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S1: That's collider.com unsupervised learning. Welcome to Unsupervised Learning, a security, AI,
S1: and meaning focused podcast that looks at how best to
S1: thrive as humans in a post AI world. It combines
S1: original ideas, analysis, and mental models to bring not just
S1: the news, but why it matters and how to respond.
S1: All right. Welcome to unsupervised learning. This is Daniel Meisler.
S1: All right. 436. In about a bunch of AI stuff here. So, uh,
S1: big religious day for me on Monday. It was, uh,
S1: WWDC by Apple. And what I said after it was Apple,
S1: just one. I they caught up and passed everyone basically
S1: in one shot. And I think this is because the
S1: thing that they get that most people don't is it
S1: it's all about the platform. It's all about the integrations.
S1: It's all about the ecosystem. And they're essentially the only
S1: one who understands that and is also good at tech.
S1: And they also have this massive platform in the iPhone. Right.
S1: So it's just they're just winning. They're so far ahead
S1: of everyone in terms of long term thinking. Like it's
S1: not even close now. This one has since been cleared up,
S1: but at the time it wasn't clear what part of
S1: what they were doing was actually Apple stuff. And what
S1: part was OpenAI. I feel like they could have done
S1: a better job explaining that because I was paying attention,
S1: and I still kind of didn't get it. Looking back,
S1: it was pretty clear somehow I missed it. And I,
S1: I mean, I used to work there, I've worked on
S1: AI quite a bit and I like, I love AI
S1: and I love Apple, and I still didn't get it.
S1: So I feel like they could have been more clear.
S1: So the answer to this question is that most of
S1: what they showed was actually all Apple stuff. It was
S1: not pure OpenAI. And that that's a huge difference, right?
S1: So so essentially what the situation is, is, I would
S1: say probably 90% of what they showed. I don't know,
S1: you can figure it out for yourself. Like if you
S1: did the the math or whatever, it doesn't really matter.
S1: The point is like 90% roughly of everything they talked
S1: about on device, the cloud stuff, the whole new cloud infrastructure,
S1: which is absolutely insane. They basically built a new AI
S1: cloud infrastructure that's completely secure, where, you know, they don't
S1: even have access to the content that's on there. They
S1: don't give that access to any third parties. It's essentially
S1: like a cloud version of the local, very secure enclave.
S1: I'm not sure it's quite that level, but it's pretty
S1: much that level. It is better than anything that exists
S1: in the industry right now in terms of like secure
S1: cloud compute, and it's just extraordinary. And they generated it
S1: just so that they could do these AI features. So
S1: that is essentially their AI story is they're going to
S1: have deep context about you because they're Apple. And this
S1: is essentially life OS is what we're talking about. And
S1: so they have all that information about you and they
S1: have the ability to bring that to AI. And what
S1: they've done is they've made it so that if you
S1: could do it locally, it does it locally, and if
S1: you can't do it locally, it goes up to the
S1: cloud using AI models by Apple. So these are Apple
S1: models running on Apple silicon right. It's not even it's
S1: not even Nvidia stuff. They're using their own chips also
S1: in the cloud and obviously on device. So that's the
S1: whole ecosystem. Now that is for all the context data okay.
S1: That's for basically all of the personal data that you have,
S1: which they don't want to share with other people and
S1: they want to be very, very private. Now in the
S1: case of like looking things up like, oh, get the
S1: current weather and stuff like that. That's where they partnered
S1: with the new Siri, with the new with OpenAI. Right.
S1: So that's that's what they did. So essentially what they
S1: did was they said, okay for live searches, OpenAI is
S1: really good at that. You know, fact checking, stuff like that.
S1: Just just calling the internet. And maybe they're not great
S1: at fact checking, but you know what I mean? So
S1: they're leveraging third party AI for a very specific thing,
S1: but they're not like uploading all this stuff to OpenAI.
S1: It's like the exact opposite of what they're doing. They
S1: built this whole new infrastructure just to keep it all
S1: very secure and very private. So it's very much two
S1: different things. Apple AI, which is like 90% of what
S1: they talked about, which has the on prem and the
S1: cloud component. And then this other third party integration, which
S1: is with ChatGPT. And they even talked about yeah, they're
S1: going to do like they're looking at possibly doing Gemini.
S1: But the point is other third party services for those
S1: kind of integrations. But don't confuse it. That is not
S1: what they're doing with our personal data. Like so Live Bovary.
S1: A whole bunch of people were like, well, how do
S1: I find an android? Like, I can't believe they're sending
S1: all my data to OpenAI. And it's like, that is
S1: not what they said. It's not what they said. It's
S1: like the opposite of what they said. So, um, already
S1: running the demos or the betas for iOS 18, uh,
S1: remarkably stable, but largely because they didn't really change that
S1: much yet, and they definitely haven't added like the AI
S1: services yet. So like, Siri is still the same. That
S1: stuff is coming out like later this year, they said.
S1: So I feel like I didn't really get the AI
S1: features that I was looking for immediately in the beta,
S1: which I was hoping to get. But whatever it happens
S1: when it happens went and trained kickboxing, I was. Bad
S1: at it. It was quite sad. I was like five
S1: times worse than a beginner, I think, because it's hard
S1: for me to like, do things slowly and I'm like
S1: imagining things in my brain, but my body won't do
S1: that thing that's in my brain. I'm just very sort
S1: of confused. So I think I'm going to do some more,
S1: like learning the basics of the body coordination, like on
S1: a bag, and then do it with live people after that.
S1: That's my current plan. Plus I'm going to be doing
S1: a Gits as well. And uh, one of the most
S1: important conversations ever on AI safety. Absolutely. Got to watch
S1: this thing. It's, uh, Leopold Aschenbrenner and Dwarkesh Patel. I've
S1: talked about this a million times, but it is really,
S1: really good. Wrote a new fabric pattern called Capture Thinkers Work.
S1: So you basically send in, like Hayek or somebody into,
S1: you know, fabric SP capture thinkers work. And I mean,
S1: it's extraordinary. In fact, I can show you, okay, so
S1: if we do a capture thinkers work here, let's do this.
S1: Let's first let's blow it up some and then we'll
S1: do who's a philosopher? Hume. We'll send that to capture
S1: thinkers work. So comes up with a single line sentence
S1: up here. Most impactful ideas primary advice or teachings. The
S1: works their main works. So you can go build a
S1: reading list based on this top quotes. Something is human
S1: if it emphasizes the role of experienced custom and sentiment
S1: over abstract reason and innate ideas, and like advice of
S1: how to like implement human concepts into your life. So yeah,
S1: love this pattern. It's a great way of like thinking
S1: about someone's work. So for example, if you do like
S1: S or Perl, which it doesn't matter if you spell
S1: it wrong, for example, it looks like maybe I spelled
S1: it wrong because it corrected it to one R and
S1: one L, but yeah. Background where they were born, the
S1: school psychotherapy just really, really powerful. Got a thread here
S1: on how to fight a good mentor. I, I put
S1: this out a very long time ago, but I think
S1: it's worth sharing. I'm going to go ahead and open
S1: it up. So it's like yeah, being mentored by someone
S1: ahead of you can really change your life. Don't overuse flattery.
S1: Ask something very specific. Behave like a future peer like don't.
S1: Don't grovel. Basically, show your you've already done a bunch
S1: of work and, you know, don't ask basic questions that
S1: you could have got by reading their blog or their book.
S1: I mean, to some extent you can, but you don't
S1: want to overdo that and ask for an opinion on
S1: something you've created. So it's like, look, I've done work,
S1: I've created something. I'm curious about a very specific question
S1: about what you think about this thing you don't want
S1: to ask open ended things. Kind of a general rule
S1: here is like, don't give them work. You're you're being
S1: respectful of their time by asking a pretty small thing
S1: and sort of like presenting yourself as a peer and
S1: you're like, hey, look, I'm looking for feedback on this.
S1: I know you've done it a lot, and that's kind
S1: of the vibe. Or you can offer an improvement or
S1: construction a constructive like comment or feedback on something they've
S1: done and most importantly, like produce something really cool and
S1: show it to them. And then they're likely to just like,
S1: treat you like a future peer and interact with you.
S1: And that's the summary here. So and it got a
S1: blog post around it okay. Let's see. Yeah. Adding source
S1: names to stories now which we'll see here in a second.
S1: So basically you'll know what the source is before you
S1: click it. New member piece on the future of AI okay.
S1: Yeah I'm going to go into this one. This one's
S1: pretty major okay. Let's dive into this. All right. So
S1: first thing is like my definition of AGI and ASI.
S1: So my definition of AGI is can functionally replace an
S1: average white collar worker in the US making 80 K
S1: in 2023. And I say 2023 because it's going to
S1: get weird. This definition will get more weird if you
S1: don't lock it into a previous time before. Like AGI.
S1: We're basically saying like, look, a white collar worker making
S1: an average salary, right? Like 80 K in the US
S1: in 2023. And ASI, I'm defining as an AGI that's
S1: smarter and more capable than any human that's ever lived.
S1: And what what this is essentially saying is like, if
S1: you can functionally replace an average white collar worker in
S1: the US, what that is referring to is generality, because
S1: what I'm talking about is the ability for the boss
S1: to say, you know what, you're actually not on that
S1: project anymore. Go get with Julie and Lisa, and you're
S1: being completely retasked to this other project, which is kind
S1: of unrelated to that. And there is some issue of
S1: like specialization, but we're assuming it's still within the specialization
S1: of that person, and it's general enough where you could
S1: be like, look, it's a completely different task. It's a
S1: completely different project. It's got a different time frame, it's
S1: got different goals, it's got different metrics. And what a
S1: human does is basically say, okay, cool, I will erase
S1: what I was thinking before. I will take the new requirements,
S1: I will come up with a new plan and I
S1: will start working on it, which means you got to
S1: break it into tasks. You got to have a timeline,
S1: you got to have a schedule, you got to have
S1: all that. So in other words, it's not just narrowly
S1: executing small tasks, it's also the planning stage. It's also
S1: constant readjustment, constant evolution of like how do I best
S1: do this. And obviously there's different tiers of quality of
S1: people who can do that as well. But that's why
S1: I say average white collar worker making $80,000. So it's
S1: not like exceptional. And it's not like the worst. So
S1: so given that as a definition of AGI, this general
S1: intelligence that can like be retasked as well as do
S1: specific tasks underneath plus answer emails plus be, you know,
S1: communicative and, you know, basically function as a good employee.
S1: That's essentially AGI. So given that what who is able
S1: to do that and basically be a thinker and be
S1: a producer of new ideas, basically smarter and more capable
S1: than any human that's ever lived. And a good way
S1: of thinking about this is like and Aschenbrenner talked about
S1: this as well in the Türkische caste. So while using
S1: their first names, Leopold talked about that in the Dwarkesh
S1: podcast conversation. It's essentially like, what if you could just
S1: make like 10,000 Einsteins or John von Neumann, right? That
S1: would be insane. So just imagine John von Neumann here,
S1: or Einstein or whoever you want to put as like
S1: the smartest person. And just imagine you can have a
S1: whole bunch of them who could go and do these tasks,
S1: like AGI tasks like, you know, most importantly, come up
S1: with new science, come up with new innovations, come up
S1: with new medicines, but also, yeah, go do all this work,
S1: figure out how how to do it better figure out
S1: better ways to do all the work we have to
S1: do and then go do it. So that's ASI. And
S1: I'm saying essentially there's a big divide here between conscious
S1: and unconscious. And it actually doesn't matter all that much.
S1: A lot of people think that consciousness is on this
S1: path over here. So it's like, oh, we're going to
S1: get artificial narrow intelligence and then we're going to get AGI.
S1: And then after that it becomes conscious and then it
S1: becomes super intelligent or some combination thereof. But I think
S1: the better way to think about this is that we
S1: could stay completely over here and not even get consciousness,
S1: but still have artificial super intelligence, right? So one does
S1: not require the other. And yeah, I've got prediction for
S1: a date later on. We are now inside the post itself. Yeah.
S1: So how I see the paths of development and the
S1: thing that got me talking about this, or thinking about
S1: this a lot more, is just this whole Leopold and
S1: Dwarkesh thing. Yeah. So I talked about that path and
S1: that's the conversation. Yeah. Okay. So this is basically my
S1: argument of like how we're going to get to AGI
S1: and ASI. So I think this is a hypothesis right.
S1: It's it's an idea. It's a thought only so many
S1: components to understand the world. Some of these are easily
S1: attainable and some are like impossible. So an impossible one
S1: is like you can't calculate all the combinations of things
S1: like numbers get crazy very quickly with combinations. So unless
S1: you can brute force everything like like God or something,
S1: it's really hard. But I think there are really realistic
S1: and approachable abstractions of that complexity. So my intuition is
S1: that there are likely abstractions of those that we haven't
S1: yet attained. I think that's pretty obvious. And then an
S1: analogy I use here is like fluid dynamics representing all
S1: the molecules in a liquid, or Newtonian physics being an
S1: abstraction for like quantum or whatever the bottom layer is.
S1: So my friend's argument is I was walking with him
S1: recently and like, I think it's really smart. He basically
S1: said there's limitations to what we can do, even with
S1: super intelligence, because of the one way nature of calculation.
S1: So basically P and P, you could try lots of things,
S1: but it's a one way function. You can see if
S1: it works, but you can't like immediately see all the options.
S1: And that's kind of like P and P. So he
S1: came up with a great definition of intelligence, which I've
S1: since seen like other people talking about as well, which
S1: is like the ability to quickly reduce a search space
S1: for a problem, like like pruning trees. So there's like
S1: 100 trillion options. What can we do to get that
S1: down to like 10 or 100 or 1000 or whatever
S1: as fast as possible. And the faster you could do that,
S1: the more you could do that, the more intelligent you are.
S1: And actually reminds me a lot of quantum computing for
S1: the first time. I've never seen like an intersection there,
S1: but imagine that you could test multiple options all at once,
S1: which is kind of the whole thing with quantum anyway,
S1: so here are the components I see leading to superintelligence.
S1: One is a super complex model of the world, like
S1: how the cell works, how medicines. Interact with cells, you know,
S1: molecular interaction with different body parts. So it's essentially like
S1: testing drugs, but doing it with models instead of like
S1: having to actually try it in labs and people and mice.
S1: So think of that at like all levels of physics.
S1: So like quantum atomic molecules, cells and bigger and bigger,
S1: eventually you're at like psychology, sociology, whatever. And in some
S1: cases it's just feeding our current understanding as text. But
S1: even better, if you like, have actual recordings of these
S1: things happening, similar to how Tesla taught its new full Self-Driving.
S1: It's just like, yeah, this is here. Here are the
S1: actual interactions. Learn from that. So the use case I
S1: kept using for this was solving human aging. So let's
S1: say it's like the telomeres or whatever the length of
S1: the telomeres. And there's a whole bunch of different possible
S1: mechanisms of how aging could be happening, and therefore how
S1: we might be able to turn it off or reverse
S1: it or whatever. But the most important thing is like
S1: have a deep understanding of how everything works, so you
S1: know where you could actually start to go and look.
S1: So the next big thing for this, in my opinion,
S1: is the patterns that analogies and metaphors. So links between things.
S1: And we're already seeing like models get really good at that.
S1: And I think the next one is the size of
S1: the working memory. Right. So not exactly sure what this means,
S1: but it's like it's it's like your Ram or your
S1: functional memory. It's like, how much of this map can
S1: you fit in your brain at once without having to
S1: slide it over? And parts of the map fall off
S1: the table and you can't see them. So maybe that's
S1: a combination of multiple tiers. So you have like you
S1: have like Ram, then you have like L1, L2 inside
S1: of the CPU, which is really the brain, not really
S1: memory but kind of all the same. Right. And then
S1: you've got disk which is much slower, which has to
S1: go into memory to get loaded up. And when there's
S1: too much on disk and not enough Ram, like I said,
S1: you got to swap. Right. So these are the types
S1: of things that I think the overall size of that
S1: memory that can do the patterns based on the world
S1: model is essentially what gets us there. That's my theory.
S1: And essentially this theory is that with scale inside of
S1: neural networks, those three things just keep getting better and better,
S1: plus some post-processing which we'll talk about, and a whole
S1: bunch of hacks and tricks that are basically just going
S1: to come with the industry, because my friend pointed out
S1: that just the size of the model enough alone is
S1: not going to be enough, and that Post-training is super critical.
S1: And once I got what he was saying, like, we
S1: definitely agree on that now, it was just something that
S1: I wasn't aware of and that got us kind of
S1: in line. So this is my way of capturing this.
S1: So my hypothesis is that there are very few fundamental
S1: components that allow AI to scale up from narrow AI
S1: to AGI to ASI. So it's a world model, sufficient
S1: training examples to allow deep enough ability to find patterns
S1: and similarities between phenomenon within that world model, sufficient working memory,
S1: and then ultimately the ability to like, model the scientific
S1: method and test things. Ideally not having to actually do
S1: that test, but being able to test things in an abstraction. Right.
S1: And I'm not sure how smart or how good that is,
S1: because if you're not testing, you're not really testing. I
S1: think this might be possible. And yeah, like I said,
S1: I'm least confident that number four is required or that
S1: it's possible. However, I would say that these three are
S1: amazing and possibly enough. And this one, if it were possible,
S1: to whatever degree, it would massively enhance the other three.
S1: And I think these are the main components. I mean,
S1: I've spent relatively little time thinking about this. I could
S1: revise it in the future. The other thing to say
S1: about this is that number four, which is the testing piece,
S1: might just be implied somehow if number one is sufficiently advanced,
S1: if the world model is good enough, maybe we start
S1: getting to this, I don't know. And maybe it requires
S1: the patterns and similarities. And obviously I think a lot
S1: of this requires the memory. So I guess what I'm
S1: saying is the whole thing might be two big things
S1: the complexity of the world model and the size of
S1: the active memory that it's able to use. And maybe
S1: the patterns and the testing stuff might come along with
S1: those two. And I guess the most controversial thing is that,
S1: and this kind of undergirds everything I'm saying is the
S1: hypothesis that the universe has like an actual complexity that
S1: is approachable or finite, and that models only need to
S1: cross a certain threshold of depth of understanding or quality
S1: of abstractions or depth of abstractions or height of abstractions
S1: to become very similar to like a Laplacian demon in
S1: the sense of like full knowledge. So I don't think
S1: they can have full knowledge. I think that's God level
S1: stuff that's supernatural. At least according to our current understanding
S1: and my belief. But that if you hit a certain
S1: threshold or depth of abstraction quality, it's functionally very similar.
S1: That is what I'm basing my intuition, my hypothesis of
S1: AGI and ASI on. I got another concept called human 3.0.
S1: So it's essentially about how many people believe that they
S1: have ideas that are useful to the world. How many
S1: people believe they could write a short book, percentage of
S1: a person's creativity that they're actively using in their life,
S1: percentage of a person's total capabilities that they actually broadcast
S1: the world as some sort of value, and the degree
S1: to which someone lives as their full spectrum self. So
S1: in human 2.0, we're essentially living and working for capitalism.
S1: Capitalism is like the most important thing. So we're kind
S1: of like 10%. We're putting like 10% of our value
S1: in what we can offer to the world, I would
S1: argue in our CV because it's like, oh, I'm really
S1: good with Microsoft Excel, and I can send emails or whatever.
S1: And it's like, is that really you? I don't think so.
S1: The other aspect is that human 2.0, I would say
S1: current model is essentially most people believing that they're not special,
S1: most people believing that only special people write books, write blogs,
S1: give presentations, do things like that, and regular people don't.
S1: So two main things bifurcated lives broken into personal and professional.
S1: This is 2.0, and the percentage of people who think
S1: that they have something to offer the world, which I
S1: would argue is extremely low and human 3.0 is the
S1: transition to a world where people understand that everyone has
S1: something to offer to the world, and they live as
S1: full spectrum people. So it's like living for other humans,
S1: living as humans, for humans, as opposed to living as
S1: humans who like it's like living to work instead of
S1: working to live and wouldn't even be working to live.
S1: Human 3.0 would even be more advanced than that. It
S1: would be. So it means like your public profile is
S1: everything about you, right? How caring you are, how smart
S1: you are, how funny you are, your favorite things in life,
S1: your best ideas, the projects you like to work on,
S1: the problems you think are important and meaningful in the world,
S1: plus your technical skills, of course. And they know that
S1: they're valuable because they are all these things, not just
S1: what they can do for a corporation. And so my
S1: thoughts on this are like, because you have to start wondering,
S1: when you listen to the Leopold and Aakash conversation, it's like,
S1: is human 3.0 in danger? Are we in danger of
S1: losing a world where humans exist and do what they
S1: do for other humans? Because AC is going to take
S1: over and whatever turn us into whatever. Maybe, hopefully? Well,
S1: I'm pushing for a possible AGI or a that allows
S1: us to do human 3.0 either. It would be kind
S1: of scary if it just took over and implemented it.
S1: That would be scary. Hopefully it would arrive at something
S1: like human 3.0 and keep us alive and thriving. Or
S1: it doesn't take over. It's still used as a tool,
S1: but it's in the hands of someone somewhat benign like
S1: the US, hopefully remains benign. And at that point we
S1: we basically give it the criteria of help us maintain
S1: a society in which we move towards and maintain something
S1: like human 3.0. Another thing I'm really worried about is
S1: like how immigration will affect UBI. So if the government
S1: is basically the jobs are gone, no, very few people
S1: can do something that I can't. And that's like, whatever 10%
S1: of the world or 1% of the world, or 25%
S1: of the world, whatever. Different times, different numbers, different ways
S1: of measuring. But let's say it's 10% and the other 90%
S1: just aren't employable because, like Harari said, you know, they're
S1: a useless class. They have nothing that they can offer
S1: the economy. And this is human 2.0. Still, this is
S1: not crossed over into this hopeful land of human 3.0.
S1: In the meantime, I mean, because some people, like Leopold
S1: are worried about ASI happening in like a number of
S1: years and just like completely redoing society and the government's
S1: obviously going to have to be involved there. They're going
S1: to have to pay people to basically not riot, not
S1: kill themselves and, you know, provide education, provide gaming, provide entertainment,
S1: provide something some sort of basis for meaning that's going
S1: to have to be included. But what does that look like?
S1: I mean, there's like a welfare state and there's like
S1: the the government's giving out too much money, and then
S1: there's a whole nother level when it's actually UBI, because
S1: no one's even able to work. Right. It's not that
S1: they can't find a job when when people are actually hiring,
S1: it's like, no. Nobody's hiring. All the work's being done
S1: by AI or whatever, 90%, and AI is producing trillions
S1: of dollars in value. And that raises another question. Okay,
S1: you're making all this new stuff. You have all these
S1: new creators, you have all this new AI generated productivity
S1: and products and services. For who? Who's buying the stuff?
S1: Who's buying the stuff? If the top 10% are the
S1: only ones who can make things and sell things. Who's
S1: buying the stuff? And the natural answer looks like, which
S1: is quite gross. It's like, I guess the government taxes
S1: all the income that's being made. Again, where's the income
S1: coming from? It's almost like there's an exchange between the
S1: government and the producers, and the producers are making all
S1: this awesome stuff. The government is using all the awesome
S1: stuff to generate all these awesome things. But then the
S1: government has to take this money, which is very strange
S1: word for it now, because it's not being generated by
S1: the people and being taxed by the government. In fact,
S1: it's like it's just a money idea. The money idea
S1: goes to the government. The government then gives the population money,
S1: the ability to purchase these goods and services, which are
S1: being generated by the small percentage of the population to
S1: the tune of whatever trillions of trillions upon trillions of dollars.
S1: And then they give that money out to the people
S1: and the people that can then use that to buy
S1: the services that are being made by this 10%. And
S1: that feels very not quite Ponzi, but like shell game,
S1: it's like, oh, it's fake money. We're moving it around.
S1: It just feels very strange when when the, the human
S1: population is not producing not participating in that creation. Now
S1: in human 3.0, I think they will be I'm not
S1: sure exactly what that economy will look like. I'm sure
S1: much smarter people can help me think about that. It's
S1: essentially more of a human to human exchange barter system.
S1: Obviously a currency like a maybe like a doctor, a
S1: coffee type of currency or something. But you can also
S1: buy regular goods and services, but you could also pay
S1: people for making you happy or whatever. Like a lot
S1: of people will be creating art and doing lots of
S1: stuff like that. So I'm hoping there's a large economy
S1: for sharing on this full spectrum that we talked about
S1: with human 3.0, and that will involve money and stuff
S1: like that. But what I'm talking about here is really
S1: human 2.0 stuff. This is like this year, next year,
S1: the next five years, the next ten years, you know,
S1: what do we do in the meantime with all these
S1: people who are going to be needing money? I mean,
S1: what happens with an economy with millions of immigrants who
S1: are undocumented, who have families, they need to feed them,
S1: they need to house them. And what if the government
S1: is like, well, what if the jobs go away? Well,
S1: maybe they leave. Maybe they don't leave, though, and they're
S1: very angry because they can no longer get work, and
S1: they're also not being paid like the local citizens. I
S1: don't know what the solution here is, but it's I'm
S1: saying we need to think about it. So my thoughts
S1: on conscious AI are, I think, fairly simple actually. I
S1: think the way humans got consciousness is that it was
S1: adaptive for helping us accelerate evolution. So basically winning and
S1: losing is what powers, evolution, blame and praise are smaller
S1: versions of winning and losing plants and insects win and
S1: lose as well, evolutionarily, but it seems like only humans
S1: and a few other species. And maybe it's a lot,
S1: but it's a subset. Very small subset, have subjective experiences,
S1: and it's not clear that anyone else other than humans
S1: has like blame and praise. Well, maybe dogs do. I
S1: feel like they understand, blame and praise. Good boy, bad girl, right?
S1: I feel like that works based on their facial expressions.
S1: Who knows if that's right. But anyway, bottom line is
S1: blame and praise might empower evolution if we experience winning
S1: and losing, being blamed and praised and feeling that at
S1: a visceral level versus having no inter like immediate repercussions
S1: of not doing well. So maybe evolution still gets you
S1: there if you don't have blame and praise and subjective experience.
S1: But I think this whole kind of container is how
S1: we ended up being conscious, because it enables this, which
S1: makes evolution better. That that's my guess. If that's right,
S1: then consciousness might have simply emerged from evolution as our
S1: brains got bigger and we became better at getting better,
S1: which means it could emerge naturally through like self similar loops,
S1: definitely some RL related stuff. Or we could kind of
S1: just like hack the system and like add it on
S1: and be like, hey, develop this so that you can
S1: have this advantage or whatever. We can try to shortcut
S1: what evolution did over millions of years. Yeah. And then
S1: one question is like, well, if we hacked it that way,
S1: would it be real consciousness? And me as a materialist,
S1: I'm like, well, what is real consciousness like? I think
S1: at some level it's all an illusion anyway. But practically,
S1: if you feel yourself feeling and it hurts, that's real, right?
S1: It doesn't matter how mechanistic the substrate below actually is,
S1: because at some point you can pick along the way
S1: and just be like, this is mechanical, this is mechanical. This.
S1: A mechanical. Therefore it's all mechanical. Doesn't matter. Friendship still
S1: is awesome. Love is awesome. Ice cream is awesome. Doesn't
S1: make it any less awesome than it's made out of atoms, right?
S1: So my AC prediction I'm ferm on my AGI prediction
S1: of 2025 and 2028. Somewhere in there, 2026 2027 is
S1: like ideal, the sweet spot for me. I'm going to
S1: give soft prediction, soft prediction of AC 2027 to 31.
S1: So what is that. That's three four plus two. So
S1: that's roughly 2029. Don't feel great about it. That's like
S1: a 30 to 40% prediction like confidence level. This one
S1: I'm saying 90% by 2028. End of 2028 I'm saying
S1: like 60% by end of 2025. So I feel a
S1: lot more confident about that one. But who knows. Predictions
S1: are hard, especially when they're about the future. Niels Bohr
S1: said that. All right, unifying thoughts. Leopold, you want to
S1: watch him on AI safety? Completely. Absolutely impressive guy. And
S1: you want to go read his essays that are released
S1: on this as well. Talked to Aakash Patel is becoming
S1: one of my favorite podcasters ever right now. He is
S1: basically an autodidact. He's a crazy reader. When Dwarkesh talks
S1: to Cowan or Cowan, that's how he pronounces it Tyler Cowan.
S1: When they talk, they jump around, they're like, oh yeah,
S1: you know, I was I was knitting this thing the
S1: other day and I was working. I was, you know,
S1: making wood figurines on my lathe in the back backyard.
S1: And I was thinking about, you know, whatever, Roman history.
S1: And then I was thinking about, you know, Adam's economics.
S1: And that reminds me of this one poem by Rilke
S1: or whatever. And they just jump from here to there
S1: and they're like, oh, that reminds me of this. Hey,
S1: what about World War Two? And this thing happened and
S1: they're just jumping from place to place and like, I
S1: feel like I'm pretty good at that. I feel like
S1: that's one of my superpowers. But Cowan, seeing Cowan do this,
S1: it is insane. And Dwarkesh is like a mini Cowan.
S1: He's like coming up so fast. It's the reason that
S1: his interviews are so good. He's, like, fiercely curious and
S1: an autodidact, reading, like, whatever, 12 books a second, whatever
S1: his current metric is, it's insane. Anyway. Follow them. It's really,
S1: really good stuff. And human 3.0. My best optimism here
S1: is we get AGI, but we don't get AC for
S1: a while, which will give us a chance to hopefully
S1: move towards 3.0 or we get AC, but it's like
S1: controlled by the US and or benign US actors and
S1: we move towards human 3.0 or AC comes online, it's
S1: benign and it kind of takes over and it builds
S1: human 3.0 because that's the best future for humanity or
S1: something that looks similar. Right. And this is what I
S1: was saying before. I don't actually care how unlikely these
S1: scenarios are, because it's kind of the only good path
S1: that I could see happening from all this AI stuff
S1: is we eventually get to human 3.0. A human world
S1: for humans, and I would say future versions of humans,
S1: which might be hybrids with AI or whatever. But you
S1: got to maintain the humanity. We got to be doing
S1: what we're doing for humans, not for the sake of tech.
S1: So I'm optimistic about that. I am trying to help
S1: build that. That's my whole purpose in, like doing all
S1: of this so that that's what I'm shooting for. And
S1: I don't care if it's a 20% chance of us
S1: getting there. First of all, nobody can know what this
S1: number is, so it doesn't really matter. But second of all,
S1: if it's a 10% chance, guess what? I'm shooting for
S1: the 10%. I'm trying to make it 11. Try to
S1: make it a 50, whatever. Try to make it a 90.
S1: If it's a 60% chance and it's a 40% chance,
S1: we're all going to die. Whatever. I'm not going to
S1: sit around thinking about the 40%. I mean, that's my vibe.
S1: I am shooting for human 3.0. And if someone's like, yeah, well,
S1: you don't know, China can take it over and blah
S1: blah blah, AC and it can kill us all and
S1: whatever paper clips and I just don't see the value
S1: of going hard down the path of like, the doomers
S1: because let's say they're right. Their answer is turn it off,
S1: don't build it. That's not going to happen. Next answer.
S1: They're like, I don't have a next answer. We should
S1: stop talking about this. We should stop building it. You
S1: have exited yourself from the adult table. You are no
S1: longer in the conversation because of Moloch. We are building this.
S1: It is happening. We are running full speed with scissors
S1: in both hands. And there's nothing we can do to
S1: stop that. My argument is, the only thing we could
S1: do is try to aim it in a good direction.
S1: Try to point towards human 3.0 and that's what I'm
S1: trying to do. All right, huge diversion, but worth talking about.
S1: That was the AI piece collection of thoughts and predictions
S1: about AI. June of 2024. All right. Security got a
S1: whole bunch of misinformation sneaking into like, little man stuffed news.
S1: So this is like manosphere, like podcasts or whatever, or
S1: news sites or whatever. They're actually republishing Russian propaganda network content.
S1: So it's like, yeah, it can seep in and go
S1: lots of different places. This is why you actually need
S1: AI to be watching everything. And basically when a new
S1: information or disinformation or misinformation propaganda campaign comes out, when
S1: it comes out, we should categorize, okay, what the core
S1: concept is, the core idea. It's trying to spread with AI,
S1: and then you monitor all the different stories coming out.
S1: It might be like Saskatchewan news from South Parish, you know,
S1: Wisconsin or whatever. And they're spreading the same story, because
S1: what the attackers will do is they'll figure out, okay,
S1: where can we get it in? Where can we push
S1: this narrative? And maybe, maybe the front door is closed,
S1: but maybe there's a thousand different backdoors. That was that one.
S1: TikTok had an issue with DMs. There was a zero
S1: day in DMs where you click it and open it.
S1: It's pretty bad. The current tactic for what? You basically
S1: get compromised for compromise of your TikTok. If you did that,
S1: and a new tactic TikTok is using to avoid the
S1: lawsuit is basically to say they're going to build out
S1: a separate algorithm and separate data just for us. So
S1: we'll see if that works. New paper claims that GPT
S1: four can autonomously hack zero day vulnerabilities. 53% success rate
S1: going to be a lot of these papers. A lot
S1: of these papers are not very good. A lot of
S1: them are decent. I will keep reading and posting these
S1: when the claims are interesting and they're from decent outfits,
S1: but you want to watch the quality of the paper
S1: very closely. Look, especially for how much of their testing
S1: methodology that they publish. Did they show exactly what they
S1: tried against these different things? Did they show all the
S1: different examples? Did they, you know, give you all the
S1: stuff to do the replication? If not, then they're kind
S1: of like hiding the ball and you can't really trust
S1: their score. And we got to analyze paper fabric pattern
S1: that you can use for this. And when I ran
S1: it against this one it got a six on rigor
S1: benchmark and Ablations look good but some details missing and
S1: stats lacking. That's why that's why we have fabric. Okay
S1: Chinese drone photographer got snagged by the US Espionage Act
S1: taking pics of military shipyards. So there's another thing that's
S1: being talked about right now with China buying up a
S1: whole bunch of land right next to military bases, they're
S1: buying like massive amounts of farmland, and they start setting
S1: up all this gear. It's like, oh, don't pay any
S1: attention to us. We're just building some stuff over here.
S1: So I hope Biden or Trump or whoever just like,
S1: finds a list of these. And just like search and seizure,
S1: good bye. Do not pass go. And maybe some of
S1: those are benign, right. But I have to basically be like, well,
S1: based on the, you know, 9566, you know, fucking billion
S1: times that you've actually tried to steal from us. And
S1: actually hacked us, then you know you're going to lose
S1: some for the home team when you're actually doing something
S1: benign and actually just setting up a farm next to
S1: a military base on accident. And they didn't actually mean
S1: anything by that. Too bad you ruined your reputation. Therefore
S1: get out. Okay. OpenAI revealed that its AI tech was
S1: used by Russia and China for sneaky influence ops. Yeah,
S1: they've said this a few times and I like it.
S1: I like the transparency. They're basically like, look, this keeps happening.
S1: We keep catching it and we're going to try to
S1: do better. All right. WWDC keynote got an analysis here.
S1: This is a fabric analysis from a TechCrunch article. As
S1: I talked about before, Apple basically killed the AI thing.
S1: They also did a bunch of really cool stuff. One
S1: of the things is you bring your phone next to
S1: someone else, you can transfer them cash. I've been waiting
S1: that for that forever. Problem is, a lot of people
S1: won't have that feature turned on, or they'll have an
S1: Android and you won't be able to do it like so.
S1: A lot of people are switching to like Zelle or Venmo.
S1: I don't like Venmo, and Zelle is slow. Whatever. I
S1: wish more people had the ability to do these swipey
S1: things and Apple things, but kind of rude to require
S1: that everyone has an iPhone, right? I mean, that's just
S1: not realistic and also not cool. All right. All the
S1: different startups that Apple killed today always fun. After WWDC
S1: or an OpenAI event, US needs 225,000 more cybersecurity workers.
S1: I never trust numbers like this because it's hard to know. Like,
S1: what are they asking? Are they asking it right? What
S1: are the questions that they accept? Like, I just feel
S1: like the numbers are bad. I feel like that number
S1: is going to go down because of AI, and I
S1: feel like it's going to go up because of AI.
S1: So it's going to go down because of AI, because
S1: the jobs are going to get easier to do without people,
S1: but so much more stuff is going to get made
S1: that we're just going to be injecting insecurity into the world,
S1: especially when it's like getting made with AI. So it's
S1: like AI going in, building a bunch of stuff and
S1: have that stuff's going to be really nasty in terms
S1: of security vulnerabilities. So it's like still going to need
S1: people for a while. Like assuming all this AI replacement
S1: stuff goes really, really fast. We're still talking about years
S1: and years. I mean, super fast for like an 80%
S1: replacement of all jobs, super fast. And these are crazy
S1: numbers I'm just throwing out. But but like Nuclear fast
S1: is like ten years now as far as starting and
S1: actually having major impact, I think it's already starting and
S1: it's already like a year or two from now we're
S1: going to start feeling it. So I think that's going
S1: to be really fast. There's going to be a regulatory response.
S1: It's slow to make any change in any organization, and
S1: the bigger and more bureaucratic it is, the slower it
S1: will be. So just because something could happen in a
S1: year or six months doesn't mean it will. And it's
S1: more likely to happen in 2 or 3 or 4
S1: or 10 years. Got to keep that in mind. It's
S1: going to go very fast. It's going to go faster
S1: than you think and slower than you think both at
S1: the same time. Apple just hit the three. $3 trillion
S1: mark again. But Nvidia jumped ahead. And now Nvidia is
S1: even more valuable than Apple. And Microsoft is still number
S1: one insane. Such as killing it over there. Cartwheel tool
S1: olama CLE wire JS for us for UI design shade
S1: map project that turns every mountain building and tree shadow
S1: for any date and time. How to think like a
S1: computer scientist. Pretty cool. Uh, interactive edition here. Starship just
S1: landed its first one. Like all the other ones either
S1: blew up on the way out or, uh, blew up
S1: on the way in, but he finally landed one very impressive,
S1: I think 5000 tons. So that's what, £20,000? Five times two, £2,000.
S1: 2000 times 5000. Holy crap, that's a lot of pounds.
S1: World's largest solar farm just went online. Three point gigawatt
S1: Marty just went live in China. Okay, here's my question.
S1: Why can't we build like ten of these in the US? Okay,
S1: we know we need all this power for AI. So
S1: I say we go Biden. Trump does this. You basically
S1: go to each state and be like, look, I need
S1: you to build 3.5GW in your state within five years.
S1: And they're like, yeah, that's impossible. We'd have to hire
S1: all these people. And the government's like, whatever. I mean,
S1: we have money printers, so we're just going to print
S1: all this money, we're going to inject it into the economy.
S1: Most importantly, giving it to these training programs will, which
S1: will train the people how to build the wind farms
S1: and the solar farms, whatever the best renewable energy is
S1: for that particular state, like Nevada should build like 100
S1: of these things for for sun. I mean, I fly
S1: over it all the time. There's nothing there. In fact,
S1: you fly over the entire United States. There's nothing there.
S1: It's all empty. And meanwhile the sun just hits it
S1: every day. Like nine times out of ten. For every
S1: day of the year the sun comes up. In fact,
S1: I think it might be higher than 90% when the
S1: sun comes up. In fact, it might be 100%. The
S1: sun always comes up and it is hitting us with
S1: like so much energy. And we just it soaks in,
S1: you know, goes off into the atmosphere. Capture it. Okay, look,
S1: we've got people who don't have jobs and don't have
S1: meaning in their lives. We know that AI is going
S1: to require all this power. So millions of people have
S1: no meaning in their lives because they don't have work.
S1: We talk about the loss of manufacturing jobs. Go build
S1: all these solar panels, go build all these solar plants,
S1: have the people working there. Eventually they'll be replaced by
S1: robots in AI. That's fine. That'll take time like we
S1: talked about before. In the meantime, let's go build 100
S1: of these, whatever, 3.5GW facilities or ten gigawatt facilities, whatever.
S1: Giant solar farms, giant wind farms. And we put millions
S1: of people to work, which gives them meaning, and they're
S1: building towards a renewable future. So, like, even if I
S1: didn't happen whatever, we got renewable energy. Now we can
S1: export that energy, we can sell it other places. We
S1: just have it for whatever purpose. And you have to
S1: build the pipelines as well, right? You got to get
S1: that energy to where it needs to go. So that's
S1: like pipes, cables, like, you know, wires, all that stuff,
S1: all the infrastructure for moving energy. Where's the downside here?
S1: It's win win jobs and and tons of energy and
S1: no reliance on external energy. And we get to move
S1: on AI faster. So really bothers me when I see
S1: China understanding this and building so quickly. And we're like, man,
S1: let's fight with each other instead. Oh, in nuclear, nuclear
S1: is the other one. Okay, so USA's although I prefer solar,
S1: but someone knows better than me. USA's solar panel manufacturing
S1: capacity jumped by 71%. I like it, I like it.
S1: Florida and Texas are leading. Let's go. What about Nevada?
S1: Where are you at? Florida. Forget about it. It rains
S1: all the time and it's sinking. It's not going to
S1: work out. I guess Florida's energy generation would have to
S1: be based on, like being flooded or winds. No, you
S1: can't do can't do wind farms and hurricanes. That would
S1: be a bit rough. All right. Humans. David Brooks argues
S1: that progressive energy has shifted from the working class to
S1: elite universities, making them more left leaning than ever. Points
S1: to protests and progressive opinions. Yeah, it's all bad. Highly
S1: recommend you read David Brooks stuff The Road to Character.
S1: My favorite one is the Second Mountain. Absolutely unbelievable book. Unbelievable.
S1: And I love his stuff. The New York Times, he's
S1: like a centrist kind of conservative, but like, yeah, logical.
S1: I like him. Okay. This one turned out to be
S1: not great. A couple people. I looked at it again,
S1: people don't like me linking to Substack. I'm going to
S1: link to Substack. The question isn't is it Substack or
S1: is it whatever, New York Times, because all these different
S1: sources can be bad. What I care about is like
S1: factually being wrong or like making claims and not backing
S1: them up in some sort of in any sort of way,
S1: or just being ideologue and just like being hateful or
S1: rambling or whatever, like, well, I'm rambling, so not quite
S1: the same. But the point is, the best people in
S1: the world at thinking and being rigorous and having good
S1: ideas are going to Substack, okay, and beehive and all
S1: these other platforms and YouTube and TikTok. Even so, it's
S1: not the source that I'm linking to. That's the problem.
S1: The quality of the content is the problem because before
S1: too long, CNBC and like Fox, first of all, Fox News,
S1: I don't link to that usually. I'm sure they have
S1: good news sometimes, but you get the point. It's not
S1: about the type of source, okay? Blogs are just as
S1: authoritative as YouTube as a lot of the media networks
S1: at this point. It's more about the individual and the
S1: actual article. I am making these links available so that
S1: someone can my link numbers or my click numbers are
S1: probably going to go down because they will be like, well,
S1: I don't want to read from this guy, or I
S1: don't want to read from Hacker News or Financial Times.
S1: I hate that place or whatever. Hopefully you don't need
S1: it anyway. Hopefully this one sentence is good enough. That's
S1: the whole point of doing the summary. All right. New
S1: York Times deep dive into devastation of Ukraine. Oh, this
S1: story was absolutely nuts. And yeah, New York Times good stuff.
S1: They're staying around at a company more than two years
S1: could massively hurt your earning potential. US job openings hit
S1: a three year low. Layoffs are surprisingly low, it says CNN.
S1: See above for commentary on sources. Viagra improves blood flow
S1: and could help to prevent dementia. University of Oxford 49%
S1: of independents think Trump should drop out because of his
S1: guilty verdict. I'm making a prediction. I think this effect
S1: wears off and Trump will be pulling even with Biden
S1: within a month. You know what I'm going to do?
S1: Let's go look at 538 538 polls. Wow. Look at
S1: the impact. Look at the impact of the guilty verdict. Yeah,
S1: it happened right in here somewhere. Oh, look. No impact.
S1: And maybe I'm reading this wrong. Maybe this doesn't account
S1: for independence. I mean, this is this is a June
S1: 12th 10th through 12th result. Echelon insights. I don't know
S1: how big this thing is. Okay, YouGov I know that's respectable.
S1: Biden 40, Trump 42. Okay. We got two different echelon
S1: ones daily cause what do we got? 4545 tide. So
S1: so I read this article and it's like, yeah, 49%
S1: of independents think Trump should drop out after his guilty verdict.
S1: Are these polls okay? That's one argument. Independents don't respond
S1: to polls. And supposedly these independents are going to switch
S1: the thing and they're going to be mad at Trump,
S1: and it's going to go for Biden. I don't buy it.
S1: If anything, in my opinion, this is just my opinion.
S1: This is politics. Like, who knows anything, right? In my opinion,
S1: these numbers are soft for Trump. I think Trump is
S1: probably winning by another 5 or 10 points right now.
S1: That's my guess. And I don't think him being convicted
S1: is going to do anything for his numbers if if not,
S1: make it better for him. Just ridiculous. This is why
S1: I think about I. I literally do not look at
S1: news anymore other other than like I brings me political
S1: news and analyses for me and then I have to
S1: go like read the story, I have to do my
S1: analysis or whatever. But like I do not pay attention
S1: to this stuff. I'm trying to build human 3.0 I
S1: don't like. I'm just so disillusioned with everything. I'm just
S1: like checked out. All right, X is doing adult content.
S1: Good for them. Ernest Hemingway wrote a deeply moving letter
S1: to friends who lost their son, saying, those who truly
S1: live never really die. I love Maria Popova. The marginalien. Yeah,
S1: I liked the old name that she had for that
S1: site before. I can't remember what it was. The yellow site. Anyway,
S1: one of the coolest life wisdom reads I've come across
S1: in a while. This is a really cool one. I'll
S1: put this one up. You can never expect honesty from
S1: people who even lie to themselves. You'll be ten times
S1: happier if you forgive your parents and stop blaming them
S1: for your misery for yourself. From society's advice, most of
S1: them have no idea of what they're doing. If you
S1: continue to wait for the right time, you'll waste your
S1: entire life and nothing will ever happen. It's a massive
S1: list of these. They're quite, quite good. You'll lose 99%
S1: of your close friends when you start to improve your life.
S1: That's bleak. I wouldn't go with 99%, but I get
S1: the point. You don't need 100 self-help books. All you
S1: need is action and self-discipline. It's a great list. It's
S1: a great list. You should check it out. Ideas and
S1: analysis I think I figured out the simplest possible answer
S1: for why Trump is still doing so well. Oh my god,
S1: I didn't include the link. Oh man, I didn't include
S1: the link in here. It's like, oh really? You did great.
S1: Thanks for not sharing it. And recommendation for the week
S1: is Tyler Cowen and Aakash Patel. As I talked about
S1: in the aphorism of the week, live as if you
S1: were to die tomorrow. Learn as if you were to
S1: live forever. Live as if you were to die tomorrow.
S1: Learn as if you were to live forever. Mahatma Gandhi.
S1: Unsupervised Learning is produced and edited by Daniel Meisler on
S1: a Neumann U87 AI microphone using Hindenburg. Intro and outro
S1: music is by zombie with the why and to get
S1: the text and links from this episode, sign up for
S1: the newsletter version of the show at Daniel meisler.com/newsletter. We'll
S1: see you next time.