Transcript
1. Introduction & Paper Overview
0:00 · How close are we to AGI? I mean, it’s interesting how much it has stuck around. We have just replaced the pineal gland with a touring machine.
0:07 · You’re a big fan of what I would call biologically inspired intelligence.
0:11 · A biological systems with a a tiny fraction of the energy and learning data could do so much more.
0:17 · Is is that fair?
0:18 · Because whatever that software does has to pass through an interpreter and the interpreter decides what it does.
0:22 · Consciousness is basically an illusion.
0:24 · One of my supervisors accused me of of writing libertarian biology because one of the results of one of my thesis is called the lore of the stack which I like dramatic names.
0:36 · MLST is proud to be sponsored by Prolific. This is Enzo Blindout.
0:41 · Yeah, that’s that’s kind of the the the the goal that we’re working towards. So we’re trying to make uh human data or human feedback uh or actually any kind of feedback at that uh um we treat it as an infrastructure problem right we try to make it accessible we make it cheaper you see this pattern in almost any company and and even in academic research as well every academic researchers cares about the quality of their data let’s abstract it away let’s put a nice API around it to make it just like the same way you also do CI/CD or
1:12 · you do model training pipelines we effectively democratize access to this data.
1:22 · Yeah. Okay. I’m trying to think of my beliefs. Give me one second.
1:28 · What were they again?
1:29 · Entirely cognizant of it.
1:31 · Uh should I look at the camera? Should I look at you?
1:33 · Look at me.
1:34 · Okay.
1:34 · Yeah.
1:35 · Um my name is Michael Timothy Bennett. I am a computer scientist who has to use his middle name because there are too many Michael Bennett in the world. I am interested in understanding AI uh intelligence um life uh the universe and the nature of existence uh and I spend all my time doing that and I have a a side hobby trying to build AI.
2:00 · I I got in touch with you actually it was quite a few months ago. It was when your paper called what the f is artificial intelligence. Did I get the name right? Yeah, I mean I just a couple of extra letters, but yeah.
2:11 · Okay.
2:11 · Yeah, that was doing the rounds and I flick through it at the time. I’ve now just spent the last couple of hours like reading it word for word and it’s actually brilliant. I do recommend that folks at home read that especially for folks in the MLST audience because uh we’re a little bit eclectic in our taste. We are ideas collectors and we like you know different approaches to AGI and also hybrid approaches and a little bit of philosophy and consciousness and whatnot. So um certainly in that respect you might be the perfect guest.
2:39 · Thank you.
2:40 · So this is all very good. This is all very good. Um in that paper you were talking about what intelligence is and various approaches to AGI and and also approaches to categorizing them.
2:52 · Tell us about that.
2. Definitions of Intelligence
2:54 · Okay.
2:54 · So uh intelligence is a hotly debated topic. It’s been for a long time. Um I sort of started off with the leg cut hutter definition of the ability to satisfy goals in a wide range of environments. Uh but as I delved more into biological intelligence and other things, I sort of arrived at a definition as um the ability uh as the efficiency of adaptation. So how sample and energy efficient you are.
3:22 · And then later I found uh Paywang’s definition which preceded mine by several years was adaptation with limited resources which I think is really succinct and clear. Uh and of course there’s myriad other definitions but my favorite is pay wings.
3:39 · Me too. I I could pay if you’re watching this I’m sorry we haven’t published your interview yet. Like basically the reason is I I loved his defining artificial intelligence paper so much that we’ve been meaning to make a special edition on it. So I kind of held back his material for quite a while and we still haven’t still haven’t done it. But even though it’s my favorite definition as well and of course it informs his Nars framework, his his non-acimatic reasoning framework you know some might
4:03 · say it’s almost a bit tortological you know just to say well yeah intelligence is about adaptation with with insufficient resources and and in his paper he went he was at pains to kind of say well we need to have simplicity we need to have fruitfulness in the definition. It’s actually a really difficult thing to get a handle on.
4:19 · Yeah. And I think a lot of people who sort of look at what is intelligence, they end up writing very long and complicated definitions and uh then you spend more time trying to figure out what the definition means than actually thinking about intelligence. Uh that’s another reason I like pace I guess.
4:35 · Yes.
4:35 · Clear.
4:36 · So as you know I’m a big uh you know fan of France. Um for much of MLST history there was a rule that I was only allowed to mention Francois’s name once per show. Um we’ve relaxed that a little bit recently but um may maybe that’s that’s a good place to start. So how does Charlay define intelligence and and you also said that Shallay was kind of inspired a little bit by Lean Hutter certainly in terms of the use of like you know cormraph complexity. Yeah, I mean his his work is very much descended from that sort of uh way of uh thinking.
5:09 · He defines it in terms of the ability to uh acquire skills um which is you know I suppose um perhaps a more benchmark focused version of pay’s definition uh which makes sense because Chile was proposing a benchmark in that paper. Uh so he was looking at each perhaps he was looking at each test question as a skill and the ability to acquire it as what it was testing. Uh which makes a lot of sense.
5:33 · Um but his uh formalism which almost seems uh an afterthought in that paper is uh is where you can see much more of uh lean hutter’s influences. It it’s once again uses comograph complexity. it sort of frames things in the same terms even though in that paper Chalet goes on to say that he doesn’t think compression is sufficient for intelligence. He he was very clear about that in the paper and then he uses it.
6:00 · Um so oh I think that might be because um yeah he describes like a a metaenerating process which is the intelligence and that produces skill programs. So the programs are a compression but the process which created them is doing something more maybe.
6:21 · Yeah.
6:23 · I felt like that part was um my uh I chose to focus on the test part at that point. I’m like oh this isn’t the bit that he was really focusing on. I don’t know. I’d have to ask him. Uh I guess uh yes but you did say something interesting is so yeah may maybe a distinction from is is that and and he thinks of LLMs as being a kind of interpretive collection of skilled programs. So he you know he thinks programs are the output of an intelligence system not the intelligence itself.
6:50 · And with um you know let’s say um AXY and we should we should talk about what that is um I don’t think the concept of a program was an explicit output artifact. is more a definition of an agent which can succeed in in an environment. Is is that fair?
3. Formal Models (AIXI, Active Inference)
7:06 · Yeah, I mean I suppose a lot of this is kind of semantics a little bit but the the model is a general reinforcement learning agent. So it just takes the standard reinforcement learning thing and tries to make it as um sort of a a uh what you might call an upperbound or a super intelligence based on that using Solomon off induction. uh and solomongh induction is uh you can think of it as a
7:30 · formalization of Okam’s razor uh it’s just if I have two explanations I pick the simpler one and so the idea is that I achieves this upper bound intelligence because if you if you accept that Okam’s razor is uh some sort of optimal huristic that you can use and it does this using complexity which is sort of the optimally compressed version of a model so if I can compress something more then it’s simpler and so if I just take the most compressible models then I can get the simplest ones. Uh and this is you know useful for thinking about what a super intelligence might do.
8:02 · And because it’s a general it’s a reinforcement learning agent we can sort of model it out. We can we can build approximations of it. Um I disagree with some of the theoretical foundations. Uh and I you know a lot of my publications are about like what we could do better but I find the overall idea of very compelling and it has informed a lot of my work.
8:24 · Yes, because I I guess for you reading reading your work, you said that if you know if we wanted to create AGI, it would be it would be something that looked like a scientist because you know if we if we frame it at the right level, a scientist can generate hypotheses and you know they’re an agent, they can act in the world, you know, they’re embedded in in environment.
8:41 · So you’re framed at at a sort of sufficient level of embedding that you can actually capture the dynamics of the system and and in that respect is an agent and it has this principle of compression and actually maybe you can contrast it to active inference because that’s quite similar.
8:57 · It’s about this agent that you know balances energy and entropy. So sort of like you know predictive control and simplicity in some you know so-called natural way. How is that different from I mean they’re very different formalisms. I would love to see someone try to active inference.
9:15 · Um but it it’s just I guess active inference has sort of it has a simplicity bias built into it. um you know there’s a like a just a regular the there’s but it’s it’s sort of a the focus is more on uh explaining something else like
9:36 · explaining I mean and also I think active inference is more okay so there’s a whole bunch of ideas there you got the free energy principle you got active inference you got all this stuff about marov blankets and maintaining the border of an organism and having an internal and external world and um
9:54 · so the targets kind of different but uh I I think that I think that’s fair. we we have an agent and the agent is doing prediction in the environment and it can act and and so on and it they are both in some sense I see and active inference trying to um produce a simple model right so there’s this assumption or principle if you like that simplistic models if they predict well must be good yeah yeah um and uh this is a very
10:23 · popular almost orthodox assumption to make cuz AAM’s razor does kind of work but uh Even as far back as 10 years ago, um there were people pointing out that this assumption is based on you know what I mean something like Solomon
10:41 · induction there’s it it performs uh reliably within within bounds uh based on the original assumptions but once you put it in an interactive setting like with iixy um now you’ve got the subjective notion of complexity that the agent has which is used for its version of simplicity because it’s it’s it’s sort of perceiving the through an interpreter. Uh in the case of a universal touring machine, but um you can think of it as just just think of it as an instruction like a language.
4. Causality, Abstraction & Embodiment
11:08 · When I when I say something in a language, how long it takes me to say it depends on the language I use. If I have some mimemetic single syllable word to describe a complicated concept, then the length of that concept is one in my language. And in in sort of my subjective world, that’s fine.
11:27 · But if I have an external world that assesses complexity in the case of this is sort of like uh like looking at leg cut hutter intelligence which is sort of a measure of intell measuring the intelligence of an agent based on the complexity of the model it comes up with. It’s got a different concept, a different sort of uh you know it it the you can make these you can make it perform arbitrarily well or arbitrarily poorly by sort of shifting the goalposts of interpretation.
11:58 · You can make it so that uh if if I am to uh you know well yeah you can essentially make simplicity completely disconnected from performance if you like. Um, not that that actually happens in reality. That’s like it’s not so cut and dried, but it’s it’s certainly not optimal. Uh, which is I think what a lot of people were hoping for with the original Yes.ization.
12:23 · And of course, it’s so interesting that actually when you dig into this deeply, it it just becomes apparent how difficult this problem actually is. You know, like many people might just think, oh yeah, defining intelligence, we we’ve got that now ages ago. I I think there was a distinction as well that certainly lean Hutter they were very focused on on this simplification of of the model and Okam’s razor and I think Shalet did overcome one hurdle which is um task generality as well this developer aware generalization. Yeah. So so for’s definition is not a general definition of intelligence.
12:54 · So it’s it’s very much a specialized definition. So it’s it’s it’s intelligence relative to a scope of tasks.
13:02 · Oh yeah. And then the generalization difficulty is is like the relative entropy from that scope of tasks for you know from the wider scope of tasks and and if I understand correctly like I I think that it’s been years since I read that 2007 paper by Lean Hutter but but it it was about an agent minimizing common complexity which can like do well on a on a the expected performance on a wide range of environments.
13:25 · The definition of task here is important. I um I agree with Chillette that the tasks are what’s important. I just disagree about what constitutes a task. Um so like something like Leen Hutter’s definition with the environments and the goals and the sort of it’s the reinforcement learning framework. You’ve got things like actions.
13:46 · These are all like highle abstractions that that we humans use to simplify the world. And uh as the uh problem of relative complexity in an interactive setting kind of illustrates, if I use a different set of abstractions to achieve the same ends, I can make it I can make something very difficult or very easy.
14:07 · Um so if I’m trying to talk about tasks, then I need to talk about embodiment as well. I can’t just rely on this idea of a software mind because whatever the software mind does depends on the the interpreter or hardware. that you have to look at the system as a whole. And in cognitive science, they’ve got the they’ve got this idea of inactive cognition uh which is not just embodied but in the environment as well because if you change the environment then the same goals change difficulty which is kind of an idea that you can see in lean Hutter’s definition but it is um it is
14:40 · something that needs to be formalized as part of the process of intelligence because if if you just sort of assume you have a set of actions or assume a an environment you’re kind of bypassing a lot of what um what intelligence needs to do to solve a task. And so it doesn’t make sense to think of a computer program, you know, in a in an absolute sense. Programs have purpose.
15:08 · They have they are situated in a context, in a world, in an environment. And I guess more broadly, you’re a big fan of what I would call biologically inspired intelligence, which is that we should create intelligence which, you know, has properties like self-organization and um delegation and causal learning and and all of this kind of stuff, you know, because that’s much more like how it works in the real world.
15:34 · Yeah.
15:34 · So to solve the um so I you know have uh you know had harder very briefly as a supervisor during my masters and then sort of continued working on that sort of thing um uh as I progressed through my PhD and I wanted to address this sort of subjective complexity subjective performance thing and that turned out to be about defining this process of coming up with an abstraction layer.
16:00 · If you think of the touring machine on which uh with respect to which is computed as a I mean or like cutter intelligence um if you think of that as an abstraction layer uh then you’ve got a software mind on a hardware abstraction layer and then that’s sort of interpreted by physics and in a conventional computer you’ve got like Python interpreted by a C program interpreted by and it just goes all the
16:25 · way down to hardware but it doesn’t really stop at hardware because hardware is sort of a state of a physical world and it’s interpreted by whatever physical laws according to which that world runs. And you could then say well knowledge of physics is kind of incomplete. So where does the abstraction end?
16:43 · And if you just if you really want to make an objective claim or a claim about objective behavior to be more abs or exact um you need to formalize what must be true of all abstraction layers, not just a uh sort of a fixed subset assuming some basic layer that you can identify cuz we we’re sort of all interacting with the world through our own abstraction layers anyway. So um if we want to make claims that generalize to other abstraction layers, it sort of helps to have this framework.
17:11 · Um, wait, where were we at the start of this?
17:16 · No, no, that that that that’s great.
17:17 · That makes sense. Let’s bring in the the causality component, right? So, in your in your paper, you were describing almost like single direction arrows of causality. So, you know, we have the the hardware, we have, you know, like the the C compiler, the interpreter, all of this kind of stuff. And so part of what we were saying is that you know to to build a living breathing lifelike system you you need to sort of like respect the causality. You can’t just you know take something out on on its own.
17:43 · But I was also more broadly interested in is it always the case that the causality goes in in one direction or is it actually kind of like quite multi-cale birectional? It is definitely multiscale birectional because in the same way that uh so uh cells uh cells can network
18:03 · right each cell has its sort of own goal- directed behavior this is in biological systems and they can uh network uh with other cells in their sort of perceptual field if you will and uh and then they are constrained by the collective of cells of which they’re part in the same way that a human uh within a legal system is constrained by the behavior of the other humans around uh they’re not going to suddenly run down the street naked.
18:27 · Um so it’s uh yeah definitely there is top down causation and we can see it in our own multiscale architecture that we’re a part of as a species. One really cool thing you did in your paper was you drew a plot and described what it is, but you had abstraction on the y- axis and delegated control on the x-axis and you gave an example of like um you know like a centralized you know form of governance would be in the top left and like a free market would be in the top right. Tell me about that.
18:59 · Yeah, I actually have a much better version of that graph uh that is coming out in the final version uh when that because it’s uh provisionally accepted. So it’s that’ll that’ll be much clearer.
19:09 · But so it’s like it’s uh the the new version of the graph has got like um like food stamps versus UBI to illustrate something that is uh different levels of delegation of control. So these things both distribute resources to uh members of an entire population. But the food stamps central uh doesn’t delegate control. It only delegates some of the resources. there people are very restricted in in what they can do with that.
19:38 · Um and so that illustrates the difference between sort of uh sort of uh decentralization and uh delegation of control. And then you can think of every system as a stack of abstraction layers not just so a computer is typically arranged into like this hardware you know um machine code assembly se all this the stack but so are human organizations.
20:01 · We’ve got um in a military organization we’ve got like soldiers then you’ve got a squad platoon and uh so you’ve got these different levels of abstraction at which you can look at the system and each layer is sort of the behavior of the parts of the layer below and in biological systems the the in the same so their behavior can be an organ and the behavior of organs can be an organism and the behavior of a set of organisms like humans can be language.
20:30 · So you just can move and so you can use this framework of abstraction layers to understand uh some of the relative advantages that biological systems have. Uh should I keep going or well does that imply that the abstractions are real?
20:48 · Right.
20:48 · So you know like I see you as an agent and I see you as factorized quite neatly into organs and brains and eyes and and whatnot. But if we want to build an artificial intelligence, one approach is we we handcraft the abstraction hierarchy. Um another one is that we adaptively learn it.
21:09 · Yeah.
21:09 · And and adaptively learning it is definitely the way to go because we we have learned the abstractions we have in order to uh because these things are useful to us. So a chair for example is useful to me. It is something that that is a cause of veilance to me or is a step on the way to causing some positive or negative veilance. It has utility table same thing. I don’t have the concept of half a chair that I think about like it’s just not useful to me to think. I have to combine this other concept of half to even describe it.
21:38 · And the world is in uh is in every aspect divided into these simplifications, these classifiers. And these are if if you know we want to go back to talking about an intelligence or something that builds programs. I’m building all these classifier programs for television, light, chair, u table, like this is all stuff that matters to me. I don’t build classifiers for things that don’t matter. And so you can even you tie this in with things like the fmy paradox of like why we don’t even notice something that might be classified as intelligent.
22:10 · its behavior is just not relevant to anything that causes us veilance which would tie in with Mike Leven’s work on mind blindness and um very good very good and you spoke about the need for um actually learning causal relationships between things as well tell me about that right I started with a bit of pearl stuff because I was reading this book of why and looking at optimal agents of course I was thinking well if it’s going to be optimal it has to learn some sort of representation of its own interventions in the world.
22:39 · Uh so that’s to say that if I want to know whether if I want to be able to get food and navigate my environment, I need to be able to tell the difference between if I’m a fly on my shoulder for example and the world moves around the fly. There’s two reasons that could have happened. Either my shoulder moved or the fly moved. Fly needs to know which or it’s going to get squished.
23:01 · Same with humans. Same with uh in and in uh and in insects all the way up. This is uh you know others have already suggested this is sort of important to the notion of subjective experience because you need a a subject to have experience right you need to have an eye to know that I did something um and
23:20 · uh so I started looking at to how you would arrive at how you would construct that as a self-organizing system uh and this came into this this this alternative to simplicity that I was working on for like optimal learning I call weak uh weak policy optimization or uh weak constraints or even uh after feeling particularly cocky called it Bennett’s razor. That one hasn’t caught on, but I’m working on it. Um well, you never know.
23:45 · Maybe if anyone uh Yeah. Benn Bennett’s razor everyone.
23:51 · Um so so we can get this uh so the point was if we can define an optimal agent that learns optimally it must construct this sort of representation of itself sort of a I call it um a causal identity for self. It’s like I I have an identity that I associate with the causal effects of my actions. But it’s also like if I don’t start off with a world divided into objects, then I would also do that for other things like a a chair, a television.
24:20 · And it wouldn’t just be a passive classifier in the sense of like a um that we think we tend to think of things as value neutral because it’s simple. But these are things are not value neutral if you have a system that is sort of uh you know impelled uh uh by sort of attraction and repulsion from the ground up, right? It’s not like it’s not like it’s going through an interpreter and having veilance attached after the fact. Uh it is uh you know in the case of like a biological organism a network of cells each of which are being attracted and repelled.
24:50 · Each collective of cells within that is sort of being pushed and pulled and by attractive and repulsive forces and the organism as a whole is being attracted and repelled.
25:00 · So you can think of this as sort of a tapestry of veilance. um and uh it’s developing representations and classifiers of the world not just of itself but of other objects and all of these objects would be sort of the causes of that veilance. So um or in in some way causally relevant to what causes veilance.
25:22 · So there was there was scale maxing which is the San Francisco thing. Yeah. And then there’s a simp maxing which let’s make it simple. So like I see would be an example of that. Yeah.
25:30 · Um, and then there was the W maxing and W I think means world.
25:34 · Uh, well, it meant weakness, but I just thought it was funny cuz it was like wind maxing, but Oh, interesting.
25:42 · What was what was the interest? Because you you were talking about things like, you know, an active cognition, you know, where we have like we consider the whole environment and everything. Is that roughly?
5. Computational Dualism & Mortal Computation
25:52 · Yeah.
25:52 · So, it’s like if if I is a sort of dualist appro. When I say dualist, right, I’m referring to like uh the for anyone who’s unfamiliar, there’s um cartisian dualism is the idea that you have mental substance and physical substance. And he was trying to come up in the 16th century with an explanation of the mind that conform to church doctrine.
26:14 · Uh and so he said that you had mental substance which are all our thoughts and things interacts with the physical substance of the world through the pineal gland and the animal spirits around the pineal gland. it sort of you know bumps it and then it bumps us and then we act. Uh now this even in the 16th century came under some criticism but it sort of stuck around and um it’s interesting how much it has stuck around because we’ve kind of done the same thing with AI. We have just replaced the the pineal gland with a touring machine.
26:46 · Um and an active cognition is the idea that uh your cognition is in the world, right? It’s not a mental substance of sort of uh it’s not just embodied in the sense of like I’m not just a body, but I am part of the world around me. I store my memory extends into the world. I can write things on a piece of paper. Um I and I enact my cognition by interacting with the people around me and the objects around me.
27:10 · Yes.
27:10 · I’m so I’m so so glad you brought this up because the other day when we were chatting you were talking about the pineal gland and and I and I thought oh my god what the what the [ __ ] is he talking about and um and and yes so you’re saying in the 1600s we had this cartisian dualism you know that the mind and the body two different ontological substances and and people at the time thought the pineal gland was almost like the mediating thing in the brain between so obviously you’re not you’re not saying that the pineal gland is the mediating thing maybe you are I don’t know I’m definitely not saying that.
27:40 · Okay, good. Just get that right. But but but the very interesting analogy is that you’re saying that there is this thing called computational dualism and that and and I want to press on this a little bit because I don’t know are you are you saying that any form of functionalism or computationalism is computational dualism or are you saying this this um use of a touring machine in some formalisms is computational dualism?
28:03 · I’m saying I’m I’m using I use computational dualism more to poke fun at the idea of uh defining just a software intelligence because uh if we just have software by itself and we just we don’t say anything about the hardware then it can’t really be intelligence if intelligent if intelligence is measured in terms of performance in the environment because whatever that software does has to pass through an interpreter and the interpreter decides what it does.
28:31 · So we can just make it arbitrarily stupid if we want.
28:35 · Yeah.
28:36 · Um and so it is really uh it was I I used that term computational dualism as almost like a rolled up newspaper to whack people on the nose because I was getting frustrated with repeating myself.
28:48 · Oh, I like it. I like it. How does this idea relate to I mean I spoke with a few folks about um mortal computation, right? You know, so touring machines and program, you know, these are great because they allow us and and also, you know, people talk about panc computationalism. It’s a really neat idea to think about computation in the abstract and think about programs in the abstract. You know, these are things which can run on any computer potentially on different substrates and and whatnot. And the world isn’t really like that.
29:16 · No. No. Um there are finitely many copies of every piece of software we make. Um I love the branding. mortal and immortal computation. But there’s no such thing as immortal computations. There’s just finitely many of uh copies of the software we make. And I get that people might quibble about but yeah, you can copy the thing. But in the context of trying to define intelligence, it is a terrible concept. There’s just there’s just mortal computation.
29:44 · Let’s not complicate the matter by adding in an extra concept that doesn’t apply. Um Yes. Yeah.
29:51 · Yes.
29:51 · And then just to help folks understand, so mortal computation is that the the stuff is the computation. There’s like no meaningful disconnection between like the program and the stuff which does it.
30:04 · I’ve seen a few people give different interpretations of it. Uh the best is probably that of Auroria and Friston where they talk about Oh yes, I interviewed him by the way, Alex.
30:13 · Oh, right. Cool.
30:14 · Yes. Cool guy.
30:15 · Yeah.
30:15 · So they’ve got like quite a rigorous definition that extends over several pages. Um the most common definition I’ve seen is the internet version which is just people going it’s immortal. Um but uh and I think it was first the the term was used as sort of an afterthought by Hinton at the end of a paper that was mostly about feed forward neural networks.
30:37 · Oh yeah. Was it his new his new proposed architect? Was it like the feed forward forward from 2022 in Europe?
30:44 · I I think so. Yeah. Yeah, I can’t remember the name of the paper, but yeah, but I think it it sort of and I remember I saw that and I’d already been writing about like embodiment and how like you you got to like take into account the abstraction layer if you want to like have uh any claims that hold up about performance.
31:03 · Um and so uh and my response to that was to come up with the term computational dualism and write an irritated paper.
31:11 · But um grumpy papers are my favorite papers which is amazing. Okay. Very good. Very good. And so in in this kind of um you know categorization of different approaches because we haven’t we haven’t spoken about you know the Silicon Valley scale maxing and so on. I mean maybe like what what drives you I mean how close are we to AGI? I mean the folks over over there think that we’ve already done it right but just by scaling.
6. Modern AI, AGI Progress & Benchmarks
31:38 · Well, I mean, if you want something that can do jobs uh and automate a lot of the economy, then sure, we’ve got some form that form of AGI. If we if you want something that’s actually intelligent like a human though, we do not have that. Uh and a lot of that is because um like even just interacting with and like I use things like you know, Grock and chat GPT.
32:02 · It’s not like I’m not interacting with this stuff, but if it was if it was anywhere near as intelligent as a human, I wouldn’t have to do all the work that I do. I would um be able to offload a lot of it’s not sample efficient. I know um there’s claims about the ARC test, but just interacting with these agents, you can see that it’s not really sample efficient. I don’t know what they did there to get those results on ARK 1, and I don’t know what uh what the claims are about ARK 2.
32:29 · It’s very hard to tell what is true when people don’t release the code and the results and everything that you can sort of puzzle through and work out. So, uh I think we’re probably a good ways off something that really resembles human intelligence and we need to look at something that isn’t just like a software innovation but hardware innovation.
32:48 · The reason I think that is because we’re using these abstraction layers in the form of like you know the the hardware we have purpose-built for very useful standardized software applications that we can roll out and copy and run on many different computers. Um but if we want something that’s as adaptive as a biological system we need something that is sort of modular and cellular and efficient like a biological system.
33:11 · We can’t be expecting some I mean a biological systems with a a tiny fraction of the energy and uh and learning data can do so much more.
33:22 · Yes.
33:23 · Um yes which you know it’s great because I like having a job. So let’s uh absolutely by the way hot off the press. Did you hear that um Elon released Grock 4? I don’t I don’t know who’s released it but um Greg you know from Arch Prize he posted this morning. Greg’s a good guy and apparently it’s scored about 16% which is sort even even because you my
33:45 · forens you know like um Muhammad and and um Jack I think they’re around 15 and a half% at the moment so yeah Grog Grog four is now in the lead I mean how do you interpret that you think it’s just sort of like dent of memorization or I don’t know I mean I’ll have to interact with it right maybe it’ll be really impressive maybe it’ll uh but I I suppose the proof’s in the pudding uh like if if this starts to do a bunch of really useful jobs across the economy.
34:12 · Um, then we can say with certainty that we’re closer to it. But these benchmarks even like and the ARGI benchmark is a great benchmark, right? I’ve been looking at that my whole PhD is it was a it’s it’s great, but it’s not perfect. None of uh Chillette would not claim it to be perfect.
34:31 · Um, and 16%‘s a great result, but uh I want to see if it can add long numbers. That probably that would be a good start.
34:39 · I don’t know. That’s like that’s my usual thing. I sort of oh, it’s a new toy. Let’s see if it can add long numbers. And it almost always can’t. It’s just Well, I mean, devil’s advocate. Um, yeah, no one’s going to argue with you if you say vanilla LLMs are, you know, basically databases. No one’s going to argue with you, right? But you can add tools to them. I mean, so, so you were
7. Hybrid AI Approaches
35:00 · starting to talk in your paper about there are some very interested interesting hybrid approaches. or you know obviously on the LLM side you add tools and there’s an interesting discussion to be had there whether you know you train them with stocastic gradient descent they use tools what can they do but you’re also talking about some other interesting like you know there’s um uh the non axiomatic reasoning system from pay and there’s the hyperon system from Ben Girtz and my
35:25 · honest like I don’t know anything I don’t know much about those systems but honestly when you were describing them it seemed a little bit like they were everything but the kitchen sink so they’re like you know they can do a bit of basian inference over here and they can do some neural networks over there and I mean that that could work but what what’s your assessment?
35:42 · Yeah. So, oh should I go around should I go over that like tool?
35:46 · Oh yeah yeah yeah. Just just sort of weave weave a path.
35:49 · So I sort of um said there like taking the inspiration from Sutton’s bitter lesson where he talked about sort of uh search and learning. Um I sort of divided in we got sort of two basic tools right we got approximation which is what the LLMs are. I mean and by definition they’re inexact and that’s really great for like you know um you know trolling through large amounts of data and coping with noisy data because it’s an approximation. You can do a lot with that and then with the computational resources we have approximation works beautifully.
36:17 · And then you got search which is like like iterating through a flow diagram or something like that and that is great for precision or things like navigation on your phone. Uh and when you combine these things you get something like alpha go or or um uh yeah I mean so you can use the approximation part for a sort of a huristic to guide the search.
36:43 · You can you can combine these in many different ways and these hybrids allow us to create much more more effective intelligent systems of some one form or another. So in the case of um you know there’s well-known examples of this are things like Alph Go or Alpha Star um but there’s also uh you know the sort of more comprehensive architectures that are meant to kind of emulate the versatility of a human mind.
37:13 · So something like uh like NARS is uh a system that sort of you can integrate many different components and I’ve seen some of the experiments involving NARS and LLM that were at the 2023 AGI conference and like Hyperon is like an inherently modular system that is just meant to allow you to sort of plug and play lots of uh well that’s uh lots of different modules and it’s meant to be decentralized and adaptable so you can plug all these things in as they develop.
37:45 · Um, and yeah, that’s that is definitely, you know, like it can include the kitchen sink if you want to plug that in. But I guess the uh I think it was Yeah, sorry. It was more um hyper on I was thinking about the kitchen s one may maybe you’d do a
38:01 · better job than me at this but r roughly speaking it’s about like building up a whole bunch of reasoning about something which I don’t know much about and then adapting and modifying it over time until which I can make you know deductions and inferences about things and I remember there was some stuff with time constraints in there so if it got stuck on something it would move on and would rank things by it would take into account the resources it had So in practice it would actually be quite useful. You can put it on a little robot, have the little robot run around the room and do stuff.
38:31 · Yes.
38:32 · Um.
38:32 · Yes.
38:33 · Which is cool. Uh but and they they seem to every year come up with better and better benchmarks results, but it doesn’t seem to get much attention in the sort of mainstream machine learning space. Uh yeah. What do you think about benchmarks by the way? Because Grock um I actually interviewed the guy who created humanities last exam the other day, Dan Hendris, and um I think it was at 26%. Today with Grock 4 it’s about 46%.
38:56 · You know what what is the point of benchmarks if they’re so easily saturated? Great marketing. Um I mean it’s it’s like we love measuring sticks.
39:06 · I it’s just it’s a it’s a it’s nice to have measuring sticks. It lets us know that we’re progressing in a direction, whatever direction we point the stick in, I guess. Um but like humans are uh adaptive. If we set up a measuring stick and it’s less than perfect, we will find a way to exploit that.
39:25 · Um, I think this is this isn’t necessarily a bad thing. It’s just that I think people should interpret benchmarks as what they are, which is measuring sticks.
8. Consciousness & The Hard Problem
39:35 · Yes. Talk to me about consciousness.
39:39 · Okay.
39:39 · Um well uh that whole spiel about um and abstraction layers kind of led me down a very long and winding rabbit hole uh with the abstraction layers thing because um after doing that I mentioned
39:57 · before the idea of coming up with a causal representation of the self and the this idea of tapestries of veilance and if you keep scaling up the ability to sort of learn these causal uh causes of veilance then you don’t just get like a a sort of a a do operator for the self
40:14 · or a representation of the self uh which others have already oh actually I should start there that self thing um when I was looking at that in the original paper I uh that I put that in I I sort of think well if you got this self and it is sort of has is inherently veanced uh and it’s sort of made up of sensory motor activity then wouldn’t that be the sort of the basis for a subjective experience and explain something of consciousness. So I I put that in the paper and people liked it and I thought this consciousness thing is great.
40:44 · I’m going to keep doing this. Um I like this and people aren’t laughing at me. So um I kept going and then one of my supervisors said hey that’s like my theory. I did a causal self thing uh and pointed me at his paper and his paper was about something called reaffirence in uh the insect central uh central complex which is also uh he was trying to show that flies have subjective experience.
41:10 · And this tiled back to some work from like 20 years ago where someone was saying that well this is this this sort of representation of the self is like where human subjective experience uh comes. we have uh something called reafference in the mamalian midbrain. Um and so many animals have this. It’s what enables us to tell when I am pressing down on the chair versus the chair pressing up on me. And this is very useful for causal relations.
41:38 · And then I kind of started thinking about consciousness more generally and how well we could make up our subjective experience with these causes of veilance. I was talking about you know things like the television, the chair, whatever. Um and someone uh started um beating me over the head with a copy of David Charalmer’s work on the hard problem of consciousness.
41:58 · Oh yes.
41:59 · Um and after a lengthy argument uh over that I decided well now I have to write about it. So I started writing a uh what and ended up as a 70page paper much to the um my supervisors at the time would please tell him please stop just graduate just just just finish the thesis. Um but I kept writing this and then it got cut down to a uh some much shorter paper. I ended up writing bringing on one of my supervisors as a collaborator.
42:31 · Found another new supervisor who was sort of expert in consciousness to come on and help me finish that and talked about the hard problem of consciousness. Um, and I propose to solve it by showing that uh what’s called a philosophical zombie is impossible in every uh conceivable world.
42:49 · And this is because if you go down all the abstraction layers, you can say well every conceivable world is um is sort of must uh whatever it is right um it must include just change or difference otherwise there’s just a sort of universal oneness and as I put it in my thesis becoming one with the universe is beyond the scope of my work.
43:13 · Um the so you got a set of states and you can build up a formalism from that and um and this formalism I argue describes all conceivable worlds. So if you can show us a philosophical zombie that is and just to explain what that is um it’s something that’s like you or me but not conscious it’s but in every way identical to you or me.
43:39 · So, it’s as efficient energetically, it’s as it’s as smart, does everything we do, just missing consciousness. And I I got really into philosophical zombies when I read the work of of Peter Watts, uh, who is this science fiction author that writes cosmic horror about philosophical zombies. Um, do you want to do you want to continue? I’ve got I’ve got a couple of questions, but No, no, let’s do the questions because I can go on ranting for ages.
44:04 · Well, no, no. I mean, this this is brilliant. Yes. So, philosophicals on me. I mean, um, I I love Charas. We’ve had them on all of the function, dynamics, and behavior without the little bit extra about the the phenomenal component.
44:18 · And so, I I might be a phenomenal zombie.
44:22 · So, I think what you’re saying is because there’s this kind of um lowercase S and capital S subjectivity.
44:30 · And if I understand correctly, you’re saying that the capital S subjectivity can be discounted. There are no phenomenal zombies. I don’t know whether you’re saying it’s because there is no hard problem of consciousness like that that consciousness is basically an illusion. So we should think about subjectivity in terms of purposeful um perspectival representations right but we shouldn’t assume that there’s some magical extra realm on top which is called consciousness.
45:01 · I suppose uh there’s a lot of lot to interpret there. I mean if we’re saying is there like a mental anything non-physical that is if if we take physical to mean something that interacts directly with the physical world and is like not sort of I suppose the advantage of doing that all conceivable worlds thing is I don’t need to talk about physical and non-physical um and I like that because then I can entertain sort of ideas of magical worlds or whatever but beside the point um
45:32 · I’m not saying that consciousness isn’t a thing or that it’s an illusion.
45:37 · Right?
45:37 · I’m saying uh that if uh we can sort of enumerate a bunch explain a whole lot of the features of consciousness as a necessary consequence of uh you know just the state of the environment changing from one to another in all possible worlds, all conceivable worlds.
45:57 · And if one accepts that my description of a conscious organism uh without having said that anything is just physical or whatever is uh is compelling um and I think it is then there is no conceivable world where you can have something that is as is as intelligent and acts the way that I do without or without without being conscious. The conscious is a necessary adaptation. uh this ex and that the idea of information processing without consciousness is uh is implausible.
46:33 · Yes.
46:33 · Very good. Very good. So you are saying basically that it is it is just something that happens when you have configurations along the lines that you are describing.
46:46 · I I guess in a way I can’t challenge you because you’re already saying they need to be biological and and like you know physical and real and so because I mean know my my obvious retort to that would be well um you know like a computer simulation of those things obviously wouldn’t be conscious but you you’re not really saying that are you?
47:05 · No. And I uh there’s one question that at the end of my thesis I get I sort of armanar about cuz it’s like uh and I like this question. I like that I couldn’t figure this out cuz I it’s fun to think about. Um so that tapestry of veilance I mentioned with like all the cells getting pushed this way and that and you can sort of say that you know a conscious state is a tapestry of veilance.
47:28 · Now if I simulate a bunch of cells and give it a tapestry veance and all the uh necessary ingredients like the first and second and third order cells that I only talked about the first order self just before but you can keep scaling the system up and getting predictions of predictions of predictions of uh stuff and if you you
47:47 · get all the necessary ingredients for consciousness and you just simulate it in a computer um there is two possibilities one either a conscious state has to be realized at a point in time by one state of the environment or it doesn’t. If it does have to be realized by a state of the environment, that is to say all of the parts of the conscious state are at the exact same moment there.
48:13 · Um and in the in the formalism that has is a much clearer statement but well um then it’s just humans and organisms and
48:29 · highly distri you could make a nanobot swarm that’s conscious but you can’t make like a single thread CPU conscious cuz it’s not really doing all this at once right it’s kind of like looking at a thing program counter sort of loads something into a register does some stuff shuffles some stuff around it’s spread smeared over time and so the idea is that smearing consciousness over time would kind of kill it. The other possibility is that how the how would we know? Am I allowed to swear on this?
48:56 · Yeah, of course.
48:57 · Yeah.
48:57 · How the [ __ ] know if we’re we’re being simulated? We don’t we nowhere know I think I’m existing at a point in time. Um and uh I could be running this whole thing could be running on a single thread CPU in some kid’s basement. Now I’m not endorsing the simulation hypothesis. I’m just saying. I mean, I like the idea that so everything is clearly there’s like an infinite stack of abstraction layers. Maybe those abstraction layers end in some kid’s basement. I don’t think they do. Doesn’t matter. That’s I’m making fun of the simulation thing.
49:23 · But um if it’s smeared across time, if I can have a single thread CPU load stuff into a register, do all simulate all the stuff that is to do with consciousness, then I could make like populations of humans or what are called liquid brains of ant. So a solid brain is like something like a a human brain with a persistent structure that supports like uh you know in the case of a human brain a bioelectric abstraction layer that can do information processing very useful but it requires a certain stability.
49:55 · It’s it has to be maintain its form. A liquid brain is something like a population of humans doesn’t have to maintain its form. Its computation is not in the form of sort of electrical signals but people moving around doing actions and interacting with the world.
50:07 · Now maybe we’ll network ourselves up. I don’t know but that’s a liquid brain and colony is another liquid brain according to my theory if if a consciousness has to be at a point in time then liquid brain not conscious if you can smear it across time then you can have a conscious liquid brain which is cool but uh has some weird implications that very cool by the way we we’ll be clipping that part so you’ll see you’ll see that part on on Twitter um there is just one potential objection
50:36 · which is that you know What a lot of theories of consciousness do is is they kind of they they brush it to one side and they treat it as something which is epifenomenal which means it it’s not like causally embedded in in the system. And we did ask Friston about this and he had a bit of an interesting response. He said that you know phenomenal states are just um parts of the generative model.
50:59 · You know it’s it’s all it’s all in there. It’s all kind of like part of the the causal nexus or whatever. What do you think about that? So I I very explicitly argue that a phenomenal state is a tapestry of veilance that that the sort of what the um that I am not just doing a representation that’s value neutral that these that my classifiers of the world like television so on is me being attracted or repelled from a physical state um in at like at different levels of abstraction at different scales all happening at once.
51:30 · So there’s a lot of sensations going on there which why it’s not it’s not just loading a file. I feel something as I try to process this information because I’m being impelled by it. But isn’t consciousness something immaterial, something unobservable? So for me it makes sense to say that it’s a coralate of something physical in in your modeling, but it has to be kind of somewhere else outside of the system.
51:57 · Yeah.
51:57 · And I guess uh so that’s the idea of like a a firsterson ontology. There’s something that I know by being conscious that you can’t know by watching me. Uh but um but if if I mean that’s the point of the abstraction layer thing I guess is that if I if I do this formalism of all conceivable worlds and I have the luxury of saying that I I have this like god’s eye view into everyone’s head. So in the formalism it’s nice and neat like that from a sort of an explanation point of view here.
52:28 · Yes, I am stuck in my first person ontology and I can only say what I’m interacting with through my abstraction layer. But it’s also kind of um kind of like saying that uh you know it’s if we if we buy into the idea that we can’t say anything about things outside of our abstraction layer, we’re basically giving up on science altogether. And I think that why would we draw a different line for consciousness than for everything else when um we clearly we could draw the same line for everything else. That’s another thing.
53:00 · I’m not saying that it’s not a line we could draw. We could say I don’t know the television’s on. It’s just it might be on. I don’t know. Uh prove it to me. Uh according to my first person ontology and I and then so you know I can continue to hold whatever belief I like really. Uh very good. Very good. I’ve enjoyed this.
9. The Diverse Intelligences Summer Institute (DISI)
53:20 · I’ve enjoyed it, too.
53:23 · Yeah.
53:23 · So, Dizzy, the Diverse Intelligences Summer Institute, uh was something I found out about about um 3 days before the application deadline. I thought, “Oh, I should do that.” Um cold weather and talking about intelligence. Uh so I have come here and met a lot of people who uh do everything from machine learning to biology to philosophy and had very intense discussions about these things for 3 days.
53:53 · I uh I’m not sure my brain still functions but I know it’s ticking away there with with something. We’re supposed to come up with a project. Uh I um I have a weird problem. There’s too many projects. There’s too many good projects.
54:08 · Yes.
54:08 · Um, but it’s it’s interesting because it sort of ties in with all this uh there’s a lot of people who are also big fans of Mike Leven’s work and I uh well I’ve been writing more recently I’ve been writing a lot with the tapestry of veillance stuff and there talking about abstraction layers everything I’m doing has become about uh that because it’s just what I’m enjoying at the moment.
10. Living Systems & Self-Organization
54:31 · I’ve been looking at say AI safety is for one thing as like well it’s not about the AI and is isolation it’s another um it’s another swarm architecture in which we are a liquid brain into which the AI plugs in it’s just a part of the organism and so rather than worrying about aligning a policy or or whatever um it’s really
54:55 · about just designing the system as a whole to accommodate these different components and make use of them in the same way that um that a biological system sort of uh you know makes use of resources available to it and then networks cells together to form stuff that that’s actually very interesting.
55:12 · Yeah. So um you know like the way biology works is that it is very decentralized and there’s this delegation and canalization and weirdly it seems quite orchestrated even though it is so decentralized. So humans um maturate in similar ways. We have similar behaviors and so on. But but we also have a degree of agency and freedom on top of that.
55:38 · I mean if you were to to design like an artificial or or augmented AI system, how could you kind of have your cake and eat it and have something decentralized and steerable? So, one of the results I um one of my supervisors accused me of of of writing libertarian biology because one of the results of of one of my of my thesis is called the law of the stack, which I like dramatic names, but um it’s basically that if you’ve got a a high level of abstraction, say um you
56:09 · know, some software running in a computer uh and it’s learning, uh its ability to learn and adapt hinges on the ability to adapt at lower levels of abstraction.
56:18 · Um and uh so like the hardware level learning and adapting there uh like in comparison if you compare biology and computers and so there’s a paper coming out um soon called uh are biological systems uh more intelligent than artificial intelligence and it is basically it says well um in addition to the waxing thing that uh that biology seems to do better um it seems to delegate adaptation down the stack.
56:49 · Whereas computers are like an inflexible bureaucracy that makes decisions only at the top. And if we want to make something as adaptive and efficient as a human body or whatever, we need to emulate this sort of delegation. And if we want to have and and then I go on to say um because I I watched way too many
57:07 · Mike Leven interviews and he was talking about cancer and how cancer is something where uh can be seen as where you’ve got a cell that becomes isolated from the informationational structure of the collective of which it’s part and it reverts to like primitive uh transcriptional behavior which basically means it just starts you know reproducing and eating itself eating and like expanding and cancer. Um, and so you could think of it as like well what what under what circumstances does part of a collective system become isolated from the informationational structure.
57:40 · So I formalized that in this stack thing and said well each each cell is like a little task with a little policy and if there are are not any correct policies available for the overall collective then the only way for the thing to continue is the the overall like higher level thing to continue is to break off some of the parts until there are some correct policies that exist.
58:01 · There’s two ways this could happen. One you impose lots of nasty stuff from the outside. Make the thing hard uh make it too hard to to have a correct policy.
58:09 · it’s just too difficult, right? So, in the case of biological organism, like lighting it on fire. Um, another way to do this uh would be to just impose too much top- down control and sort of just sort of cut yourself off at the ankles and eliminate otherwise good policies by over constraining the members of your collective.
58:29 · And if you think of this in terms of like humans, like uh this would be like an overly um this is why my supervisor was making jokes about libertarian biology because I was saying that it’s just like if you over constrain the members of the collective then they’re going to break off and and do crazy [ __ ] Um so it’s like having too much of a totalitarian state or something like that. Too many laws, too much restrictions. And you could I used to live in Italy and I could see this in the way people used lines. Um they didn’t line up. They just kind of Yeah. Yeah.
58:59 · Went towards the front of the shop. There were rules for everything in Italy. So, nobody obeyed any of them.
59:04 · Yeah.
59:04 · Um, other than that, it’s a great place.
59:07 · Yeah. I loved living there. Um, as soon as I got over the whole, you know, nobody standing in line thing, it was fine.
59:13 · Yeah. And don’t drive.
59:14 · Yeah. Don’t Well, I did a lot of that.
59:16 · That was terrifying.
59:17 · Um, I had to stop at the side of the road and my boss, uh, who was Italian and he’s like yelling at me. He’s like, “Why aren’t you driving?” I’m like, “I had to stop. Everyone’s crazy. They’re all driving like maniacs.” I know. I know. It’s insane. Yeah. Go on.
59:31 · The AI if you over if you put too many constraints on it, it’s like you’re more likely to end up with that. You want to just constrain it in the areas you actually need it to be constrained if you’re trying to sort of avoid some sort of dangerous behavior. Don’t sort of like constrain something to a set of impossible circumstances. It’s just going to break.
59:48 · Yes.
59:48 · Yes. Because I’m I’m interested in this concept of what it means to be alive, right? Because you because we’re kind of dancing around this a little bit. We want to create artificial systems that that have the the the vibrancy, you know, and and we could have perspectives on what that means, you know, maybe it’s sort of like certain patterns of information processing, diffusion and whatnot. And there are some existence proofs like you look at Conway’s game of life, you know, artificial life and linear, and you look at these things, you think, wow, that seems very lifelike. I can’t really put into words why it is, but it seems like it is.
1:00:18 · And then on on the other side of things, I love building even with computers distributed agent-based systems using the actor pattern. You know, I I love I love sort of like separating things out into autonomous little units of computation that can run distributed, but but they’re very very much not alive. I mean, they have some cool computational properties, but you know, they’re they’re brittle. You know, maybe I could make them do meta programming. So, you know, have a little LLM and it can kind of heal itself and update itself, but still still not really like, you know, it’s knocking on the door of it, but it’s not really where we want to go.
1:00:47 · There are some interesting approaches like neuroscellular automter where you sort of do this emergentist sort of optimization where you know it’s self-organizing and it can heal itself from the bottom up but that’s quite domain specific. So we’re dancing around this idea of creating systems which which are alive. Yeah. Okay. There is there’s two things I want to talk about there.
1:01:10 · Yeah.
1:01:11 · One is you mentioned healing, right? So I was interested as simply [Music] extended universe stuff gets there’s only so much stuff you can cram into a a bounded system or finite space and so um if I want my system to say I’ve got you know a biological system made of a just coming up with abstraction layers to process information then as it delegates control in order to make efficient use of space it’s going to weaker constraints take simple forms.
1:01:45 · And so if you delegate as much as you can, you’re generally going to get uh simplating with waxing um as you sort of scale things up and do this. So this means that something that maintains homeostasis like a self-reping organism is going to need to delegate control a lot in order to do this. And that then so being alive almost requires this kind of combination of simping and waxing.
1:02:08 · And I was thinking about this because um I wanted to know uh why things are alive and I figured this would be a fun side gig with my thesis. Um so I uh I’m waiting for examiner feedback on this.
1:02:23 · But um I thought well a rock uh is something that simpes and just kind of persists through simping as in by just simp maxing as the universe sort of transitions from one state to another it’s going to destroy some objects and preserve others. And if something is simple why would it persist? Well, it would persist because something that’s simple is more likely to sort of stumble into a kind of a suit a weak constraint because there is this sort of basic correlation thing we’ve got going on.
1:02:54 · Um, and so rocks and lots of objects like that in the universe persist by being simple.
1:03:02 · Y but then if you take something that self-repairs, it’s doing the opposite. It is becoming more complex than the same thing if it didn’t self-repair. It has increased complexity and massively increased its ability. It’s its sort of ability to embody weak constraints. And so I would argue life is that which uh which waxes at the expense of simp which are not alive just can simp.
1:03:30 · And we were just talking about healing and and like selforganization and so on. And it doesn’t just come from within. It also comes from the outside. interactions with the surrounding environment.
1:03:41 · Exactly.
1:03:41 · Because I think like a lot of what agency and intelligence is is about storing a history of information. It’s like, you know, it’s almost like you’ve got like there’s there’s a there’s a hard drive of human knowledge which is our culture and and this is all being stored and it survives and and it’s kind of like ontoically um imbued into all of us during our lifetimes. and and and this is why David Krakow has said that culture is evolution at light speed, right?
1:04:09 · Because we’ve we’ve like language and culture help us to transgress the physical limitations of DNA and physical evolution. So it’s there are just many different ways of thinking about how this like machine works. I sent an abstract of my thesis to David Krakow last night and I don’t I don’t know what he thinks of it yet. I I will wait and see. He he might think it’s terrible. Um but I think he would be honest with you if if it was.
11. Closing Thoughts
1:04:38 · Yeah.
1:04:38 · But um but I think these ideas are compatible. I mean I’m I’m sort of I don’t know. With the start of my PhD, I was I sort of saw the free energy principle thought what is this? I don’t know about this. And then I read more and more and I’m like oh okay I can come around to it now. I like it. So um so maybe there’s maybe there’s something I’ve missed but I I feel pretty confident in this. And just to be clear, the the the life is waxing without simpaxing thing is not something that has gone through peer review yet.
1:05:06 · I have submitted it for peer review and we’ll wait and see what sort of hate mail I get in return. Um but um it’s it’s definitely something I would want to run past Fristant if I get the opportunity in future and and hear what he thinks of it.
1:05:23 · Um yeah.
1:05:25 · Yeah.
1:05:26 · Very cool. Very cool. Well, Michael, what I will say is you should join our Discord server.
1:05:30 · All right. I think I think the the folks would love it if maybe one Friday afternoon we all got together and, you know, shoot the [ __ ] for a little while.
1:05:37 · I’d love that. Yeah, that sounds you’d have a lot of fans on there. Um, Michael, this has been amazing. Thank you so much.
1:05:42 · Thanks so much for having me on.
1:05:44 · Awesome.