in the end, most fundamentally, literally, the mother of all problems is who designed us. So, so assume success, and that we're going to transition to the machines making machines, and all of these new sort of social systems we're describing will help manage them and curate them and democratize them. If we close the gap, I just let off with of 10 to the 10 to 10 to the 18 between chip Fab and you um, we're ultimately in marrying communication, computation, and Fabrication going to be able to create unimaginable complexity um, and how do you design that, and so I'd say the deepest of all questions that I've been working on goes back to the oldest part of our genome. So in our genome, what are called Hox genes, and these are morphogenes, and nowhere in your genome is the number five. It doesn't store the fact that you have five fingers um, what it stores is what's called the developmental program. It's a series of steps, and the steps have the character.

of like grow up a gradient or break symmetry, and at the end of that developmental program, you have five fingers. So you are stored not as a body plan, but as a growth plan. And there's two reasons for that. One reason is just compression. Billions of genes can place trillions of cells, but the much deeper one is evolution doesn't randomly perturb. Almost anything you did randomly in the genome would be fatal or inconsequential, but not interesting. But when you modify things in these developmental programs, you go from like webs for swimming to fingers, or you go from walking to wings for flying. It's a space in which search is interesting. So this is the heart of the success of AI. In part, it was the scaling we talked about a while ago, and in part, it was the representations for which search is effective. AI has found good representations. It hasn't found new ways to search, but it's found good representations of search. And that's, you're saying, that's what biology, that's what evolution has done, is creative representation structures.

biological structures through which search effective and so the the developmental programs in the genome beautifully encapsulate the lessons of AI, and this is It's embody it's it's molecular intelligence it's AI embodied in our genome. It it it it's every bit as profound as the cognition in our brain, but now this is sort of thinking in molecular thinking in how you design and so um. I'd say the most fundamental problem we're working on is it's kind of tautological that when you design a phone, you design the phone, you represent the design of the phone, but that actually fails when you get to the sort of complexity that we're talking about, and so there's this profound transition to come once I can have self-reproducing assemblers placing 10 to the 18 parts, um, you need to not sort of metaphorically but create life in that you need to learn how to evolve. But evolutionary design has a really misleading trivial meaning. It's not as simple as you randomly mutate things.

it's this much more deep embodiment of AI and morphogenesis. Is there a way for us to continue the kind of evolution of design that led us to this place from the early days of bacteria, single-cell organisms, to ribosomes, and the 20 amino acids? You mean for human augmentation or for life augmentation? I mean, what would you call assemblers that are self-replicating and placing parts? What is the dynamic complex things built with digital fabrication? What is that? That's the light. So, yeah. So, ultimately, absolutely, if you add everything I'm talking about, it's building up to creating life in non-living materials. Yes. And I don't view this as copying life. I view it as driving life. I didn't start from how does biology work, and then I'm going to copy it. I start from how to solve problems, and then it leads me to, in a sense, rediscover biology. So, if we go back to Valentina in Ghana making her circuit board, she still needs a chip fab.

very far away to make the processor owner circuit board for her to make the processor locally for all the reasons. We described you actually need the Deep things we were just talking about and so it really does lead you. So let's see there's a wonderful series of books by gingery book one is how to make a charcoal furnace and at the end of book Seven you have a machine shop. So it is it it's sort of how how you do your own personal Industrial Revolution uh isru is what NASA calls in-situ resource utilization and that's how do you go to a planet and create a civilization uh isru has essentially assumed gingery you go through the Industrial Revolution and you create the inventory of a hundred thousand resistors. What we're finding is the way you the minimum building blocks for a civilization is roughly 20 parts so what's interesting about the amino acids is they're not interesting they're hydrophobic or hydrophilic basic or acidic they have typical but not extremal properties but

they're good enough. You can combine them to make you. So what this is leading towards is technology doesn't need enormous Global Supply chains. It just needs about 20 properties you can compose to create all technology as the minimum building blocks for a technological civilization. So there's going to be 20 basic building blocks based on which the self-replicating assemblers can work, right? And I say that not philosophically, just empirically, sort of that's where it's heading. And yeah, that I like thinking about how you bootstrap a civilization on Mars, that problem. There's a fun video on bonus material for the movie where with a neat group of people we talk about it because it has really profound implications back here on Earth about how we live sustainably. What is that Civilization on Mars looks like that's using a isru that's using these 20 building blocks and does self-assembly. Yeah, go go through primary, secondary, tertiary, quaternary, um, you know, you extract properties like conducting, insulating.

semiconducting, magnetic, dielectric, flexural - these are the kind of, you know, roughly 20 properties. Um, with those, those are enough for us to assemble logic, and they're enough for us to assemble actuation. Um, with logic and actuation, we can make micro robots. The micro robots can build bigger robots. Um, the bigger robots can then take the building block materials and make the structural elements that you then do to make construction. And then you boot up through the stages of a technological civilization. By the way, where in the span of logic and actuation to the sensing come in. Oh, I skipped over that, but my favorite sensor is a step response. So if you just make a step and measure the response to the electric field, that ranges from user interfaces to positioning to material properties. And if you do it at higher frequencies, you get chemistry. And you can get all of that just from a step in an electric field. So for example, once you have time resolution in logic, something as simple as two electrodes let you do

The deepest question: Who designed life on Earth? | Neil Gershenfeld and Lex Fridman

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The deepest question: Who designed life on Earth? | Neil Gershenfeld and Lex Fridman

The deepest question: Who designed life on Earth? | Neil Gershenfeld and Lex Fridman

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