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S1: you go to Vanta comm slash unsupervised. That's vanta.com/supervised for
S1: $1,000 off. Welcome to Unsupervised Learning, a security, AI, and
S1: meaning focused podcast that looks at how best to thrive
S1: as humans 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. All right. What do
S1: we have here? All right. So saw this funny thing.
S1: So basically, um, some terrorist funding news. Al Qaeda rebrands
S1: as I. Qaeda raises a $1 billion. Cool. I am
S1: rereading Alex Hormozi. Two business books. Uh, really? I think
S1: Alex Hormozi is, like the best business content generator right now,
S1: at least for my type of business, although he's like
S1: a gym person. Um, so it's not the same type
S1: of business, but he's just extraordinary at, like, user acquisition,
S1: like all sorts of stuff. And I love the format
S1: of his content. Like he's doing a whole bunch of
S1: shorts now. He's just making content probably, I don't know,
S1: it's got to be making 4 or 5 pieces a day.
S1: It's just so much and it's really good. My buddy
S1: Jacoby is an Army veteran, really cool security guy, and
S1: he's struggling really hard right now and he's got a
S1: gofund me. So if you could, uh, add to that,
S1: I would really appreciate it. Fabric made it to the
S1: front page of Hacker News. Uh, next run of augmented
S1: is on July 26th. You want to go sign up there?
S1: Got to spend some in-person time with a couple of
S1: fellow entrepreneurs creators over the weekend. Doing this stuff in
S1: person is invaluable. Like, we're talking about wanting to do
S1: a digital version of The thing that we built, and
S1: I'm reticent to do that because I. I want it
S1: to happen only in person, but we can make it
S1: digitally and then just interact with it in person. So
S1: that's what we're going to do, because it's too valuable
S1: not to be digital, but highly recommend setting up some
S1: similar sessions with your friends because it's really, really powerful.
S1: And my friend Monica Verma is running our CSO masterclass
S1: soon and she also has a newsletter, so go check
S1: those out. My work just finished a resource I've been
S1: working on for over a week called Raid. Real world
S1: AI definitions, not redundant array of inexpensive disks, which is
S1: the first thing I think of whenever I hear Raid.
S1: Either that or Bug Killer. But yeah, real world AI definitions.
S1: And I'm actually going to go into it and talk
S1: about it. So I'm going to go ahead and jump
S1: in here. Okay. So I see a lot of definitions
S1: for different AI terms out there. And I want to
S1: put my own thoughts into a long form version. And
S1: that's basically why I did this. And it's mostly for
S1: me because I love this quote. I forget the name
S1: of the woman. Uh, I need to memorize that. But
S1: she basically says, um, if you are thinking without writing,
S1: you only think that you're thinking. And I think that
S1: is such a powerful statement, because when you are writing
S1: and this is, um, this is why I call this
S1: a Feynman approach to learning, it's basically Feynman said, if
S1: you think you know something, try to teach it and
S1: you will figure out pretty quickly that you probably don't
S1: know it as well as you thought you did. And
S1: I've always known this for a very long time. So
S1: starting back in 1999, I started writing tutorials to myself
S1: to explain things. So when I was learning tech, I
S1: literally would try to learn the tech from textbooks and
S1: then it wouldn't make sense to me. So I would
S1: go write my own explanation and then I would just
S1: use that whenever I wanted to remember something. And that
S1: turned into my tutorials page, which is how I got
S1: started on the internet. So table of contents here, the
S1: expanded definitions table, the one liner definitions table, and then
S1: I break out AGI and ASI into two different sections.
S1: So we're going to start with the one liner AI
S1: definitions table. And this is coming off of Hamel Hussein's
S1: I bullshit knife which gave violently short definitions for a
S1: bunch of I terms. And this is my expanded version
S1: of that. Okay. So this is made to like crystallize
S1: in your brain what these things are in very, very
S1: crisp conversational form. And we're going to go into more
S1: detail in the further ones. But I tech that does
S1: cognitive tasks that only humans could do before. And the
S1: reason I have that only humans could do before is
S1: because that's a moving bar. So before there was a
S1: chess playing robot or a chess playing AI, everyone said, well, yeah,
S1: but it can't play chess. It never will be able
S1: to play chess because that's a human thing. And of
S1: course you could do that now. Image identification. Being able
S1: to tell the difference between a dog and a cat,
S1: that was a very, very hard problem for a very
S1: long time. You have to think in terms of like
S1: what currently is a thing that only humans can do
S1: that I cannot do. Yet that is the barrier which
S1: determines AI or not, because the moment you cross over
S1: into that and I suddenly becomes available to do that,
S1: that's what's currently called I. Now, what's interesting is as
S1: it moves further and further out, people forget that that's
S1: I like image recognition. A lot of people are like,
S1: that's not AI, that's ML. Well, it's still AI, first
S1: of all, because machine learning is a subset of AI,
S1: but also because it's doing something that only humans could
S1: do before. So I, I love that definition. Very flexible
S1: machine learning AI that can improve just by seeing more data.
S1: And we're going to talk about this more, uh, later
S1: on prompting clearly articulate what you want from an AI rag.
S1: Provide context to an AI that's too big or expensive
S1: to fit in a prompt, an agent, an AI component
S1: that does more than just LM call to respond. So
S1: it's not just call and response, that's just regular. I,
S1: an agent, does more than that, taking on more of
S1: the work chain of thought. Tell the I to walk
S1: through its thinking and steps. Zero shot ask an AI
S1: to do something without any examples. Multi-shot. Ask an AI
S1: to do something and provide multiple examples. Prompt injection tricking
S1: an AI into doing something bad. Jailbreaking, bypassing security controls
S1: to get full execution ability, or at least as full
S1: as possible, doesn't necessarily need to be full AGI general
S1: AI smart enough to replace an $80,000 white collar worker,
S1: and ASI, which is superintelligence. General AI that's smarter and
S1: or more capable than any human. So I think that's
S1: a pretty clean list for thinking about these concepts. And
S1: now I want to go into more detail for each.
S1: So AI. AI is technology that does cognitive tasks or
S1: work that could previously only be done by humans. So
S1: cognitive tasks or work, and I might clean that up
S1: at some point and just make it work or cognitive tasks. Anyway,
S1: lots of different ways to define AI. So this is
S1: going to be controversial. And this is what I talked about.
S1: Well yeah of course I can do this. But it
S1: still can't do this and it probably never will be
S1: able to. And then the narrator voice says, yeah, that
S1: happened seven months later. And this is why I like
S1: the definition is because it moves over time. Machine learning
S1: a subset of AI that enables a system to learn
S1: from data alone, rather than needing to be explicitly programmed.
S1: This is a super exciting to me as a definition. Actually,
S1: as a concept. I can't believe the program is the same,
S1: but you change the data and the program gets smarter.
S1: That blows my mind. And it's like the most important.
S1: In fact, if you want to go tighter learning from
S1: data alone or a technology system that's able to learn
S1: from data alone, like lots of different ways you can
S1: skin this thing. Prompt engineering. Prompt engineering is the art
S1: and science of using language, usually text, to get AI
S1: to do precisely what you want it to do. Yeah,
S1: a lot of people think prompt engineering is like this
S1: really special thing. Others think it's not important at all.
S1: I think it's absolutely an art and a science because
S1: it's more about clear thinking than about text itself. Just
S1: like writing, the best prompt engineer is the same. It
S1: comes from deeply understanding the problem and be able to
S1: break out your instructions to the AI in a very
S1: methodical and clear way, So the clearer you can think
S1: and the clearer you can think about problems and describe methodologies.
S1: That's that's what's going to make you really good at
S1: prompt engineering. Don't think of it as an AI thing.
S1: Think of it as a thinking thing and a writing thing. Retrieval.
S1: Augmented generation Rag is the process of taking large quantities
S1: of data, which are either too large or too expensive
S1: to put in a prompt directly, and making that data
S1: usable as vectorized embeddings to AI at runtime. And I
S1: say it's important to understand that Rag is a hack
S1: that solves a specific problem, which is that people and
S1: companies have way too much data gigabytes, terabytes, petabytes of
S1: data that they want their AI to be aware of
S1: when performing tasks. But I can only handle small amounts
S1: of that data per interaction. And that's what Rag is for.
S1: So the solution we come up with is to use
S1: embeddings and vector databases to encode relevant information and send
S1: that along with the prompts. And it's not really clear
S1: what the successor is going to be. But one option
S1: is to just have massive context windows and put the
S1: raw content in there. But then you have cost problems,
S1: so you would need that to be very cheap for
S1: that to work. Agents. An AI agent is an AI
S1: component that interprets instructions and takes on more of the
S1: work in a total AI workflow than just LLM response,
S1: for example, executing functions, performing data lookups, etc. before passing
S1: on results. Yeah, so there are lots of different definitions
S1: for agent. I think the trick is to go back
S1: to the Latin, the agents or Arjun's, which is to
S1: do or to act. So I think eventually this is
S1: going to be like an AI component that has its
S1: own mission and goals and can use its resources and
S1: capabilities to accomplish them in a self-directed way. That is
S1: the true nature of agent or agents from the Latin.
S1: But I think in this current version, I think the
S1: best way to say it is that anything that acts
S1: on behalf of the mission and something that performs multiple
S1: steps towards the final goal, and that's why I'm using
S1: this definition for agent chain of thought, a way of
S1: interacting with AI in which you don't just say what
S1: you want, but you give the steps that you would
S1: take to accomplish the task. So you're basically teaching it
S1: how you think, which which is essentially teaching it how
S1: to think like a human. And then it uses that
S1: template to sort of do that every time it's going
S1: to solve the same type of problem. And to me,
S1: it's kind of an example of what we talked about
S1: in prompt engineering, clear thinking. It's walking the AI through
S1: how you think when you're solving a problem yourself. Prompt
S1: injection a method of using language, usually text, to get
S1: AI to do something it's not supposed to do. And
S1: a lot of people confuse prompt injection with jailbreaking. And
S1: I think the best way to think about this. And
S1: thanks to Jason Haddox for talking through this with me,
S1: because he had a very similar definition, uh, is to
S1: say prompt injection is a method. It's an attack avenue.
S1: It's not a payload, whereas jailbreaking is a goal. We're
S1: trying to bypass the security on a system. Prompt injection
S1: is not bypassing the security on a system. Well, it's
S1: a method for doing that. It's a way of tricking
S1: the eye into doing something it's not supposed to do.
S1: But that could be telling a joke that it's not
S1: supposed to tell. That could be showing a picture it's
S1: not supposed to show. It doesn't mean it's bypassing the
S1: security on a system so you can execute commands. So
S1: it could be data exfil, it could be code execution,
S1: it could be image generation. It could be anything. So easiest,
S1: cleanest way, at least in my mind, to think about this.
S1: Prompt injection is a method. Jailbreaking is a goal. All right.
S1: We talked about jailbreaking. Going to pass that one. Artificial
S1: general intelligence. The ability for an AI system, whether a model,
S1: a product or a system to fully do the job
S1: of an average US based knowledge worker. In other words,
S1: it's not just competent at doing a specific thing, which
S1: is called narrow AI oftentimes, but many different things, which
S1: is why it's called general. So basically it's some combination
S1: of sufficiently general and sufficiently competent, and the amounts of
S1: which are, you know, going to be debated so generally
S1: competent at many other cognitive tasks, sample efficient so we
S1: can learn quickly from like one example or a low
S1: number of examples. And the system should be able to
S1: apply new information, concepts and skills that learns to additional
S1: new problems. So it's not just generally intelligent from its training,
S1: but when it learns new things, it's generally intelligent at
S1: applying those new things that learn to everything else in
S1: the future. And I like this definition focusing on the
S1: worker replacing a worker, because I think this is arguably
S1: what matters most to humans when we talk about AI discussion,
S1: which is the future of humans in a world of AI,
S1: regular people don't care about processing speed or model size
S1: or model weights or any of that crap. What they
S1: care about is if and when any of this is
S1: going to actually affect them. And that means job replacement mostly,
S1: or at least that's the most important thing. So these
S1: are the levels that I see within that job replacement
S1: of this average white collar worker. So first level is
S1: better but with significant drawbacks. And I'm not going to
S1: go through all of these. But it's like the interface
S1: the language, the confusion, the errors, flexibility, basically. Basically, real
S1: world work environments are kludgy and lame and they change
S1: all the time. And it's basically a giant mess. This
S1: level one is basically saying, yes, we brought in an
S1: AI worker to replace that person, or we didn't hire
S1: a person. We used an AI worker instead for for
S1: growth if you want to be nice. So that's what happened.
S1: We brought in this level one AGI one. And here's
S1: the problem. It's kludgy. So so the problem is that
S1: they're doing the work. But you have to clean up
S1: after them. So the interface proprietary cumbersome the language that
S1: you have to use. It's still kind of like AI language.
S1: It's almost like you're prompting. You're not just normally talking
S1: to them. Um, it gets confused a decent amount of
S1: the time. There are frequent mistakes which need to be
S1: fixed by humans, and if you want to change what
S1: this thing is doing, it needs to be largely retooled.
S1: Not not like in a major way, but you've got
S1: to have a conversation with it. You've got to change
S1: some documentation, you've got to do some stuff. So going
S1: back to the top level of this one better but
S1: with significant drawbacks. So it is right at the line
S1: of being too much work and not good enough to
S1: replace a human right. That's the trick. It's just barely better. Barely.
S1: And sometimes you can't tell if it's better or not.
S1: It might be too much work. Next one. It removes
S1: a lot of those problems. So it's like in the middle.
S1: It's like decent, competent but imperfect. So mostly normal workflows,
S1: mostly human with exceptions. Doesn't get confused very often. Fewer
S1: mistakes and the mistakes aren't as bad, and it just
S1: decently well to new direction from leaders. So that's like
S1: the middle level. And then AGI three is full worker replacement.
S1: So this can replace this average 80 K worker in 2022.
S1: White collar worker in the US. It can fully replace them.
S1: So when you talk to them, it's you talk to
S1: them just like any other employee. You could text them,
S1: you could voice them, you could do video with them.
S1: It's just like an employee. You talk to them exactly
S1: like an employee. They're confused the same amount or less
S1: than the $80,000 employee. Keep in mind, humans have all
S1: these problems too, right? Humans also get confused and need
S1: to be retasked or whatever. So it's not like we're
S1: competing with perfection here. Same or fewer mistakes than an
S1: $80,000 employee. Employees also make mistakes and just as flexible
S1: or more than $80,000 employee employees also have trouble like
S1: completely being retests. It's like I thought it was working
S1: on that. What are you doing? Like, so this is
S1: basically saying it's just as good as them or or better.
S1: So this is the top level of complete worker replacement.
S1: I think this is going to take a decent amount
S1: of time. And then the next question is like okay,
S1: but what if it's like that? It's a full replacement
S1: for that worker. So it's AGI three, but with an
S1: IQ of 150. Well that's that's really powerful. And I'm
S1: not working on that axis. I'm only working on how
S1: good of a worker are they. And that's the three
S1: levels for AGI okay. Let's do AC a level of
S1: general AI that's smarter and more capable than any human
S1: that's ever lived. So this is interesting for a number
S1: of reasons. One, it's a threshold that sits above AGI
S1: and people don't really agree on that definition. And the
S1: second thing is, at least as I'm defining it, it
S1: has a massive range. And third, it blends with AGI
S1: because AGI just really means general incompetent. And ASI is
S1: also general incompetent. So really ASI is also AGI. It's
S1: just uh, at a level beyond any human. So AGI
S1: is replacement for a human worker making SDK in the
S1: US in 2022, which is pretty AI, prision AI and
S1: ASI is more smart and more competent than any human.
S1: And the model I'm using there is like John von Neumann,
S1: because unlike Einstein and Newton, he was, uh, very broad.
S1: He was a generalist, right. So he did game theory, physics, computing,
S1: lots of other stuff. But I don't think being smarter
S1: is the only thing that matters, because I think it
S1: comes down to multiple things. So if I look at
S1: all the different things that I'm putting into ASI, it's
S1: like your ability to model abstractions of reality at these
S1: different levels. It's the ability to take action, to perceive, understand, improve, solve, create, destroy.
S1: And then it's like, okay, what fields are we innovating in?
S1: What problems are we able to solve and what scale
S1: are we actually working? You know, thinking about, I guess
S1: I should add universe or whatever, but all the way
S1: from Quark to Galaxy is like the scale. So what's
S1: really cool about this is you can now turn these
S1: into functional phrases. So you could say, like an AI
S1: capable of curing aging by creating new chemicals that affect DNA. Okay.
S1: So we have a problem here creating new chemicals. That's
S1: like a net new thing, right? So that you're seeing this,
S1: you know, verbs and nouns here, maintaining a full city
S1: by monitoring and adjusting all public resources in real time.
S1: And the AI capable of taking over a country by
S1: manufacturing a drone army and a new energy based weapon.
S1: So this is like new science here, and an AI
S1: capable of faster than light travel by discovering completely new physics.
S1: So that's like a level above, right? So let's look
S1: at the different levels here. AC one superior, an AI
S1: capable of making incremental improvements in multiple fields and managing
S1: up to city size entities on its own. So here
S1: are the things that it has smarter and more capable
S1: than any human on many topics. Able to move progress
S1: forward in multiple scientific fields. Able to recommend novel solutions
S1: to many of our main problems. Able to copy and
S1: surpass the creativity of many top artists. Able to fully
S1: manage a large company or city itself and keep, keep
S1: in mind, copy and surpass some of the best artists. Now,
S1: what this really means is like in a superhuman way, right?
S1: So it's not just like better in one area. It's like, okay,
S1: it's Eminem, but it's way better than Eminem. It's Kendrick Lamar,
S1: but five times better, right? Same with these types of things.
S1: Novel solutions, moving progress forward, which obviously no human has done.
S1: So they're doing it better, but it doesn't have these.
S1: It can't create. Net new physics or net new material science.
S1: It's unable to fundamentally improve itself by orders of magnitude.
S1: So next phase dominant okay. First one was superior. Next
S1: one is dominance. This one has all of AC one.
S1: But it's also able to completely change how we see
S1: multiple fields able to completely solve most of our current problems.
S1: Able to fully manage a country by itself. So this
S1: is a huge jump over the first one. Able to
S1: fundamentally improve itself by orders of magnitude, but still can't create.
S1: Net new physics or run an entire planet. And then
S1: you have AC three, another massive jump AI capable of
S1: creating net new physics, completely new materials, manipulation of near
S1: fundamental reality or fundamental reality. If it's possible to do
S1: that and can run an entire planet. And keep in mind,
S1: I might add AC for um, if I have a
S1: like a clear picture of what that looks like. Um, Yoshi. No. Rishi,
S1: my buddy Rishi mentioned, um, what about someone? An AI
S1: that's smarter than all of humanity put together? And I
S1: was like, well, isn't that confusing knowledge with intelligence and reason?
S1: But I haven't heard back from him yet. Other things
S1: to consider here. So this one adds the ability to
S1: modify reality at a fundamental or near fundamental level, able
S1: to manage an entire planet simultaneously. And maybe its primary
S1: concerns become like sun expansion, which is going to like
S1: eat up the earth or populating the galaxy and beyond,
S1: or the heat death of the universe, which is like
S1: escaping this reality. Yeah, or breaking out of the simulation.
S1: I should add that. So as a summary, I terms
S1: are confusing. It's nice to have simple, practical versions. It's
S1: useful to crystallize your own definitions on paper, both for
S1: your own reference and to see if your definitions are
S1: consistent with each other. And I think these eye definitions
S1: work best when they're human focused and practically worded, because
S1: it is very easy to try to be technical with
S1: these definitions, and you instantly open up like this giant
S1: mess the moment you go a level down in technicality.
S1: And what you'll find is if you get 20 AI
S1: experts in in a room and a bunch of ML experts,
S1: they've been doing this 30 years and you ask them
S1: their opinions. They don't agree. The textbooks don't agree, the
S1: experts don't agree. The moment you go a layer down,
S1: it's a giant mess. And that giant mess stops conversations
S1: from happening because they end up, they'll get two hours
S1: into a podcast and be like, oh, that's what you
S1: meant by I. That's not what I mean by I.
S1: And I'm like, okay, let's stop messing with let's stop
S1: wasting two hours in a conversation because we're not talking
S1: about the same thing. Let's level up to an abstraction
S1: level where it's useful in a conversation. That's why it's
S1: called real world AI definition. All right. So that's that one.
S1: All right. Stories. Cloudflare has a new free tool to
S1: stop bots from scraping websites. Remember I told you about
S1: Cloudflare how they just do like the coolest stuff and
S1: they just Cloudflare is a beast. I am telling you.
S1: They just they see a problem that is annoying everybody
S1: and they go and just pour a little bit of
S1: like cement in that seam or that crack or that
S1: hole or whatever, and they're just like, we're going to
S1: solve this. And if no one else does it, they're
S1: just going to be the solution for this. Like they're
S1: doing this with Captcha. They're doing this now with AI
S1: scraping like somebody over there is going, this is going
S1: to be a problem in the future or this is
S1: already becoming a problem. Let's get there first. And I
S1: massively respect it. And before too long, the whole whole
S1: internet's going to be Cloudflare, because over the course of time,
S1: they've made like 500 of these tools at some point. Right.
S1: And it's like all those random little tools that they
S1: made become the structure of the internet. Twilio says, uh,
S1: somebody's got phone numbers because of an unsecured auth API endpoint,
S1: 33 million phone numbers. Russian experts say they fully analyzed
S1: the structure of the American attack Miss Atacms missile, and
S1: they believe that's going to help them fighting Ukrainian missiles,
S1: which is not good. Thanks to tines for sponsoring. North
S1: Korea has switched its state TV broadcast from a Chinese
S1: satellite to a Russian one. And now fewer people can
S1: watch Hezbollah launch over 200 rockets and drones at Israel
S1: after Israel killed a senior Hezbollah official. Thanks to nudge
S1: security for sponsoring. US intelligence community is diving into generative
S1: AI to enhance intelligence operations, using it for search, discovery,
S1: counterargument generation. Uh, I'm going to make so much Intel stuff,
S1: I cannot wait to build that product in substrate. I
S1: cannot wait. There's some analysis saying I cannot be funny
S1: from Anya, Jeremy Greenwald, and I think I disagree. And
S1: here's an example. My buddy Joseph posted this one. It's
S1: a glyph app and it makes memes. Look at this
S1: bug bounty, bro. Like I legitimately laughed at some of these.
S1: Show me show you my leet skills. Use the inspect
S1: element I only hack ethically dos school website in eighth grade.
S1: I mean, first of all, these are real. They're good
S1: in terms of like they actually apply to the field
S1: that they're making fun of. And, uh, my POC is flawless.
S1: It's a Rick roll link. I generated this. Okay. I've
S1: been saying for a while that being funny is a
S1: major milestone in human AI, in AI becoming real. And
S1: just like a lot of other things in AI, there
S1: are so many people who are like, oh yeah, it can.
S1: It could tell a cat from a dog, but it's
S1: never going to make me laugh. Whatever. The stuff's been
S1: out for like 18 minutes, this just happened at the
S1: end of 2022. Like this is still day one. I'm
S1: telling you right now, there will be funny I very
S1: soon and this is the first version of it. I mean,
S1: this is already funny. I technically the real standard is
S1: going to be a stand up routine, a deepfake of
S1: a face, or actually a deepfake. Okay, I am watching
S1: a stage. There is a deepfake, uh, person standing there.
S1: It doesn't even have to convince me. It could be
S1: like uncanny valley. But if there's a deepfake person standing
S1: there talking, doing a stand up routine and it makes
S1: me laugh the way it makes me laugh when I
S1: watch other stand ups, that is a major milestone. That
S1: is extraordinary. I don't know how long that's going to. Oh,
S1: I should do that on a prediction market. Yeah, I'm
S1: curious about that. I'm guessing like a year and a
S1: half to two years. I think it's going to be
S1: a little while because that one's really, really difficult. But
S1: it could be, honestly, it could be opus, it could
S1: be opus 35 or GPT five. It could happen in
S1: this next iteration, which is going to be around the
S1: end of this year or beginning of next year. Okay.
S1: Nvidia is set to make 12 billion by selling over
S1: a million HG, H20 GPUs. That sounded impressive. I thought
S1: it was like, how are you going to send those?
S1: I thought we had export controls, but these are within
S1: the export controls, so they must be like pretty nerfed.
S1: Apple might soon announce a deal to bring Google Gemini
S1: to iOS 18, so that means they're working with all three.
S1: They already announced OpenAI in the announcement. They mentioned meta
S1: a while back and now Google. And by the way,
S1: I'm bothered with Apple and also with OpenAI because they
S1: both did demos of things that aren't in the betas.
S1: Some reporting is saying this Siri stuff, and all the
S1: AI stuff won't even be until next spring. And I'm like,
S1: stop showing me demos of stuff that won't be out
S1: for months, if ever. But that's just me being annoyed.
S1: Greece is moving to a six day working week. Weird. Oh,
S1: this is actually for a different story. That's a mistake here.
S1: Do not like, um, this was about somebody, uh, Apple
S1: putting cameras on AirPods, which I was hoping was going
S1: to be actual cameras. But of course, that's going to
S1: be more headgear that's going to require like, uh, like
S1: a collar or like a neck piece or like a
S1: heavy earphone or headphone because you need to power this, uh,
S1: this camera that's running all the time. So you're going
S1: to have battery issues, plus lenses and stuff like that.
S1: But what I really want is I want the AirPods
S1: or this headset or whatever it is, but it's got
S1: to be every day. But I want it to see
S1: behind me. That's super critical. If you look at my
S1: big eye piece, I want it to see in front
S1: of me. I want it to see behind me. It'd
S1: be nice if it saw to the sides and it
S1: could connect them for 360. That would be nice. But
S1: most importantly, in front and behind And then that all
S1: being processed and it's like, hey, there's a dangerous thing
S1: in front of you. Hey, watch out for this. This
S1: car is coming in your direction. Whatever. Someone's sneaking up
S1: behind you. Be great for women. Like walking at night.
S1: It's like this person is following you. Yeah, but no,
S1: the thing that actually is getting shipped, it's for gesture controls.
S1: So you get like, you know, go like this and
S1: you could do things with your AirPods. That's the rumor.
S1: David Brooks sat down with Steve Bannon to understand his
S1: vision for the global populist movement. Um, number of U.S.
S1: high school graduates is expected to peak in 25 and
S1: then decline for years. I don't understand this. Schools and
S1: colleges are closing. Faculty members are being laid off. I,
S1: I what I don't understand why there's going to be
S1: fewer people graduating. Is there a few people going into
S1: the system? Is the population declining? I'm not sure exactly
S1: what's going on here. US dollar just hit a 38
S1: year peak against the yen. Peoples whose eyes dilated more
S1: performed better on tests of working memory, suggesting that pupil
S1: size is linked to how well we can process and
S1: remember information. And this is from Scientific American. The FDA
S1: approved Eli Lilly's Alzheimer's drug Kazuna. Why was that hard
S1: to pronounce? Kazuna. Donanemab is the real name which shows
S1: disease progression, slows disease progression by about a third. These
S1: drug companies are killing it with Ozempic and now Alzheimer's.
S1: High work in progress is killing your business, innovation and morale.
S1: The more tasks you juggle, the slower everything moves. Yeah,
S1: tell me about it. I'm struggling with this right now. Ideas.
S1: The I class gap. There's a separation that is really
S1: freaking me out about AI right now, which is dramatically
S1: increased separation between social groups or classes, socioeconomic classes because
S1: of AI. So basically one small group at the top
S1: of like 1% or whatever, how you want to call
S1: it 5%, we'll use AI to have a staff of
S1: 10,000 executive assistants, tutors and analysts working constantly to run
S1: their businesses, optimize their entire lives, accountants doing their paperwork,
S1: filing their taxes, optimizing everything. Just think about that. A
S1: lot of people, this top 1%, are going to have
S1: like a team of 10 or 50 or 100 or
S1: 10,000 executive assistants, which are basically independent eyes, all working
S1: towards the same goals, optimizing everything in your life. That's
S1: the top 1%. And a lot of other people either
S1: won't be using AI at all, or they'll only be
S1: using it for gaming, watching media porn entertainment, watching videos,
S1: watching Netflix, just wasting time like slightly more efficiently using AI.
S1: And this is largely the same split as between like
S1: voracious readers and everyone else today. But it's going to
S1: be way worse with AI, because AI is going to
S1: lift the top 1% way more. Even more than being
S1: a heavy reader. Discovery self publishing a tech book mma AI.
S1: This guy is predicting MMA fight outcomes with very high
S1: accuracy just using AI, and all he's doing is he's
S1: looking at the, um, the people that they fought in
S1: the past. Sam Parr launched Sam's List, a database of CPAs,
S1: accountants and tax strategists, and he's already made $32,000 in
S1: just two months. And illustrated transformer visual intuitive guide to
S1: understanding how Transformers work. 11 labs has a new voice isolator.
S1: This thing's scary. Rachel Toback, my buddy, talked about this, uh, talking.
S1: I forgot how to pronounce this. Um, talking. Yeah, I
S1: feel like I should know how to pronounce that. Anyway,
S1: Ursula K Le Guin did a translation of this, which
S1: I've not read this thing yet. I really need to
S1: read this thing in full. And the recommendation of the
S1: week is ground news. Ground news. This is like the
S1: best website right now for news, especially going into this, uh,
S1: political election series, uh, situation in the US. So it
S1: shows you bias in how stories are covered. In fact,
S1: let me just pull this up. It's the whole purpose
S1: of being on, uh, on video. Look at this thing. Okay.
S1: I'm logging in, maybe and doxing myself while I log in.
S1: That's cool. Okay, look at these. You see these outlines?
S1: It's got an outline for the type of coverage that
S1: it's mostly getting. Look at this bar. Left 17% center 16%.
S1: Right 67% okay. And you have these bars all over
S1: the place. And what's really cool blind spot. Look at
S1: this blind spot thing for the left. This is exposing
S1: you to things that the right is talking about, that
S1: the left is not. And same for over here, for
S1: the right exposing you to things that the left is
S1: talking about, that the right is not. So this this
S1: helps you basically balance yourself, or at least have more
S1: of an option of balancing yourself. Highly recommended. Especially if
S1: you know somebody who claims to be wanting to be
S1: less biased. And they're like, well, I'm just looking for
S1: a good source or whatever. Send them this. Okay. And
S1: the aphorism of the week, clear writing and clear speaking
S1: are simply outputs of clear thinking. Naval clear writing and
S1: clear speaking are simply outputs of clear thinking. Naval Unsupervised
S1: Learning is produced and edited by Daniel Miller on a
S1: Neumann U87 AI microphone using Hindenburg. Intro and outro music
S1: is by Zomby with the Y, and to get the
S1: text and links from this episode, sign up for the
S1: newsletter version of the show at Daniel miessler.com/newsletter. We'll see
S1: you next time.