Why do biologists refer to something, excreted by one cell and taken up by another, as a signal? Doesn’t this invest the cell with intentionality that it doesn’t really have? Why don’t biologists just refer to the process of chemical exchange as simply a chemical reaction? A molecule passed between cells is called a “signal,” if it sets off a response that is ultimately good for both cells and/or the body they’re part of. So a signal has a function for survival and maintenance, and the cells have been evoloved by natural selection to process signals. A chemical in the body that does not have this kind of effect is not a signal. Biosemiosis offers real explanatory power for science. It explains why people believe things that aren’t true. It explains why we can have allergic reactions to neutral things. It explains how life first emerged. It explains how meaningless things can acquire new meanings. It explains how creativity is possible. It explains why AI fails compared to Biological intelligence when it comes to adapting to contexts.
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.
We have been hearing a lot about “fake news” and “propaganda” lately, and it is as important as ever to use our critical thinking skills. But we also need to understand how propaganda works and why it is so difficult to counteract with logic. Propaganda takes advantage of the way our brains function when we are not paying attention. When we are paying attention our analytical skills are engaged. When we are not, our brains go on processing information in a non-analytical way, using what might be called a poetic logic, based mainly upon similarities, coincidental patterns, associations, repetition, and emotion. There are sound biological reasons for this mindless type of processing, which actually helps us learn faster, retain memories longer, and make appropriate decisions without really thinking. In this presentation, we will explore how and why art and poetry may actually be more helpful in developing critical thinking skills. Art also works with the poetic logic of subconscious processing, but does so in a way that is not manipulative, deceptive or dishonest.
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.
Finding Genius Podcast
“In biosemiotics, we say that the human ability to interpret signs—which is the ability to think really, to think creatively and adaptively and learn new things—didn’t just emerge with animals; rudimentary sign reading emerged in the simplest forms of life with single-celled organisms,” says Victoria Alexander, biosemiotician, Director of Dactyl Foundation, Fulbright specialist, and author of The Biologist’s Mistress: Rethinking Self-Organization in Art, Literature, and Nature. What’s biosemiotics, you ask? Alexander explains biosemiotics as the study of sign use in biological systems, where signs can be almost anything in the environment—a word, a pictogram, a smell, a sound, or a chemical gradient. Signs are intelligence; we can train a computer to read signs, but can we train a computer to interpret signs that depend on the context within which they exist? Answering this question is one aspect of the investigation into the differences between biological and artificial intelligence, which is what Alexander’s work is currently focused on. She explains that biological cells don’t just react to the chemistry around them, but interpret and respond to it. She goes on to explain how this underlies a system of interpretation that’s related to random change in a way that’s changing the direction of evolution. In this way, Alexander argues that it is within the organism that evolutionary agency is found and explained, rather than in the mechanisms of randomness and competition.Alexander offers an exciting and informative conversation that touches on a variety of topics, including metabolic pathways, the association of properties and the placebo effect, the idea that dysfunctional semiotic habits are the cause of some syndromes and conditions, cell differentiation, self-organization in nature, and butterfly wing pattern development. Press play to hear the full conversation.
Course in Quantitative versus Qualitative methods
Digital Humanities Lab
Surpassing the Turing Test: What is the difference between art and science, biological intelligence and artificial intelligence, qualitative and quantitative approaches?
The goal of a quantitative approach to copying Rembrandt’s style of portraiture would be to create a typical painting, such as the one produced by the deep-learning algorithm used in the Next Rembrandt experiment. The goal of a qualitative approach to copying Rembrandt’s style would be to create a unique and original painting such as Old Woman Cutting her Nails (1655-60), a painting previously attributed to Rembrandt that is now considered to be the masterful work of one of his students. We can say that Rembrandt’s student learned the Rembrandt algorithm for painting portraits. Old Woman Cutting her Nails attracts many museum visitors, whereas more typical paintings by Rembrandt, such as those used to train the algorithm in the Next Rembrandt experiment, do not attract the interest of as many visitors or Rembrandt scholars. While the AI-produced painting may pass the Turing Test, Old Woman Cutting her Nails surpasses it. This may lead us to ask, What is the difference between the way a biological organism—like one of Rembrandt’s students—develops algorithms and the way that machines (currently) do? This course will explore the biological mechanisms that are employed when new procedures are discovered that repeat the old procedures with a significant difference. We will try to understand why, despite advances in computing power, predicting the behavior of nonlinear systems (e.g., people) with certainty (or creating an algorithm that can produce significantly original works of art) does not improve in proportion to the amount of data collected about past behaviors and procedures—at least not with current computing methods.
Our investigations will draw upon Alan Turing’s late work in morphogenesis and the field of biosemiotics, which studies the, sometimes formulaic, sometimes original, mechanisms of biological computation. Using a qualitative approach might be called using “artistic insight” into natural phenomena and it is an extremely important and often overlooked tool for scientists. We will launch into the course with the work of artists who were also scientists, Johann von Goethe and one of his “students,” Vladimir Nabokov, and from there we will dive into a very broad and eclectic survey of the history of subjective versus objective knowledge, with a view to bringing the humanities and the sciences together in fruitful ways.
Representative Democracy, Capitalism, Communism, Socialism or Anarchy? No matter what philosophy you begin with, over time political systems tend to concentrate wealth and power. Government and individual freedom should really be co-creative of one another. Why is it that we can’t seem to achieve this? As a biosemiotician, I have learned that creative and intelligent behavior emerge in complex systems when individuals have semiotic freedom and enabling constraints. Government/culture should provide the enabling constraints (language, tradition, borders, laws, courts, currency, public buildings, hospitals, schools, mass transportation, energy and communication networks) but the people making use of those constraints should have the semiotic freedom (i.e., the ability to interpret rules and even misinterpret rules) to make their own decisions, set their own goals, and enjoy/suffer the consequences.
[This is a version of a talk I originally presented at the 2018 Biosemiotics conference in Berkeley last June, presented on Dec 9th to a Biosemiotic study group online organized by Pille Bunnell. The video is a little rough. The brilliant Qs that sparked some of my As were cut because I neglected to get permissions from all the participants beforehand. Thank you, Pille, for organizing the session.]
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.