Carlos Farias discusses the mysteries of memory transformation and evolution.

On Memory as a Self-Adapting Agent

Estimated read time: 1:20

Summary

In this enlightening discussion, Carlos Farias delves into the fascinating topic of memory as a self-adapting entity, exploring the remarkable transition from caterpillars to butterflies as a case study. Farias explains how memories are not just static records but dynamic, adaptable agents influenced by both cognitive and environmental changes. He discusses the concept of memory transfer, where RNA from trained organisms can potentially impart learned information to naive ones, suggesting memory's incredible adaptability. The conversation extends into the realms of synthetic biology and cognitive science, providing insights into how memories adapt and transform, reflecting the ever-evolving nature of life itself. This thought-provoking dialogue invites listeners to reconsider the conventional understanding of memory as mere storage, presenting it instead as a living, evolving process.

Highlights

The caterpillar to butterfly transition showcases memory adaptation in a new organismal state 🐛.
RNA from trained organisms can impart knowledge to naive ones, exemplifying memory's dynamic nature 🌱.
Memories are reinterpreted and expanded upon depending on the current context and needs 🚀.
This transformative view of memory challenges the traditional perspective of it being static 🏛️.
The integration of synthetic biology and cognitive science provides new insights into memory processes 🔬.

Key Takeaways

Memories can adapt and transform like the transition from caterpillar to butterfly 🦋.
RNA memory transfer is a remarkable phenomenon where learned information can be transferred between organisms 🧬.
Memory is not simply a storage system, but a dynamic and adaptable process 🔄.
Thinking about memories as independent agents opens up new avenues in synthetic biology and cognitive science 🤯.
The discussion highlights the role of memories in influencing an organism's past, present, and future 😊.

Overview

What if your memories were more than just a record of the past? Carlos Farias explores this idea by discussing the transition of a caterpillar into a butterfly, where memories from the caterpillar are somehow retained and used by the butterfly in a completely new context. This fascinating process suggests that memories can adapt and evolve, much like the living beings that store them.

In a deeper dive, the conversation touches on RNA's role in memory transfer, a revelation that opens up new possibilities in understanding how information is stored and transferred biologically. Such findings are not only intriguing but also remind us of the flexibility and creativity inherent in biological systems, pushing the boundaries of how we traditionally understand memory as mere static data.

Farias's insights encourage us to rethink the concept of memory entirely. By viewing memories as agile, transformative entities, we can start to appreciate them as active components in both personal and evolutionary development. This perspective connects the fields of synthetic biology and cognitive science, offering thrilling possibilities for future research and understanding of life's most intricate processes.

Chapters

00:00 - 01:30: Introduction and Context In 'Introduction and Context,' the discussion centers on the adaptive reuse of information, using analogies of a caterpillar's encoding and a butterfly's decoding. It highlights experiments showcasing memory transfer, like injecting RNA from a trained slug into a naive donor, demonstrating the potential to transfer memory. The chapter suggests that instead of just persisting, entities should aim to expand and alter themselves for colonization of new spaces.
01:30 - 02:30: Interview Introduction The chapter delves into the theme of change and persistence, highlighting the paradox where altering too much might lead to losing one's original identity. It poses the question of whether it's acceptable to evolve to a point where one becomes something entirely different. The chapter also features Michael Levan, a scientist at Tus University, whose laboratory focuses on understanding anatomical and behavioral decision-making within biological, artificial, and hybrid systems.
02:30 - 06:00: Lab Highlights and Recent Work Michael's work covers a range of fields including developmental biology, artificial life, bioengineering, synthetic morphology, and cognitive science. In this conversation, Michael discusses his recent paper on the concept of self-improving memory, which he describes as a flexible cognitive 'glue' that dynamically reinterprets memories. This marks his fourth conversation on the platform, reconnecting after 18 months.
06:00 - 10:00: Discussion on Self-Improving Memory The chapter titled 'Discussion on Self-Improving Memory' begins with a reconnection between the speakers after years, expressing excitement about discussing recent papers published. The speaker queries about highlights from the lab for the year 2024. The response indicates numerous developments and a substantial number of papers being published.
10:00 - 15:00: Butterfly and Caterpillar Remarkable Observations The chapter discusses significant advancements in anthr robots technology, particularly in tracking communication between embryos. There is a focus on understanding collective intelligence at the cellular level and investigating gene regulatory networks below the cell level. The chapter hints at upcoming developments that were primarily made in the year 2024, with some research already published.
15:00 - 20:00: Metamorphosis and Memory Transfer The chapter covers exciting topics in novel transcriptomics, focusing on anthrobots and xenobots, highlighting the innovative work of a particular research team. The discussion zeroes in on three recently published papers, organized in a past-present-future structure. The chapter introduces one of the papers titled 'Self-Improved Ivising Memory' that falls in the 'past' category.
20:00 - 25:00: Philosophical Insights on Change and Persistence This chapter explores the concept of stress sharing as a cognitive process that acts as a 'glue' in human interactions and decision-making. The chapter also discusses artificial intelligence as a medium to bridge diverse forms of intelligence and its implications for the future of humanity. Although there were plans to cover all these papers, the chapter primarily aims to delve into the philosophical perspectives on change and persistence.
25:00 - 30:00: Bow Tie Architecture Concept This chapter delves into the concept of Bow Tie Architecture, beginning with the author's contemplation of the caterpillar to butterfly transformation. It highlights the distinct characteristics of a caterpillar as a soft-bodied organism that navigates a 2D world using a specialized brain to consume leaves. This transformation serves as the foundation for exploring deeper architectural concepts.
30:00 - 35:00: Conclusion and Future Research Directions The chapter discusses the metamorphosis of certain creatures, highlighting how their brains undergo massive changes to adapt to new functionalities. During the metamorphosis process, many neural connections are severed, and numerous cells die, which enables the organism to entirely reconstruct a new, adapted brain. The research findings on this remarkable transformation were initially made by the shaman group and have since been observed in caterpillars. The chapter underscores the complexity and the wonder of physiological changes during metamorphosis, and it implies directions for future research into understanding these processes more deeply.

On Memory as a Self-Adapting Agent Transcription

00:00 - 00:30 the caterpillar wants to encode and the Butterfly wants to decode in a way that um reuses the information adaptively and we see this we see this a lot in experiments and memory transfer you know they train a se- slug and they and they extract the RNA from from its nervous system and then they inject it in the into the into the brain of a of a naive donor and then you can see memory transfer me it's fabulous work wow most likely you don't just want to persist you want to expand into other spaces which means you want to alter yourself in way that would allow you to colonize
00:30 - 01:00 as as many other thinkers and as many other um action spaces as you can and then of course they face exactly the same Paradox that we started out with which is that if you change enough you're no longer what you used to be and so is that okay is that persistence or is that you know you've now you're gone now and some something else exists Michael Levan is a scientist at tus University his lab studies anatomical and behavioral decision-making across biological artificial and hybrid systems
01:00 - 01:30 his work spans developmental biology artificial life bioengineering synthetic morphology and cognitive science today we'll discuss his recent paper self-improving memory a perspective on memories as a gental dynamically reinterpreting cognitive glue please like And subscribe and I hope you enjoy our fourth conversation together Michael L thank you for coming on for the fourth time yeah thanks for having me good to see you again great to see you it has been a while maybe 18 months to uh to 2
01:30 - 02:00 years uh since we last connected and I'm so excited to cover the the papers the recent papers um that that you've published here was we'll discuss today i' like to get us started off if if you wouldn't mind you know for the year of 2024 do you have any uh say broad highlights that you want to share with the about the lab um let's see highlights about the lab boy it's been there's been a lot of stuff uh I know yeah yeah um well a number of a number of papers some things
02:00 - 02:30 have um really moved along um on the on the anthr robots front um with respect to uh our ability to track communication between embryos so so attempts to like really understand um collective intelligence above the cellular level uh also some new work below the cell level looking at the gene regulatory networks and how they solve problems um so yeah some some some there's some stuff coming actually soon that was developed mostly in 24 some publish it but some things on
02:30 - 03:00 um novel transcriptomics of anthr robots and xenobots and uh yeah lots of lots of interesting things lots of stuff that's so cool it's so cool to follow uh all of your work and your your your team's work um and today in this discussion we'll cover we're going to try to cover three of uh your recently published papers and I'm going to try to structure it uh like past present future so we have the past will focus on this paper called self self-improved I vising memory and then
03:00 - 03:30 uh the present second act will be the second paper on stress sharing as cognitive glue and then the third uh AI a bridge toward diverse intelligence and Humanity's future so that'd be ideal to cover all three of those I know touch but if we if we get into one and we're you know very in-depth into one that's totally fine we can know we can just cover one or two of the papers but love to get a started with this self-improving memory and uh could you explain the central thesis of this paper
03:30 - 04:00 yeah well it it it started when I uh when I started thinking more deeply about what's going on with the caterpillar butterfly transition so for years uh I've been thinking about so let's just let's just say what the what the data are so the data are sure a caterpillar um it's a soft body creature has a particular kind of controller that it uses to move around basically in a two-dimensional World eating leaves uh which is this very specific type of brain that it has and it was it it it turned into a butterfly which is a
04:00 - 04:30 hardbodied kind of creature completely different controller um flies in three-dimensional space and in order for that to happen the brain is is is massively remodeled so most of the connections are broken many of the cells are killed off and then it rebuilds basically rebuilds a new brain so the remarkable observation this was made uh for um certain kinds of certain kinds of metamorphic systems years ago uh by uh uh by the shaman group um and then more recently uh in in caterpillars and and
04:30 - 05:00 by Doug Blackiston who's currently a staff scientist in our in our Center was that if you train the caterpillar the butterfly remembers the original information now one of the cool things about that of course is that uh we have here a computational medium that um where the information survives massive refactoring so we don't have any Technologies like that where you can store some data and then you you sort of rip up the medium reconfigure it completely and everything is everything works so so I've thought about this a
05:00 - 05:30 lot over the years as a um as a as a challenge to our computational kinds of architectures you know what what does it mean to store information in a way that survives that kind of uh that that that kind of massive damage and and remodeling but um I I realized uh earlier this year that the much more interesting feature there isn't the fet maintaining the Fidelity of the information but it's actually remapping the information onto a completely new uh
05:30 - 06:00 uh in a in a completely new kind of being with new problems because the butterfly doesn't need the exact memories of the caterpillar in fact it can't use them the specific memories of the caterpillar are of no use to the butterfly because it doesn't move the same way it doesn't want the same things it doesn't want the leavs that the caterpillar was trained to to find at a particular color it's not going to move the same way uh you can't you can't just keep the same memories what you have to do is remap these memories onto a new substrate so and and and make them make
06:00 - 06:30 sense make those memories be useful to you in a completely novel configuration so if you think more deeply about it it you you realize that this isn't some weird unusual um feature of this of this lifestyle you know this metamorphic lifestyle but it's actually extremely fundamental in biology and this goes back to a paradox and and um uh I used to think it was bat and's Paradox I'm actually not sure who who came up with it but the idea of the paradox is is
06:30 - 07:00 this if as a as a species if you remain the same you will eventually die out when the environment changes you can't keep up and you'll die out but if you do change then you're not the same species anymore so once again you're gone right that that original being is is no longer there so the Paradox is how do you survive and persist while necessarily changing fundamentally and this is true for all of us this is this is the the the consequences of Education of transformative experience of of puberty
07:00 - 07:30 of the the question is you know is is is the past me still still around or is this or is this different in some way this being different so so I started thinking about this and I and I generalized this in the in the following way um that that paper the self- improvising paper does a couple of things that I think are are are useful one is that it um proposes this architecture which is which is uh useful both on the kind of uh
07:30 - 08:00 time scale of a single cognitive agent but also on the evolutionary time scale and the and the and the system looks like a big bow tie it's also uh it also looks exactly like in autoencoder architecture for uh used in in computer science and and machine learning so what you have is you have this this big funnel on the left side and um then there's a there's a thin um node or layer in the middle and then there's a big funnel going out right so you can imagine that kind of bowang and so the idea is this so let's just let's just imagine this for our for the for the cognitive case
08:00 - 08:30 um you as a as a as a as a as an active agent you have inputs stimuli experiences that you have so these come in with very specific details if you're a cell it means there are specific molecular things going on let's say on your membrane if you are an animal you're receiving signals from from certain receptors that you have in sens sense organs and so on um so you so all of those are coming in but but you can't you can't afford to store the exact details because by trying to track the
08:30 - 09:00 micro details right by by trying to be a kind of lassan demon that that that just tracks the micro details you will be eaten and and die and no the real world does not afford you the the time and the energy that it takes to do all that so instead what you have to do is you have to generalize you have to con compress all of those instances into some kind of generative some kind of very simple generative kernel that um is going to capture the what's essential about all the different details like lots of those
09:00 - 09:30 details are are not essential you know if you saw a particular stimulus the the exact shade of color and you know where the pixels were on your right now those are not typically the useful things but but there's something else about that experience that um maybe you maybe you've inferred a pattern of in the stimuli you know so intelligence does is is is it goes from from from particular instances to a general rule so then you remember this pattern you you learn and you remember this pattern and so that's what you have access to uh it as as a memory engram so you you you uh you you
09:30 - 10:00 store that as a memory engram but then then later and it might not be very much later it might be you know this you can think of the center of the v as the now moments you've got the past right you've got this now moment and then you've got your future and that now moment moves along and and very very shortly thereafter you now have to reinflated um those generative uh memory traces into some kind of um coherent story about what's happening now and what it means and how you can use those
10:00 - 10:30 memories and so another thing that I try to do in that paper is to recast your memories as messages from your past self so to use the same kind of um formalism that we use to analyze communication between agents at a given time so laterally right the way you communicate with others is um you can also think about um all of your memories as Communications from a past version of you and all of your actions and the things you learn now are messages that you are recording for your future self
10:30 - 11:00 um I long after I read that I I wrote that I I read a funny uh I think it was a tweet by Sam gersman who said that your most important collaborator is you six months ago and he's not answering emails and that's you know that's that's really very good that's that's good yeah if I had had that back then I would have used it as this as the subtitle of a of a section in that paper because yeah it's basically what what what it reminds us of is that you don't have access to the past but you have access to are the memory traces the engrams that have been
11:00 - 11:30 left by your interaction with the past in your mind your brain and body and now now at every at every given moment and and this I think is interesting for kind of the definition of what it means to be an agent or to to to have a mind is to be in charge of constantly and and be be um driven to constantly figuring out what do my memories mean you know you don't you don't think of it typ I think typically we don't think about it we just assume we have memories and we know what that mean well right that might be
11:30 - 12:00 true in our computational systems where the uh the bottom layers are highly reliable and and and there's this abstraction where when you program at a higher level you don't expect uh you don't have to worry about the the data in your register sort of floating off and and changing into something else and so on so we can get into that too with the difference with computation but but the thing about biology is that uh you are you are always working in an unreliable medium and so this is where so so um just finish finish the
12:00 - 12:30 the thought on the on the cognitive side so so you've got these engrams and then you have to re-inflate them and in order to reinl them here's here's the thing about re-inflating them is that you've lost information uh there are lots of um uh correlations and other things that were specifically squeezed out of that data when it was when it was written down but now that you have it you don't know exactly what it means you don't know exactly how your past self interp it you don't you don't have access to that what
12:30 - 13:00 you have access to is the recording itself and now it's up to you to re reinterpret it and you might reinterpret it in exactly the same way but you might not and you don't have to you don't have any allegiance to that you you have to biology um tries to make use of information at the moment in whatever best way that it can you know and it's it's sort of like um I mean in literature they have this concept that when when when somebody write something you are not forced to interpret it the same way that they did even though they say well I it I know what it means and
13:00 - 13:30 you say well guess what I got something else out of it so so now I think this is what it means right so it's that's it's that sort of it's that sort of thing and it it relates to the poly Computing um Paradigm that Josh bongart and I have developed where the idea is um you you know there isn't there isn't an objective fact of the matter about what a particular physical event is Computing and it doesn't matter if if someone wrote the algorithm and says no I know what it does I I wrote the algorithm well there are multiple observers that
13:30 - 14:00 could potentially look at those events and have a different model of what's being computed and they're equally equally valid to the extent that they can make some use of it right you have to be able to adaptively use it so um that means that uh the uh the left side of the funnel is largely algorithmic because you know compression and inference and so on you can imagine that as an algorithmic process but the right side of the funnel is creative because it's underdetermined you you can't simply uh deduce what was meant by the memory traces you have you have to
14:00 - 14:30 interpret them in the current context so it's a view of cognition as cons um a continuous sensemaking where you're trying to make models of of yourself and your world and you have to reconstruct them all the time because all you have is the traces that were left to you by the past self and your past self may be very much like your current self in which case it might not be too hard but also it it might not and so uh and so that that then leads to uh to an interesting uh thing where you can apply
14:30 - 15:00 these Concepts on a on an evolutionary time scale and you can then see immediately the uh the origin of the incredible plasticity and the problem solving capacities of cells and tissues and molecular networks and so on because unlike um unlike in our computer technology biology is working with a fundamentally unreliable medium not only when you come into the world as a new being not only can you not be certain of what your environment is going to be but you also can't even be certain of your own parts in fact you're guaranteed that
15:00 - 15:30 your own parts are going to change they're going to mutate there's going to be errors they're going to all kinds of things um and this is why you know I think we've talked before about like one of my favorite examples of this of this n kidney tubule right that that that you you can make these these nudes with different copies of chromosome number and basically the cells adjust their size to the amount of DNA and then morphogenesis adjusts itself using novel mechanisms to make a perfectly good N Out of cells that are completely the wrong size in fact different building
15:30 - 16:00 the same pattern but in quite different ways and so as a new as as a as a creature coming to this world you can't be sure of much you know you can't be sure that of your environment but you you also can't be sure of your own Parts you don't know how many copies of your genetic material you're going to have you don't know uh how many what how what the size of the number of your cells you have to construct a viable way to move forward on the fly right this is you know Play Hands your delt kind of kind of thing and so that if from the beginning biology and
16:00 - 16:30 evolution uh commits to this idea of an unreliable substrate where you don't really know what's going to happen but you have to interpret that information left for you so the genome was left for you by eons of experience of your ancestors but you're under no obligation to interpret it the same way and this is why with the exact same genome we can have plaria that make heads of the wrong species we can have xenobots made from with a frog genome we can make anthr robots made of a totally normal human
16:30 - 17:00 genome the meaning of that information is not hardcoded at all right issues and so and so this is this is the the origin I think of that incredible plasticity in biology and I also think that this provides kind of a um an intelligence ratchet where once you once you uh start down the road of making um active problemsolving agents not solutions to fixed problems which I think most most organisms are not there may be a few
17:00 - 17:30 exceptions um but most organisms uh and cells and even you know even unicellular organisms are problemsolving agents once you start down that road the meaning of that information becomes less and less determined because the agents are going to be better at re reinterpreting them and So eventually you start projecting that kind of uh willingness to confabulate I mean literally this is this is where confabulation is a is a is a feature not a bug because the ability to ignore whatever the information used
17:30 - 18:00 to mean and to make up a story that helps you right now is is is really helpful in a wide range of of of context and so I think that kickstarts what we eventually recognize as intelligence which is increasingly um creative uh uh on the-fly reinterpretation of information based on whatever problem you're trying to solve and then the last bit which we can we can talk about the the the title of the um the title of the of the paper actually is is really only touched upon
18:00 - 18:30 at the very end which is uh this idea of the memories themselves as agents and so we talk about that that's a whole other thing we can talk about as patterns as agents and so on yeah well that was wonderful you answered about six or seven of my questions already so that was great thank you Michael um and that actually actually is one of the let's see I go so many different places here let's start off with what you just mentioned about memories as a genal um what do you mean by that yeah how do you
18:30 - 19:00 define agential memories and what evidence do you have to support that idea yeah um okay so so let's first uh let's first just nail down um how how how I think about agency so so there's there's a there's a feeling among many that uh there's there are certain certain kinds of things are agents so let's say humans and maybe some animals and so on um and that the use of that word in other cont context it's some sort of a category error so so
19:00 - 19:30 my feeling on all of this is that these categories are are not sort of given to us by on high and that then we are then required to stick by them I think the category should follow the science and that means that the way you know when you have agency is you take the the the tools that we normally have so these are the tools of behavior science of cognitive science and so on the tools that you normally used to interact with agents and you and and then you empirically you don't sort of sit back and assume but but empirically you apply
19:30 - 20:00 them to other types of things that being molecular Network cells tissues organs um cyborgs biobots whatever and you see how far that gets you and you see where where that gives you advantages and disadvantages so I see agency as something that's only um applied um it's a term that can only be applied after experimental study and you and you need to have a specific hypothesis about what problem space it's working in and by the way that problem space does not need to be three-dimensional space so when people talk about embodiment and they mean some sort of wheel robot or
20:00 - 20:30 something that runs around and does things I'm talking about solving problems in transcriptional space and anatomical morphos space I all these other spaces that are that biology navigates but are hard for us to to to visualize so so that's that's my take on agency so now so now okay so now let's think about the you know the spectrum of of agency what kinds of things are on that Spectrum so um I've previously talked about placing some some unusual things on that Spectrum like and tissues and
20:30 - 21:00 and you know and slime molds and and even molecular networks we've shown learning in molecular Network models and so on uh but I think we can get much weirder than that even and I think that's a good idea um because it's good to push through um push past our uh typical kind of limitations and thinking about these things because I think you know we've inherited some some very specific firw from our life on Earth and spec and and especially you know our our our um our latest history on the Savannah or you know all that kind of
21:00 - 21:30 stuff we've we've inherited specific ways that were very expedient for thinking about things but I think I think they're quite um constraining actually and so so now it's right now it's time to break through some of that so so I started thinking about the following uh the following and the following dichotomy because we typically Mo most people typically think well okay we have we have uh actual physical um beings and so those might be biological beings they might be some sort of you know um engineered machines or whatever but you
21:30 - 22:00 we have things things that do things um and then we have patterns we have data we have we have information that these things process we have uh patterns in various media so so whether they be in a cognitive medium or patterns in in a you know in a digital memory Med what we have patterns so we have patterns and then we have we have cognizers or we have thoughts and we have thinkers and um William James said something interesting he said that thoughts are also thinkers so how would that how would that work so so so first let's um
22:00 - 22:30 just to kind of warm us warm us up let's let's think about a science fiction story and this is I think based on a story that I read many years ago I'm actually not sure if that's really the case or what the story was but uh and and no doubt I I I've bent it completely out of shape but anyway but but but here's the but I think it's good and here's the here's the story so so just imagine uh from the from the core of the Earth from the center of the Earth come these beings they sort of work their way up and uh they're incredibly dense because they come from you know the
22:30 - 23:00 center of the earth so so they're incredibly dense um their vision is I don't know in the gamma range or something like that and they're walking around what what do they see well the first thing they see is uh that the Earth is enveloped In This Very tenuous kind of plasma um that's all of us and everything that we see as physical objects they don't see any of this they uh to to them they are so dense that to them all of these kind all of the things that seem to us are just um you know
23:00 - 23:30 just ethereal wispy kinds of gaseous patterns that exist around the earth and um uh much like much like when you walk through a garden there's all sorts of patterns of of of of pollen and smells and there's all this stuff and you just sort of walk through it you don't even see it it's you know these patterns in the in the in the media so they're walking around and and and uh kind of stomping through everything and and one of them is a scientist and he says um to the others you know I've been I've been I've been watching ing the gas that that our planet is surrounded by and I and I
23:30 - 24:00 see these kind of patterns in the gas I say what what what kind of patterns well they're sort of temporarily persistent patterns they kind of hang around for a while and they seem to be doing things it's almost like they almost they almost seem a gental they almost seem like they you know they move around and they try to protect themselves from from dissipating and they have certain goals and it almost looks like they're they're doing stuff and the others say well that's well that's crazy but like we're real we're physical patterns patterns can't be agential and by the way how
24:00 - 24:30 long do these patterns stick around well about a 100 years well that's nuts nothing nothing interesting can in the space of 100 years um and so and so uh you know and and and I've I added to this uh to this story there's a there's a blog post that I have where where I kind of do a dialogue between you know um the one of those one of those core scientists and one of these patterns and and he says look uh you know I feel I feel crazy talking to you because because you're just a pattern in this in this medium and the human which is the
24:30 - 25:00 of course the pattern is trying to convince them no we're we're we're real too it's just a matter of perspective like we you know we're we're real um and it's funny that there's there's another story which I'm quite sure is is a is a is a is a real sci-fi story about a a a a stream of plasma pattern that gets ejected from the Sun and the humans are flying by in some sort of spacecraft and they don't realize that this is also a sensan being that's just been sort of ejected from this uh from from its home and and so on so anyway so so the point
25:00 - 25:30 of all of that is to re remember and to remind ourselves that we are actually also temporary patterns we are um temporary patterns in metabolic space and we persist for some amount of time let's say on the average of a 100 years or so on the scale and uh and and we try to keep ourselves together but much like hurricanes and solons and gliders in The Game of Life and various other you know temporary um self-organizing and self- persisting
25:30 - 26:00 patterns one can take that view and so that that reminds us that this this a distinction that everybody makes very categorically between real be you know real things or thinkers and the patterns within them or thoughts is really a Continuum it's very much a continuous measure that's up to the eye of an observer to to note and so that then uh suggests the following which where now just beginning to um this is something
26:00 - 26:30 that you know Josh bongard and I and and um and um Richard Watson and Chris fields and some others are starting to think about which is what if what if you turn the standard Computing Paradigm on its head so normally you have these Turing machines you got the machine and that's the agent doing things and then you've got its memory tape and so it's writing things and so what if um what if what if you look uh from the perspective of the tape so in in fact not not just the tape itself but the patterns on the
26:30 - 27:00 tape because in a certain sense they run the show the machine is going to do what it does based on what the information on the on the tapes say and so you can imagine uh with these so so now so now back to Memories as as agents so so imagine this Continuum so you've got you've got these um you've got fleeting thoughts so these are patterns that run through a cognitive system and then you know wink out of existence they disappear so they're very short shortl lasting but then you've got some persistent thoughts or recurrent
27:00 - 27:30 thoughts that are kind of difficult to get rid of and we know from Clinical Psychiatry that that there are there are those kinds of thoughts that once they establish in certain minds they um they're hard to they're hard to get rid of depressive thoughts and you know obsessive thoughts and things like that and in fact some of these thoughts do something interesting they do a kind of Niche construction meaning that they they actually the more you have those thoughts the easier it is to have more of those thoughts they literally change the brain there have been studies on on how uh you know brain Ultra structure changes with with those kinds of those
27:30 - 28:00 kinds of thoughts it makes it easier to continue having that cycle right so these thoughts these kinds of thoughts are a little more permanent um they're a little more uh they they contribute a little more to their own Survival they're in fact changing the Thinker uh by their presence and then then you sort of move up the Spectrum and you can say well what about uh dissociative identity disorder personality fragments they are even more agential they have they have goals some of can talk uh they will
28:00 - 28:30 certainly um affect the Thinker in ways that that changes how you know how they persist and how others persist and then you know eventually then you have a full human personality and then who knows what's after that right transpersonal psychology suggests there may be something past that so you can imagine these different much like like you have for um quote unquote physical objects of which all of us living things are really just metabolic temporary metabolic patterns um you have you have a you have a spectrum of of of agency there and
28:30 - 29:00 then you can have a spectrum of agency in these kind of patterns too so so that's so that's what I'm talking about you know this and this is just the very beginning of this research program so uh uh the the conceptual part of it is to start looking at it as active data so so yes you have the machine that's moving the data around what if you look at it from the other direction and especially in um in systems like biological systems where the information patterns themselves and these might be patterns in the neural substrate so these might
29:00 - 29:30 be like fullon um thoughts you know traditional thoughts but they also might be patterns in physiological State space they must they might be um uh patterns of of of stress or they might be you know all all kinds of things that physiologists and and uh and and different kinds of uh but you know therapists deal with right there could be all sorts of unusual patterns and to what extent can you think of uh data as as driving the show and having its own uh its own life and trying to persist in
29:30 - 30:00 its environment the way that the way that we P try to persist in ours um so so that that has all kinds of interesting uh practical implications for for example regenerative medicine and that's the kind of thing that we're working on now so so could we you know could we look at some of the um you know one way to think about it for examp for example look at our bioelectric patterns right you can you as as we have for years describe describe the bioelectric patterns that we see during morphogenesis during
30:00 - 30:30 regeneration as literally the thoughts of the morphogenetic collective intelligence so you have a cellular collective intelligence it's trying to navigate morphogenetic space to get from from an egg to a to a full body or to regenerate a limb or something so it's navigating that space and the bioelectric patterns are the thoughts of and we can read and rewrite them now to some extent they they literally are the thoughts of that agent in exactly the same way that electrophysiology in the brain represents the cognitive content of beings navigating threedimensional space so that's the the the more
30:30 - 31:00 conventional story as weird as that is but but but that's a that's a more conventional story The the new way that um I'm starting to explore now is what if it's actually backwards what if the physical body that we're looking at is the tape and it's the it's the bi electric patterns that are really the driving agent and that what we see when we look at the consequences of that which are changes in in second second messenger function gene expression uh chromatin you know epigenetic changes
31:00 - 31:30 and then finally cell Behavior changes and morphogenetic changes what if what if that's the tape right the physical body is the uh is the is the memory medium and uh and and there's there's significant um significant action going on at the level of the physiological patterns themselves and so that suggests some more applications uh and ways to test these ideas and and that's what we're doing now this is very very early days H that's fascinating so I have so
31:30 - 32:00 many different questions I could go here but I do want to stick on this um concept of memories as agents so correct me if I'm wrong and uh I'm going to try to restate some of this so we can think of memories as let's say Transmissions from the past and we have to interpret those they're not just given to us memory and I'm sure many many many listeners know but not not everybody knows that memory is not like a storage
32:00 - 32:30 cabinet you don't just go in pull out the thing you have to actually not confabulate that's going too far probably maybe not but you have to um recreate the memory um there's some trace of it memory trace of it and you have to actively I mean we do it we do it spontaneously right it's uh not a conscious construction but we have to do that so if we think of them as agents is then do we have to think about say
32:30 - 33:00 information patterns more broadly as being agential so some of the examples you gave memory is like say one example but is any pattern of information potentially agential then yeah that's a great that's a point but potentially yes we don't know you keep you can't automatically um decide that that's the case anymore then you can do that with with with physical objects but yeah potentially that's the case which means that you have to uh you
33:00 - 33:30 have to try to apply the tools that exist for this to see to see whether that gives you an advantage and if you find one then then then there you go um you know you can imagine and and so this is this is a part that I left out about these um these patterns these memory patterns that have to be reinterpreted uh from from from sort of time slice to time slice of a being when you look at it from the perspective of the of the being themselves you see that uh you see that um uh bowai architecture so you see that
33:30 - 34:00 okay you're the recipient of a bunch of compressed information and now you have to creatively expand that engram into what do I do now you know the choice okay that's that's from the perspective of the of the of the Thinker now from the perspective of the thought itself it might be and I'm not I'm I'm much like with our basil cognition models I'm certainly not claiming that uh these that these thoughts are high high level agents like humans you know I mean some of them might be you know the dissociative um sub personality alars
34:00 - 34:30 are close I mean they're you know they they they they have a lot of those features but but some could be very low-level intelligences doesn't you know you don't have to be a high level self-reflective mind to be a to be some kind of intelligence but but but from the perspective of that system what what might your goals be well one goal might be simply to persist that that you know that might be a simple kind of darwinian way to think about it so so so from that perspective if you're a pattern what you
34:30 - 35:00 would seek to do is to change yourself and also the the thinker or the system around you in a way that makes it easy more easy for you to um propagate into the future or in fact uh you know most likely that's not a sufficient story most likely you don't just want to persist you want to expand into other spaces which means you want to alter yourself in a way that would allow you to colonize as as many other thinkers and as many other um action spaces as
35:00 - 35:30 you can so for example you know so so that means that the the caterpillar wants to encode and the Butterfly wants to decode in a way that um reuses the information adaptively but the information itself uh might work in to to uh to have features that make it easier to be encoded decoded and propagated into new uh into into into new embodiments and and we see this we
35:30 - 36:00 see this a lot in experiments and memory transfer um they kind of um you know you you like like like when when David gansman does does the the the RNA you know they train a se- slug and they and they extract the RNA from from its nervous system and then they inject it in the into the into the brain of a of a naive donor and then you can see memory transfer I mean it's fabulous work wow yeah and and there's been I'm not familiar with that oh yeah yeah so this is so this is David glans man's work um looking at the the
36:00 - 36:30 basis of memory and there are there's a lot of other work in the past that's been done about moving either either chemical extracts or pieces of tissue from a trained animal to a naive animal and so on but but to me one of the most amazing things about that kind of work is that you know when you introduce let's say the RNA extract into the donor into the host recipient you don't micromanage where the RNA goes you don't put it into the right neurons to run the thing like a puppet you just sort of you
36:30 - 37:00 just sort of inject it somewhere into the brain and yeah you know no it works it's no problem it just kind of picked out and so so this idea we have we have other examples that are that are that are still unpublished of of of something similar in morphogenetic space so so yeah you know I I think there are incentives on both sides actually for these to be uh reinterpret and for the for the agent to be good at reinterpreting them and for the memories to be good at uh at being the subject of
37:00 - 37:30 that kind of process and thus colonizing the future you know colonizing the and expanding into into new spaces and then of course they face exactly the same Paradox that we started out with which is that if you change enough you're no longer what you used to be and so is that okay is that persistence or is that you know you've now you're gone now and some something else exists yeah it's beautiful that makes me think of you say persistence a few times the Persistence of memory by Sol Vador Dolly just makes me think of that uh
37:30 - 38:00 that painting the and I know I wanted we wanted to cover three papers but I actually think that there's so much meat here I'd love to stay on this one if if that's okay with you um and the bow tie architecture in particular that's something I wanted to definitely dive into because it's not a familiar concept to myself and I imagine for many listeners it it won't be either but it seems like there's something about this this shape this structure that you know cuts across it's like a pattern across
38:00 - 38:30 patterns right um so can you tell us a little bit like how did you discover this I mean it also looks like a cognitive light cone I mean it's just like this pattern that you see over and over again how did you first come across this idea or I don't know if you came up with it or um brought it together but can you tell us more about it well I certainly didn't come up with the bowai architecture so so that's been been around for a really long time uh been around in biology if you look at um
38:30 - 39:00 things like signaling networks you know a real um common one is there's a million different things that cause calcium fluxes and calcium fluxes cause a million other different things so you have this bow where like all this different stuff feeds into calcium and then and then it fans out again and and there's a lot of discussion in the community okay but how does the specificity work if everything boils down to calcium how do you figure out on the other end what which which one of these things and that's the whole point is that it's not meant to be a onetoone mapping uh it's not that the end the
39:00 - 39:30 endpoint tries to figure out okay so I know you're encrypted but which one but I'm going to decrypt you to know exactly which micro State caused it that's exactly not the point of these networks um the other the other place that this cropped up and I didn't invent that either is um the autoencoder architecture which is used machine learning where the idea is that you have these these layers of of a of a neural network like structure but in the middle there's a very thin layer that forces um generalization it's it's it's thin and
39:30 - 40:00 it's and it's uh its information capacity is small such that you cannot afford to remember details the only way the information is going to come through in a in an Adaptive way is that you uh you compress and you generalize so and that and that forces the generalization by not by by putting a layer or or several layers in the middle that are um that are uh very um very very thin in terms of how much information they can propagate you're forcing the system to generalize it's a bottleneck that that
40:00 - 40:30 that that requires you to uh uh to learn Concepts and not try to not try to remember individual details and so that's very important for intelligence because the whole point is that you should abstract patterns and what's happened before and apply those patterns to scenarios you haven't seen before so so that so that architecture has been used in a few different um in a few different systems but what I do think is new in this and and there's another there's another paper related to this
40:30 - 41:00 um that's that's come out well it's a it's a preprint that that came out recently by Kevin Mitchell and Nick Cheney that also looks at this at this concept where uh we can now use that architecture to understand what's going on in biology the idea that these really are I mean the I I I I think there's two fundamentally um unconventional things in this in this paper like two big themes one is the Symmetry or the
41:00 - 41:30 invariance between cognition and uh and and and development so um development broadly speaking the you know morphogenesis and so so the idea is that uh there's a reason you know uh why um uh there are there are there are deep deep in deep fundamental similarities between how you construct bodies and how you construct minds and so what I'm after is the what those those principles and what is what is happening with the information that
41:30 - 42:00 requires morphogenesis to be an intelligent process and how that works during on an individual scale but also on an evolutionary scale I mean that's the other nice thing about this is that you can apply this to whole lineages you can apply this to an individual being uncertain about what their memories mean or you can apply this to a um uh an evolutionary lineage where you come into the world and you have this DNA but you know you you're going to need to reinterpret it and this is why by the way this this is this is um something that's going to come out in the next uh in the next uh month I guess
42:00 - 42:30 or so uh is some of our work on transcriptomics in um in xenobots and anthr robots so the bottom line is that they have a they have a r both of them have a radically different transcriptome than um than the tissue of origin in Vivo and so the G DNA is the same but in your new environment and more I mean the environment's not that different actually the environment's almost the same what's different is your embodiment you have a new shape a new a new way of
42:30 - 43:00 getting around a new a new function new behaviors and how are you going to use the affordances you receive from Evolution all the DNA and all the the other uh cytoplasmic components that you have how are you going to use them for your new for your new life and so that's so that that I think that that invariance and that that scaling across space across time the the the movement of of Concepts from from cogn and behavior onto the construction of bodies you know morphogenesis so I
43:00 - 43:30 think that's that's kind of that's kind of new and also uh this this idea of uh the information that moves through these um these these kinds of B eyes as being potentially uh an agent too which means asking ourselves what does the world look like from the point of view or from the perspective of that information that's interesting some perspective information I mean I think a very it's just like comical
43:30 - 44:00 but going through it to a bottleneck right the information I mean as if it had a perspective or as if it could see but um imagine that being very quite scary actually to be condensed down and compressed down but then of course there's there's the way through and then more expansion on the other side of it yeah so right so so I think I think scary is the is the right term because this and and you know I not to get into matter that are kind of above my pay grade but but uh it it does sound like a
44:00 - 44:30 lot of things that um people who and and I've had a lot of um contact from from people who work in therapy and psycho Psychiatry and and and on on these kinds of ideas this idea that you're going to go through a bottleneck what comes out on the other end if you if you want to compare details it's not going to be you because you're not going to be the same on the other end of it um but that's the price you pay for Improvement for learning for growth for projecting into
44:30 - 45:00 new problem spaces for creating new meaning and so right so so that is scary and it's especially scary if you commit to a kind of um uh object permanence with respect to the self so if you think you are a stable thing then yeah you're in trouble because no matter what you're not going to be here for very long but if you have a more processed view where what you are are a kind of pattern with certain features in you have the ability to shape those features over time and
45:00 - 45:30 that already means that you the old you is not going to be here but you get to shape the new you and and and but the environment is going to try to shape you as well so there's some some tensions there but but yeah yeah I think I think I think you're exactly right it's this it's it's back to that same Paradox that that that kind of architecture gives you the plasticity and the intelligence to uh to adapt and exploit other uh other Realms and other domains of of activity
45:30 - 46:00 and and and so on but that means you are not going to be the same right there also this brings to mind of course it's a specul very speculative idea uh the idea of like white holes black holes and white holes on the other side of them and I see this pattern I'm I have the paper in front of me and I can't help but think of that connection there potentially I know that's way out there um sci-fi land here but do you think gives any Credence to that idea or it's it's interesting I mean I I don't know
46:00 - 46:30 this is this is the kind of thing you know Paul Davies or somebody would probably want to want to talk about that too so I I don't have the physics to know what's supposed to happen to patterns as they as they go through a wormhole like that but um I I don't think it's crazy to to ask the question uh with respect to I mean especially if okay under under normal under sort of conventional theories you wouldn't expect anything like like that uh you you wouldn't expect there to be any reason why the the these things would
46:30 - 47:00 map on to to that to that Wormhole scenario but if you buy into some of the um evolutionary universes approaches like I think Lee Mo and hwood and some um then then it becomes uh I think perfectly a perfectly reasonable hypothesis to say that the same dynamics that led to this bowai architecture in the biological world if if those Dynamics exist at the scale of of whole universes maybe maybe maybe they give
47:00 - 47:30 rise to exactly the same kinds of information Dynamics through the through these wormholes I mean this is like Way Beyond you know anything I know as far as realistic physics but um I I I think I think if you take the longer view of like some of those some of those some of those models then then I don't see why not yeah it's fascinating the other thing too I'd love to a big topic um in the paper you talk about confabulation and actually would you mind potentially
47:30 - 48:00 defining confabulation uh for the audience and you talk about in the paper a bit but I just wanted to get like um can we think about it like as being like hallucinations with AI I know that's something that's brought up or I've heard that term discussed with AI uh before is that the same kind of idea are those different so I I I I okay um I I don't have any reason right
48:00 - 48:30 now to think that the kinds of phenomena that we see in uh in in current language models are the same have have the same origin where where the confabulation has the same origin as it does for us I will put a asterisk there that we can talk about which I think is that we have to be very humble about our claims of even though we write these things and we make them and whatnot for for a number of reasons that we could talk about I think we have to be um quite agnostic still at
48:30 - 49:00 this point about what's actually going on there but um but but the end point is is actually I think quite quite similar which is the desire to uh or the or the functional drive to uh output behaviors that are um more adaptive given current circumstances versus the circumstances that gave rise to them so an allegiance to saliency and adaptive quality not to
49:00 - 49:30 history or veracity or uh or Fidelity of the data and so so so let's just let's just Define um what what we mean when we talk about um confabulation um here are some here are some examples that uh that that people have found in in human human patients uh some of the some of the um earliest ones were from split brain patients where you sever the Corpus colossum and so you have the the you know there's a speaking
49:30 - 50:00 typically there's speaking Hemisphere and one that doesn't but but the one that doesn't is operating half of the body and when that half of the body does certain things uh the the speaking half makes up stories about what's going on there even though we know so so so so so we can put you know we can you you put a piece of a piece of cardboard between the eyes like this and you show one side of the brain uh some kind of thing and then you ask the uh the the opposite um you ask the opposite hand to pick out a
50:00 - 50:30 relevant object and then you ask the language speaking side hey why did you pick up this object well it has no actual idea because it did not see the prompt but it'll come up with some story that vaguely makes sense and it doesn't feel like lying to the subject it just feels completely natural because we are driven to make stories about ourselves in our world that make sense that's a that's a fundamental thing and um uh it's another example I can think of uh there's a there's was a video on um that I saw where a patient had a um he had an
50:30 - 51:00 electrode uh I think it was for epilepsy um in his brain and it happened to be uh touching a region that corresp that that induces laugh laughing behavior and so the uh the scientist pushes the button and the the person's mouth starts laughing and then you ask then then he's asked so why are you laughing and the answer isn't gee I don't know I was sitting here thinking of serious things and suddenly my mouth starts laughing that's never the answer the answer answer is Oh I thought of a funny joke and and again this isn't this isn't them
51:00 - 51:30 trying to fool anybody this is this is what it feels like to them because because because all of us are trying to uh uh continuously modify our models of ourselves in our world to make it to make things make sense and so so that that kind of uh basic fundamental feature I think is really important now when it goes too far when the Horizon gets really short and you lose track of long-term patterns then that's not adaptive either because because then you end up with explanations that have maybe
51:30 - 52:00 immediate value but in the in the long run they're you know and and I think this is functionally I think this is what's happening with these language models they they tell you something at the moment that is plausible of the kind of thing you want to see but but big picture they're not if they're not tied to what's actually if if they're not good at reinterpreting the past then then this then this doesn't work I mean it's a deep skill to be able to do that so so I think it's I think it's fundamental but I think we we still don't understand I mean the biggest mystery to all of this is like the one
52:00 - 52:30 the one thing that um you know I've hardly uh cracked this this deep issue here but all I've done is is is draw attention to a new way of thinking about it the Deep issue is how the creative interpretation actually works when you're handed these engrams decoding them in a way that uh that is adaptive that's really important I think if we understood how that works we would have much more insight um into into ourselves but also into new uh computational Frameworks that would do a better job than than than our current
52:30 - 53:00 efforts okay interesting okay so it's a little different than it's a little a skew from what I was thinking of originally are you familiar with it all with um Greg henriquez he's a psychologist at James Madison University yeah this reminds me a little bit of his one of his course Concepts around uh that we're self-justifying apes and so when you say something like yes one part of the brain is explaining something that has no access to the thing that we're doing all the time much
53:00 - 53:30 of the time is is is sort of justifying stuff is that right or is am I I mean I mean yes yes I think I think there's a lot of value in that but also um and I don't remember who said this it might been Yuval horari Harari or somebody said that you know humans are fundamentally storytellers like yes but this isn't just about humans this is about all agents so all good agents if if if you're not a good Storyteller in a in a in a primitive way right meaning
53:30 - 54:00 making models making actionable models of yourself in your outside world you will never get out of the Single Cell phase in fact in fact you will not survive as a single cell and I don't think you'll survive as a persistent you know chemical pathway either you this this this storytelling uh you know by the time you get to humans we call it storytelling but but that fundamental thing that active inference kind of loop that causes you to to store some priors and to try to figure out what exactly is going on in a way that is going to allow me to make
54:00 - 54:30 the decisions which are you know are coming up very you know constantly you have to right in in the real world you're going to run out of energy and die and be eaten very quickly if you're not constantly taking actions uh that requires modeling and that does not wait till we get to human stage that was there from day one of evolution and possibly before that H storytelling are there any let's see I'll say patterns
54:30 - 55:00 or huh in terms of these information patterns do you notice any patterns that resemble anything like storytelling Frameworks or elements of Storytelling that we are more familiar with as humans yeah well I I I do think it would be a it would be an amazing project for somebody uh and and you know maybe there's there's a few people I've I've talked about this around here that that might want to do it is to take something like Joseph Campbell's um righty of of
55:00 - 55:30 of archetypes or or you know these kinds of things and try to recast them as what what what do those things look like for single cells what do they look like for Pathways and and I mean here's right I like I think I think that would be that would be completely fascinating um but uh you're speaking my language that's like amaz that that is a great idea yeah I think I think that would be really
55:30 - 56:00 interesting and you know like like one pattern I mean I'll I'll tell you one one thing that I can think of right off the top of my head uh and I'm no I'm no you know expert on on myths or anything like that but but but one thing that I think is really fundamental is uh seeing agency in the world telling stories about agents doing things I think is really critical and here's why if and and I think and I think any any real realistic agent that that evolved under
56:00 - 56:30 constraints of energy and time is going to need to do this because again just to come Circle back to the beginning if you you you as a living system that is vulnerable you know these mortal computations as as a few people have called them um you do not have the luxury of being a lassan demon that says I don't believe in mesostates or large scale pattern I'm just going to track microstates all I care about is every particle every atom
56:30 - 57:00 I can measure and and I'm just going to track them okay well you don't have time to do that you you'll be dead if you if you try that strategy so the only things that survive that filter are agents that are good at core screening so what they do is they say okay I'm going to take a bunch of these these all of these states I'm gonna I'm G to ignore what's different about them but I'm going to establish a category that I'm going to treat them all the same way I'm going to generalize and I'm going to say all of this is you know that it's this this is hunger or this is a chemical attack or this is
57:00 - 57:30 danger or this is stress or this is um you know what whatever right and so and and and then later on it's like oh this is a tiger and I don't care if the pixels are this way or that way or there's lighting shadowing you know and so on so um you have to get good at it in order to survive you don't have the computational uh resources to to avoid that and and that leads to that that kind of thing if you become an agent that is constantly telling stories about other agents in the world doing things
57:30 - 58:00 that then leads to you making models of them well how do what are the properties that these agents have do they what do they notice what do they you know can I hack them can they hack me are they dangerous are they positive whatever uh and then eventually you turn that on yourself and you say wait a minute I'm an agent too that does things and now you've got a model of yourself as This Magnificent you know uh Moral Moral being that that exists right this kind of self-reflective loop but but you can but you can in fact must do that long before you're capable of of doing that
58:00 - 58:30 um that self- referential you know that self- referential Loop so I think that one you know uh that fundament that that concept of an agent in the world doing things not just not just uh standing back and saying here's a bunch of stuff that happened and I've totaled up all the atoms that zigged and zagged and whatever but but but I've I've course grain them into a into a some kind of a larger scale pattern where the pattern makes decisions it has memories and by the way it pays off if I try to make a model of well what kinds of things does
58:30 - 59:00 it remember for how long what does it like what does it not like uh some level of you know uh some level of of predictive description that I think is is extremely [Music] fundamental yeah and even well and I think about it in terms of more broadly the the effectiveness of great storytelling and how yes you might have a con concept a complicated thing in physics right but someone a great
59:00 - 59:30 educator can hone it down and break it down and have an anecdote about it and maybe give some personalities to the quirks and electrons so that we can kind of glom on to certain things and we interpret those and we kind of make sense of them in a better way even though they don't have personalities per se um I like to think of about them as having personalities or having yeah not not personhood but um attributes let's say or or things that um like I can
59:30 - 60:00 understand um I'm kind of talking in circles here but yeah yeah it's a really cool idea I like that yeah I mean it makes what what you just said makes makes sense for in two ways one is that like in that paper we give a table um or or I give a table of things that act as uh as these bow ties and uh scientific papers language literature all of those things act in this bow MTI fashion you have some very complex mental states you have some very complex um synaptic you
60:00 - 60:30 know molecular States there's no hope of you communicating those States and mapping them onto my brain so that we understand each other my brain is different it's not going to work anyway what we do have is a very thin interface at you know however many bits per minute we can do it that's language that allows this very complex set of events to come forward and to then in then to be expanded in my brain into whatever it takes for me to understand whatever part of what you said I
60:30 - 61:00 understood you know whatever whatever I'm supposed to get out of it and so the same thing with science papers right so these brilliant people have these incredible ideas they boil it down to a you know to a nature paper that's like this compressed you know squee a couple of pages representation and then all everybody else the rest of us read it and try to reinflated it and say well what does this mean what am I going to get out of it and and right so so a lot of these things are are like that yeah yeah great I know you have to run I just want to ask you one more one last question um So based on the conclusions of this paper uh are you going to follow
61:00 - 61:30 us up with any research do you have anything uh what comes after this basically yeah yeah we yeah we have we have a ton of stuff so so well conceptually uh what comes off after is some computational work that we're doing to tie um uh the uh the poly Computing framework into all of this and so um a Tusa parksa who uh was was a student with Josh bonarden was responsible for a lot of the actual primary work on the poly Computing I mean she she drove the
61:30 - 62:00 the early poly Computing work um she's she's now in my in my group and we're going to uh basically turn this whole thing into a a model of an evolutionary model a model of for a new type of um you know computational platform and so on but then but then the biology of it uh becomes really a search for the mechanisms of this creative and a so when you do have uh when you do have these engrams what are the mechanisms by
62:00 - 62:30 which those get mapped onto whatever the novel um scenario and the novel problems are and that's that's something that we're using in our um synthetic morphology models like anthr robots xenobots and some other things that will come out this year where we can actually start to ask because because they're I mean one reason for making those things is that there the problem is is stands the starkest you're you're you're a zenbot you've been given some DNA was that any of that DNA about how to be a zenbot most of it wasn't at all there's
62:30 - 63:00 never been any zenbot there's never been selection to be a good zenbot um you know there's some physiology in there that you have in common but but but a lot of it and the same thing the same thing for the OTS when you one of the values of making these uh synthetic models is that you force them to break free of a specific evolutionary history and then you get to find out how do they reinterpret the the affordances that they've been given um and where do these novel patterns come from they have new patterns of behavior and and so on so so
63:00 - 63:30 memories that were not specifically encoded for them right because if you I mean just think about this if if you have the ability to reinterpret these little these little um engrams that you've been given that creative ability you can now nucleate that off of a lot of other things you know you could there are lots of prompts that that you can apply that same capacity to it doesn't have to be the same the same materials that you were given before if you have the ability to interpret specific memory
63:30 - 64:00 and grams that were used for Behavioral memory you can turn that capacity onto almost anything so I almost visualize that like the right side so you get this bow tie you know they meet in the middle I almost like visualize unmoral and disease and and and so on oh so cool all right Mike thank you so much
64:00 - 64:30 for your time we we want to cover three papers I want to but we got we got deep into one which I think is I I prefer that in terms of going deeper into one thing than glossing over maybe three but maybe we can talk again and we go into the uh stress sharing paper and I can reach out to uh to you and Emma and uh sure sure yeah yeah yeah yeah got some time uh and we'll we'll we'll talk about the other stuff yeah no problem great thanks so much Mike appreciate it thank you good to see