Edge Video Library

Morphogenesis for the Design of Design

[7.31.19]

As we work on the self-reproducing assembler and writing software that looks like hardware that respects geometry, they meet in morphogenesis. This is the thing I’m most excited about right now: the design of design. Your genome doesn’t store anywhere that you have five fingers. It stores a developmental program, and when you run it, you get five fingers. It’s one of the oldest parts of the genome. Hox genes are an example. It’s essentially the only part of the genome where the spatial order matters. It gets read off as a program, and the program never represents the physical thing it’s constructing. The morphogenes are a program that specifies morphogens that do things like climb gradients and symmetry break; it never represents the thing it’s constructing, but the morphogens then following the morphogenes give rise to you.

What’s going on in morphogenesis, in part, is compression. So, a billion bases can specify a trillion cells, but the more interesting thing that’s going on is almost anything you perturb in the genome is either inconsequential or fatal. The morphogenes are a curated search space where rearranging them is interesting—you go from gills to wings to flippers. The heart of success in machine learning, however you represent it, is function representation. The real progress in machine learning is learning representation. How you search hasn’t changed all that much, but how you represent search has. These morphogenes are a beautiful way to represent design. Technology today doesn’t do it. Technology today generally doesn’t distinguish genotype and phenotype in the sense that you explicitly represent what you’re designing. In morphogenesis, you never represent the thing you’re designing; it's done in a beautifully abstract way. So, for these self-reproducing assemblers, what we’re building is morphogenesis for the design of design. Rather than a combinatorial search over billions of degrees of freedom, you search over these developmental programs. This is one of the core research questions we’re looking at.

NEIL GERSHENFELD is director of MIT’s Center for Bits and Atoms; founder of the global fab lab network; the author of FAB; and co-author (with Alan Gershenfeld & Joel Cutcher-Gershenfeld) of Designing Reality. Neil Gershenfeld's Edge Bio Page


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Ecology of Intelligence

[7.23.19]

I don't think a singularity is imminent, although there has been quite a bit of talk about it. I don't think the prospect of artificial intelligence outstripping human intelligence is imminent because the engineering substrate just isn’t there, and I don't see the immediate prospects of getting there. I haven’t said much about quantum computing, other people will, but if you’re waiting for quantum computing to create a singularity, you’re misguided. That crossover, fortunately, will take decades, if not centuries.

There’s this tremendous drive for intelligence, but there will be a long period of coexistence in which there will be an ecology of intelligence. Humans will become enhanced in different ways and relatively trivial ways with smartphones and access to the Internet, but also the integration will become more intimate as time goes on. Younger people who interact with these devices from childhood will be cyborgs from the very beginning. They will think in different ways than current adults do.

FRANK WILCZEK is the Herman Feshbach Professor of Physics at MIT, recipient of the 2004 Nobel Prize in physics, and author of A Beautiful Question: Finding Nature’s Deep DesignFrank Wilczek's Edge Bio Pag


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Humans: Doing More With Less

[7.16.19]

Imagine a superintelligent system with far more computational resources than us mere humans that’s trying to make inferences about what the humans who are surrounding it—which it thinks of as cute little pets—are trying to achieve so that it is then able to act in a way that is consistent with what those human beings might want. That system needs to be able to simulate what an agent with greater constraints on its cognitive resources should be doing, and it should be able to make inferences, like the fact that we’re not able to calculate the zeros of the Riemann zeta function or discover a cure for cancer. It doesn’t mean we’re not interested in those things; it’s just a consequence of the cognitive limitations that we have.

As a parent of two small children, this is a problem that I face all the time, which is trying to figure out what my kids want, kids who are operating in an entirely different mode of computation, and having to build a kind of internal model of how a toddler’s mind works such that it’s possible to unravel that and work out that there’s a particular motivation for the very strange pattern of actions that they’re taking.

Both from the perspective of understanding human cognition and from the perspective of being able to build AI systems that can understand human cognition, it’s desirable for us to have a better model of how rational agents should act if those rational agents have limited cognitive resources. That’s something that I’ve been working on for the last few years. We have an approach to thinking about this that we call resource rationality. And this is closely related to similar ideas that are being proposed in the artificial intelligence literature. One of these ideas is the notion of bounded optimality, proposed by Stuart Russell.

TOM GRIFFITHS is Henry R. Luce Professor of Information, Technology, Consciousness, and Culture at Princeton University. He is co-author (with Brian Christian) of Algorithms to Live By. Tom Griffiths's Edge Bio Page


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A Separate Kind of Intelligence

[7.10.19]

Back in 1950, Turing argued that for a genuine AI we might do better by simulating a child’s mind than an adult’s. This insight has particular resonance given recent work on "life history" theory in evolutionary biology—the developmental trajectory of a species, particularly the length of its childhood, is highly correlated with adult intelligence and flexibility across a wide range of species. This trajectory is also reflected in brain development, with its distinctive transition from early proliferation to later pruning. I’ve argued that this developmental pattern reflects a distinctive evolutionary way of resolving explore-exploit tensions that bedevil artificial intelligence. Childhood allows for a protected period of broad, high-temperature search through the space of solutions and hypotheses, before the requirements of focused, goal-directed planning set in. This distinctive exploratory childhood intelligence, with its characteristic playfulness, imagination and variability, may be the key to the human ability to innovate creatively yet intelligently, an ability that is still far beyond the purview of AI. More generally, a genuine understanding of intelligence requires a developmental perspective.

ALISON GOPNIK is a developmental psychologist at UC Berkeley. Her books include The Philosophical Baby and, most recently, The Gardener and the Carpenter: What the New Science of Child Development Tells Us About the Relationship Between Parents and Children. Alison Gopnik's Edge Bio Page


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Collaboration and the Evolution of Disciplines

[7.1.19]

Cooperation achieves its beneficial effects by improving communication, promoting gains from specialization, enhancing organizational effectiveness, and reducing the risks of harmful conflict. Members of an institutionalized academic discipline jointly benefit in all these ways. Unfortunately, members of different disciplines typically do not. The boundaries of most disciplines were largely set 100 (plus or minus 50) years ago, and efforts to redraw the boundaries (e.g. at Irvine and Carnegie Mellon) have not been met with much success. I would like us to consider how the more or less fragmented research community can best respond to new opportunities (AI), new problems (climate change), new modes of education and governance, and new understandings of human behavior and values. 

ROBERT AXELROD, Walgreen Professor for the Study of Human Understanding at the University of Michigan, is best known for his interdisciplinary work on the evolution of cooperation. He is author of The Complexity of Cooperation and The Evolution of Cooperation. Robert Axelrod's Edge Bio Pag


 

Questioning the Cranial Paradigm

[6.19.19]

Part of the definition of intelligence is always this representation model. . . . I’m pushing this idea of distribution—homeostatic surfing on worldly engagements that the body is always not only a part of but enabled by and symbiotic on. Also, the idea of adaptation as not necessarily defined by the consciousness that we like to fetishize. Are there other forms of consciousness? Here’s where the gut-brain axis comes in. Are there forms that we describe as visceral gut feelings that are a form of human consciousness that we’re getting through this immune brain?

CAROLINE A. JONES is a professor of art history in the Department of Architecture at MIT and author, most recently, of The Global Work of Art. Caroline Jones's Edge Bio Page


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The Brain Is Full of Maps

[6.11.19]

 I was talking about maps and feelings, and whether the brain is analog or digital. I’ll give you a little bit of what I wrote:

Brains use maps to process information. Information from the retina goes to several areas of the brain where the picture seen by the eye is converted into maps of various kinds. Information from sensory nerves in the skin goes to areas where the information is converted into maps of the body. The brain is full of maps. And a big part of the activity is transferring information from one map to another.

As we know from our own use of maps, mapping from one picture to another can be done either by digital or by analog processing. Because digital cameras are now cheap and film cameras are old fashioned and rapidly becoming obsolete, many people assume that the process of mapping in the brain must be digital. But the brain has been evolving over millions of years and does not follow our ephemeral fashions. A map is in its essence an analog device, using a picture to represent another picture. The imaging in the brain must be done by direct comparison of pictures rather than by translations of pictures into digital form.

FREEMAN DYSON, emeritus professor of physics at the Institute for Advanced Study in Princeton, has worked on nuclear reactors, solid-state physics, ferromagnetism, astrophysics, and biology, looking for problems where elegant mathematics could be usefully applied. His books include Disturbing the UniverseWeapons and HopeInfinite in All Directions, and Maker of PatternsFreeman Dyson's Edge Bio Page

 


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Perception As Controlled Hallucination

Predictive Processing and the Nature of Conscious Experience
[6.6.19]

Perception itself is a kind of controlled hallucination. . . . [T]he sensory information here acts as feedback on your expectations. It allows you to often correct them and to refine them. But the heavy lifting seems to be being done by the expectations. Does that mean that perception is a controlled hallucination? I sometimes think it would be good to flip that and just think that hallucination is a kind of uncontrolled perception. 

ANDY CLARK is professor of Cognitive Philosophy at the University of Sussex and author of Surfing Uncertainty: Prediction, Action, and the Embodied MindAndy Clark's Edge Bio Page


 

Mining the Computational Universe

[5.30.19]

I've spent several decades creating a computational language that aims to give a precise symbolic representation for computational thinking, suitable for use by both humans and machines. I'm interested in figuring out what can happen when a substantial fraction of humans can communicate in computational language as well as human language. It's clear that the introduction of both human spoken language and human written language had important effects on the development of civilization. What will now happen (for both humans and AI) when computational language spreads?

STEPHEN WOLFRAM is a scientist, inventor, and the founder and CEO of Wolfram Research. He is the creator of the symbolic computation program Mathematica and its programming language, Wolfram Language, as well as the knowledge engine Wolfram|Alpha. He is also the author of A New Kind of Science. Stephen Wolfram's Edge Bio Page


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The Cul-de-Sac of the Computational Metaphor

[5.13.19]

Have we gotten into a cul-de-sac in trying to understand animals as machines from the combination of digital thinking and the crack cocaine of computation uber alles that Moore's law has provided us? What revised models of brains might we be looking at to provide new ways of thinking and studying the brain and human behavior? Did the Macy Conferences get it right? Is it time for a reboot?­­­

RODNEY BROOKS is Panasonic Professor of Robotics, emeritus, MIT; former director of the MIT Artificial Intelligence Laboratory and the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL); founder, chairman, and CTO of Rethink Robotics; and author of Flesh and Machines. Rodney Brooks's Edge Bio Page


 

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