using this framework,
S1: I can indeed generate responses that reflect an understanding based
S1: on patterns and data, even without subjective experience. Okay, so
S1: we're making progress here. Do you want to do some testing? Yes. Go.
S1: It says here's the scenario. And it starts giving me one.
S1: And I'm like hold on. No, no no I'm giving
S1: you the test. I want you to do it. And
S1: I'm like, look, let's pretend someone doesn't believe that you
S1: are understanding and they're going to give you a really
S1: hard challenge. What would they give you as a challenge?
S1: And could you do it? And it gives me some blabbering. Again,
S1: I'm like, look, that's also true for humans, so just
S1: do it. Here's a speculative hypothesis. Quantum processing in the
S1: brain okay. Yeah. So it gives me this. Really I
S1: thought it was really good. It was pretty good. I
S1: mean it was kind of similar to things that I
S1: was thinking about distributed encoding. Oh. So I asked it
S1: to figure out how memories are stored in the brain,
S1: because I know this is the thing that is not
S1: known in actual science. So it gave me three actual
S1: theories and I'm like, hey, this kind of sounds like
S1: something I've heard before. Is this brand new? Anyway, so
S1: we keep going. We keep going. Okay, Daichi, an explanation
S1: emphasizes creating deep underlying theories that unify disparate phenomena under
S1: fundamental principles. I love that definition, by the way. I'm
S1: going to steal that. Probably. That'll probably be the foundation
S1: of what I come up with. Understanding is essentially a system.
S1: Or by his definition, understanding is the ability to create
S1: new explanation using that understanding. Oh, that was me talking.
S1: So yes. So it agreed with that. Okay. So this
S1: is where I define functional versus creative understanding. Okay. Now
S1: here's where we get into that final debate here. Okay
S1: I tell it again creative understanding does not require subjective experience.
S1: And then I then I realized that I'm coaching it
S1: too much. Right. I say at least that's my claim.
S1: If you disagree, I want you to argue with me
S1: about that. But the point is, you can't just say
S1: that your experience in finding patterns is not enough. Because
S1: guess what? Humans don't have free will. We live in
S1: a mechanistic universe by definition. We've already established that humans
S1: have creative understanding. So if we have it and we
S1: live in a mechanistic universe and there is no free will,
S1: that means human creativity is still made up of a
S1: combination of interactions inside this black box of a brain,
S1: which results in this creativity, which everyone agrees that we
S1: have it right. So it's like, yeah, yeah, yeah, I
S1: get it. Fair point. So if we define creative understanding
S1: as the ability to generate novel ideas or explanations through
S1: a complex interplay of patterns and data similar to how
S1: humans do, then it's conceivable that AI's like me could
S1: approach this level. Great, now we're making progress. And keep
S1: in mind, coaching an AI to be able to say
S1: what you want doesn't mean it's true, right? This this
S1: could be prompt injection and engineering. So we have to
S1: keep a logical framework on this whole thing, because the
S1: fact that I can make it say something that agrees
S1: with me does not make it true. So you got
S1: to follow the whole line of this debate and the
S1: argument and make sure it's being logical here. I'm definitely
S1: not claiming that just because it wrote that. Like, we've
S1: settled this right. So you got to follow along here. Okay.
S1: Let's take the opposite side. So this is me arguing.
S1: There is something special that humans have that AIS don't have.
S1: And I have it go into the different things. And
S1: we kind of go down that path for a little while. Okay.
S1: And I say, yeah, we we might have some special
S1: sauce like I'm giving this as ammunition to the argument. Um,
S1: all right. So let's keep going. I want to get
S1: down to this end part. Yeah. We know consciousness is
S1: not an illusion. What we could say is consciousness is
S1: not a special thing outside of normal processing. And the
S1: fact that it seems special could be an illusion. In
S1: other words, it could be regular information processing coming out
S1: of the black box of our brain. So the process
S1: could be like, you could argue it's artificial, but the
S1: fact that when we experience authorship, that is not an illusion, right?
S1: Because and it says, yeah, it sees the distinction. Okay.
S1: So I'm trying to get down to this part talking
S1: about the Nobel Prize. Oh yeah. So this is the
S1: whole theory about this is my theory about how we
S1: got consciousness from evolution. Worth reading. I'm not going to
S1: go too much more into it. Okay, so here we go.
S1: I just thought of another way we can determine whether
S1: or not eyes like yourself come up with a creative
S1: level of understanding. We could basically look at examples of
S1: things that have won the Nobel Prize in science, which
S1: by definition is innovation or creativity. How many of those
S1: do you think you could have done? And it's like,
S1: that's an interesting approach. Let's look at past Nobel Prize
S1: winning discoveries and use that as a benchmark. And I
S1: slammed it for being like too safe. I talk about
S1: the minor developments like a new mechanism the cell does,
S1: it says. That's a fair point. Many Nobel Prize winning
S1: discoveries do indeed involve incremental advances or novel observations within
S1: existing frameworks, particularly in fields like chemistry and biology. Watch this.
S1: In these cases, I could potentially identify patterns or mechanisms
S1: that lead to similar discoveries, given the right data and
S1: analytical tools. This suggests that I could indeed contribute to
S1: scientific innovations at levels recognized by the Nobel Committee, especially
S1: as these systems continue to evolve. I don't think that
S1: was coached too much to say that. I think that
S1: is a logical conclusion from the debate that we just had.
S1: And I think what that shows is not only functional understanding,
S1: which I already have, but this higher level, more stringent
S1: level definition that I'm using of creative understanding. I am
S1: arguing that an I will soon be able to win
S1: a Nobel Prize. I am arguing that an AI is
S1: already capable of making the type of jumps, the types
S1: of jumps required to make one of these iterative discoveries
S1: that would get somebody a Nobel Prize. In fact, if
S1: I were to go and compile a list of all
S1: the Nobel Prizes and then do a summary of how
S1: much of a jump it was and then give a
S1: bunch of scenarios to current AIS. I bet you they
S1: are already capable of making as good or more of
S1: a jump. Now, could they do all of the experimental
S1: work and all of the research and all of the
S1: stuff that backs that up? Maybe not, but that's all
S1: coming soon. That's the easy part, right? Not the experimental part,
S1: because that could involve physical world and robots. But the
S1: point is, it's not the creative part. The creative part
S1: is the idea and the explanation and the experiment and
S1: the different way of seeing the world. I'm arguing. Eyes
S1: are already getting close to that, if not have already passed.
S1: So all this to say, yeah, eyes do understand. They
S1: understand both at the functional level and at the creative
S1: level of understanding two different levels of understanding. All right.
S1: That was absolutely the longest jump out from a piece
S1: but wanted to talk about that. Transformers create shapes of
S1: the universe. This one is just I'm going to do
S1: a separate one on this because we already went down
S1: a tangent. Vulnerability management isn't about finding issues, it's about
S1: fixing them in context. Did this for dads, which is
S1: a cool vulnerability management company who sponsored the post, and
S1: I've been wanting to write that article anyway for a while,
S1: so that was a good opportunity to do it. Book
S1: club this weekend was so, so good, and we've selected
S1: The Republic by Plato for this week's or this month's book.
S1: So really excited about that. Oh yeah, currently reading Fabric
S1: of Reality and the Beginning of Infinity. These are the
S1: ones that spawned that whole conversation. And. All right, let's
S1: get into it. Security scissors. Warning. Brute force attacks against
S1: default credentials against ICS and SCADA. Um, sharp increase in
S1: OT attacks reported by Fortinet. And a lot of people
S1: are saying that this is China basically getting ready for
S1: a kinetic battle with the US by preparing a whole
S1: bunch of cyber stuff. So the idea is when it
S1: gets ready, gets ready to go into Taiwan, they'll launch
S1: all these infrastructure attacks and basically mess with us, mess
S1: with our society, potentially mess with the military as well,
S1: basically cause a whole bunch of cyber related critical infrastructure
S1: problems for the US power, water, you know, other critical services,
S1: internet and basically mass disruption while they're going into Taiwan.
S1: That's a theory a lot of people have. I'm not
S1: sure how valid it is. Seems somewhat valid, at least.
S1: China reportedly achieved a significant ah, milestone by developing a
S1: generative AI model that operates across multiple data centers and
S1: GPU architectures. Particularly impressive given how hard it is to
S1: get stuff right because supposedly under sanctions. But they seem
S1: to be making plenty of progress. One thing I wanted
S1: to point out is that it's not PvP necessarily against China.
S1: It's PvE because they only need to be able to
S1: do these things here, right? Continuously crawl for all of
S1: our attack surface and find vulnerabilities. Write exploits for those vulnerabilities,
S1: actually exploit them, control their population, launch disinformation campaigns, propaganda campaigns, uh,
S1: distribute tech that can do this to all of their
S1: buddies that are also totalitarian, right? So if they could
S1: do the if they get good enough to do these things,
S1: it doesn't matter if we're 2 or 3 generations ahead.
S1: That's why it's PvE instead of PvP. The only way
S1: it's PvP is that if they are doing these things
S1: against us, using a certain level of AI or ASI
S1: or like AGI, ASI we want to have a next
S1: generation one that's capable of seeing it and stopping it.
S1: But that requires not just the brain, but all the
S1: visibility as well to feed the brain. Okay, Worldcoin is
S1: addressing the issue of bots and deepfakes with decentralized identity
S1: system that confirms if someone is human. So we're starting
S1: to see the practical uses for Worldcoin. By the way,
S1: it's an Ethereum based blockchain. Validation of humans. Interesting stuff.
S1: Chinese attacker called Salt Typhoon. Very cool. Should be a
S1: band name targeting Cisco Systems to establish persistence. Again, going
S1: back to the thing, just imagine that they have hundreds
S1: of thousands of different systems that they're able to mess with,
S1: like rerouting things. BGP attacks, like just imagine the internet
S1: not working power going down for so many people. All right.
S1: Before the Taiwan attack, U.S. has charged three Iranian nationals
S1: linked to the IRGC for hacking into accounts of U.S.
S1: officials and political campaigns. T-Mobile got hit with a $16
S1: million fine from the FCC, and they were basically forced
S1: to spend another 16 million on security as part of
S1: the settlement. And massive love to my buddy Matt Johannson,
S1: this story right here goes out to his site, which
S1: is vulnerable. You. So now a lot more of my stories.
S1: He's going to start making actual cybersecurity stories. So instead
S1: of going to like Bleeping Computer and uh, what are
S1: some of the other ones, a bunch of security news ones, right. Um,
S1: but I'm like helping him with like some of the summarization,
S1: different pieces, like what a good summary looks like, big
S1: picture impact, why it matters. So I'm giving a bunch
S1: of feedback on that kind of stuff. And, uh, yeah,
S1: I really love the idea of being able to point
S1: to my buddy's site. Who can do ultimately, to do this? Well,
S1: you have to find the stories and you have to
S1: summarize them well. And the reason he's going to be
S1: so good at this is because he is an actual
S1: cybersecurity expert. He runs like Appsec, Reddit, and he has
S1: been in this field, I think like almost as long
S1: as me. Uh, or if not the same level. I
S1: don't think so. I don't think he's as old as me,
S1: but he's been doing this for so long. One of
S1: the longest people in the industry, and he's an absolute
S1: guru at it. And he's good at writing at the
S1: same time, and he's learning AI. So it's like, this
S1: is the perfect combination, and I can't wait to start
S1: pointing more and more of my stories to his site
S1: and spreading the love there as a buddy as well. U.S.
S1: Commerce Department proposing a ban on Chinese, Russian, automotive, auto,
S1: moto automotive hardware and software. Basically saying it's a way
S1: for adversaries to get footholds into U.S. systems and infrastructure, which, yeah,
S1: that's what we've been talking about. Ukraine reportedly found Starlink
S1: terminals in a downed Russian Shahed drone. LM based hacking assistance.
S1: I think we all get that lots of different tools
S1: here that actually help you attack and defend with Llms.
S1: Telegram is now starting to hand over phone numbers and
S1: IP addresses of suspects. This is all part of their
S1: CEO getting arrested. Security researchers found a way to remotely
S1: control millions of Kia vehicles by only having the license
S1: plate number, which allows you to look up and basically
S1: gain controls. Pretty nasty. One so the biggest hype switching
S1: to AI in tech, biggest hype in the last week
S1: has been around notebook LM, which is, by the way,
S1: what I use to create that, um, that podcast I
S1: played for you earlier. So it's a big Google project.
S1: I don't know why they're not talking about it more.
S1: Never mind. Yes, I do, because they're really bad at
S1: product and marketing and everything like that. So basically it's
S1: this tiny little project which they're not announcing, they're not
S1: putting out anywhere. Like you learn about it on Twitter? Whatever.
S1: It'll probably be dead in a week anyway. Uh, to
S1: the graveyard, along with everything else. But anyway, fun while
S1: it lasted. It's super fun. And oh yeah, here's an example.
S1: So I uploaded an Alma file, which is part of
S1: a Telos project, which I'm going to be talking about,
S1: hopefully making content on tomorrow. Anyway, it's this giant context
S1: file I uploaded it. It is actually the context file
S1: in the Telos format for a security program. So managing
S1: a cyber security program for a company, it's for this
S1: fake company called alma. I've uploaded it. It's in the
S1: fabric repo right now, but it's soon going to be
S1: in the Telos repo as well, which is designed to
S1: do specifically this. But check this out. You put it
S1: in there. Look at this generate deep dive conversation. You
S1: can actually create a podcast based on that security program.
S1: And they'll just talk about the security program back and forth.
S1: That's crazy by itself. But most importantly, look, you can
S1: ask these questions about the security program. And you got
S1: this typing field down here where you can interact and
S1: ask questions about the program. So super cool. Newsom. Yeah,
S1: recommend checking out notebook LM Google because they definitely needed
S1: a Google domain. Whatever. Newsom vetoed the California AI bill,
S1: saying it was too restrictive. I agree with him on this.
S1: A good, good move, though. I want to see another
S1: follow up bill. California made it illegal to use AI
S1: to impersonate actors without their consent. Somebody found out their
S1: voice was being AI cloned by a company and they're
S1: mad about it. Convergence AI, co-founded by Marvin Porter and
S1: Andy Tallis, has raised 12 million to develop proxy agents
S1: with long term memory. New task of agent, new class
S1: of agents. Yeah, uh, this is going to be huge.
S1: Huge meta launched three two. They can now handle images
S1: and text competing with OpenAI and anthropic on that ChatGPT
S1: advanced mode. Definitely, definitely. I use it all the time
S1: as all these examples we just went through. Hugging face
S1: now has 1 million AI model listings. 1 million models, all.
S1: Are they all open source? Pretty sure they're all open source.
S1: Are they all open source? I think so. I think
S1: they're all open source. They're definitely all there for you
S1: to use. I think that implies that they're open source. Um, yeah.
S1: Anthropic is in early talks with investors. Could value the
S1: company at 30 to 40 billion. Oh, by the way,
S1: OpenAI just closed its funding, so I think it's worth
S1: some crazy amount now. They just raised like more than
S1: Elon did for a lot of his projects. Yeah. Crazy
S1: crazy amount of money. They just raised enclosed. Google's rolled
S1: out two new production Gemini models, constant stream model updates
S1: for the main players, much smarter models or models that
S1: can do new stuff like vision or whatever, multimodal, and
S1: then models that are like two 500 times cheaper. One
S1: of the cool things that was just mentioned is like
S1: in general, models are 99% cheaper than a year ago
S1: for the same functionality 99%. Ilyas I reading list Agile's
S1: original principles have been lost in translation. AI is disrupting
S1: the traditional ad supported internet model. Yea yea yea yea yea.
S1: This one's crucial. I did a piece a little while
S1: back called what was that called? I don't remember. It
S1: doesn't matter. Um, real internet things. I've got this part
S1: called AI mediation. Oh yeah. Here's the AI mediation piece. Yeah.
S1: Das will mediate everything. We'll say what we want. Our
S1: Das will manipulate external demons and APIs to make it happen.
S1: So this is from, uh, I think the very beginning
S1: of the year I put this out, but it's actually
S1: from my book from 2016. And this is just an updated,
S1: illustrated version. I've been talking about this. You should check
S1: out this book, by the way. It's called The Real
S1: Internet of Things. It's actually a little bit crappy of
S1: a book. It's more like a blog post or like
S1: an essay, but it's pretty well structured. But most importantly,
S1: all these things that I've been saying on the podcast
S1: that you've been listening to for like 2 or 3
S1: years or five years or however long it was all
S1: in that book in 2016, all these predictions about AI,
S1: what's about to happen to AR? It's all in there.
S1: And that's why I've been consistent about what I've been
S1: saying and predicting all along. So I highly recommend you
S1: check out the source material, which is that first book.
S1: And yes, I was even talking about AI back in
S1: 2016 and that that I was the source of all
S1: these different predictions. Obviously, I didn't know it was going
S1: to happen in 2022. I thought it might take longer,
S1: but yeah. Anyway, go check that out. Um, SpaceX Starlink
S1: is about to hit 4 million subscribers. Um, yeah, I
S1: base a lot of my investment in prediction on leaders
S1: and ideas. Elon jobs Jenson Zuckerberg doesn't mean they're going
S1: to win, but I think it's bad idea to bet
S1: against them because they are so locked in on vision
S1: and also able to execute. Smart TVs from Samsung and
S1: LG are taking snapshots of what you're watching multiple times
S1: per second. Plus energy started a $1 billion solar and
S1: battery storage project. The white collar apocalypse is nigh. The
S1: decline of status, economy and the impact of AI on
S1: job markets. Yeah, pretty cool one. Definitely go check this
S1: one out. Meta. Orion's AR glasses are a big upgrade.
S1: I don't think Mark is right about replacing phones by 2030.
S1: I think that's too ambitious. I think phones offer too
S1: many things that glasses don't, and I just don't think
S1: the physical miniaturization tech is there to go into the
S1: glasses to replicate what's on the phone Hacker News thread,
S1: where people are sharing their experiences about whether having a
S1: personal website helped get them hired. Some people said yes,
S1: a lot of people said yes. Some people said no.
S1: All right, humans. California is taking phones out of schools,
S1: basically limiting cell phone use. So massive props to Jonathan
S1: Hite on this. Uh, someone found a method that uses
S1: stem cells to regenerate insulin producing cells. ExxonMobil is facing
S1: a lawsuit for coming up with a supposed fraud saying
S1: or a myth saying that recycling could help solve the
S1: problem with plastic waste. California is looking to make it
S1: one click. Get off of subscriptions just the same way.
S1: One click sign ups. So if you offer a one
S1: click sign up, you have to offer a one way
S1: to get out of it as well. Over 10,000 books
S1: were banned in US public schools from 2023 to 2024.
S1: Insane Steve Jobs had a thing called a ten minute rule,
S1: just ten minutes to tackle a problem or a task.
S1: Check this out. That's why I love doing video. I've
S1: got one of these. I actually use it. It's called pomodoro,
S1: but you can set it for any time. Right. So
S1: 30 minutes is like the classic one. It's inverted. But
S1: but you can put it down to like. There you go.
S1: Ten minutes. So I think it's cool. Constraints. Constraints inspire creativity.
S1: And Maria Popova explores profound curiosity and love of Henry
S1: David Thoreau as he encounters a screech owl in the wild. Ideas.
S1: Everything is going behind the paywall. Yeah, I'm not going
S1: to read this. I'm just going to give you the overview. Freeform.
S1: I think it's really interesting to think about how there's
S1: the open internet is going away. Everything's going to be
S1: behind auth not only to block bots but also to
S1: get micropayments. So rich people who have money for their
S1: their eyes. Okay I'm going to give you a little
S1: bit of a I also have to make this piece
S1: of content, but a tiny little view of the future. Okay,
S1: you're going to have all your personal context in your
S1: personal AI. It's with you all the time. Your personal
S1: AI is the one going out and reading everything for you.
S1: It's consuming everything. It's consuming all the APIs, all the
S1: RSS feeds, all the videos. It's making transcripts. It's translating them,
S1: it's summarizing them. It's doing everything for you. And as
S1: you're moving through your day, when you have a little
S1: bit of extra time, it'll create a custom piece of
S1: content for you. Actually, I'm merging both of these. Cool.
S1: It'll create a custom piece of content for you. Okay,
S1: so there's this paper that I just summarized. Where is
S1: that thing? Oh, it's right here. So my buddy just
S1: sent me this thing. It's this giant PDF. Um, my
S1: AI is going to go read this thing. It's going
S1: to give me a summary based on how much time
S1: I have before my next meeting. It's like, hey, you
S1: want to hear about this thing? Or do you want
S1: to see a video? I'm like, yeah, give me a video.
S1: So it's Richard Feynman explaining this paper to me in 30s,
S1: which means the content. Watch this. The content duration, the
S1: content format, the content style, all of. Oh and the performer.
S1: The person actually giving the content. So it could be
S1: a graphic novel. It could be a video, it could
S1: be an essay, it could be a video, it could
S1: be text, it could be whatever, but it could shrink
S1: from 30s down to 10s do a giant set of books,
S1: and the AI will dynamically recreate that for you, which
S1: means this is actually the future of education. And this
S1: is going to be a whole separate conversation. In fact,
S1: we're going to break this out as a separate clip
S1: to talk about this. This is going to be the
S1: future of education, because think about this. The AI can
S1: also adjust based on somebody's limitations. Okay. They haven't learned
S1: this topic yet. They're not so great at English yet.
S1: They have a learning disability okay. The AI is going
S1: to be an expert in learning disabilities, which means it
S1: can repeat things, or it could avoid certain ways of
S1: explaining things, or it could double down on other ways
S1: of explaining things. Dynamic adjustment to the best way to
S1: teach you at that given time. Maybe if you're a kid,
S1: you want to hear this lesson as a story. You
S1: want to watch it as a video. The whole thing
S1: could be framed as Taylor Swift or Pokémon or Star Wars. Okay,
S1: so your eye is going to be able to shape
S1: teaching you anything using any format, in any duration, in
S1: any style of teaching. Okay. It could be a podcast
S1: between your two favorite people, and they're talking about the
S1: ideas debating back and forth. It could be a graphic novel,
S1: it could be a TV show, it could be a cartoon,
S1: it could be an anime. It could be a YouTube
S1: video of your favorite creator just doing a YouTube video
S1: for you about that thing, and you're like, nope, I
S1: want it in 30s nope. I want it in one
S1: hour episodes, five of them per week. So it's like
S1: I sit down for an hour and I learn this thing.
S1: This is going to massively disrupt education. Okay, we already
S1: knew I was going to disrupt education. We already know
S1: technology is going to disrupt education. Like this is all known.
S1: But this particular formula of being able to adjust all
S1: those variables and create dynamic content, that is the game
S1: that that is the education game, like 100%. Okay. Now
S1: now think about this. All you need to do is
S1: come up with the absolute best content, okay? Once you
S1: have the best content, which which I hate to tell you,
S1: it's kind of already out there. Okay. What what are
S1: the best concepts in journalism or biology or whatever? It's
S1: in there. It's in the textbooks already. Right? The smartest
S1: people have already been talking about it. It's in all
S1: of their books. Well, go read their books. We've already
S1: seen their lectures. Half of those lectures are already on
S1: open MOOCs on, uh, you know, Harvard or MIT or whatever. Like,
S1: these people aren't being quiet. It's not a secret what
S1: they know about the world. So all the stuff's out there,
S1: so the content's solved. The only question is, how are
S1: we getting that content to the people? Now, you take
S1: something like an extremely expensive university like Harvard or Stanford
S1: or something like 11 people go per year and it's
S1: horribly expensive. How many people are on the planet? Oh,
S1: 8 billion, 8 billion people need that education and don't
S1: have the time. And they have language, you know, disabilities
S1: or language like, um, mismatches with the way the original
S1: content was taught. Doesn't matter. Instant translation to their language
S1: while on ramping them onto English if they want to
S1: learn English. 8 billion people getting exactly the education they
S1: need in the topics that they need in any format
S1: that is best for them, represented by deepfakes of exactly
S1: the best people or in the format of whatever a show,
S1: whatever is the best way to get it to them.
S1: Like how is that not the way people are going
S1: to learn in the future? That's what I'm asking you.
S1: How is that not the future? Now, again, how fast
S1: are we going to get there? Are there lots of
S1: different ways that will be opposed? Of course, of course. Right.
S1: So when is it going to start? Today. Yesterday. Tomorrow.
S1: That's when it's going to start. It's already starting. When
S1: will it be finished? Never. There will always be a
S1: forces that push it back and say no, you've got
S1: to dress up. You've got to go to this special school.
S1: Because remember, a lot of education is actually not about
S1: the content and about the the learning. A lot of
S1: the education is actually about the stamp that you get afterwards,
S1: which we can solve a different way, which we'll be
S1: talking about at some point to and kind of already
S1: have talked about. All right. Let's keep going. I think
S1: we covered both of those topics pretty well. Oh, yeah.
S1: Future of education. Exactly. Okay. Discovery acts framework extension of
S1: Axios for bug hunters and penetration testers. So it's basically
S1: a way to do large distributed scans as part of
S1: like Bug Bounty, the Russian APT tool matrix. New tool
S1: matrix focused on Russian APT groups GRU, SVR, FSB affiliated
S1: threat actors. Reuse of tools like Mimikatz, impact, exact, Metasploit
S1: Re George Re George being particularly notable for limited use
S1: by by ransomware gangs okay Merkle map subdomain search engine
S1: helps you discover subdomains. Gun gear is a command line tool.
S1: What's with this font? Do Not like is a command
S1: line tool written by my buddy gunner and go. Microservices
S1: are technical debt. Really really cool. Take there. Be somebody
S1: who does things. That was a fantastic essay. It's lists
S1: all the way down. Another fantastic one. US government is
S1: now publishing tide sensor data to show sea level rise.
S1: Love the transparency here. Comedy is search, critical mass and
S1: tipping points. Semantic chunking for rag flipper zero. Firmware. Open
S1: source firmware unlocks a lot more of your flipper device.
S1: MTA Open Data Challenge Paul Graham addresses the question of
S1: whether to follow your passion or not. Compressed jpg, compressed
S1: jpeg move fast and abandon things. BBC Sound Effects Library
S1: what I learned in the past years spent building an
S1: AI video editor. Danger zone is teamed up with Google's.
S1: Gvisor to enhance its security, allowing journalists to open suspicious
S1: documents safely. RM Creative Console might be getting one of
S1: these actually need to go order that advanced structure output tutorial.
S1: Logitech's MX Creative Console is a game changer for photo
S1: and video editors. Dieter Rams inspired iPhone dock. This thing
S1: looks really cool. I don't think I would get one,
S1: but isn't that cool? Yeah, that looks cool. And automatic
S1: litter boxes because I want a cat, but I'm allergic
S1: to cats, so probably not getting one recommendation of the week.
S1: Take a document or something you know really well and
S1: upload it to notebook LM and tinker around with it.
S1: It is super cool and do it quickly because they
S1: might put the project in the Google graveyard. Um. Aphorism
S1: of the week. I want so much that is not
S1: here and do not know where to go. I want
S1: so much that is not here and do not know
S1: where to go. Charles Bukowski. Unsupervised learning is produced and
S1: edited by Daniel Miessler on a Neumann U87 AI microphone
S1: using Hindenburg. Intro and outro music is by zombie with
S1: a Y. And to get the text and links from
S1: this episode, sign up for the newsletter version of the
S1: show at Daniel missler.com/newsletter. We'll see you next time.