Lichtenstein: I suspect that blending the boundary between humans and computers is going to be a much snearer term thing that we're going to have to worry about in this domain. And i think there is a really interesting question about motivations, an the fact that with any living system, we can tell very quickly how to motivate it. We know what it likes, what it dislikes, because we can train it using positive and negative reinforcement. These chimeric constructions are really important to push t to to develop better concepts,. Because we don't know really what it means to have intrinsic preferences or how they might carry over to hybrid devices. All that needs needs a lot of well
As a semi-outsider, it’s fun for me to watch as a new era dawns in biology: one that adds ideas from physics, big data, computer science, and information theory to the usual biological toolkit. One of the big areas of study in this burgeoning field is the relationship between the basic bioinformatic building blocks (genes and proteins) to the macroscopic organism that eventually results. That relationship is not a simple one, as we’re discovering. Standard metaphors notwithstanding, an organism is not a machine based on genetic blueprints. I talk with biologist and information scientist Michael Levin about how information and physical constraints come together to make organisms and selves.
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Michael Levin received his Ph.D. in genetics from Harvard University. He is currently Distinguished Professor and Vannevar Bush Chair in the Biology department at Tufts University, and serves as director of the Tufts Center for Regenerative and Developmental Biology. His work on left-right asymmetric body structures is on Nature’s list of 100 Milestones of Developmental Biology of the Century.
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