VN Alexander, Fulbright Specialist, available for art/science workshops

Would your non-US institution be interested in facilitating interaction between the sciences and the arts and humanities by holding two-to-four week workshop(s) with support from the US Fulbright program? These workshops would bring together students studying computer science, biology, literature, or philosophy.

The Fulbright Specialist program is supported by the U.S. Department of State’s Bureau of Educational and Cultural Affairs (ECA) and World Learning, Non-US institutions interested in hosting workshops can apply at https://fulbrightspecialist.worldlearning.org/eligibility-host-institutions/ Write to me alexander@dactyl.org about your program and I can help you contact the Fulbright offices in your country.

Lecture #1
The Artist as Scientist: Nabokov’s Unorthodox Theory of Insect Mimicry.
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. Vladimir Nabokov, renowned Russian-American novelist and butterfly scientist, 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 thought natural selection, a mere proofreader with no real creative powers, could not make a butterfly look exactly like a dead leaf, complete with faux fungus spots. He did not 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. Look-a-likes could be better explained by other natural mechanisms, such as constraints on self-organized pattern formation, hybridization or large-scale “lucky” mutations. 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?  See lecture sample at https://www.youtube.com/watch?v=hAKgIsYSZwA

Proposed Workshop designed to explore the possibilities of undertaking further research on the role of reaction-diffusion processes in forming butterfly mimicry

Butterfly mimics or butterfly look-a-likes?
After noticing that some butterfly mimics are actually disadvantaged by their similarity to something else, Nabokov began looking for a better explanation for mimicry and what he called Nature’s practical jokes. Can a computer program be designed to model the reaction-diffusion processes that form butterfly wing patterns? Can this program be used to determine the probability of similar-looking patterns emerging without the pressure from natural selection?

Research:

Alexander, V. N., (2019). The mechanism for mimicry: Instant biosemiotic selection or gradual Darwinian fine-tuning selection? Biosemiotics, in press.

Alexander, V. N.,  (2016).Chance, nature’s practical jokes, and the ‘non-utilitarian delights’ of butterfly mimicry. Fine Lines: Vladimir Nabokov’s Scientific Art. Stephen Blackwell and Kurt Johnson (Eds.). New Haven: Yale University Press, 2016.

Alexander, V. N., (2003) Nabokov, teleology, and insect mimicry. Nabokov Studies 7, 177-213.

Alexander, V. N. (2001). Neutral evolution and aesthetics: Vladimir Nabokov and insect mimicry. Working Papers Series 01-10-057. Santa  Fe: Santa Fe Institute.

Lecture #2
Siri Fails the Turing Test: What Can Art Teach Us About Artificial Intelligence?
Apps can translate spoken English sentences into spoken Chinese. “Deep learning” programs find patterns and can help doctors diagnose cancer or help singles find mates. In some court systems, judges use software to analyze patterns in criminal behavior before passing sentences. Every time we use a search engine, purchase an item online, write an email, or post something on social media, we interact with computer algorithms that adapt to our Internet activity. We call our phones and our weapons “smart,” and all of these advances in technology are said to use artificial “intelligence.”

We may wonder, What is intelligence? What is the difference, if any, between the way an organism and a machine can seek an object, read signs, or identify a pattern? Both can obtain goals, set either by evolution or design. Do organisms and machines use similar methods for learning, classifying, remembering and interpreting? Are animals and people really just organic machines? If so, could science eventually make machines that can learn to make up their own minds as robots do in science fiction?

In this lecture, we will talk about some of the differences between the present-day artificial intelligence and biological intelligence. Specifically, we will learn about biologists studying cell signaling who say that even the simplest unit of life can make interpretations in ways that smart machines do not. Animals can take advantage of chance associations, which machines are usually designed to ignore, and machine learning programs are not designed to invent new knowledge – not yet anyway. Examining smart technologies can inspire us to learn about our own learning processes and help us decide whether or not it’s a good idea to rely on machines to make judgements. A video sample: “Siri Fails the Turing Test,” can be found at https://www.youtube.com/watch?v=d15yJoB-jhk

Proposed Workshop designed to explore the possibilities of undertaking further research on the role of reaction-diffusion processes to mimic more realistic (i.e. poetic) intelligence artificially.

Why can’t machines get jokes? 
AI translation apps still have trouble with jokes and puns such as, “Time flies like an arrow, while fruit flies like a banana.” This may be due to the fact that while computers use strict codes and develop algorithms apart from contexts, according to research in Biosemiotics, living cells use flexible signs and develop semiotic habits within contexts. Is an organism’s ability to use contexts to understand jokes or to make better decisions partly due to the collective activities of biological cells that produce traveling waves, which form synchronized virtual assemblies, which in turn, further constrain individual cellular activity?

Research

Alexander, V. N., (2019). AI, stereotyping on steroids and Alan Turing’s biological turn. Democratizing AI. Andreas Sudmann (Ed.). Berlin: transcript Verlang, in press.

Alexander, V. N., (2019). Siri fails the Turing test: Computation, biosemiosis and artificial life. Researches Semiotique/Semiotic Inquiry, in press.

Alexander, V. N., Grimes, V. A. (2017). Fluid biosemiotic mechanisms underlie subconscious habits. Biosemiotics 10(3) 337–353.