...direct from the Schizoid M.I.T. 'department' of "none of the above".
FabLabs portend the birth of the Tofflerian Prosumer!
...So there's so much potential for good, so much capacity for good that FabLabs and the ability and the tools of creation really unlock that potential.
- I don't say that as sort of dewy-eyed naive. I say that empirically from just years of seeing how this plays out in communities.
- I wonder if it's the early days of personal computers though, before we get spam.
- In the end, most fundamentally, literally the mother of all problems is who designed us? So assume success in that we're gonna 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 led off with of 10 to the 10 to 10 to the 18 between chip fab and you, we're ultimately, in marrying communication, computation, and fabrication, gonna be able to create unimaginable complexity. 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 gene, and these are morphogenes, and nowhere in your genome is the number five. It doesn't store the fact that you have five fingers. What it stores is what's called a 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 you're saying that's what biology, that's what evolution has done, is created representations, structures, biological structures through which search is effective.
- And so the developmental programs in the genome beautifully encapsulate the lessons of AI. And it's embodied, it's molecular intelligence. It's AI embodied in our genome. 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 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-reducing assemblers placing 10 to the 18 parts, 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 as much more deep embodiment of AI and morphogenesis.
- Is there a way for us to continue the kind of evolutionary design that led us to this place from the early days of bacteria, single cell organism to ribosomes and the 20 amino acids?
- You mean for human augmentation? - For life- 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 life.
- So ultimately, absolutely, if you add everything I'm talking about, it's building up to creating life in non-living materials. 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 gonna copy it. I start from how to solve problems and then it leads me to, in a sense, rediscover biology. So if you go back to Valentina in Ghana making her circuit board, she still needs a chip fab very far away to make the processor in her 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.
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. It's sort of how you do your own personal industrial revolution. ISRU is what NASA calls in situ resource utilization. And that's how do you go to a planet and create a civilization. ISRU has essentially assumed Gingery. You go through the industrial revolution and you create the inventory of 100,00 resistors.
What we're finding is 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, that's where it's heading. And 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 does that civilization on Mars look like that's using ISRU, that's using these 20 building blocks and does self-assembly.
- Go through primary, secondary, tertiary, quaternary. You extract properties like conducting, insulating, semiconducting, magnetic, dielectric, flexural. These are the kind of roughly 20 properties. With those, those are enough for us to assemble logic and they're enough for us to assemble actuation. With logic and actuation, we can make microrobots. The microrobots can build bigger robots. 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 did 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 amazingly capable sensing. So we've been talking about all the work I do, there's a story about how it happens, where do ideas come from?
- That's an interesting story. Where do ideas come from?
- So I had mentioned Vannevar Bush and he wrote a really influential thing called the Endless Frontier. So science won World War II. The more known story is nuclear bombs. The less well known story is the RAD lab. So at MIT, an amazing group of people invented radar, which is really credited as winning the war. So after the war, grand old man from MIT was charged with science won the war, how do we maintain that edge? And the report he wrote led to the National Science Foundation and the modern notion we take for granted but didn't really exist before then of public funding of research, of research agencies. In it, he made what I consider an important mistake, which is he described basic research leads to applied research leads to applications leads to commercialization leads to impact. And so we need to invest in that pipeline.
The reason I consider it a mistake is almost all of the examples we've been talking about in my lab went backwards. That the basic research came from applications. And further, almost all of the examples we've been talking about came fundamentally from mistakes. Essentially everything I've ever worked on has failed, but in failing, something better happened. So the way I like to describe it is ready, aim, fire is you do your homework, you aim carefully at something, a target you wanna accomplish, and if everything goes right, you then hit the target and succeed. What I do you can think of is ready, fire, aim. So you do a lot of work to get ready, then you close your eyes and you don't really think about where you're aiming, but you look very carefully at where you did aim, you aim after you fire. And the reason that's so important is if you do ready, aim, fire, the best you can hope is hit what you aim at.
More about MIT's FabLabs
