1 00:00:05,200 --> 00:00:09,080 Speaker 1: Why do you think of your thirty trillion cells as 2 00:00:09,080 --> 00:00:13,160 Speaker 1: a self? Does an ant colony have a sense of self? 3 00:00:13,600 --> 00:00:16,040 Speaker 1: And could you think of all those ants as a 4 00:00:16,160 --> 00:00:19,120 Speaker 1: liquid brain? What does any of this have to do 5 00:00:19,640 --> 00:00:23,520 Speaker 1: with how the brain of a caterpillar transitions to the 6 00:00:23,560 --> 00:00:26,840 Speaker 1: brain of a butterfly, or how we might think of 7 00:00:26,920 --> 00:00:31,520 Speaker 1: a memory as a pattern that stays alive and has 8 00:00:31,560 --> 00:00:37,680 Speaker 1: its own life. Welcome to Inner Cosmos with me David Eagleman. 9 00:00:38,240 --> 00:00:41,559 Speaker 1: I'm a neuroscientist and an author at Stanford and in 10 00:00:41,640 --> 00:00:45,320 Speaker 1: these episodes we dive deeply into our three pound universe 11 00:00:45,680 --> 00:00:53,640 Speaker 1: to uncover some of the most surprising aspects of our lives. 12 00:00:59,240 --> 00:01:02,120 Speaker 1: In the last piso, I talked about who we are 13 00:01:02,240 --> 00:01:06,920 Speaker 1: and how we change through time. The pieces and parts 14 00:01:06,959 --> 00:01:10,840 Speaker 1: of every single cell in your body degrade and get 15 00:01:10,920 --> 00:01:16,679 Speaker 1: replaced continuously, such that you are physically speaking, a totally 16 00:01:16,760 --> 00:01:21,360 Speaker 1: new person every few years, and yet we experience the 17 00:01:21,480 --> 00:01:24,840 Speaker 1: illusion that we are the same person we've always been. 18 00:01:25,040 --> 00:01:29,399 Speaker 1: We have this illusion of constancy. So last week we 19 00:01:29,520 --> 00:01:34,199 Speaker 1: explore this by considering the thought experiment of the Ship 20 00:01:34,319 --> 00:01:35,319 Speaker 1: of Theseus. 21 00:01:35,360 --> 00:01:37,080 Speaker 2: The story here is that. 22 00:01:37,200 --> 00:01:41,240 Speaker 1: Each plank of a famous ship is replaced one by 23 00:01:41,360 --> 00:01:45,920 Speaker 1: one over time, which raises the question, is it's still 24 00:01:46,040 --> 00:01:50,720 Speaker 1: the same ship even after every plank has been replaced 25 00:01:50,920 --> 00:01:55,320 Speaker 1: and nothing of the original remains. We consider this question, 26 00:01:55,400 --> 00:01:59,440 Speaker 1: of course, because like the ship, we too exist with 27 00:01:59,520 --> 00:02:04,680 Speaker 1: physical changes that never stop, and yet we perceive ourselves 28 00:02:04,720 --> 00:02:09,720 Speaker 1: as constant through time. The planks of theseus's ship map 29 00:02:09,800 --> 00:02:12,800 Speaker 1: onto the cells and molecules in our bodies. So what 30 00:02:13,000 --> 00:02:17,360 Speaker 1: maintains your sense of self over time? Okay, so maybe 31 00:02:17,360 --> 00:02:21,520 Speaker 1: the thing that links our different selves through time is 32 00:02:21,639 --> 00:02:25,639 Speaker 1: our memory. But there's a problem here as well, which 33 00:02:25,680 --> 00:02:31,600 Speaker 1: is that memory is notoriously unreliable. It's constantly being reshaped 34 00:02:31,600 --> 00:02:35,320 Speaker 1: and revised. So this is all very strange. And there's 35 00:02:35,360 --> 00:02:39,040 Speaker 1: an added difficulty, which is that we're constantly changing, and 36 00:02:39,120 --> 00:02:42,200 Speaker 1: even when we recognize that we have changed, we always 37 00:02:42,240 --> 00:02:45,919 Speaker 1: assume that we're not going to change very much into 38 00:02:45,919 --> 00:02:49,480 Speaker 1: the future, and that's always incorrect. This is a cognitive 39 00:02:49,520 --> 00:02:53,400 Speaker 1: bias called the end of history illusion. We tend to 40 00:02:53,480 --> 00:02:59,200 Speaker 1: believe that our current preferences and personalities are fixed, even 41 00:02:59,280 --> 00:03:03,480 Speaker 1: though we will in fact continue to evolve. So that's 42 00:03:03,520 --> 00:03:07,839 Speaker 1: our situation. We constantly change, but we're reaching back into 43 00:03:07,960 --> 00:03:11,200 Speaker 1: our not so good memory to try to understand who 44 00:03:11,240 --> 00:03:12,120 Speaker 1: we are, or. 45 00:03:12,120 --> 00:03:13,080 Speaker 2: At least who we were. 46 00:03:13,600 --> 00:03:17,200 Speaker 1: All this encourages us to start looking for new frameworks 47 00:03:17,240 --> 00:03:20,160 Speaker 1: when we think about the self. For example, what if 48 00:03:20,160 --> 00:03:23,919 Speaker 1: we thought of the memories themselves like their own little 49 00:03:24,000 --> 00:03:28,240 Speaker 1: creatures and they are competing to stay alive. So in 50 00:03:28,280 --> 00:03:31,600 Speaker 1: this episode, I want to dive into new ways of 51 00:03:31,639 --> 00:03:33,960 Speaker 1: thinking about all of this, and there is no one 52 00:03:34,000 --> 00:03:37,760 Speaker 1: better to ring up for that than my colleague Michael Levins. 53 00:03:37,840 --> 00:03:40,480 Speaker 1: He's one of the most energetic and original thinkers in 54 00:03:40,520 --> 00:03:45,960 Speaker 1: the field. Michael is a developmental biologist and a synthetic biologist, 55 00:03:46,080 --> 00:03:49,560 Speaker 1: meaning he makes new kinds of organisms. He's a Toughts 56 00:03:49,680 --> 00:03:53,160 Speaker 1: University and what I love about Michael is his extremely 57 00:03:53,320 --> 00:03:59,280 Speaker 1: broad interests like bioelectrical signals by which cells communicate, and 58 00:04:00,080 --> 00:04:04,320 Speaker 1: what cognition looks like across totally different body plans, and 59 00:04:04,920 --> 00:04:08,600 Speaker 1: how you get similar forms and functions across all kinds 60 00:04:08,600 --> 00:04:11,800 Speaker 1: of different scales and biology. So I'm going to have 61 00:04:11,880 --> 00:04:14,400 Speaker 1: Michael back to talk about some of these other topics 62 00:04:14,400 --> 00:04:17,640 Speaker 1: on a future episode, but today I want to zoom 63 00:04:17,680 --> 00:04:21,160 Speaker 1: in with him on his recent thinking about the self 64 00:04:21,640 --> 00:04:29,200 Speaker 1: and memory. So Mike, tell us how you think about 65 00:04:29,480 --> 00:04:30,440 Speaker 1: the self. 66 00:04:31,160 --> 00:04:33,599 Speaker 2: I work in diverse intelligence. I'm interested in all different 67 00:04:33,680 --> 00:04:37,640 Speaker 2: kinds of implementations of minds, and I think a self 68 00:04:37,880 --> 00:04:41,240 Speaker 2: is a useful way to think about selves is as 69 00:04:41,480 --> 00:04:46,000 Speaker 2: a system that has goals of some particular size. It's 70 00:04:46,160 --> 00:04:48,640 Speaker 2: what the dark hop Statu would call a strange loop. 71 00:04:48,680 --> 00:04:51,839 Speaker 2: It's an observer of the outside world, but it's also 72 00:04:51,880 --> 00:04:55,040 Speaker 2: an observer of itself. It's something that can loop back 73 00:04:55,080 --> 00:04:58,520 Speaker 2: in and interpret what its own memories mean and what 74 00:04:58,880 --> 00:05:01,000 Speaker 2: it is doing, and make decision and going forward. Those 75 00:05:01,000 --> 00:05:03,279 Speaker 2: are the components of being a self. And so do 76 00:05:03,560 --> 00:05:07,520 Speaker 2: selves change? Yeah? Part of the paradox of being a 77 00:05:07,560 --> 00:05:10,000 Speaker 2: self is that you have to change in order to 78 00:05:10,000 --> 00:05:12,039 Speaker 2: stay alive. You have to change in order to persist. 79 00:05:12,279 --> 00:05:14,279 Speaker 2: Every time you learn something, every time you make a 80 00:05:14,320 --> 00:05:17,320 Speaker 2: decision that then feeds back and alters the inputs that 81 00:05:17,360 --> 00:05:20,000 Speaker 2: you receive and thus changes your own cognitive system and 82 00:05:20,040 --> 00:05:23,120 Speaker 2: your own behavior going forward. You've now changed. I you know, 83 00:05:23,400 --> 00:05:27,200 Speaker 2: we selves face this this paradox that in order to 84 00:05:27,200 --> 00:05:30,960 Speaker 2: to to to persist, they must change and transform over time. 85 00:05:31,320 --> 00:05:34,320 Speaker 1: Why do you suppose we have the illusion of ourselves 86 00:05:34,360 --> 00:05:37,640 Speaker 1: when we think about you know yourself and your life, 87 00:05:37,800 --> 00:05:40,320 Speaker 1: you have the illusion that it's unchanging. 88 00:05:40,800 --> 00:05:43,440 Speaker 2: What do you suppose that's about. Yeah, I think that, 89 00:05:43,760 --> 00:05:48,320 Speaker 2: Uh well, let's let's just think about very primitive, basic, fundamental, 90 00:05:48,400 --> 00:05:51,120 Speaker 2: basic agents at the very beginning of of of of 91 00:05:51,160 --> 00:05:53,839 Speaker 2: that of that spectrum. If if you're any kind of 92 00:05:53,839 --> 00:05:56,279 Speaker 2: a system that's going to survive in the real world, 93 00:05:56,560 --> 00:05:58,279 Speaker 2: what you can't afford to do is to be a 94 00:05:58,360 --> 00:06:01,599 Speaker 2: kind of placium demon where where you're tracking the micro 95 00:06:01,640 --> 00:06:05,080 Speaker 2: states of you know, every single stimulus, every molecular impact 96 00:06:05,160 --> 00:06:07,120 Speaker 2: on your membrane or whatever you can. You can't afford 97 00:06:07,160 --> 00:06:09,160 Speaker 2: to track all that. If you try to do that, 98 00:06:09,400 --> 00:06:11,480 Speaker 2: you will run out of time, You'll run out of energy, 99 00:06:11,480 --> 00:06:13,159 Speaker 2: You'll be eaten and dead in no time. And so 100 00:06:13,960 --> 00:06:17,360 Speaker 2: I think that what we have in at least at 101 00:06:17,400 --> 00:06:21,520 Speaker 2: least in biological evolution, is an incredible pressure to coarse grain, 102 00:06:21,800 --> 00:06:23,800 Speaker 2: that is, to look out into the world and to 103 00:06:24,000 --> 00:06:30,160 Speaker 2: group inputs and sensations and stimuli and observations that you make, 104 00:06:30,400 --> 00:06:33,000 Speaker 2: to group them into categories, and to try to understand 105 00:06:33,000 --> 00:06:36,279 Speaker 2: your world by reducing its complexity, and by trying to 106 00:06:37,120 --> 00:06:39,640 Speaker 2: make models of the world that have these large things 107 00:06:39,680 --> 00:06:42,400 Speaker 2: in it that are themselves agents. You know that do things, 108 00:06:42,440 --> 00:06:44,840 Speaker 2: and that you can then make decisions about what they 109 00:06:44,839 --> 00:06:46,600 Speaker 2: do and you don't have to pay attention to all 110 00:06:46,600 --> 00:06:49,360 Speaker 2: the micro states. If you're going to do that, you 111 00:06:49,440 --> 00:06:54,000 Speaker 2: fundamentally need a way to make models of persistent things. 112 00:06:54,320 --> 00:06:57,560 Speaker 2: You have to have some way of saying that this 113 00:06:57,680 --> 00:06:59,800 Speaker 2: is an object I need to stay away from. More conversely, 114 00:06:59,800 --> 00:07:01,800 Speaker 2: this something I need to approach, or this is something 115 00:07:01,839 --> 00:07:05,400 Speaker 2: I need to recognize meanwhile, of course, so let's just 116 00:07:05,400 --> 00:07:07,120 Speaker 2: look at vision, you know. For example, So you have 117 00:07:07,160 --> 00:07:11,680 Speaker 2: these these pixels and impacting onto your retina, these these 118 00:07:11,880 --> 00:07:14,680 Speaker 2: visual stimuli, and depending on how you're looking at something, 119 00:07:14,720 --> 00:07:17,560 Speaker 2: the details will be completely different, the lighting will be different, 120 00:07:17,600 --> 00:07:20,440 Speaker 2: the motion, the way that it's angled. But your job 121 00:07:20,600 --> 00:07:25,360 Speaker 2: as a successful cognitive system is to recognize as well 122 00:07:25,400 --> 00:07:27,640 Speaker 2: as possible that this is always the same thing, right 123 00:07:27,680 --> 00:07:30,360 Speaker 2: that you can, despite the fact that it's now just shifted, 124 00:07:30,400 --> 00:07:32,520 Speaker 2: tinted green or whatever it is, that that hey, wait 125 00:07:32,520 --> 00:07:34,520 Speaker 2: a minute, I recognize it. I know what this is. 126 00:07:34,840 --> 00:07:37,800 Speaker 2: And so I think from the very beginning to be 127 00:07:37,880 --> 00:07:42,000 Speaker 2: a successful being that can survive in a world war, 128 00:07:42,080 --> 00:07:45,400 Speaker 2: time and energy are extremely precious. These are precious resources. 129 00:07:45,600 --> 00:07:48,280 Speaker 2: You have to get good at noticing and in fact 130 00:07:48,360 --> 00:07:51,440 Speaker 2: magnifying invariance. You know, things that stay the same. And 131 00:07:51,480 --> 00:07:53,400 Speaker 2: then I think you turn that on yourself and you say, 132 00:07:53,400 --> 00:07:55,360 Speaker 2: wait a minute, I am also an agent that does things. 133 00:07:55,400 --> 00:07:58,040 Speaker 2: And I don't mean consciously, and we don't do this consciously. 134 00:07:58,200 --> 00:08:01,720 Speaker 2: But every system, every living system, I think, has an 135 00:08:01,760 --> 00:08:04,760 Speaker 2: internal self model, and part of that model is to 136 00:08:04,800 --> 00:08:08,160 Speaker 2: recognize you as a persistent thing even though things change. 137 00:08:08,440 --> 00:08:10,680 Speaker 2: And then and then you know, those those of us 138 00:08:10,680 --> 00:08:13,520 Speaker 2: that have self consciousness then have a have a story 139 00:08:13,560 --> 00:08:16,760 Speaker 2: that we tell ourselves about being a persistent thing as 140 00:08:16,760 --> 00:08:19,760 Speaker 2: opposed to I think what we much more close to 141 00:08:19,800 --> 00:08:22,000 Speaker 2: what we really are, which is a flowing process. 142 00:08:23,600 --> 00:08:26,160 Speaker 1: So so we all walk through life and we have 143 00:08:26,280 --> 00:08:29,680 Speaker 1: our notion of self, and we observe the selves in 144 00:08:29,840 --> 00:08:32,439 Speaker 1: others that we love, and we try to figure out 145 00:08:32,440 --> 00:08:35,520 Speaker 1: what this is all about. You have a very cool approach, 146 00:08:35,559 --> 00:08:40,080 Speaker 1: which is that you think about diverse intelligences. Tell us 147 00:08:40,120 --> 00:08:41,200 Speaker 1: what that term means. 148 00:08:42,000 --> 00:08:45,000 Speaker 2: Yeah, diverse intelligence is the name of a of a 149 00:08:45,160 --> 00:08:47,160 Speaker 2: of a feel that on the one hand is the 150 00:08:47,200 --> 00:08:50,120 Speaker 2: emerging now and it's a very exciting kind of feel. 151 00:08:50,360 --> 00:08:52,560 Speaker 2: On the other hand, this is a collection of ideas 152 00:08:52,600 --> 00:08:54,560 Speaker 2: that have really been around for a very long time. 153 00:08:54,840 --> 00:08:58,640 Speaker 2: And it goes back to the efforts of trying to 154 00:08:58,720 --> 00:09:03,400 Speaker 2: understand what what do we actually mean by intelligence, by minds, 155 00:09:03,600 --> 00:09:07,679 Speaker 2: by cognition, by all all these kinds of all these 156 00:09:07,720 --> 00:09:10,800 Speaker 2: kinds of terms. And we have familiar examples of them 157 00:09:10,800 --> 00:09:13,920 Speaker 2: in brainy animals and and and you know, in ourselves. 158 00:09:14,200 --> 00:09:17,280 Speaker 2: But actually the key thing that that we need to 159 00:09:17,320 --> 00:09:20,280 Speaker 2: understand is that all of us, at one point, both 160 00:09:20,400 --> 00:09:23,080 Speaker 2: on an evolutionary scale and on a developmental scale, we 161 00:09:23,080 --> 00:09:25,520 Speaker 2: were all a single sell once. You know, at one 162 00:09:25,520 --> 00:09:28,280 Speaker 2: point we were a little blob of quies and cytoplasm 163 00:09:28,320 --> 00:09:30,800 Speaker 2: and unfertilized noocyte. And we would look at that and 164 00:09:30,840 --> 00:09:33,720 Speaker 2: we would say, well, there's there's a little a little 165 00:09:33,720 --> 00:09:36,880 Speaker 2: blob of chemicals that obeys physics and chemistry. And then 166 00:09:36,920 --> 00:09:38,840 Speaker 2: at some point we have something that we would say 167 00:09:38,840 --> 00:09:41,400 Speaker 2: has an inner perspective, It has a mind, it has preferences, 168 00:09:41,400 --> 00:09:43,920 Speaker 2: it has goals, it has memories, and and you know 169 00:09:44,040 --> 00:09:46,719 Speaker 2: it and it has a full blown self conscious kind 170 00:09:46,720 --> 00:09:49,520 Speaker 2: of meta cognition and so on. Now, how did we 171 00:09:49,559 --> 00:09:51,560 Speaker 2: get there? Right? Because we started out you know, this 172 00:09:51,679 --> 00:09:53,920 Speaker 2: this amazing journey from physics to mind. You know, you 173 00:09:54,000 --> 00:09:56,120 Speaker 2: start off with with a little blob of chemistry, and 174 00:09:56,160 --> 00:10:00,240 Speaker 2: then eventually here we are, so diverse. Intelligence is is 175 00:10:00,480 --> 00:10:04,280 Speaker 2: I think, is predicated on the on the idea that 176 00:10:04,800 --> 00:10:07,720 Speaker 2: we need to understand how it is that minds are 177 00:10:07,760 --> 00:10:10,720 Speaker 2: embodied in the physical world, and that by understanding that 178 00:10:10,760 --> 00:10:13,360 Speaker 2: we emerge slowly and gradually, this is we are not. 179 00:10:13,640 --> 00:10:15,920 Speaker 2: You know, there's no there's no magical category called human 180 00:10:15,960 --> 00:10:18,920 Speaker 2: that suddenly snaps into existence at some particular moment of 181 00:10:18,960 --> 00:10:22,840 Speaker 2: embryonic development. But this is a slow, gradual process. It's 182 00:10:22,880 --> 00:10:25,800 Speaker 2: it's an effort to understand what those processes are that 183 00:10:25,920 --> 00:10:29,400 Speaker 2: give rise to collective intelligence. And by the way, all intelligence, 184 00:10:29,440 --> 00:10:31,920 Speaker 2: I think is collective intelligence because all of us are 185 00:10:31,920 --> 00:10:33,880 Speaker 2: made of parts, and we have to understand how the 186 00:10:33,880 --> 00:10:36,760 Speaker 2: competencies of those parts give rise to whatever the larger 187 00:10:36,800 --> 00:10:39,800 Speaker 2: scale system is capable of doing. And as soon as 188 00:10:39,840 --> 00:10:41,520 Speaker 2: as soon as you frame the problem that way, to 189 00:10:41,640 --> 00:10:44,880 Speaker 2: understand what is it that the components are doing to 190 00:10:44,960 --> 00:10:48,000 Speaker 2: scale that cognition from the competencies of a single cell 191 00:10:48,040 --> 00:10:50,480 Speaker 2: to that of an animal or a human, then then 192 00:10:50,480 --> 00:10:52,680 Speaker 2: immediately you start to ask yourself, okay, so not only 193 00:10:52,760 --> 00:10:56,640 Speaker 2: is there a history going backwards where we see increasingly 194 00:10:56,720 --> 00:11:00,440 Speaker 2: simpler forms and ask what is their intelligence like? But 195 00:11:00,480 --> 00:11:04,520 Speaker 2: then we can really try to shed some of the 196 00:11:04,960 --> 00:11:08,200 Speaker 2: limitations that we're given to us by our evolutionary firmware 197 00:11:08,520 --> 00:11:10,800 Speaker 2: that make it easy for us to recognize, you know, 198 00:11:11,040 --> 00:11:16,559 Speaker 2: large intelligence, large large, medium sized objects moving at medium 199 00:11:17,520 --> 00:11:19,760 Speaker 2: speeds through three dimensional space, and we so, okay, that's 200 00:11:19,800 --> 00:11:22,480 Speaker 2: an intelligent animal doing whatever it's doing. But to learn 201 00:11:22,520 --> 00:11:26,040 Speaker 2: to recognize intelligence and unfamiliar guises, you know, what other 202 00:11:26,160 --> 00:11:28,720 Speaker 2: spaces can intelligence be operating and what what else can 203 00:11:28,720 --> 00:11:32,200 Speaker 2: it be made of? What other processes give rise to 204 00:11:32,360 --> 00:11:35,439 Speaker 2: the scale up of intelligence. That's diverse intelligence. It's the 205 00:11:36,040 --> 00:11:39,400 Speaker 2: broad attempt to understand intelligence as it can be. What's 206 00:11:39,440 --> 00:11:40,160 Speaker 2: an example of that? 207 00:11:40,280 --> 00:11:44,720 Speaker 1: For example, ants in a colony understanding that as an 208 00:11:44,760 --> 00:11:48,319 Speaker 1: intelligence system where they're laying down memories in particular ways. 209 00:11:48,360 --> 00:11:49,920 Speaker 1: It's very different than the way we think about the 210 00:11:49,960 --> 00:11:51,560 Speaker 1: human brain. Is that an example? 211 00:11:52,400 --> 00:11:54,319 Speaker 2: Yes, I'll give you a few examples and get the 212 00:11:54,400 --> 00:11:59,680 Speaker 2: sort of progressively progressively weirder with them. Traditional collective intelligence 213 00:11:59,720 --> 00:12:03,280 Speaker 2: is like ants and beehives and termite colonies and blocks 214 00:12:03,280 --> 00:12:07,280 Speaker 2: of birds and things like that. Those are typical collective intelligence. 215 00:12:07,559 --> 00:12:10,520 Speaker 2: They are what our Carsole calls liquid brains because they're 216 00:12:10,559 --> 00:12:13,280 Speaker 2: moving around relative to each other. Our neurons tend to 217 00:12:13,320 --> 00:12:16,400 Speaker 2: stand still relative to each other. And yet we too 218 00:12:16,440 --> 00:12:18,840 Speaker 2: are a collective intelligence, right, We are a pile of 219 00:12:18,880 --> 00:12:21,839 Speaker 2: neurons and other cells that work together to give rise 220 00:12:21,880 --> 00:12:25,120 Speaker 2: to an emergent being with memories and preferences and goals 221 00:12:25,120 --> 00:12:27,599 Speaker 2: that don't belong to any of the individual cells. So, 222 00:12:28,040 --> 00:12:29,800 Speaker 2: no matter what you are, whether you're a collection of 223 00:12:29,800 --> 00:12:32,360 Speaker 2: neurons or your collection of birds or of termites or 224 00:12:32,360 --> 00:12:34,800 Speaker 2: anything like that, what you need is a kind of 225 00:12:34,800 --> 00:12:38,240 Speaker 2: cognitive glue. You need these policies that are implemented by 226 00:12:38,280 --> 00:12:40,760 Speaker 2: the pieces that allow the collective to be more than 227 00:12:40,880 --> 00:12:43,560 Speaker 2: the sum of its parts in relevant ways. And so 228 00:12:43,600 --> 00:12:48,679 Speaker 2: if we define intelligence as problem solving competencies in some space, 229 00:12:48,880 --> 00:12:51,120 Speaker 2: so the ability to and so this is kind of 230 00:12:51,120 --> 00:12:54,560 Speaker 2: William James's definition of intelligence, same goal by different meats, right, 231 00:12:54,600 --> 00:12:57,240 Speaker 2: the ability to navigate some space to reach your objective 232 00:12:57,640 --> 00:13:02,000 Speaker 2: and do so despite novel despite perturbations, and and so on. 233 00:13:02,440 --> 00:13:06,319 Speaker 2: Then we can get we can get progressively more inclusive 234 00:13:06,320 --> 00:13:08,800 Speaker 2: with this. We can say, okay, so we have we 235 00:13:08,840 --> 00:13:13,240 Speaker 2: have animals that solve problems. We have unfamiliar things like 236 00:13:13,280 --> 00:13:16,040 Speaker 2: slime molds, right, so so so there are people including us, 237 00:13:16,040 --> 00:13:20,080 Speaker 2: who study slime mold behavior, in slime mold learning and 238 00:13:20,120 --> 00:13:22,960 Speaker 2: things like that. So slime molds are a very unusual organism. 239 00:13:23,040 --> 00:13:25,040 Speaker 2: It's a single cell. It can be quite large, and 240 00:13:25,120 --> 00:13:27,960 Speaker 2: yet it can it can solve all kinds of problems. 241 00:13:28,040 --> 00:13:32,280 Speaker 2: People have studied memory and learning in in bacteria, in 242 00:13:32,600 --> 00:13:36,000 Speaker 2: unicellular organisms and plants, and and then you can get 243 00:13:36,040 --> 00:13:39,520 Speaker 2: then you can get you know, even even more sort 244 00:13:39,559 --> 00:13:41,400 Speaker 2: of broad with this, and you can ask, well, what 245 00:13:41,480 --> 00:13:44,160 Speaker 2: happens beyond three dimensional space? For example, the cells that 246 00:13:44,200 --> 00:13:46,360 Speaker 2: make up our bodies and our and our tissues and 247 00:13:46,400 --> 00:13:50,600 Speaker 2: our organs, they operate in physiological state space, so the 248 00:13:50,640 --> 00:13:54,080 Speaker 2: space of all possible physiological conditions. They operate in gene 249 00:13:54,120 --> 00:13:59,360 Speaker 2: expression space. Embryonic and regenerative cells operate in anatomical space. 250 00:13:59,480 --> 00:14:01,800 Speaker 2: They have to navigate from whatever shape they have now 251 00:14:01,880 --> 00:14:05,360 Speaker 2: to some kind of complex organ structure. And in all 252 00:14:05,360 --> 00:14:07,679 Speaker 2: of these spaces you can see if you look for 253 00:14:07,720 --> 00:14:11,000 Speaker 2: them using the techniques of behavioral science, you can find 254 00:14:11,000 --> 00:14:13,439 Speaker 2: them solving problems. You can find them getting to their 255 00:14:13,440 --> 00:14:16,640 Speaker 2: goal in very creative ways despite various problems So that's 256 00:14:16,720 --> 00:14:19,040 Speaker 2: kind of the range of the diverse intelligences. And then 257 00:14:19,040 --> 00:14:22,400 Speaker 2: beyond that you have embodied robotics, and you have maybe 258 00:14:22,400 --> 00:14:27,480 Speaker 2: software intelligent agents, and maybe someday exobiological beings and so on. 259 00:14:28,760 --> 00:14:32,280 Speaker 1: Okay, so the challenge is that everything's changing all the 260 00:14:32,360 --> 00:14:36,640 Speaker 1: time in our brains and all these other biological systems 261 00:14:36,640 --> 00:14:40,120 Speaker 1: and colonies whatever, things are changing all the time. Cells 262 00:14:40,160 --> 00:14:45,840 Speaker 1: are dying or aging or getting mutations. And yet somehow, 263 00:14:45,920 --> 00:14:50,480 Speaker 1: when it comes to memory of oneself, we tend to 264 00:14:50,520 --> 00:14:53,120 Speaker 1: have some sort of memory. It's not perfect, it only 265 00:14:53,160 --> 00:14:56,120 Speaker 1: has a few details, things like that, but we retain 266 00:14:56,240 --> 00:15:00,720 Speaker 1: this memory through time. Now, what is your framework for 267 00:15:00,840 --> 00:15:01,640 Speaker 1: thinking about that? 268 00:15:02,600 --> 00:15:06,400 Speaker 2: Yeah, I'm not sure we retain memory through time as 269 00:15:06,440 --> 00:15:10,440 Speaker 2: much as we reconstruct memory through time. And of course 270 00:15:10,600 --> 00:15:12,960 Speaker 2: there's a lot of human neuroscience being done about this. 271 00:15:12,960 --> 00:15:14,280 Speaker 2: But I want to take a step back and give 272 00:15:14,320 --> 00:15:17,480 Speaker 2: you another example that kind of drives my thinking on this. 273 00:15:17,720 --> 00:15:20,880 Speaker 2: Consider what happens from a butterfly to a caterpillar. So 274 00:15:21,040 --> 00:15:23,520 Speaker 2: in the caterpillar, you have the soft bodied creature that 275 00:15:23,560 --> 00:15:26,160 Speaker 2: lives in basically a two dimensional world where it crawls 276 00:15:26,160 --> 00:15:29,600 Speaker 2: around and eats leaves. Now, one thing that it has 277 00:15:29,640 --> 00:15:31,280 Speaker 2: to do is that has to turn into a butterfly. 278 00:15:31,440 --> 00:15:36,240 Speaker 2: During that process of metamorphosis, its brain is massively rebuilt. 279 00:15:36,320 --> 00:15:38,920 Speaker 2: Many of the cells die, all the connections are broken, 280 00:15:38,960 --> 00:15:42,080 Speaker 2: it's completely refactored, and you get a new brain suitable 281 00:15:42,080 --> 00:15:45,160 Speaker 2: for driving a now hard bodied kind of vehicle, completely 282 00:15:45,160 --> 00:15:48,640 Speaker 2: different controller you need for that. And now it flies 283 00:15:48,760 --> 00:15:51,440 Speaker 2: and it lives in the three dimensional world and so on. 284 00:15:51,960 --> 00:15:55,800 Speaker 2: Now it turns out that if you train the caterpillar 285 00:15:57,040 --> 00:16:00,000 Speaker 2: the let's say you train it to associate a specific 286 00:16:00,080 --> 00:16:02,640 Speaker 2: color to a specific leaf that it wants to eat, 287 00:16:02,840 --> 00:16:05,840 Speaker 2: what you will find is that the butterfly, despite having 288 00:16:05,880 --> 00:16:09,240 Speaker 2: a totally refactored brain, will remember that information. And for 289 00:16:09,360 --> 00:16:11,520 Speaker 2: years I thought the amazing thing here was how do 290 00:16:11,560 --> 00:16:14,520 Speaker 2: you hold on to information when your information medium is 291 00:16:14,520 --> 00:16:17,040 Speaker 2: being totally ripped apart and refactored. Right, we don't, you know, 292 00:16:17,040 --> 00:16:19,520 Speaker 2: our computer technology doesn't doesn't like that. We don't have 293 00:16:19,560 --> 00:16:23,360 Speaker 2: anything that has that that robustness to it. But if 294 00:16:23,360 --> 00:16:26,680 Speaker 2: you think about it more, what turns out is that 295 00:16:27,120 --> 00:16:29,640 Speaker 2: the amazing thing is not holding onto the memory because 296 00:16:29,640 --> 00:16:33,440 Speaker 2: actually the memories, the exact memories of the caterpillar, are 297 00:16:33,440 --> 00:16:36,080 Speaker 2: of no use to the butterfly whatsoever, because it doesn't 298 00:16:36,080 --> 00:16:38,560 Speaker 2: move the same way. It doesn't want the leaves a 299 00:16:38,640 --> 00:16:42,400 Speaker 2: drinks nectar, right, And so just holding onto that memory 300 00:16:42,520 --> 00:16:44,920 Speaker 2: is of no use. What you need is to actually 301 00:16:45,680 --> 00:16:49,680 Speaker 2: transform and remap that memory into a completely novel context 302 00:16:49,920 --> 00:16:53,360 Speaker 2: where it's like not leaves but food for example. Right, 303 00:16:53,400 --> 00:16:55,240 Speaker 2: So you have to generalize a little bit, and you 304 00:16:55,280 --> 00:16:57,320 Speaker 2: have to now remap it on a different on a 305 00:16:57,320 --> 00:17:01,080 Speaker 2: different controller. And you know, numerous people who have done 306 00:17:01,120 --> 00:17:04,280 Speaker 2: experiments in memory transfer, most recently David Landsman and others 307 00:17:04,480 --> 00:17:07,400 Speaker 2: show that that it's it's really wild how you can 308 00:17:07,440 --> 00:17:10,879 Speaker 2: move either tissues or extra extracts let's say, RNA extracts 309 00:17:10,960 --> 00:17:14,480 Speaker 2: or whatever, and introduce them into a new into a 310 00:17:14,520 --> 00:17:16,959 Speaker 2: new creature and and have that creature like take on 311 00:17:17,040 --> 00:17:18,639 Speaker 2: that that initial learning. 312 00:17:18,960 --> 00:17:20,480 Speaker 1: So let me interrupt for a second. So tell us 313 00:17:20,480 --> 00:17:24,800 Speaker 1: about David Landsman's experiments in these sea slugs. Yeah, Well, 314 00:17:24,840 --> 00:17:30,120 Speaker 1: specifically in David's work, what he was doing is training 315 00:17:30,119 --> 00:17:33,320 Speaker 1: these c slugs to a particular to a particular task. 316 00:17:33,359 --> 00:17:36,160 Speaker 1: I mean it was a simple, you know, simple reflex 317 00:17:36,560 --> 00:17:40,800 Speaker 1: and extracting RNA from from the brain. Because the hypothesis 318 00:17:40,880 --> 00:17:42,840 Speaker 1: was that memory is stored in some way in this 319 00:17:42,960 --> 00:17:46,600 Speaker 1: in this medium of RNA, and then he was injecting 320 00:17:46,600 --> 00:17:48,760 Speaker 1: it into a naive animal and showing that they now 321 00:17:48,880 --> 00:17:51,880 Speaker 1: have you know, they show a recall of that information 322 00:17:52,119 --> 00:17:54,160 Speaker 1: I mean to me, I mean, it's it's a fabulous 323 00:17:54,200 --> 00:17:54,720 Speaker 1: body of work. 324 00:17:55,240 --> 00:17:57,640 Speaker 2: To me. One of the most interesting things about it 325 00:17:57,680 --> 00:18:00,280 Speaker 2: is that when you've got this RNA, you don't have 326 00:18:00,359 --> 00:18:03,520 Speaker 2: to go put it exactly in the right neuron where 327 00:18:03,600 --> 00:18:05,679 Speaker 2: it was supposed to be, you know, and that you 328 00:18:05,840 --> 00:18:09,000 Speaker 2: just kind of inject it somewhere in the brain of 329 00:18:09,080 --> 00:18:12,080 Speaker 2: a second of a second sea slug exactly of the 330 00:18:12,119 --> 00:18:14,560 Speaker 2: recipient right of the host seasug, and it somehow gets 331 00:18:14,560 --> 00:18:17,639 Speaker 2: taken out. And that's that's a that's a theme that 332 00:18:17,640 --> 00:18:20,679 Speaker 2: that is like central to all this because what I 333 00:18:20,680 --> 00:18:22,920 Speaker 2: think this is, this is telling us is so now, 334 00:18:22,960 --> 00:18:25,040 Speaker 2: so now let's let's walk into this from from the 335 00:18:25,040 --> 00:18:28,600 Speaker 2: other end. Whatever you are, human or anything else, you 336 00:18:28,640 --> 00:18:31,080 Speaker 2: don't have access to the past. What you have access 337 00:18:31,119 --> 00:18:33,439 Speaker 2: to is the end grams, the memory traces that the 338 00:18:33,480 --> 00:18:36,240 Speaker 2: past has left in your brain or body or wherever 339 00:18:36,480 --> 00:18:39,000 Speaker 2: that were formed by past experience. And so what you 340 00:18:39,080 --> 00:18:41,399 Speaker 2: now have to do is to at any given moment 341 00:18:41,840 --> 00:18:43,399 Speaker 2: and it's you know, these moments. I don't know how 342 00:18:43,400 --> 00:18:46,080 Speaker 2: many milliseconds they are, but but some some number of milliseconds. 343 00:18:46,320 --> 00:18:50,520 Speaker 2: You have to reconstruct that memory to be meaningful to 344 00:18:50,600 --> 00:18:53,840 Speaker 2: you now because you don't know what it used to mean. 345 00:18:53,880 --> 00:18:55,399 Speaker 2: This is you have to do this on the on 346 00:18:55,480 --> 00:18:57,760 Speaker 2: the fly. And I think there's a lot of good 347 00:18:57,760 --> 00:19:00,280 Speaker 2: neuroscience showing how plastic these memories are and how there 348 00:19:00,400 --> 00:19:03,480 Speaker 2: how you know, even even recall of the memories means 349 00:19:03,480 --> 00:19:07,240 Speaker 2: they're getting changed. There's no non destructive read. Accessing these 350 00:19:07,240 --> 00:19:09,880 Speaker 2: memories changes them and and and so so the way 351 00:19:09,920 --> 00:19:12,680 Speaker 2: I think about this is as an architecture in the 352 00:19:12,720 --> 00:19:14,679 Speaker 2: shape of a kind of bow tie. And this is 353 00:19:14,920 --> 00:19:17,280 Speaker 2: for the computer science listeners. You might imagine like a 354 00:19:17,400 --> 00:19:21,639 Speaker 2: like a an auto encoder architecture where there's a funnel 355 00:19:21,680 --> 00:19:25,280 Speaker 2: on the left side which receives the primary experiences, the 356 00:19:25,359 --> 00:19:28,560 Speaker 2: raw data that come in the sense impressions, and that 357 00:19:28,840 --> 00:19:31,080 Speaker 2: the process of learning has to and and and this 358 00:19:31,160 --> 00:19:36,040 Speaker 2: is fundamental to intelligence, is abstracting from individual instances of 359 00:19:36,080 --> 00:19:38,800 Speaker 2: things you experience to a rule, to some kind of 360 00:19:39,560 --> 00:19:41,480 Speaker 2: some kind of pattern, and it's the pattern that you 361 00:19:41,520 --> 00:19:45,200 Speaker 2: remember it, so you compress all those experiences, you throw 362 00:19:45,200 --> 00:19:47,720 Speaker 2: away all the irrelevant details and you form a memory. 363 00:19:47,760 --> 00:19:50,280 Speaker 2: You store that in some sort of enngram. Uh. You know. 364 00:19:50,560 --> 00:19:53,800 Speaker 2: Sometimes people think of this as synaptic modifications. Sometimes other 365 00:19:53,840 --> 00:19:55,560 Speaker 2: people think it's in the RNA or in a cyber 366 00:19:55,600 --> 00:19:59,000 Speaker 2: skeleton wherever. So so you store this but now but 367 00:19:59,080 --> 00:20:02,160 Speaker 2: now here comes here comes the really interesting part. When 368 00:20:02,200 --> 00:20:04,840 Speaker 2: you need to recall this. You have to know, here 369 00:20:04,880 --> 00:20:06,640 Speaker 2: comes the right side of that, on the right side 370 00:20:06,680 --> 00:20:09,040 Speaker 2: of that bow tie. You have to sort of reinflate 371 00:20:09,080 --> 00:20:13,320 Speaker 2: that compressed memory. And because you've lost all kinds of information, 372 00:20:13,800 --> 00:20:18,679 Speaker 2: this process, this, this recall process is creative because you 373 00:20:18,720 --> 00:20:22,240 Speaker 2: don't have all the details that were there, and nor 374 00:20:22,320 --> 00:20:24,960 Speaker 2: can you really be sure at a later time what 375 00:20:25,000 --> 00:20:27,639 Speaker 2: the meaning was to your past self. In other words, 376 00:20:27,800 --> 00:20:31,200 Speaker 2: I also think of memories as literally messages from your 377 00:20:31,240 --> 00:20:33,439 Speaker 2: past self. So I tend to think of memory and 378 00:20:33,480 --> 00:20:36,479 Speaker 2: recall as communication events. And it's just you know, it's 379 00:20:36,480 --> 00:20:38,440 Speaker 2: a communication with a past version of you, but it's 380 00:20:38,440 --> 00:20:40,880 Speaker 2: the same. They sent you a message. It was encrypted 381 00:20:40,960 --> 00:20:44,960 Speaker 2: and compressed in these n grams, and then you try 382 00:20:44,960 --> 00:20:47,360 Speaker 2: to reinflate it. And your goal at any given time 383 00:20:47,760 --> 00:20:51,200 Speaker 2: is not to have any kind of allegiance to what 384 00:20:51,280 --> 00:20:54,880 Speaker 2: the memory meant before. Your goal now is to reinterpret 385 00:20:54,880 --> 00:20:57,040 Speaker 2: it the way the butterfly does in whatever is the 386 00:20:57,080 --> 00:21:01,760 Speaker 2: most optimal adaptive way that makes sense. Now, so this really, 387 00:21:01,800 --> 00:21:03,480 Speaker 2: you know, the first part is kind of algorithmic, which 388 00:21:03,520 --> 00:21:06,160 Speaker 2: is the compression. But now here comes a creative process. 389 00:21:06,200 --> 00:21:09,000 Speaker 2: It's not really deductive. It's a creative process where you 390 00:21:09,040 --> 00:21:11,920 Speaker 2: take that prompt. It's more of a prompt than anything else, 391 00:21:11,920 --> 00:21:13,880 Speaker 2: and you say, okay, what does this mean to me? Now? 392 00:21:13,880 --> 00:21:17,680 Speaker 2: How can I incorporate this into my current constantly evolving 393 00:21:17,720 --> 00:21:19,600 Speaker 2: model of the self, of the outside world and on. 394 00:21:19,760 --> 00:21:22,000 Speaker 2: So it's very much And this goes on, goes on 395 00:21:22,080 --> 00:21:26,080 Speaker 2: all the time. And this is consistent with the plasticity 396 00:21:26,119 --> 00:21:30,160 Speaker 2: of memories. It's consistent with confabulation, which has been seen 397 00:21:30,200 --> 00:21:32,879 Speaker 2: in all kinds of experiments with human subjects, you know, 398 00:21:33,000 --> 00:21:33,879 Speaker 2: split brain and so on. 399 00:21:33,960 --> 00:21:36,800 Speaker 1: Wait, let's take a second to talk about confabulation. So 400 00:21:37,640 --> 00:21:41,280 Speaker 1: this is where brains seem to make something up. You know, 401 00:21:41,320 --> 00:21:44,240 Speaker 1: there are these experiments, for example, the cutaneous rabit illusion. 402 00:21:44,240 --> 00:21:47,280 Speaker 1: If I tap you twice somewhere on your forearm, and 403 00:21:47,280 --> 00:21:49,439 Speaker 1: then I tap you a third time. Let's say, in 404 00:21:49,480 --> 00:21:53,120 Speaker 1: a different location, you will feel like you felt three taps, 405 00:21:53,600 --> 00:21:56,160 Speaker 1: that we're all that we're moving in the direction. Even 406 00:21:56,200 --> 00:21:58,639 Speaker 1: though the second tap was in the same spot, you 407 00:21:58,760 --> 00:22:01,679 Speaker 1: feel like you felt it on the way to the 408 00:22:01,720 --> 00:22:05,640 Speaker 1: third one. This is one example of lots of confabulation 409 00:22:05,680 --> 00:22:09,879 Speaker 1: where the brain is retrospectively making things up. Now, the 410 00:22:09,960 --> 00:22:15,280 Speaker 1: interesting part is we look at confabulation generally as something bad. 411 00:22:15,359 --> 00:22:20,000 Speaker 1: For example, with large language models, we talk about hallucinations. 412 00:22:20,080 --> 00:22:22,760 Speaker 1: But there is another way to look at this, which 413 00:22:22,840 --> 00:22:27,600 Speaker 1: is as hypothesis, as generating new creative ideas. So tell 414 00:22:27,680 --> 00:22:30,520 Speaker 1: us how that fits into the way you think about confabulation. 415 00:22:31,040 --> 00:22:34,440 Speaker 2: Yeah, I mean so, here's another another example of confabulation 416 00:22:34,600 --> 00:22:38,760 Speaker 2: that is similar to what I'm talking about. Two examples. 417 00:22:38,800 --> 00:22:43,240 Speaker 2: One is there was a patient that had an electrode 418 00:22:43,240 --> 00:22:45,800 Speaker 2: that was placed in their brain for I think aplepsy 419 00:22:45,920 --> 00:22:49,000 Speaker 2: was the idea, and it happened to land in a 420 00:22:49,040 --> 00:22:51,040 Speaker 2: region of the brain that when you stimulate that electrode, 421 00:22:51,040 --> 00:22:53,480 Speaker 2: it makes them laugh, makes the mouth laugh. So the 422 00:22:53,520 --> 00:22:56,080 Speaker 2: person will be sitting there thinking about something serious. You 423 00:22:56,119 --> 00:22:58,280 Speaker 2: can see and there's a video of this I saw somewhere. 424 00:22:58,400 --> 00:23:01,399 Speaker 2: The scientist pushes the button. They start laughing, and then 425 00:23:01,400 --> 00:23:04,040 Speaker 2: you ask them why are you laughing, And the answer 426 00:23:04,119 --> 00:23:05,840 Speaker 2: is never, Gee, I don't know. I was sitting here 427 00:23:05,840 --> 00:23:08,040 Speaker 2: thinking about something serious and then my mouth started laughing. 428 00:23:08,080 --> 00:23:10,520 Speaker 2: That's never the answer you get. The answer you get is, well, 429 00:23:10,760 --> 00:23:12,360 Speaker 2: I thought of something funny. I thought of a joke, 430 00:23:12,760 --> 00:23:14,919 Speaker 2: And you get the same thing out of split brain patients. 431 00:23:14,960 --> 00:23:16,919 Speaker 2: When the one hemisphere causes the other side of the 432 00:23:16,920 --> 00:23:19,400 Speaker 2: body to do something that the language hemisphere doesn't understand, 433 00:23:19,880 --> 00:23:21,600 Speaker 2: what they usually do is make up a story about 434 00:23:21,640 --> 00:23:23,760 Speaker 2: it on the spot, and they don't know, you know, consciously, 435 00:23:23,800 --> 00:23:26,080 Speaker 2: they won't report that they're making up a story. So 436 00:23:26,119 --> 00:23:29,360 Speaker 2: I think what we mean by confabulation is really a 437 00:23:29,359 --> 00:23:33,919 Speaker 2: fundamental skill and necessity of sense making of your world. 438 00:23:34,119 --> 00:23:35,760 Speaker 2: You have a model of the outside world. You have 439 00:23:35,800 --> 00:23:37,399 Speaker 2: a model of yourself and what you are and how 440 00:23:37,440 --> 00:23:41,440 Speaker 2: you behave and sometimes that model gets updated, but sometimes 441 00:23:41,440 --> 00:23:45,320 Speaker 2: you just incorporate other world events that the go on. 442 00:23:45,359 --> 00:23:47,439 Speaker 2: You incorporate them into that model and you interpret them 443 00:23:47,480 --> 00:23:49,679 Speaker 2: in a way that makes sense to you. Now, you know, 444 00:23:49,720 --> 00:23:53,040 Speaker 2: and something similar to what you said about the tapping, 445 00:23:53,040 --> 00:23:54,840 Speaker 2: you've seen the rubber hand illusion. 446 00:23:55,080 --> 00:23:57,560 Speaker 1: Yeah, in the rubber hand illusion, your hand Let's say 447 00:23:57,560 --> 00:23:59,720 Speaker 1: your left hand is covered up. You're not seeing it, 448 00:24:00,040 --> 00:24:03,639 Speaker 1: but you're seeing a rubber hand, and you see somebody 449 00:24:04,640 --> 00:24:08,000 Speaker 1: stroking that rubber hand, and every time that rubber hand 450 00:24:08,040 --> 00:24:10,760 Speaker 1: gets stroked, you feel a stroke on your hand too. 451 00:24:10,880 --> 00:24:13,600 Speaker 1: The person is stroking them both at the same time, 452 00:24:13,640 --> 00:24:16,320 Speaker 1: even though you can't see your own hand, and then 453 00:24:16,680 --> 00:24:20,080 Speaker 1: they hit that hand with a hammer and you withdraw 454 00:24:20,640 --> 00:24:23,200 Speaker 1: in terror because it feels like it's become your hand. 455 00:24:23,680 --> 00:24:26,680 Speaker 2: Yeah. So, to me, the amazing thing about that that 456 00:24:26,920 --> 00:24:30,440 Speaker 2: is the plasticity. Look, we've been tetrapods for I think 457 00:24:30,480 --> 00:24:33,560 Speaker 2: almost four hundred million years something like that, and so 458 00:24:34,080 --> 00:24:37,400 Speaker 2: for millions of years we had a brain that knows 459 00:24:37,440 --> 00:24:40,720 Speaker 2: exactly how many limbs you have, and within what seven 460 00:24:40,800 --> 00:24:43,800 Speaker 2: minutes of new experience, you now have decided that you 461 00:24:43,800 --> 00:25:00,119 Speaker 2: have this extra hand. It's the plasticity is crazy. 462 00:25:03,160 --> 00:25:05,760 Speaker 1: By the way, One thing that has been extraordinary on 463 00:25:05,800 --> 00:25:09,640 Speaker 1: this is experiments in VR, where, for example, you give 464 00:25:09,680 --> 00:25:12,280 Speaker 1: somebody a third arm that comes out of their chest, 465 00:25:12,800 --> 00:25:16,040 Speaker 1: and you control your natural two arms with two controllers, 466 00:25:16,040 --> 00:25:18,080 Speaker 1: and you can see your arms and you also see 467 00:25:18,119 --> 00:25:22,679 Speaker 1: this third arm which you control by changing your wrist orientations, 468 00:25:22,720 --> 00:25:24,879 Speaker 1: and that can control the third arm, and within a 469 00:25:24,920 --> 00:25:28,080 Speaker 1: few minutes people can get very good at controlling this 470 00:25:28,200 --> 00:25:29,960 Speaker 1: third arm. And you know they're doing a game where 471 00:25:30,000 --> 00:25:33,439 Speaker 1: you pick up boxes of certain colors, and yes, so 472 00:25:33,480 --> 00:25:38,000 Speaker 1: you can add limbs and subtract limbs readily. The homunculus, 473 00:25:38,080 --> 00:25:41,399 Speaker 1: the little model of your body, is totally flexible in 474 00:25:41,440 --> 00:25:41,720 Speaker 1: that way. 475 00:25:42,640 --> 00:25:48,400 Speaker 2: That right there, the ability to adapt to novel situations 476 00:25:48,440 --> 00:25:51,240 Speaker 2: in this way that can fabulation to tell a story 477 00:25:51,280 --> 00:25:53,960 Speaker 2: that makes sense, not that it's necessarily true relative to 478 00:25:54,000 --> 00:25:57,320 Speaker 2: what your past was giving you, but to what makes 479 00:25:57,359 --> 00:26:00,600 Speaker 2: sense for you now is fundamental to buyology. And I 480 00:26:00,600 --> 00:26:04,040 Speaker 2: think biology doesn't preserve the fidelity of memories. It preserves 481 00:26:04,040 --> 00:26:07,399 Speaker 2: the salience of memories. It tries to remap them in 482 00:26:07,480 --> 00:26:09,320 Speaker 2: the way that it makes sense to you now, not 483 00:26:09,359 --> 00:26:12,280 Speaker 2: necessarily to what it meant. And I think that this 484 00:26:12,320 --> 00:26:15,679 Speaker 2: is fundamentally an intelligence ratchet for life. And here's what 485 00:26:15,720 --> 00:26:18,800 Speaker 2: I mean by that. Let's look instead of the memories 486 00:26:18,840 --> 00:26:20,880 Speaker 2: of a single of a single human or animal, Let's 487 00:26:20,880 --> 00:26:23,840 Speaker 2: look on an evolutionary timescale. You come into the world 488 00:26:23,840 --> 00:26:26,000 Speaker 2: as an embry Oh, You've been given all of this 489 00:26:26,359 --> 00:26:30,320 Speaker 2: genetic and cytoplasmic and other kinds of information that are 490 00:26:30,440 --> 00:26:35,159 Speaker 2: the accumulated really the memories of your of your lineage agent, right, 491 00:26:35,200 --> 00:26:37,320 Speaker 2: the lineage that's been through that's been through the evolution 492 00:26:37,680 --> 00:26:41,840 Speaker 2: and has accumulated all this useful information. There are some 493 00:26:42,000 --> 00:26:45,400 Speaker 2: animals and at least as far as we know, maybe 494 00:26:45,400 --> 00:26:49,119 Speaker 2: nematodes like C. Elegance, that are extremely hardwired. We know 495 00:26:49,160 --> 00:26:50,720 Speaker 2: exactly how many cells are going to have, all the 496 00:26:50,760 --> 00:26:53,400 Speaker 2: cells of the same position that's determined by lineage. They're 497 00:26:53,520 --> 00:26:57,840 Speaker 2: very hardwired. But the majority of living forms aren't like that. 498 00:26:58,240 --> 00:27:03,159 Speaker 2: They take that information and they reinterpret it in novel ways. 499 00:27:03,240 --> 00:27:05,520 Speaker 2: I think that now now in normal development, we always 500 00:27:05,560 --> 00:27:07,280 Speaker 2: see the same thing happening, so we kind of assume 501 00:27:07,359 --> 00:27:09,920 Speaker 2: that it's some kind of hardwired mechanical process, but it's 502 00:27:09,960 --> 00:27:12,240 Speaker 2: not that at all. For this reason, we can take 503 00:27:12,320 --> 00:27:14,600 Speaker 2: we can make a tadpole which has no eyes in 504 00:27:14,640 --> 00:27:16,280 Speaker 2: the head, but it has an eye on its tail, 505 00:27:16,560 --> 00:27:19,240 Speaker 2: and that those those animals can see right out of 506 00:27:19,280 --> 00:27:21,320 Speaker 2: the box. They don't need new rounds of selection or 507 00:27:21,359 --> 00:27:24,280 Speaker 2: evolution or adaptation or any of that. They can see immediately. 508 00:27:24,640 --> 00:27:27,440 Speaker 2: We can take cells from an early frog embryo and 509 00:27:27,560 --> 00:27:31,320 Speaker 2: they become zenobots, and they do interesting things. There have 510 00:27:31,440 --> 00:27:34,399 Speaker 2: never been any zenobots. There's never been any selection to 511 00:27:34,480 --> 00:27:37,760 Speaker 2: be a good xenobot, right, so that plasticity can you 512 00:27:37,800 --> 00:27:41,320 Speaker 2: can you explain to the audience with zenebot is sure? Yeah, 513 00:27:41,400 --> 00:27:43,600 Speaker 2: So so we make we make in our group, we 514 00:27:43,640 --> 00:27:47,680 Speaker 2: make zenobots and anthrobots. These are these are living systems 515 00:27:47,760 --> 00:27:50,560 Speaker 2: that we call them biobots because we use them for, 516 00:27:50,800 --> 00:27:53,600 Speaker 2: among other things, bio robotics kinds of applications. They are 517 00:27:53,920 --> 00:27:57,720 Speaker 2: living uh organisms made of cells in the In the 518 00:27:57,720 --> 00:27:59,920 Speaker 2: case of xenobots, they come from frog cells, and the 519 00:28:00,080 --> 00:28:03,800 Speaker 2: case of anthrobots, they come from adult human tracheal epithelial cells. 520 00:28:04,000 --> 00:28:06,480 Speaker 2: They are self motile. They will move around a dish. 521 00:28:06,600 --> 00:28:08,439 Speaker 2: They sort of swim around the dish on their own. 522 00:28:08,480 --> 00:28:12,080 Speaker 2: And they have lots of interesting capabilities. For example, the anthrobots, 523 00:28:12,160 --> 00:28:14,720 Speaker 2: if they find a neural wound, they will heal They 524 00:28:14,760 --> 00:28:18,000 Speaker 2: will heal the peripheral innervation by taking the two sides 525 00:28:18,040 --> 00:28:20,119 Speaker 2: of the neural wound and kind of connecting them together. 526 00:28:20,720 --> 00:28:22,840 Speaker 2: You know, who would have known that your tracheal cells 527 00:28:22,840 --> 00:28:24,840 Speaker 2: that sit there for you know, quietly in your body 528 00:28:24,840 --> 00:28:27,680 Speaker 2: for decades, have the ability to form a self motile 529 00:28:27,720 --> 00:28:29,600 Speaker 2: little creature that runs around and do these things, and 530 00:28:29,640 --> 00:28:32,640 Speaker 2: does these things so the plasticity and people have been 531 00:28:32,680 --> 00:28:36,160 Speaker 2: noticing this, this kind of thing for a very long time. 532 00:28:36,200 --> 00:28:39,800 Speaker 2: Developmental biologists is that living systems will play they hand 533 00:28:39,880 --> 00:28:42,400 Speaker 2: their depth. They don't just automatically, at least most of 534 00:28:42,400 --> 00:28:45,160 Speaker 2: them don't just automatically do the same thing. They will 535 00:28:45,160 --> 00:28:48,640 Speaker 2: try to as much as we try to telecoherence story 536 00:28:48,680 --> 00:28:53,720 Speaker 2: in confabulation and linguistic space, they will confabulate in transcriptional 537 00:28:53,760 --> 00:28:58,400 Speaker 2: space meaning gene expression space, in physiological space, and in 538 00:28:58,880 --> 00:29:04,120 Speaker 2: anatomical space to put together some kind of a coherent lifestyle. 539 00:29:04,280 --> 00:29:08,560 Speaker 2: Given novel, novel circumstances, the environment can be novel. You 540 00:29:08,560 --> 00:29:11,640 Speaker 2: can interface living tissue with all kinds of weird materials. 541 00:29:12,320 --> 00:29:15,080 Speaker 2: They will always try to make something, And I think 542 00:29:15,120 --> 00:29:18,840 Speaker 2: that's because they never assume that you can take the 543 00:29:18,880 --> 00:29:22,920 Speaker 2: past literally. They have to on the fly put something together. 544 00:29:23,160 --> 00:29:27,080 Speaker 2: One of my favorite examples is what happens in the 545 00:29:27,320 --> 00:29:30,000 Speaker 2: new to kidney tubules. You have a newt If you 546 00:29:30,040 --> 00:29:32,200 Speaker 2: take a section through the kidney tubule, you see eight 547 00:29:32,280 --> 00:29:34,520 Speaker 2: or ten cells are making like a circle, and they 548 00:29:34,520 --> 00:29:36,640 Speaker 2: make this. They make this two well, one of the 549 00:29:36,640 --> 00:29:39,000 Speaker 2: things that the people have found is that you can 550 00:29:39,400 --> 00:29:41,920 Speaker 2: treat them in a way that makes multiple copy number 551 00:29:41,920 --> 00:29:44,200 Speaker 2: of their chromosomes so they have the genetic material. Instead 552 00:29:44,200 --> 00:29:46,400 Speaker 2: of two n they will have four and five and 553 00:29:46,480 --> 00:29:48,720 Speaker 2: six end and so on. If you do that, the 554 00:29:48,760 --> 00:29:51,600 Speaker 2: cells get bigger, but the nude stays the same size. 555 00:29:51,760 --> 00:29:53,440 Speaker 2: So that's kind of amazing. So you take a cross 556 00:29:53,440 --> 00:29:55,320 Speaker 2: section through the two bule and you see, oh, the 557 00:29:55,360 --> 00:29:58,240 Speaker 2: cells are bigger, but there's fewer of them and they 558 00:29:58,280 --> 00:30:01,120 Speaker 2: still form the exact same structure. Well, you can make 559 00:30:01,600 --> 00:30:05,320 Speaker 2: a highly polyploid neud like that that has gigantic cells, 560 00:30:05,520 --> 00:30:08,680 Speaker 2: and in that case, one single cell will bend around itself, 561 00:30:09,000 --> 00:30:11,200 Speaker 2: leaving a hole in the middle, which is a completely 562 00:30:11,200 --> 00:30:13,840 Speaker 2: different molecular mechanism. Right, it's not cell to cell communication. 563 00:30:13,880 --> 00:30:16,560 Speaker 2: That's some kind of cideoscalarle bending. So now think about 564 00:30:16,600 --> 00:30:18,640 Speaker 2: what this means if you're a nude coming into this world. 565 00:30:18,880 --> 00:30:21,160 Speaker 2: You can't count on how much genetic material you're going 566 00:30:21,200 --> 00:30:23,280 Speaker 2: to have. You can't count on and never mind not 567 00:30:23,320 --> 00:30:25,280 Speaker 2: being able to count on the environment. Right, who knows 568 00:30:25,600 --> 00:30:27,520 Speaker 2: what the pha your water is and all that. Forget 569 00:30:27,560 --> 00:30:29,680 Speaker 2: that you can't even count on your own parts. You 570 00:30:29,680 --> 00:30:31,760 Speaker 2: don't know what your chromosome number is going to be, 571 00:30:31,880 --> 00:30:33,240 Speaker 2: you don't know how many cells you're going to have, 572 00:30:33,360 --> 00:30:35,239 Speaker 2: you don't know the size of your cells. You have 573 00:30:35,320 --> 00:30:37,680 Speaker 2: to do something coherent in that case build an actual 574 00:30:37,760 --> 00:30:41,920 Speaker 2: neud when everything changes. And that's why I think that 575 00:30:41,920 --> 00:30:44,840 Speaker 2: that's the fundamental thing about confabulation is that if you 576 00:30:44,960 --> 00:30:47,200 Speaker 2: commit to the idea, which I think biology has to. 577 00:30:47,320 --> 00:30:52,200 Speaker 2: Unlike our computer technology, which relies on a highly reliable hardware, 578 00:30:52,480 --> 00:30:54,920 Speaker 2: right when you code, you don't worry about your you know, 579 00:30:55,000 --> 00:30:57,120 Speaker 2: cpu doing something weird. You just assume it's going to 580 00:30:57,200 --> 00:30:58,800 Speaker 2: do what it needs to do. You don't think, you 581 00:30:59,240 --> 00:31:00,800 Speaker 2: know your copper is going to go off or something. 582 00:31:00,960 --> 00:31:04,200 Speaker 2: In biology, that's not the case. The medium is completely unreliable. 583 00:31:04,320 --> 00:31:06,400 Speaker 2: You have no idea what you know, how many proteins 584 00:31:06,440 --> 00:31:07,960 Speaker 2: have we given the type you have, or if they're 585 00:31:07,960 --> 00:31:09,760 Speaker 2: going to get a little bit teen natured, or you 586 00:31:09,800 --> 00:31:12,040 Speaker 2: know what's going to happen. If you assume that your 587 00:31:12,120 --> 00:31:16,880 Speaker 2: medium is unreliable, then instead of this kind of hardwired 588 00:31:16,920 --> 00:31:19,440 Speaker 2: here's how we do it every single time. Idea, what 589 00:31:19,480 --> 00:31:23,160 Speaker 2: evolution is going to produce are sense making problem solving 590 00:31:23,240 --> 00:31:25,720 Speaker 2: agents in different spaces. It can be very simple things 591 00:31:25,760 --> 00:31:28,240 Speaker 2: bacteria and you know, but already you're off to the 592 00:31:28,280 --> 00:31:31,120 Speaker 2: racist because you can't count on your environment being the same, 593 00:31:31,160 --> 00:31:33,240 Speaker 2: and you can't count on yourself being the same. You're 594 00:31:33,240 --> 00:31:36,200 Speaker 2: going to mutate, right, your parts will mutate, Everything will change. 595 00:31:38,200 --> 00:31:39,680 Speaker 1: So this is one of the first things that I 596 00:31:39,800 --> 00:31:44,040 Speaker 1: was absolutely intrigued with in biology when I was very young, 597 00:31:44,120 --> 00:31:48,160 Speaker 1: which is, how in the world does a mouse's heart 598 00:31:48,400 --> 00:31:52,600 Speaker 1: and an elephant's heart do the same thing? When you 599 00:31:52,640 --> 00:31:54,720 Speaker 1: know these two cases, you've got totally different number of 600 00:31:54,760 --> 00:31:57,000 Speaker 1: cells making this, and yet it makes the same structure 601 00:31:57,480 --> 00:31:59,800 Speaker 1: that does the same thing. So what is the way 602 00:31:59,840 --> 00:32:05,800 Speaker 1: to understand how biology can code for these higher order structures. 603 00:32:06,560 --> 00:32:13,040 Speaker 2: Yeah, I think that there are key elements of understanding 604 00:32:13,040 --> 00:32:15,560 Speaker 2: what's going on that come from behavioral science. This is 605 00:32:15,880 --> 00:32:18,320 Speaker 2: we are not going to get to this purely by 606 00:32:18,480 --> 00:32:21,200 Speaker 2: the concepts of chemistry and physics, although those are crucial 607 00:32:21,240 --> 00:32:25,400 Speaker 2: to understand. What we have here are problem solving collective intelligences. 608 00:32:25,600 --> 00:32:27,880 Speaker 2: So when you have a bunch of molecular networks that 609 00:32:27,920 --> 00:32:30,400 Speaker 2: make a sell that's a coherent organism like a like 610 00:32:30,400 --> 00:32:32,560 Speaker 2: an amebo or a lachrom area or something that that 611 00:32:32,680 --> 00:32:36,120 Speaker 2: do all these interesting things. They are making a next 612 00:32:36,160 --> 00:32:39,000 Speaker 2: They are contributing to a next level collective intelligence that 613 00:32:39,080 --> 00:32:41,720 Speaker 2: does that, that operates in some kind of space and 614 00:32:41,840 --> 00:32:44,560 Speaker 2: has a small cognitive light coone work and do certain 615 00:32:44,600 --> 00:32:46,880 Speaker 2: things that have a little bit of predative power forward, 616 00:32:46,920 --> 00:32:49,280 Speaker 2: a little bit of memory backward. When those cells come 617 00:32:49,320 --> 00:32:51,920 Speaker 2: together and form an organism, once again, you have a 618 00:32:51,920 --> 00:32:55,000 Speaker 2: collective intelligence that now projects into a new space. Whereas 619 00:32:55,000 --> 00:32:58,760 Speaker 2: the cells we're solving problems in physiology and metabolics and 620 00:32:58,840 --> 00:33:01,200 Speaker 2: gene expression, you know, now have a system that solves 621 00:33:01,240 --> 00:33:04,360 Speaker 2: problems in anatomy. So so when you take an early embryo, 622 00:33:04,440 --> 00:33:06,240 Speaker 2: let's say, an early mammalian memory, and you cut it 623 00:33:06,240 --> 00:33:08,640 Speaker 2: in half, you don't get two half embryos. You get 624 00:33:08,680 --> 00:33:11,800 Speaker 2: too perfectly normal monozygotic twins because each side has to 625 00:33:11,840 --> 00:33:14,080 Speaker 2: figure out, oh way this is missing, why I have 626 00:33:14,160 --> 00:33:17,080 Speaker 2: to rebuild and so on. And so my point is 627 00:33:17,120 --> 00:33:20,200 Speaker 2: not that we attribute to uh, you know, high order 628 00:33:20,360 --> 00:33:22,720 Speaker 2: human level self consciousness to these things. I'm not saying 629 00:33:22,760 --> 00:33:25,479 Speaker 2: they have the metacognition to know what they're doing. What 630 00:33:25,520 --> 00:33:29,520 Speaker 2: I'm saying is we have a simplified version of intelligence, 631 00:33:29,520 --> 00:33:31,440 Speaker 2: which we know there had to be because we came 632 00:33:31,560 --> 00:33:34,080 Speaker 2: that that is our origin. We know there has to 633 00:33:34,120 --> 00:33:36,240 Speaker 2: be a version of intelligence that is, you know, sort 634 00:33:36,240 --> 00:33:38,080 Speaker 2: of on the on the left end of that spectrum 635 00:33:38,120 --> 00:33:40,959 Speaker 2: going all the way back to primitive cells and before 636 00:33:41,000 --> 00:33:43,200 Speaker 2: that actually, And that's that's how we need to think 637 00:33:43,240 --> 00:33:46,880 Speaker 2: about this as as as problem solving, continuous dynamical problem solving. 638 00:33:47,240 --> 00:33:49,240 Speaker 1: Great, So let's take where we are now and return 639 00:33:49,240 --> 00:33:53,360 Speaker 1: to the issue of memory. So how does memory work 640 00:33:53,600 --> 00:33:55,040 Speaker 1: in a brain? 641 00:33:55,360 --> 00:33:57,080 Speaker 2: So I'll just address to you know, a couple of 642 00:33:57,240 --> 00:33:59,760 Speaker 2: things that I can speak to. What one is that 643 00:34:00,080 --> 00:34:04,640 Speaker 2: I think the conventional story that memories are some sort 644 00:34:04,640 --> 00:34:08,640 Speaker 2: of fine tuning of synaptic connections. I think that story 645 00:34:08,680 --> 00:34:11,239 Speaker 2: is very incomplete, and there there are many people, you know, 646 00:34:11,320 --> 00:34:13,960 Speaker 2: like Landsmen and sam Gershman and many others that are 647 00:34:14,840 --> 00:34:17,399 Speaker 2: that are working on that. I I tend to think 648 00:34:17,440 --> 00:34:19,359 Speaker 2: if I had to guess, I would say that there 649 00:34:19,400 --> 00:34:22,719 Speaker 2: probably isn't one substrate of memory. I would look at 650 00:34:22,760 --> 00:34:27,319 Speaker 2: memory as an interpretation process, which I think neurons are 651 00:34:27,400 --> 00:34:30,759 Speaker 2: very good at this of interpreting a reservoir. That reservoir 652 00:34:30,880 --> 00:34:33,200 Speaker 2: is everything else the cell is doing. The side of 653 00:34:33,200 --> 00:34:36,000 Speaker 2: skeletal states, the molecular networks. I mean, some people pick 654 00:34:36,040 --> 00:34:40,600 Speaker 2: up have picked up transcriptional uh signatures of certain memories 655 00:34:40,640 --> 00:34:43,799 Speaker 2: that mice have had, and so on. Every everything in 656 00:34:43,840 --> 00:34:46,279 Speaker 2: the cell, all the complexity that is going on, can 657 00:34:46,320 --> 00:34:48,880 Speaker 2: be used as a reservoir in a sense of reservoir computing, 658 00:34:49,160 --> 00:34:54,440 Speaker 2: to be used as prompts to reinterpret these those prompts 659 00:34:54,480 --> 00:34:59,640 Speaker 2: as memories that are useful and with so again maximizing salience, 660 00:34:59,680 --> 00:35:04,680 Speaker 2: not as early fidelity, but salience useful in their novel context. 661 00:35:04,840 --> 00:35:07,960 Speaker 2: So I think I think memory is a lot about creativity. 662 00:35:08,000 --> 00:35:11,319 Speaker 2: I think it's a lot about uh, having prompts that 663 00:35:11,600 --> 00:35:14,560 Speaker 2: that that push you into new new kinds of problem solving, 664 00:35:14,719 --> 00:35:17,000 Speaker 2: you know. And and if if your if your body 665 00:35:17,040 --> 00:35:21,080 Speaker 2: and your environments stay extremely constant, then it just looks 666 00:35:21,200 --> 00:35:23,040 Speaker 2: like the old version of memory, where you store a 667 00:35:23,040 --> 00:35:25,480 Speaker 2: piece of data, you read it out and that's it, right, 668 00:35:25,520 --> 00:35:27,319 Speaker 2: That's how it looks like. That's what it looks like 669 00:35:27,320 --> 00:35:29,400 Speaker 2: from the outside. But I don't think that's what's going on, 670 00:35:29,840 --> 00:35:30,040 Speaker 2: you know. 671 00:35:30,120 --> 00:35:32,560 Speaker 1: In my in my book Live Wire, I make the 672 00:35:32,680 --> 00:35:35,080 Speaker 1: argument that even though in Silicon Valley we think about 673 00:35:35,080 --> 00:35:38,040 Speaker 1: everything as being a trim and efficient layer of hardware, 674 00:35:38,160 --> 00:35:41,040 Speaker 1: and then you build uh trim and efficient software on 675 00:35:41,120 --> 00:35:43,760 Speaker 1: top of that. That's not at all how the brain's working. Instead, 676 00:35:43,800 --> 00:35:46,400 Speaker 1: you've got this constant reconfiguration. And I know that you 677 00:35:46,960 --> 00:35:51,279 Speaker 1: also reject that dichotomy between a computation layer and a 678 00:35:51,360 --> 00:35:52,600 Speaker 1: passive code layer. 679 00:35:53,320 --> 00:35:56,040 Speaker 2: So how do you think about that? Yeah, well, I 680 00:35:56,280 --> 00:35:59,560 Speaker 2: think I think there's a couple of major differences between 681 00:36:00,320 --> 00:36:04,320 Speaker 2: how how we build hardware now you know, comput computational devices, 682 00:36:04,360 --> 00:36:06,640 Speaker 2: and what biology is doing. The first thing we've just 683 00:36:06,640 --> 00:36:10,640 Speaker 2: talked about, which is the reliability the idea that in 684 00:36:10,640 --> 00:36:13,000 Speaker 2: in in the computational where you have levels of abstraction 685 00:36:13,400 --> 00:36:15,600 Speaker 2: and you try to screen every layer from all the 686 00:36:15,680 --> 00:36:18,080 Speaker 2: vagaries of the level below. So if you're coding in 687 00:36:18,440 --> 00:36:20,680 Speaker 2: you know, c or something, you're not worried about what 688 00:36:20,719 --> 00:36:22,879 Speaker 2: the copper is doing and what the silicon is doing. 689 00:36:22,920 --> 00:36:26,040 Speaker 2: You you you assume that the function calls you have 690 00:36:26,200 --> 00:36:27,480 Speaker 2: are going to do what they need to do and 691 00:36:27,960 --> 00:36:31,200 Speaker 2: you go from there. Biology isn't like that. All all 692 00:36:31,239 --> 00:36:34,480 Speaker 2: the all the layers are somewhat unreliable, and you need 693 00:36:34,520 --> 00:36:37,239 Speaker 2: to be interpreting it at all at all times. Josh 694 00:36:37,320 --> 00:36:40,000 Speaker 2: Bongard and I are working on a framework called polycomputing, 695 00:36:40,440 --> 00:36:43,719 Speaker 2: and the idea and this is this is partially based 696 00:36:43,760 --> 00:36:46,640 Speaker 2: on some amazing work that is student to Saparsa had 697 00:36:46,680 --> 00:36:50,160 Speaker 2: done showing that the same set of physical events can 698 00:36:50,160 --> 00:36:53,759 Speaker 2: be interpreted as different computations by different observers the exact 699 00:36:53,800 --> 00:36:56,240 Speaker 2: same set of physical events. So give us an example. 700 00:36:57,360 --> 00:37:00,799 Speaker 2: An example is I mean in her work, they were 701 00:37:00,800 --> 00:37:03,239 Speaker 2: looking at the vibrations of particles and you look at 702 00:37:03,239 --> 00:37:04,719 Speaker 2: them in one way and you see an end gate, 703 00:37:04,719 --> 00:37:05,920 Speaker 2: and you look at them a different way and you 704 00:37:05,920 --> 00:37:09,759 Speaker 2: see an ore gate. That's one example in biology. What 705 00:37:09,800 --> 00:37:12,640 Speaker 2: it means in biology is that. And by the way, 706 00:37:12,680 --> 00:37:14,759 Speaker 2: he and I wrote this paper called this plenty of 707 00:37:14,840 --> 00:37:17,960 Speaker 2: room right here kind of riffing off of finements, a 708 00:37:18,080 --> 00:37:20,080 Speaker 2: comment that there's plenty of room at the bottom because 709 00:37:20,120 --> 00:37:23,440 Speaker 2: because biology has this thing where every level is already occupied, 710 00:37:23,480 --> 00:37:25,279 Speaker 2: there is no room at the bottom because every level 711 00:37:25,320 --> 00:37:28,440 Speaker 2: is occupied. How do you as if your evolution, how 712 00:37:28,440 --> 00:37:31,680 Speaker 2: do you put in novel functionality when every level already 713 00:37:31,680 --> 00:37:34,080 Speaker 2: has something. And by the way, when you make changes, 714 00:37:34,239 --> 00:37:35,920 Speaker 2: you're going to screw up. If you make changes in 715 00:37:35,920 --> 00:37:37,880 Speaker 2: the given subsystem, you're going to screw up all the 716 00:37:37,920 --> 00:37:40,480 Speaker 2: other systems. That depend on what it's doing. So one 717 00:37:40,480 --> 00:37:43,200 Speaker 2: thing that I think happens in biology is this poly 718 00:37:43,239 --> 00:37:47,400 Speaker 2: computing where you don't necessarily change the system. You add 719 00:37:47,600 --> 00:37:50,840 Speaker 2: other systems that see what's already going on in a 720 00:37:50,840 --> 00:37:53,279 Speaker 2: different way and make use of it as a computation 721 00:37:53,560 --> 00:37:56,640 Speaker 2: but from a different perspective. So, if you're some kind 722 00:37:56,680 --> 00:37:59,279 Speaker 2: of chemical pathway that mitochondria are using as part of 723 00:37:59,320 --> 00:38:01,920 Speaker 2: the metabolic path way, some other system can look at 724 00:38:01,920 --> 00:38:04,400 Speaker 2: that and say, well, I'm gonna use it as a 725 00:38:04,400 --> 00:38:06,560 Speaker 2: as a clock, I'm gonna I'm gonna take I'm gonna 726 00:38:06,600 --> 00:38:08,239 Speaker 2: use it to regulate my timing, or I'm going to 727 00:38:08,320 --> 00:38:10,880 Speaker 2: use it, you know, in some other, some other signaling capacity. 728 00:38:11,360 --> 00:38:13,680 Speaker 2: And so so I think what we have in biology 729 00:38:13,800 --> 00:38:16,680 Speaker 2: is not this linear stack first of all, not a 730 00:38:16,680 --> 00:38:19,640 Speaker 2: linear stack, but a kind of a super a society 731 00:38:19,680 --> 00:38:24,000 Speaker 2: of multiple nested, cooperating and competing agents which all have 732 00:38:24,080 --> 00:38:27,200 Speaker 2: their own perspectives and they all interpret everything that goes 733 00:38:27,200 --> 00:38:29,920 Speaker 2: on around them in whatever way they can. Uh so, 734 00:38:30,080 --> 00:38:32,480 Speaker 2: And and you know, we're used to the fact that 735 00:38:32,520 --> 00:38:37,920 Speaker 2: in a computation, we supposedly know what a given algorithm 736 00:38:37,960 --> 00:38:39,759 Speaker 2: is doing, right, you can and if you don't know 737 00:38:39,800 --> 00:38:41,160 Speaker 2: you can ask the person who wrote it and they'll 738 00:38:41,160 --> 00:38:43,360 Speaker 2: tell you this is what this thing is computing. But 739 00:38:43,440 --> 00:38:46,280 Speaker 2: in biology, I don't think there is any one fixed 740 00:38:46,320 --> 00:38:49,120 Speaker 2: answer to this. It's doing whatever you as an observer 741 00:38:49,239 --> 00:38:52,239 Speaker 2: can usefully think it's doing that. That doesn't mean anything goes. 742 00:38:52,280 --> 00:38:54,359 Speaker 2: If you have a story that doesn't help you get 743 00:38:54,360 --> 00:38:56,520 Speaker 2: around in the world and thrive, then you don't know 744 00:38:56,560 --> 00:38:59,200 Speaker 2: what it's doing. But but but multiple observers can have 745 00:38:59,280 --> 00:39:01,279 Speaker 2: different stories about the about the same thing. 746 00:39:01,719 --> 00:39:04,200 Speaker 1: So give us if you can't give us another specific 747 00:39:04,280 --> 00:39:05,080 Speaker 1: example of that. 748 00:39:05,680 --> 00:39:07,560 Speaker 2: So the cite of skeleton, on the one hand, is 749 00:39:07,680 --> 00:39:09,319 Speaker 2: used by the cell to get around, and so you 750 00:39:09,360 --> 00:39:12,600 Speaker 2: might say, well, this is this is my my movement machinery. 751 00:39:12,640 --> 00:39:16,120 Speaker 2: That's that's that that I'm counting on to maintain certain 752 00:39:16,120 --> 00:39:18,560 Speaker 2: cell shapes and so on. But at the same time, 753 00:39:18,600 --> 00:39:20,600 Speaker 2: there's other data showing that the site of skeleton can 754 00:39:20,600 --> 00:39:24,200 Speaker 2: actually be can be storing memory. It's also serving as 755 00:39:24,200 --> 00:39:26,560 Speaker 2: a scaffold for other molecules to find where they need 756 00:39:26,560 --> 00:39:28,640 Speaker 2: to go. They're moving around with motor proteins and things 757 00:39:28,719 --> 00:39:32,239 Speaker 2: like that, and they're just there's just lots of lots 758 00:39:32,239 --> 00:39:37,360 Speaker 2: of different uses that any given mechanism is performing at 759 00:39:37,400 --> 00:39:40,520 Speaker 2: any one time, and there are multiple different readout systems 760 00:39:40,600 --> 00:39:42,120 Speaker 2: and this is this is why you know, there's the 761 00:39:42,160 --> 00:39:47,279 Speaker 2: same molecule induces eyes in one context, that induces UH. 762 00:39:47,360 --> 00:39:49,440 Speaker 2: You know, it might induce a kidney rudiment in a 763 00:39:49,480 --> 00:39:52,840 Speaker 2: different UH contest. There are transcription factors that have that 764 00:39:52,880 --> 00:39:55,759 Speaker 2: have many different roles depending on the context. In our 765 00:39:55,760 --> 00:39:59,839 Speaker 2: work on bioelectrics, the exact same stimulus induces a tail 766 00:39:59,880 --> 00:40:02,440 Speaker 2: to regenerate on a tackle, but a leg to regenerate 767 00:40:02,440 --> 00:40:04,840 Speaker 2: on a froglet, and they never get confused, so that 768 00:40:05,320 --> 00:40:09,120 Speaker 2: specificity is not in the in the treatment, it's in 769 00:40:09,160 --> 00:40:12,320 Speaker 2: the surrounding cells being able to interpret that exact same 770 00:40:12,640 --> 00:40:15,920 Speaker 2: signal in whatever way makes sense for them. The ability 771 00:40:15,920 --> 00:40:19,759 Speaker 2: of these subsystems to cooperate in UH, in in in 772 00:40:19,840 --> 00:40:23,960 Speaker 2: groups and solve problems together is is really like a 773 00:40:24,000 --> 00:40:27,759 Speaker 2: fundamental thing in which biology is is different than the 774 00:40:28,239 --> 00:40:30,440 Speaker 2: kind of you know, control systems that we have now 775 00:40:30,440 --> 00:40:47,360 Speaker 2: in computer science. You know, Stephen J. 776 00:40:47,480 --> 00:40:51,719 Speaker 1: Gould wrote about exaptation, where you have something that develops 777 00:40:51,760 --> 00:40:53,520 Speaker 1: and then it turns out to have a use in 778 00:40:53,600 --> 00:40:56,560 Speaker 1: another way. But what you're talking about is even more 779 00:40:56,600 --> 00:40:58,960 Speaker 1: sophisticated in that in the sense that it can retain 780 00:40:59,080 --> 00:41:02,400 Speaker 1: its first use and be used for a second thing 781 00:41:02,440 --> 00:41:04,480 Speaker 1: and the third thing all the same time, just by 782 00:41:04,520 --> 00:41:06,520 Speaker 1: reading that data out in different ways. 783 00:41:06,680 --> 00:41:10,640 Speaker 2: Is that right? That's that's exactly right. And you know, 784 00:41:10,760 --> 00:41:13,480 Speaker 2: some of some of the latest the stuff that I've 785 00:41:13,480 --> 00:41:16,319 Speaker 2: been thinking about really tries to turn this whole thing 786 00:41:16,360 --> 00:41:19,240 Speaker 2: on its head. And you know so, so in the 787 00:41:19,280 --> 00:41:22,239 Speaker 2: standard touring computing paradigm, you have a machine and you 788 00:41:22,280 --> 00:41:23,960 Speaker 2: have the data. Right, so you have a you have 789 00:41:24,200 --> 00:41:26,320 Speaker 2: the process. You have this machine that reads the tape 790 00:41:26,440 --> 00:41:30,719 Speaker 2: and it and it records, you know, the byproducts of 791 00:41:30,760 --> 00:41:33,440 Speaker 2: the computation onto the tape and so on. So typically 792 00:41:33,480 --> 00:41:35,760 Speaker 2: we look at this from the perspective of the machine. 793 00:41:35,760 --> 00:41:39,279 Speaker 2: That is, we are the whether the where, the cell 794 00:41:39,360 --> 00:41:41,200 Speaker 2: or the human or whatever. We're forming memories and we're 795 00:41:41,200 --> 00:41:43,840 Speaker 2: writing it down into some memory medium. The memory medium 796 00:41:43,920 --> 00:41:47,040 Speaker 2: is passive. The memories themselves are passive. They're just marks 797 00:41:47,040 --> 00:41:48,879 Speaker 2: on a tape, and then we can read them out 798 00:41:48,920 --> 00:41:52,000 Speaker 2: when we want. Some of the latest work that we've 799 00:41:52,000 --> 00:41:54,600 Speaker 2: been doing, it starts out by thinking about it backwards 800 00:41:54,600 --> 00:41:56,520 Speaker 2: and saying, well, what does this look like from the 801 00:41:56,520 --> 00:41:59,640 Speaker 2: perspective of the data, right, data that are not passive. 802 00:41:59,680 --> 00:42:03,800 Speaker 2: They're not passive patterns within some medium. They're actually active patterns. 803 00:42:04,000 --> 00:42:06,719 Speaker 2: And from the perspective of the tape, the tape runs 804 00:42:06,800 --> 00:42:09,000 Speaker 2: the show that machine is going to do things depending 805 00:42:09,040 --> 00:42:11,000 Speaker 2: on what is written on the tape. So if I'm 806 00:42:11,040 --> 00:42:13,880 Speaker 2: a pattern on this tape, I can make the machine 807 00:42:14,040 --> 00:42:17,080 Speaker 2: do things. From my perspective, I'm in control. And so 808 00:42:17,160 --> 00:42:18,960 Speaker 2: now it sounds it sounds a little crazy to say 809 00:42:18,960 --> 00:42:20,919 Speaker 2: that that these patterns are doing things and that they're 810 00:42:20,920 --> 00:42:23,399 Speaker 2: agential and whatever. But let's keep in mind we are 811 00:42:23,440 --> 00:42:27,000 Speaker 2: patterns too. We are temporary metabolic patterns within an excitable 812 00:42:27,239 --> 00:42:31,719 Speaker 2: medium the way that people study you know, other temporary 813 00:42:32,520 --> 00:42:36,560 Speaker 2: patterns like solitons and whirlpools, and you know, all different 814 00:42:36,640 --> 00:42:39,640 Speaker 2: kinds of all different kinds of systems. And from from 815 00:42:39,680 --> 00:42:43,439 Speaker 2: that perspective you can you can see that different kinds 816 00:42:43,440 --> 00:42:46,360 Speaker 2: of patterns persist in different media, be they cognitive media 817 00:42:46,440 --> 00:42:49,960 Speaker 2: or just computational media. And asking what does the world 818 00:42:49,960 --> 00:42:52,560 Speaker 2: look like from their perspective and how much, how much 819 00:42:52,600 --> 00:42:56,319 Speaker 2: problem solving capacity, how much agency in fact, might those 820 00:42:56,320 --> 00:43:00,960 Speaker 2: patterns have has massive implications not only for new computational architectures, 821 00:43:01,120 --> 00:43:04,160 Speaker 2: but also, for example, for regenerate medicine, where you want 822 00:43:04,200 --> 00:43:08,080 Speaker 2: to understand what are the persistent information structures that cause 823 00:43:08,160 --> 00:43:11,840 Speaker 2: cells to do or not do various things in disease states, 824 00:43:11,880 --> 00:43:15,160 Speaker 2: and you know, pro regenerative states and so on. So 825 00:43:15,239 --> 00:43:16,120 Speaker 2: let's double click on that. 826 00:43:16,200 --> 00:43:19,640 Speaker 1: So what would that mean for a memory to be 827 00:43:19,800 --> 00:43:21,840 Speaker 1: like an agent to be doing something. 828 00:43:22,640 --> 00:43:26,600 Speaker 2: I'll tell I'll tell a story that I read. I'm 829 00:43:26,640 --> 00:43:29,279 Speaker 2: sure at least part of this was motivated years ago 830 00:43:29,320 --> 00:43:31,799 Speaker 2: by a science fiction story that I'm not exactly sure 831 00:43:31,920 --> 00:43:33,719 Speaker 2: what it was. I think it was a it was 832 00:43:33,760 --> 00:43:35,719 Speaker 2: an Arthur Clock story, but I'm not one hundred percent sure. 833 00:43:35,800 --> 00:43:37,920 Speaker 2: So let's just and I'm sure I've also twisted it 834 00:43:37,920 --> 00:43:39,640 Speaker 2: in a different way. But let's just let's just let's 835 00:43:39,680 --> 00:43:43,320 Speaker 2: just visual it's because your memory is creative to totally. 836 00:43:43,400 --> 00:43:45,600 Speaker 2: There may have been no story. I have no idea, so, 837 00:43:46,480 --> 00:43:47,879 Speaker 2: you know, I just want to give credit in case 838 00:43:47,920 --> 00:43:51,080 Speaker 2: there was. So so let's just let's just visualize this. 839 00:43:51,120 --> 00:43:54,240 Speaker 2: So from the center of the earth come these creatures, 840 00:43:54,239 --> 00:43:56,000 Speaker 2: these core creatures, right, they live at the center of 841 00:43:56,000 --> 00:43:58,000 Speaker 2: the earth. They come out onto the surface. They are 842 00:43:58,480 --> 00:44:01,560 Speaker 2: incredibly dense because they live at the core, they have 843 00:44:01,680 --> 00:44:04,880 Speaker 2: vision that operates, let's say, in them in gamma rays. 844 00:44:04,920 --> 00:44:06,840 Speaker 2: And so they come up to the surface. What do 845 00:44:06,920 --> 00:44:09,960 Speaker 2: they see, Well, pretty much nothing, because everything that we 846 00:44:10,000 --> 00:44:14,799 Speaker 2: see here is like a fine ethereal plasma. To them, 847 00:44:14,800 --> 00:44:16,880 Speaker 2: they are so dense. All of the stuff that we 848 00:44:16,960 --> 00:44:21,600 Speaker 2: think of as real objects are basically not even within 849 00:44:21,680 --> 00:44:26,399 Speaker 2: their within their ability to perceive directly. So they're walking around, 850 00:44:26,480 --> 00:44:29,040 Speaker 2: stomping through everything. And you know the same way that 851 00:44:29,080 --> 00:44:32,000 Speaker 2: when we walk past, you know, some kind of flower bed, 852 00:44:32,000 --> 00:44:34,960 Speaker 2: there's all kinds of like fine you know, patterns of 853 00:44:35,000 --> 00:44:37,080 Speaker 2: ascents and so on, we just sort of walk right 854 00:44:37,080 --> 00:44:38,960 Speaker 2: through it, mix that all up. So they're stepping all 855 00:44:39,000 --> 00:44:40,880 Speaker 2: over everything, and well, one of them, one of them, 856 00:44:40,960 --> 00:44:43,759 Speaker 2: is a scientist, and he's taking some careful readings of 857 00:44:43,800 --> 00:44:47,200 Speaker 2: what's going on around him, and he says to the others, Hey, 858 00:44:47,200 --> 00:44:50,400 Speaker 2: you know there's this there's this like fine invisible gas 859 00:44:50,440 --> 00:44:53,759 Speaker 2: around our planet. This is like plasma around and there's 860 00:44:53,800 --> 00:44:56,440 Speaker 2: patterns in this there's regular patterns in this plasma that 861 00:44:56,560 --> 00:44:59,000 Speaker 2: kind of hole together. And he say, so, what, well, 862 00:44:59,360 --> 00:45:01,080 Speaker 2: I've been watching some of these patterns, and you know, 863 00:45:01,640 --> 00:45:04,520 Speaker 2: they seem to be almost like they do things. They 864 00:45:04,520 --> 00:45:07,960 Speaker 2: almost seem agential, they almost seem like they have goals, 865 00:45:07,960 --> 00:45:10,319 Speaker 2: and like they you know, they're not they're they're they're 866 00:45:10,360 --> 00:45:13,560 Speaker 2: sort of like you would see waves or solitons moving 867 00:45:13,600 --> 00:45:15,120 Speaker 2: through water, you know, and they look like they hold 868 00:45:15,160 --> 00:45:17,359 Speaker 2: together for a period of time. And they say to him, well, 869 00:45:17,400 --> 00:45:20,000 Speaker 2: how long do these hold together? Well about one hundred years. 870 00:45:20,000 --> 00:45:22,720 Speaker 2: Well that's crazy, nothing, nothing interesting can happen that that quickly. 871 00:45:22,840 --> 00:45:25,359 Speaker 2: You know, They're just temporary. They're temporary, fleeting, you know, 872 00:45:25,400 --> 00:45:27,719 Speaker 2: sorts of sorts of patterns. And and by the way, 873 00:45:27,760 --> 00:45:30,799 Speaker 2: we've been watching the ecosystem here, and some of these 874 00:45:30,800 --> 00:45:33,320 Speaker 2: patterns are really like not conducive to the health of 875 00:45:33,360 --> 00:45:35,399 Speaker 2: the ecosystem, you know, these patterns are really are really 876 00:45:35,440 --> 00:45:37,560 Speaker 2: like screwing things up. So they're they're kind of like 877 00:45:37,760 --> 00:45:42,279 Speaker 2: these these recurrent but the unhelpful patterns. So so I 878 00:45:42,520 --> 00:45:44,840 Speaker 2: have a blog post which has this this this fictional 879 00:45:44,880 --> 00:45:47,920 Speaker 2: dialogue between that creed, that that that core scientist, and 880 00:45:47,960 --> 00:45:49,759 Speaker 2: he tries to talk to one of the patterns. We 881 00:45:49,800 --> 00:45:53,400 Speaker 2: of course are the patterns, and so the human says 882 00:45:53,400 --> 00:45:57,040 Speaker 2: to him, it's really imperative that you guys understand that 883 00:45:57,080 --> 00:45:59,919 Speaker 2: we are alive and and we have we are, we matter, 884 00:46:00,080 --> 00:46:01,680 Speaker 2: we you know, in a moral sense, we have goals, 885 00:46:01,680 --> 00:46:04,400 Speaker 2: we have memories, We persist. And he says, well, I 886 00:46:04,480 --> 00:46:06,600 Speaker 2: feel like I'm crazy. I'm talking to a pattern and gas. 887 00:46:06,640 --> 00:46:08,560 Speaker 2: You know you can't be real. He says, I'm real. 888 00:46:08,600 --> 00:46:11,280 Speaker 2: I'm solid. I live for you know, millions of years. 889 00:46:11,840 --> 00:46:14,680 Speaker 2: You're a temporary pattern in this gas. How can I 890 00:46:14,719 --> 00:46:18,239 Speaker 2: take you seriously as a coherent intelligence? And so just 891 00:46:18,280 --> 00:46:20,799 Speaker 2: thinking about it that way reminds us that all of 892 00:46:20,840 --> 00:46:24,520 Speaker 2: this is relative, and that we two are patterns, and 893 00:46:24,800 --> 00:46:29,879 Speaker 2: what other patterns around us have a degree of coherence 894 00:46:30,200 --> 00:46:34,680 Speaker 2: and live and strive and have different kinds of degrees 895 00:46:34,800 --> 00:46:38,480 Speaker 2: of problem solving competency and other kinds of things that 896 00:46:38,520 --> 00:46:40,680 Speaker 2: we don't know. And so once we think about that, 897 00:46:40,719 --> 00:46:44,040 Speaker 2: once we realize that this distinction between you know, real 898 00:46:44,239 --> 00:46:47,560 Speaker 2: solid beings like us and the temporary pattern like we 899 00:46:47,600 --> 00:46:49,640 Speaker 2: are all on that spectrum. We are all patterns. So 900 00:46:49,640 --> 00:46:51,600 Speaker 2: once you think about it that way, it unlocks the 901 00:46:51,640 --> 00:46:56,400 Speaker 2: ability to take the tools that we use to understand 902 00:46:56,560 --> 00:47:00,200 Speaker 2: real embodied beings and ask ourselves, how do some of 903 00:47:00,200 --> 00:47:03,719 Speaker 2: those tools and concepts from behavioral science and so on, 904 00:47:03,920 --> 00:47:06,400 Speaker 2: how would they apply to certain other kinds of patterns 905 00:47:06,400 --> 00:47:13,359 Speaker 2: in other media. So what are patterns in media? Well, 906 00:47:13,880 --> 00:47:16,400 Speaker 2: thoughts within the cognitive system are patterns. You can have 907 00:47:16,800 --> 00:47:19,440 Speaker 2: fleeting thoughts that sort of come and go. You can 908 00:47:19,520 --> 00:47:21,880 Speaker 2: have persistent thoughts, you know, thoughts that are hard to 909 00:47:21,880 --> 00:47:24,920 Speaker 2: get rid of. Right, then it's all many examples of that, 910 00:47:25,360 --> 00:47:27,200 Speaker 2: and some of those thoughts actually do a little bit 911 00:47:27,200 --> 00:47:30,120 Speaker 2: of niche construction. Niche construction in biologies, when an animal 912 00:47:30,360 --> 00:47:33,440 Speaker 2: modifies its environment that makes it easier for them to persist. 913 00:47:33,560 --> 00:47:35,640 Speaker 2: So you're doing something to the environment that makes it 914 00:47:35,680 --> 00:47:37,960 Speaker 2: easy for yourself to stick around. Well, there are data 915 00:47:37,960 --> 00:47:40,960 Speaker 2: that depressive thoughts, persistent thoughts, those kinds of things actually 916 00:47:41,000 --> 00:47:43,640 Speaker 2: modifying brain issue in ways that makes it easier to 917 00:47:43,719 --> 00:47:46,200 Speaker 2: keep having those kinds of thoughts. Right, So you got 918 00:47:46,200 --> 00:47:49,120 Speaker 2: your fleeting thoughts, you got your kind of persistent thoughts. 919 00:47:49,680 --> 00:47:54,160 Speaker 2: Then maybe you have some dissociative personality alters which are 920 00:47:54,440 --> 00:47:57,680 Speaker 2: way more coherent than a simple persistent thought and in 921 00:47:57,719 --> 00:48:00,520 Speaker 2: fact somewhat agential. So they have a is and they 922 00:48:00,560 --> 00:48:04,560 Speaker 2: have memories or whatever, but not a full on human personality. 923 00:48:04,640 --> 00:48:06,439 Speaker 2: So then you have you have that, and then who 924 00:48:06,480 --> 00:48:09,040 Speaker 2: knows what's beyond that? Right, trans personal psychology will say 925 00:48:09,040 --> 00:48:11,560 Speaker 2: that maybe maybe there are there are bigger things past that. 926 00:48:12,200 --> 00:48:16,000 Speaker 2: So so I think that, uh, you know, this this 927 00:48:16,120 --> 00:48:20,040 Speaker 2: idea of having patterns within a medium and maybe within 928 00:48:20,040 --> 00:48:22,400 Speaker 2: a cognitive meaning, but also a computational medium. If you're 929 00:48:22,520 --> 00:48:27,200 Speaker 2: data in a database of being being shuffled depending on 930 00:48:27,200 --> 00:48:30,040 Speaker 2: that architecture, maybe you can take the perspective of that 931 00:48:30,120 --> 00:48:31,920 Speaker 2: data and ask yourself, what does the world look like 932 00:48:31,960 --> 00:48:36,240 Speaker 2: from my perspective, right from the perspective of the pattern, 933 00:48:36,440 --> 00:48:39,520 Speaker 2: and what is the pattern doing or not to facilitate 934 00:48:39,600 --> 00:48:43,440 Speaker 2: its own persistence and to facilitate its own transformation that 935 00:48:43,600 --> 00:48:46,040 Speaker 2: usually is required if you're going to persist over long 936 00:48:46,080 --> 00:48:48,160 Speaker 2: periods of the time, you may need to change. So 937 00:48:48,360 --> 00:48:50,880 Speaker 2: that's that's you know, that's some cutting sort of cutting 938 00:48:50,960 --> 00:48:52,960 Speaker 2: edge stuff as far as what we're thinking about to 939 00:48:53,080 --> 00:48:55,440 Speaker 2: understand some of what goes on in these kind of 940 00:48:55,440 --> 00:48:57,799 Speaker 2: complex biological cases that we want to be able to 941 00:48:57,800 --> 00:48:59,160 Speaker 2: control in medicine and so on. 942 00:48:59,440 --> 00:49:01,719 Speaker 1: And presume will you think about that in a Darwinian 943 00:49:01,800 --> 00:49:05,920 Speaker 1: context in terms of if I'm a thought and you know, 944 00:49:06,000 --> 00:49:07,799 Speaker 1: so I'm some pattern that is a thought and I'm 945 00:49:07,840 --> 00:49:13,240 Speaker 1: trying to keep myself alive. There are certain mutations perhaps 946 00:49:13,320 --> 00:49:15,239 Speaker 1: that I can have, or certain things that I can 947 00:49:15,320 --> 00:49:18,080 Speaker 1: do that give me an advantage in that domain. 948 00:49:18,920 --> 00:49:22,320 Speaker 2: That's part of it. But I think that the bare 949 00:49:22,400 --> 00:49:27,759 Speaker 2: bones Darwinian paradigm, which is short term self interest, competition, 950 00:49:28,360 --> 00:49:33,360 Speaker 2: and random change, those three things I think are woefully 951 00:49:33,400 --> 00:49:37,120 Speaker 2: incomplete as a story both of actual evolutionary change in 952 00:49:37,120 --> 00:49:40,239 Speaker 2: biology and the kinds of things that we're talking about here. 953 00:49:40,360 --> 00:49:42,800 Speaker 2: The alternative to this, of course, is that a system 954 00:49:43,040 --> 00:49:45,680 Speaker 2: that changes with some sort of foresight. Now that doesn't 955 00:49:45,719 --> 00:49:48,439 Speaker 2: mean long term purpose. I am not saying that there's 956 00:49:48,480 --> 00:49:50,960 Speaker 2: some sort of human or above level of a plan 957 00:49:51,480 --> 00:49:54,960 Speaker 2: that is executing the changes that are happening. What I'm 958 00:49:55,000 --> 00:49:58,520 Speaker 2: saying is that we cannot necessarily assume that the change 959 00:49:58,520 --> 00:50:01,360 Speaker 2: that is happening is completely blinde and we cannot assume 960 00:50:01,400 --> 00:50:04,359 Speaker 2: that there isn't some computational process done at the level 961 00:50:04,360 --> 00:50:07,719 Speaker 2: of the lineage that is actually guiding the changes that 962 00:50:07,760 --> 00:50:10,279 Speaker 2: are that are happening. One way to think about this 963 00:50:10,360 --> 00:50:11,960 Speaker 2: is to think about the whole lineage, you know, I 964 00:50:12,000 --> 00:50:15,160 Speaker 2: don't know, fifteen million years of alligators or something. Think 965 00:50:15,200 --> 00:50:18,400 Speaker 2: about that that whole lineage as a giant, single agent 966 00:50:18,520 --> 00:50:21,080 Speaker 2: distributed over time, bigger than we're used to thinking about, 967 00:50:21,280 --> 00:50:25,759 Speaker 2: where every each individual animal is a hypothesis of that 968 00:50:25,800 --> 00:50:28,279 Speaker 2: agent about the outside world. Some of those hypotheses are good, 969 00:50:28,360 --> 00:50:31,319 Speaker 2: some are not. The thinking evolves as time goes on, right, 970 00:50:31,360 --> 00:50:32,960 Speaker 2: So again, this is cutting edge stuff, you know, this 971 00:50:33,040 --> 00:50:35,680 Speaker 2: is this is I'm not at all saying that we 972 00:50:35,719 --> 00:50:37,440 Speaker 2: have all this worked out. This is just these are 973 00:50:37,520 --> 00:50:41,160 Speaker 2: things that we're working on and some ideas going forward. 974 00:50:41,280 --> 00:50:44,239 Speaker 2: But there are lots of people thinking about how much 975 00:50:44,320 --> 00:50:48,400 Speaker 2: and including Richard Watson, how much and what kind of 976 00:50:48,400 --> 00:50:51,319 Speaker 2: computation is done by populations like this that is not 977 00:50:51,480 --> 00:50:55,480 Speaker 2: captured in this very simple uh competition for resources random 978 00:50:55,600 --> 00:50:59,520 Speaker 2: change model. The way that you think about memories in 979 00:50:59,560 --> 00:51:02,920 Speaker 2: the brain as being like their own agents, patterns that 980 00:51:02,960 --> 00:51:07,080 Speaker 2: stay alive, and the recollection of memory as sort of 981 00:51:07,080 --> 00:51:12,880 Speaker 2: the creation from some physical evidence that's there, recreation into 982 00:51:12,920 --> 00:51:15,239 Speaker 2: your current world. Does this tell you anything. 983 00:51:15,000 --> 00:51:19,680 Speaker 1: About Ribou's law, which is the oldest rule in neurology, 984 00:51:19,719 --> 00:51:22,960 Speaker 1: which is that older memories are more stable than more 985 00:51:23,040 --> 00:51:24,040 Speaker 1: recent memories. 986 00:51:24,880 --> 00:51:27,480 Speaker 2: Have you thought about that at all? I've not thought 987 00:51:27,520 --> 00:51:33,520 Speaker 2: about that specifically. It sort of makes sense that you're 988 00:51:33,719 --> 00:51:35,839 Speaker 2: If you're a pattern that has managed to stick around 989 00:51:35,880 --> 00:51:38,800 Speaker 2: for a really long time by interaction with the surrounding 990 00:51:38,840 --> 00:51:41,279 Speaker 2: cognitive system in a way that causes it to keep 991 00:51:41,280 --> 00:51:43,720 Speaker 2: you around and for you to persist, it makes sense 992 00:51:43,760 --> 00:51:49,520 Speaker 2: that you have now picked up on whatever properties, residents, whatever, 993 00:51:49,920 --> 00:51:53,000 Speaker 2: that allows you to be pretty stable in the system. 994 00:51:53,440 --> 00:51:56,600 Speaker 2: There's this term that people use sometime about a breakthrough 995 00:51:56,640 --> 00:51:59,640 Speaker 2: where you reinterpret a number of things that happen in 996 00:51:59,680 --> 00:52:02,080 Speaker 2: your life. If you find out that this person, maybe 997 00:52:02,120 --> 00:52:05,400 Speaker 2: that you were mad at, had some other problem going on. 998 00:52:05,920 --> 00:52:08,480 Speaker 2: They knew that they had cancer, but they didn't tell 999 00:52:08,520 --> 00:52:12,279 Speaker 2: you that, and suddenly they're lashing out at you, you 1000 00:52:12,320 --> 00:52:14,919 Speaker 2: have a totally different interpretation of it. You're going back 1001 00:52:14,960 --> 00:52:18,319 Speaker 2: through your memory and recasting everything in a different light. 1002 00:52:18,400 --> 00:52:21,600 Speaker 2: Do you have any interpretation of that in your framework? 1003 00:52:22,360 --> 00:52:25,760 Speaker 2: I mean that sounds to me like a very sophisticated 1004 00:52:27,160 --> 00:52:32,399 Speaker 2: human level cognitive version of a process that happens all 1005 00:52:32,480 --> 00:52:35,719 Speaker 2: the time, going all the way back to our simplest ancestors, 1006 00:52:35,960 --> 00:52:39,719 Speaker 2: which is that circumstances change and it forces you to 1007 00:52:39,840 --> 00:52:43,280 Speaker 2: reuse whatever information you had from the past, whatever tools 1008 00:52:43,320 --> 00:52:45,399 Speaker 2: you had from the past, to make sense of what's 1009 00:52:45,440 --> 00:52:47,440 Speaker 2: going on now, and that I think is the fundamental 1010 00:52:47,480 --> 00:52:50,680 Speaker 2: basis of intelligence. That's why I think this requirement to 1011 00:52:50,719 --> 00:52:55,360 Speaker 2: confabulate because everything changes is an intelligence ratchet. It requires 1012 00:52:55,400 --> 00:52:57,960 Speaker 2: cells to get good at solving problems in their spaces, 1013 00:52:58,080 --> 00:53:02,200 Speaker 2: which eventually bubbles up as collective intelligence scales and the 1014 00:53:02,239 --> 00:53:05,640 Speaker 2: cognitive light gones expand. It then eventually starts to look 1015 00:53:05,719 --> 00:53:07,920 Speaker 2: like the kind of intelligence that we we're used to seeing. 1016 00:53:08,120 --> 00:53:10,520 Speaker 2: But that that that fundamental process I think is is 1017 00:53:11,080 --> 00:53:14,120 Speaker 2: very ancient and fundament and basic. Mike, does this change 1018 00:53:14,160 --> 00:53:19,120 Speaker 2: anything about how you think about yourself? For me? I 1019 00:53:19,160 --> 00:53:25,879 Speaker 2: think it's it's very very important to face this, this 1020 00:53:25,880 --> 00:53:28,440 Speaker 2: this paradox, right, the paradox which which we face this 1021 00:53:28,760 --> 00:53:31,560 Speaker 2: as cognitive systems, but also species face this as well. 1022 00:53:31,960 --> 00:53:35,319 Speaker 2: If you don't change, you will likely die out when 1023 00:53:35,640 --> 00:53:38,879 Speaker 2: when circumstances change. But if you do change to meet 1024 00:53:38,920 --> 00:53:42,120 Speaker 2: those circumstances, you're no longer the same, You're not you anymore. 1025 00:53:42,160 --> 00:53:44,120 Speaker 2: So So what does that mean? Right, that's the paradox? 1026 00:53:44,160 --> 00:53:46,839 Speaker 2: How how can you possibly persist in this idea of 1027 00:53:47,160 --> 00:53:51,080 Speaker 2: persisting as a as a as a pattern and uh 1028 00:53:51,520 --> 00:53:55,120 Speaker 2: realizing that because things change all the time and this, this, 1029 00:53:55,160 --> 00:53:57,760 Speaker 2: I think is is fundamental. What is in our control 1030 00:53:58,040 --> 00:54:00,680 Speaker 2: are not the thoughts that we have right now. What's 1031 00:54:00,800 --> 00:54:04,360 Speaker 2: what's in our control is the long term application of 1032 00:54:04,400 --> 00:54:07,120 Speaker 2: effort to modify our own cognitive system to have different 1033 00:54:07,120 --> 00:54:09,040 Speaker 2: thoughts in the future, the thoughts you would like to 1034 00:54:09,080 --> 00:54:11,239 Speaker 2: have more of, and behaviors you would like to have 1035 00:54:11,280 --> 00:54:14,560 Speaker 2: more of versus something else. So this idea of committing 1036 00:54:14,600 --> 00:54:18,880 Speaker 2: to a consistent, long term process of self change, you know, 1037 00:54:18,920 --> 00:54:21,600 Speaker 2: the Buddhists, you also call it the body step of 1038 00:54:21,600 --> 00:54:24,480 Speaker 2: a vow, This idea of enlarging your cognitive light cones 1039 00:54:24,520 --> 00:54:27,960 Speaker 2: so that you're able to have the goal of compassion, 1040 00:54:28,239 --> 00:54:32,200 Speaker 2: you know, beyond our current limited human kind of scale 1041 00:54:32,200 --> 00:54:34,480 Speaker 2: that we can actually you know, work towards the goals 1042 00:54:34,480 --> 00:54:37,680 Speaker 2: of a certain size. Yeah, that's that's that's what motivates me. 1043 00:54:37,719 --> 00:54:41,080 Speaker 2: And the plasticity is really I find it incredibly hopeful 1044 00:54:41,120 --> 00:54:44,200 Speaker 2: and positive, this idea, this this incredible plasticity that has 1045 00:54:44,200 --> 00:54:48,239 Speaker 2: intelligence at its core, that every single cell is intelligent 1046 00:54:48,400 --> 00:54:52,560 Speaker 2: within and it's exerting intelligence in its cooperation and competition 1047 00:54:52,680 --> 00:54:55,319 Speaker 2: with others to form larger scale structures that can be 1048 00:54:55,400 --> 00:54:58,480 Speaker 2: molded top down, molded over time, to be better and 1049 00:54:58,560 --> 00:54:59,520 Speaker 2: to improve over time. 1050 00:55:07,280 --> 00:55:11,239 Speaker 1: That was Mike Levin, a professor and biologist at Tufts University. 1051 00:55:12,040 --> 00:55:14,840 Speaker 1: So wrapping up this two part episode about the self, 1052 00:55:15,320 --> 00:55:18,480 Speaker 1: we saw that everything in your biology is changing all 1053 00:55:18,560 --> 00:55:19,000 Speaker 1: the time. 1054 00:55:19,080 --> 00:55:20,600 Speaker 2: Your cells are. 1055 00:55:20,160 --> 00:55:23,759 Speaker 1: Constantly turning over their pieces and parts, but we have 1056 00:55:24,320 --> 00:55:28,960 Speaker 1: memory to bind the use together. Now, I've talked in 1057 00:55:29,040 --> 00:55:32,839 Speaker 1: several episodes about how memories change in their character. They're 1058 00:55:32,920 --> 00:55:36,040 Speaker 1: not like a file of zeros and ones that are 1059 00:55:36,320 --> 00:55:39,200 Speaker 1: written down in a computer and then read back out perfectly. 1060 00:55:39,600 --> 00:55:41,160 Speaker 2: And the way Michael Levin. 1061 00:55:40,880 --> 00:55:44,839 Speaker 1: Thinks about this is that memories get compressed. They get 1062 00:55:44,920 --> 00:55:49,320 Speaker 1: encoded down into the neurons or the connections between neurons, 1063 00:55:49,640 --> 00:55:52,680 Speaker 1: or the inner cosmos of proteins and side neuron and 1064 00:55:52,719 --> 00:55:58,480 Speaker 1: then when these memories get reinflated later they find themselves 1065 00:55:58,520 --> 00:56:01,920 Speaker 1: in a different world, they get it interpreted by the 1066 00:56:02,040 --> 00:56:05,480 Speaker 1: new brain that is looking at them. So I want 1067 00:56:05,520 --> 00:56:08,400 Speaker 1: to make this model clear. So here's my analogy to 1068 00:56:08,480 --> 00:56:12,360 Speaker 1: capture that. Imagine that the world out there has lots 1069 00:56:12,360 --> 00:56:15,920 Speaker 1: of things that need to be bolted down, and so 1070 00:56:15,960 --> 00:56:19,960 Speaker 1: you create a wrench, and your metal wrench is in 1071 00:56:20,000 --> 00:56:24,120 Speaker 1: some sense a compressed representation of the world out there, 1072 00:56:24,520 --> 00:56:27,560 Speaker 1: a world full of bolts. So when you see the wrench, 1073 00:56:27,600 --> 00:56:28,440 Speaker 1: that reminds you. 1074 00:56:28,440 --> 00:56:29,879 Speaker 2: Of all the bolts that are out there. 1075 00:56:30,239 --> 00:56:35,640 Speaker 1: Okay, Now, imagine that you bury that wrench, and some 1076 00:56:35,880 --> 00:56:39,799 Speaker 1: other creature, some future human creature, digs it up in 1077 00:56:39,880 --> 00:56:43,880 Speaker 1: a thousand years and she doesn't see it as a wrench, 1078 00:56:44,239 --> 00:56:48,120 Speaker 1: but to her it's maybe a weapon, or it's an 1079 00:56:48,200 --> 00:56:50,840 Speaker 1: instrument for conducting electricity. 1080 00:56:50,239 --> 00:56:51,120 Speaker 2: On her spaceship. 1081 00:56:52,000 --> 00:56:55,880 Speaker 1: Or she takes it to be a ceremonial artifact, or 1082 00:56:55,920 --> 00:56:59,759 Speaker 1: she uses it for physical exercise, or she looks at 1083 00:56:59,800 --> 00:57:03,560 Speaker 1: its clean, balanced design and uses it for a piece 1084 00:57:03,600 --> 00:57:07,480 Speaker 1: of art. The point is that what you buried is 1085 00:57:07,520 --> 00:57:11,200 Speaker 1: not what gets exhumed in a new world of the future. 1086 00:57:11,680 --> 00:57:15,520 Speaker 1: And that's what happens to memories too. You bury something 1087 00:57:15,880 --> 00:57:18,680 Speaker 1: that has some meaning in the now, but what you 1088 00:57:18,800 --> 00:57:22,040 Speaker 1: dig up is interpreted through the eyes of. 1089 00:57:22,040 --> 00:57:22,960 Speaker 2: The future you. 1090 00:57:23,600 --> 00:57:25,760 Speaker 1: And if there's one thing we can count on, it's that, 1091 00:57:26,200 --> 00:57:29,960 Speaker 1: despite all your intuitions to the contrary, that future you 1092 00:57:30,600 --> 00:57:34,200 Speaker 1: will not be the same as the you now. It'll 1093 00:57:34,240 --> 00:57:37,320 Speaker 1: be someone you don't know, who doesn't share all your 1094 00:57:37,440 --> 00:57:41,760 Speaker 1: values and opinions, and is someone you can't accurately predict 1095 00:57:42,320 --> 00:57:45,600 Speaker 1: the ship of theseus with all those changes does not 1096 00:57:46,040 --> 00:57:51,960 Speaker 1: in fact remain the same ship. I was recently talking 1097 00:57:51,960 --> 00:57:55,080 Speaker 1: with my friend Lisa Joy, and she said she thinks 1098 00:57:55,120 --> 00:58:00,000 Speaker 1: it's strange that the longevity community cares so much about 1099 00:58:00,200 --> 00:58:06,840 Speaker 1: extending their lifespan by decades, because that future person will 1100 00:58:06,880 --> 00:58:10,600 Speaker 1: be somebody potentially very different from who they are now. 1101 00:58:11,200 --> 00:58:13,560 Speaker 1: So who are you saving if you go through a 1102 00:58:13,600 --> 00:58:17,440 Speaker 1: lot of trouble now to extend your life. Whoever you're saving, 1103 00:58:17,840 --> 00:58:21,720 Speaker 1: it's a stranger to you. You're doing all this work 1104 00:58:22,040 --> 00:58:25,160 Speaker 1: for someone you don't know. So, coming back to the 1105 00:58:25,240 --> 00:58:28,800 Speaker 1: question of why we have this notion of an unchanging self, 1106 00:58:29,240 --> 00:58:32,840 Speaker 1: Michael's answer is that the job of the brain is 1107 00:58:32,880 --> 00:58:37,320 Speaker 1: to make models of consistency, like this is what a 1108 00:58:37,440 --> 00:58:40,600 Speaker 1: chair is, this is what a backpack is, this is 1109 00:58:40,600 --> 00:58:43,560 Speaker 1: what a bicycle is. And even though there may be 1110 00:58:43,640 --> 00:58:46,000 Speaker 1: a lot of variety in the specifics that you come 1111 00:58:46,040 --> 00:58:49,520 Speaker 1: across and things might change, you nonetheless are good at 1112 00:58:49,600 --> 00:58:55,640 Speaker 1: summarizing things as objects. You lump them into unchanging categories. 1113 00:58:56,200 --> 00:59:00,760 Speaker 1: And so Michael argues, the same cognitive machinery is turned 1114 00:59:00,800 --> 00:59:03,920 Speaker 1: on to our selves. Even though there's a lot of 1115 00:59:04,240 --> 00:59:08,200 Speaker 1: fluctuation of what that refers to. We lump the self 1116 00:59:08,240 --> 00:59:12,240 Speaker 1: into one object that we call me, and that high 1117 00:59:12,360 --> 00:59:17,080 Speaker 1: level cognitive model just doesn't change much. And so as 1118 00:59:17,120 --> 00:59:21,040 Speaker 1: we close, we are left with this remarkable paradox that 1119 00:59:21,080 --> 00:59:25,680 Speaker 1: we move through life carrying memories and stories and beliefs 1120 00:59:25,680 --> 00:59:29,360 Speaker 1: about who we are, and we carefully preserve them like 1121 00:59:29,480 --> 00:59:32,320 Speaker 1: relics in the soil of our minds, and we expect 1122 00:59:32,360 --> 00:59:35,560 Speaker 1: them to stay the same. But with each retrieval, every 1123 00:59:35,600 --> 00:59:41,120 Speaker 1: time we unearth them, they are interpreted afresh. They're reshaped 1124 00:59:41,200 --> 00:59:46,040 Speaker 1: by the hands of a self that is itself ever shifting. 1125 00:59:46,720 --> 00:59:49,000 Speaker 1: But the illusion our brains create for us as a 1126 00:59:49,080 --> 00:59:53,240 Speaker 1: model of the self as unchanging, a fixed point in 1127 00:59:53,280 --> 00:59:56,520 Speaker 1: a fluctuating world. And it's a comforting thought that we're 1128 00:59:56,560 --> 01:00:01,920 Speaker 1: a single thread woven through time. Maybe there's also a 1129 01:00:02,040 --> 01:00:06,240 Speaker 1: beauty in realizing that each of your future selves is 1130 01:00:06,280 --> 01:00:10,880 Speaker 1: a stranger unto you, an explorer who picks up that 1131 01:00:11,040 --> 01:00:15,480 Speaker 1: wrench of memory, holding it to the light and interpreting 1132 01:00:15,560 --> 01:00:17,120 Speaker 1: something new each time. 1133 01:00:18,120 --> 01:00:20,360 Speaker 2: Who we are, what we hold dear. 1134 01:00:20,480 --> 01:00:23,560 Speaker 1: Maybe these aren't artifacts that are meant to be saved 1135 01:00:23,640 --> 01:00:24,840 Speaker 1: or preserved perfectly. 1136 01:00:25,400 --> 01:00:27,280 Speaker 2: They are living stories. 1137 01:00:27,800 --> 01:00:33,560 Speaker 1: They're reimagined and repurposed by every future version of us. 1138 01:00:33,920 --> 01:00:34,480 Speaker 2: So when you. 1139 01:00:34,480 --> 01:00:38,400 Speaker 1: Think of your future self, who you will be tomorrow 1140 01:00:38,560 --> 01:00:40,880 Speaker 1: or a month from now or a decade from now, 1141 01:00:41,440 --> 01:00:46,280 Speaker 1: think of that stranger that future you, and maybe smile 1142 01:00:46,520 --> 01:00:50,400 Speaker 1: at the mystery of what that person will even remember, 1143 01:00:50,480 --> 01:00:55,080 Speaker 1: what they'll care about, what they'll let go of. After all, 1144 01:00:55,280 --> 01:00:58,320 Speaker 1: part of the adventure of life is not just holding 1145 01:00:58,360 --> 01:01:03,320 Speaker 1: on to who we were. It's also about meeting time 1146 01:01:03,360 --> 01:01:12,280 Speaker 1: and again who we are becoming. Go to eagleman dot 1147 01:01:12,320 --> 01:01:16,400 Speaker 1: com slash podcast for more information and find further reading. 1148 01:01:16,920 --> 01:01:20,160 Speaker 1: Send me an email at podcast at eagleman dot com 1149 01:01:20,200 --> 01:01:23,520 Speaker 1: with questions or discussion, and check out and subscribe to 1150 01:01:23,680 --> 01:01:27,400 Speaker 1: Inner Cosmos on YouTube for videos of each episode and 1151 01:01:27,440 --> 01:01:28,480 Speaker 1: to leave comments. 1152 01:01:29,200 --> 01:01:30,000 Speaker 2: Until next time. 1153 01:01:30,240 --> 01:01:33,400 Speaker 1: I'm David Eagleman and this is Inner Cosmos.