It’s a commonplace to say that good science requires imagination, yet scientist aren’t really encouraged to read poetry or to take up painting. Maybe they should. This talk presents the example of Vladimir Nabokov, renown Russian-American novelist and butterfly scientist who used his artistic knowledge to understand how evolution can work. He went against the prevailing theories of his day and was attacked for being unscientific, but recently some of his work has been vindicated by DNA analysis, showing that his artistic guesses were amazingly accurate and precise. Nabokov didn’t think natural selection, a mere proofreader with no real creative powers, could make a butterfly look exactly like a dead leaf, complete with faux fungus spots. He didn’t think natural selection had gradually made the tasty Viceroy species butterfly look like the bitter tasting Monarch, allowing it to survive better. Although he believed that natural selection had shaped many of nature’s forms, he thought the one thing natural selection could not create was mimicry, which could be better explained by other natural mechanisms. This heresy infuriated scientists who thought insect mimics were the best illustration of the gradual powers of selection. More than fifty years later, Nabokov’s genius is finally being recognized. What was it about Nabokov’s way of thinking that allowed him to see what others could not? And how did his understanding of nature inspire his fiction? Talk based on “Chance, Nature’s Practical Jokes, and the “Non-utilitarian Delights” of Butterfly Mimicry” by V N Alexander, in Fine Lines: Vladimir Nabokov’s Scientific Art. Eds. Stephen Blackwell and Kurt Johnson. New Haven: Yale University Press.
The ubiquity of technologies using artificial intelligence (AI)—Google learning algorithms, Apple smart phones and weaponized robots—should give us pause. What is intelligence? What might be the difference, if any, between intelligence in machines and organisms? Both can obtain goals, set either by evolution or design. Machines can be programmed to perform computations, seek objects, read signs, and even preserve themselves. But do organisms and machines use different methods for learning, remembering and interpreting in order to perform these intelligent actions? Artificial Intelligence (AI) designers try to mimic human brain capabilities with “self-learning” neural networks trained by selection processes. Yet decades on, AI still fails the Turing Test. While computers use codes and develop algorithms apart from contexts, living cells use signs and develop semiotic habits within contexts. This difference, I argue, is partly due to the collective activities of biological neurons that produce traveling waves, which, in turn, further constrain neural activity. It appears wave patterns function as contexts for the local connections. At the time of his death, Alan Turing was investigating the organizing role of emergent wave patterns on biological development, dappled animal fur patterns, root growth, and embryonic differentiation. Had he lived to continue this work — which thirty years later was revived as Artificial Life (AL) — he might have reoriented AI research, which has become merely a tool for stereotyping and regularizing, not thinking.
Adding a biosemiotic perspective to AL research, I investigate how the behavior of individual neurons may lead to emergent patterns at the collective level. How do neurons learn to organize with other neurons? Origin of life researchers ask similar questions about how interacting molecules can “program” themselves or “optimize” their “algorithms” such that functional choices are made, resulting in collective outcomes that can be retained by natural selection or not. This is the wrong question. I argue functionality arises when semiotic transformations at the lowest levels begin to flow efficiently and form a semiotic cycle. A semiotic habit is a machine that resets itself. Instead of attempting to hypothesize about how neural algorithms may be trained by the environment with a more or less logical or statistically significant selection process, I propose, using Crutchfield’s Epsilon-Machine concept and Turing’s morphogenesis research, that semiotic habits simply flow to the lowest possible energy state, following the stochastic resonance of similar and proximate signs. This fluid and cyclical nature of biological computation distinguishes it from artificial machine computation.
Although research into the biosemiotic mechanisms underlying the purposeful behavior of brainless living systems is extensive, researchers have not adequately described biosemiosis among neurons. As the conscious use of signs is well-covered by the various fields of semiotics, we focus on subconscious sign action. Subconscious semiotic habits, both functional and dysfunctional, may be created and reinforced in the brain not necessarily in a logical manner and not necessarily through repeated reinforcement. We review literature that suggests hypnosis may be effective in changing subconscious dysfunctional habits, and we offer a biosemiotic framework for understanding these results. If it has been difficult to evaluate any psychological approach, including hypnosis, this may be because contemporary neuroscience lacks a theory of the sign. We argue that understanding the fluid nature of representation in biological organisms is prerequisite to understanding the nature of the subconscious and may lead to more effective of treatments for dysfunctional habits developed through personal experience or culture. Download article https://link.springer.com/article/10….
How can art and science interact meaningfully? Based on a talk at the Leonardo Art and Science Rendezvous (LASER) meeting in NYC on April 12, 2014, Victoria N Alexander, PhD discusses how art can benefit science through a biosemiotic perspective.
What happens in your body when you decide to go right or left? What makes your choices? your Self? What do we mean by “choice” anyway? Victoria N Alexander, PhD discusses the science of making choices through a complexity science-biosemiotic perspective.