Algorithm, Analog Computing, Art, Automata, Bacteria, Biological Computation, Biology, Cybernetics, Deep Learning

Beyond design: cybernetics, biological computers and hylozoism (Pickering 2008)

AI, Algorithm, Automata, Biological Computation, Code, Cybernetics, Deep Learning, Emergence, Man/Machine, Neural Networks, Robots, Science, Social intelligence, Society

Can a robot be too nice?

“Designing artificial entities perfectly groomed to meet our emotional needs has an obvious appeal, like creating the exact right person for a job from thin air. But it’s also not hard to imagine the problems that might arise in a world where we’re constantly dealing with robots calibrated to treat us, on an interpersonal level, exactly the way we want. We might start to prefer the company of robots to that of other, less perfectly optimized humans. We might react against them, hungry for some of the normal friction of human relations. As Lanier worried, we might start to see the lines blur, and become convinced that machines—which in some ways are vastly inferior to us, and in other ways vastly superior—are actually our equals.”


Anthropology, Art, Biological Computation, Biology, Biometrics, Brain, Deep Learning, Music, Neural Networks, Psychology, Science, Social intelligence, Society, Sound

The Neuroscience of Improvisation

Charles Limb has been investigating rap. “It’s what kids are doing spontaneously when growing up… and improvisation is a strong theme. It incorporates language and rhythmic music very equally.” Limb has been scanning the brains of rappers the same way he looked at jazz musicians: comparing fMRIs when they recited memorized passages to when they “freestyled,” or improvised in rhyme. Although the study is still in progress, preliminary data suggest “major changes in brain activity when you go from memorized rap to freestyle.” Can studies of improvisation unlock more general secrets of creativity? Limb hopes to do similar investigations of artists as they draw or paint. The moderator ended with an inevitable question about art and science: “It is worth the effort to measure and quantify something abstract and artistic… to demystify what we enjoy the mystery of?” Limb saw nothing “threatening or reductionist” in the work of neuroscientists. “Humans are hardwired to seek art, and there are very few things that engage the brain on the level that music does. To understand the neural basis of creativity teaches us something fundamental about who we are, why we’re here.” Improvisation “shows us what the mind can do,” Marcus added. “The ability of human beings to improvise tells us a lot about the ultimate scope of our capabilities.”


AI, Algorithm, Automata, Biological Computation, Brain, Code, Cybernetics, Deep Learning, Logic, Man/Machine, Mathematics, Neural Networks, Science, Social intelligence

Neural Networks and Deep Learning

“Will we understand how such intelligent networks work? Perhaps the networks will be opaque to us, with weights and biases we don’t understand, because they’ve been learned automatically. In the early days of AI research people hoped that the effort to build an AI would also help us understand the principles behind intelligence and, maybe, the functioning of the human brain. But perhaps the outcome will be that we end up understanding neither the brain nor how artificial intelligence works!”