S1: Unsupervised Learning is a podcast about trends and ideas in cybersecurity,
S1: national security, AI, technology and society, and how best to
S1: upgrade ourselves to be ready for what's coming. There's a
S1: ton of discussion everywhere about AGI and ASI and whether
S1: or not they're possible to achieve. I think they are.
S1: And I want to talk about one way we could
S1: possibly pursue that. So I'm going to step through definitions
S1: of AGI and ASI, why we should care about them,
S1: and a system for pursuing them. First, on the definitions themselves,
S1: I think a big problem with AGI and ASI definitions
S1: are really around AI at all is that they're too technical.
S1: They tend to be too technical and therefore not usable.
S1: Not really useful in conversation. I think the best definition
S1: for these things needs to be something that's very human centric.
S1: It should be obvious, and I think we should use
S1: this as a benchmark. Why should I care? We should
S1: be able to look at these definitions and know why
S1: we should care, or at least have a hint towards
S1: why we should care. And I think if we can't
S1: get that from the definition, then it's probably not a
S1: very good one. So my definition for AGI is an
S1: AI system that's able to perform most or all cognitive tasks,
S1: as well as an average US based knowledge worker from 2022.
S1: And I say a US based knowledge worker, because most
S1: people probably won't doubt that there's some kind of base
S1: level smart at doing lots of different tasks, which is
S1: the general in AGI, right? AGI is artificial general intelligence.
S1: So it's general tasks that you do in knowledge work.
S1: And I think if someone's making, you know, a decent
S1: salary as a US based knowledge worker, aren't too many
S1: people that are are going to say that this person
S1: doesn't have general intelligence. So we're using humans as the
S1: baseline for having true general intelligence. And I say before 2023,
S1: because that's when modern AI kicked off. And we don't
S1: want to have the definition keep shifting because humans get
S1: more and more augmented with AI. So so the bar
S1: keeps moving, right. So we want to lock that in place.
S1: ASI is a bit harder and a bit easier to
S1: define at the same time. It's a little more intuitive
S1: because it should be super or above human, but it's
S1: also harder to think about because unlike human level generality,
S1: we've never actually seen anything that's smarter than us. So
S1: you have to actively imagine that. And I think both
S1: of these definitions here are simple enough, and it's obvious
S1: by looking at them why you should care for AGI.
S1: It could replace knowledge workers, which is going to affect
S1: the economy massively. And for ASI you could do a
S1: whole lot more than that. So the next thing is,
S1: why do we care about AGI and ASI? Like what
S1: are they actually going to produce as output. I think
S1: the most important output, or at least the most tangible one,
S1: is invention. Like coming up with. Net new things, ideas, concepts, products, services,
S1: whatever in the same way that humans do. And whenever
S1: I think of that, I have one main question. Well,
S1: how do humans do it? Like what is that actual methodology?
S1: And I saw a recent episode of Lex Fridman's podcast.
S1: He had an evolutionary biologist on and he was talking
S1: about during the enlightenment, there were people meeting and sharing
S1: ideas and like different shops and salons and whatever, wine bars.
S1: I'm not sure where they actually went, but they would
S1: take their ideas, they would share their ideas, and they
S1: would try to copy each other's ideas. But sometimes they
S1: would make mistakes and those mistakes would make even better ideas.
S1: But this idea exchange is like the natural way that
S1: we had tons of innovation during the enlightenment. And this
S1: tracks for me because I've always seen innovation as like
S1: bombarding your brain like a particle accelerator with ideas from
S1: multiple sources, right? You talk with your your smart friends,
S1: you talk about cool ideas, you read a whole bunch
S1: of books, you watch a whole bunch of videos. Whatever
S1: you do, and all these ideas like go into your
S1: brain getting bombarded by other ideas that may be different
S1: or the same or whatever, and they just kind of
S1: percolate in there and kind of reproduce in there. And
S1: then as you sleep and you dream and you think
S1: about other things and work on other things, all of
S1: a sudden you'll be like, wait a minute and you'll
S1: have like these moments where actual innovation happens. So the
S1: idea here is really simple. Let's copy how humans do
S1: this right. How do humans do this at an individual scale?
S1: And let's use automation and AI to orchestrate and scale
S1: that process, which looks, I think, something like this. So
S1: you have your own ideas. Ideas from books, ideas from
S1: other people, ideas from wherever. And you basically put that
S1: into an idea repository. And you could look at this
S1: project right here called substrate, which I put together a
S1: couple of years ago. And it's basically crowdsourced ideas, crowdsourced problems,
S1: crowdsourced solutions. This is a way for us to pull
S1: together ideas and solutions and problems all into a place
S1: that we can crowdsource them and see them and work
S1: on them. And most importantly, we can now hand this
S1: to AI to start thinking about them all together. Then
S1: you have this idea of an idea combination system, and
S1: this is where you combine ideas. You vary them slightly,
S1: change them in a subtle way, add randomness, whatever, and
S1: then fold those back into the idea store. and so
S1: the list of ideas just keeps growing. And then you
S1: have the testing stuff. This testing stuff is absolutely critical.
S1: And the most difficult actually, where you actually test the
S1: ideas against the problems and you need to have a
S1: way to experiment, right? And this is why so many
S1: startups are actually spinning up labs like material science labs
S1: or bio labs, where you can actually build molecules and
S1: test them against living tissue. Right. And you have to
S1: be able to do this. Otherwise you can't know whether
S1: or not the idea worked or not. Uh, in some
S1: cases you can in some like digital cases, you could
S1: do like a B testing or something like that, and
S1: you could say, yes, this is good enough to say
S1: this actually worked. But in a lot of cases it's
S1: hard science, it's hard reality. You actually have to have
S1: a lab to do that. But what you do once
S1: you have all these components, the ideas, the problems, the
S1: idea combination engine and then the experimentation engine, You. Now
S1: just run through this. You iterate through this. So we
S1: have taken the human system of trying these different things,
S1: and we've sort of broken it into its components of
S1: the scientific method. And we are scaling it with AI,
S1: with crowdsourcing and automation, you know, using pure tech to
S1: scale the crap out of an already awesome human process.
S1: And keep in mind, this is not just for like
S1: a new type of keyboard or a better car battery
S1: or something like that. The list of problems could be
S1: anything from like marketing campaigns to figuring out better ways
S1: to connect with kids who need to learn math or whatever.
S1: We could put all of humanity's problems into these problem buckets, right?
S1: And as we get better and better ways to test them,
S1: we accelerate, right? We accelerate this entire process of automating
S1: the scientific method. So this ends up being an algorithm
S1: for solving general problems and testing them. And instead of
S1: doing it at the scale of like the few universities
S1: that we have and the few researchers that we have,
S1: we now can do this at AI scale. And with
S1: the bottleneck really only being, you know, how much testing
S1: we actually need to do in the real world. Uh,
S1: and I'm just really excited about this because, I mean,
S1: we're talking about, I don't know, five x ten x
S1: 100 x 1000 x million x. Whatever. Our current iterations,
S1: our current, you know, attempts on goal for doing the
S1: scientific method, but just scaling that to an insane level.
S1: So I don't think this system is actually needed for
S1: AGI or ASI, to be clear. But this chart here
S1: I think, shows how it is actually just a continuum
S1: going from bottom to top. So you go from the
S1: bottom subhuman level of general intelligence or cognitive capability. You
S1: move up through AGI and then into AC at the top.
S1: But I do think a system like this that we've
S1: talked about is a way to actually make the transition
S1: from where we are into AGI and then beyond into AC. Now,
S1: my current guess, as I've sort of captured here in
S1: this chart for AGI is 2027. And I think that's
S1: going to instantiate as a true knowledge worker replacement agent
S1: that actually you just hire as a company. It comes in,
S1: it basically logs in and starts doing onboarding. It reads
S1: the slack messages, it reads Confluence and Google Docs and
S1: basically onboards like a regular employee. And this will be
S1: our first instance of AGI will be like a commercial
S1: project like that Um, or a commercial product like that.
S1: And again, I think that's going to be around 2027.
S1: My original range that I gave in 2023 was 25
S1: to 28. So I'm, you know, well within those bounds.
S1: And then for ASI, I have a lot less strong
S1: of an intuition, but I'm guessing 2028 to 2030 for ASI.
S1: And hopefully this has been helpful. Cool way to sort
S1: of think about this uh scientific method algorithm. And we'll
S1: see you next time. Unsupervised learning is produced on Hindenburg
S1: Pro using an SM seven B microphone. A video version
S1: of the podcast is available on the Unsupervised Learning YouTube channel,
S1: and the text version with full links and notes is
S1: available at Daniel Mysa.com newsletter. We'll see you next time.