Microbes Mind Forum - Futures of Intelligence

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  Microbes-Mind Forum
Art Curator – Piero Scaruffi
Collaborative Intelligence Experiment – Zann Gill

ART: Artists Redefine Turing

There are two kinds of creation myths: those where life arises out of the mud, and those where life falls from the sky. In this creation myth, computers arose from the mud, and code fell from the sky.

George Dyson, Turing's Cathedral (2012)

Artists, inventors, and scientists use their minds to fashion mud into the next imagined reality. In this 2012 Centenary of the birth Alan Turing, famous for envisioning “machines who think,” a group of artists describe how they’ve been inspired by Turing to explore potential futures of intelligence. To this experiment each artist brings a unique perspective and body of work. Each explores a different spinoff from Turing and speculates differently about futures of intelligence, proposing alternatives and redefining Turing’s legacy for the future.

Alan Turing characterized intelligence as “effective search,” choosing the simplest possible explanation to translate George Berkeley’s concept of mind into computational terms in his paper, “Computing, Machines, and Intelligence” (1950). He wrote, “The popular view that scientists proceed inexorably from well-established fact to well-established fact, never being influenced by any improved conjecture, is quite mistaken. Provided it is made clear which are proved facts and which are conjectures, no harm can result. Conjectures are of great importance, since they suggest useful lines of research” (Turing 1950). Similarly, in evolution each organism is a conjecture: “something like this might work in this ecosystem.” Artists evolve their art through conjecture and search, redefining Turing in new ways. This was an emergent experiment, which we’ll describe first and decode later.

Miu Ling Lam – Deciphering nature's codes
From spiral galaxies and wind-combed dunes to coral reefs and peacock feathers, patterns in nature offer us visual and aesthetic stimulus. Self-organized forms and structures in natural systems inspired legendary artist-scientists, from Leonardo da Vinci to Ernst Haeckel and Alan Turing. Fibonacci numbers in phyllotactic architecture allow plants to optimize access to resources like moisture and sunlight, producing stunning fractal structures in some plants.

The last, unpublished work of Alan Turing, the father of computer science and artificial intelligence, explained Fibonacci phyllotaxis. Turing, well-known for his remarkable achievements, from cracking the German Enigma Machine during World War II to the Turing Test, has not received the recognition he deserves for his pioneering work on mathematical biology. During his last few years, Turing developed his Reaction-Diffusion Theory of Morphogenesis (or the Turing Instability) to explain the mechanism of pattern formation in biological systems, such as animal skin pigmentations (Turing 1952). More than fifty years later, biologists provided the first biological evidence to show that Turing's model experimentally controls hair follicle spacing in mice (Sick 2006). Recently tissue engineering scientists discovered the left-right symmetry breaking of vascular stem cells due to mechanical stimuli, and successfully created targeted cellular patterns based on Turing's morphogenetic model (Chen 2012). Turing's Reaction-Diffusion Theory not only explains many amazing patterns in nature, but also has significant implications for developmental biology, regenerative medicine and complex systems.

My art project, Turing, deciphering nature's codes, is a tribute to Alan Turing for his groundbreaking and counterintuitive idea of using Reaction-Diffusion systems to explain the complex mechanism of patterning in nature. I have developed a set of computer programs to mimic these Reaction-Diffusion systems. I have also extended Turing's two-component Reaction-Diffusion model to three-components, using the three additive primary colors, red, green, and blue, to represent three types of morphogens that react and diffuse in the system. Similar to oscillating chemical reactions, the density of morphogens in the domain will change in an iterative fashion due to reaction and diffusion, finally reaching a stable equilibrium state. Such stable equilibrium does not always occur; it requires careful selection of initial conditions, such as the diffusion and reaction rates. These initial conditions, and the initial placement of morphogens, also affect the periodicity of the resultant patterns. Based on the anisotropic migration model described in Turing (1952), I modified the system to guide the diffusion direction of morphogens, which transforms resultant patterns from labyrinthine to unidirectional.

Chris Chafe - My Musical "Touring" Test
Mid-century pioneers in computer music, who paved the way for today’s digital synthesis, saw the promise of creating sound in software, from mathematics, via the right algorithm, which requires understanding the sound you aim for. My work explores the underlying physics of sound sources and the intricacies of our perceptual system, since what you want to hear isn’t always what you get.

My practice as a composer / performer taps into “extra-musical” projects for data as a source of musical materials. I find that our ears are especially suited to detecting patterns and subtle shades of difference when the outside world is incorporated into music. Internet traffic patterns, environmental signals (air quality), brain wave data, seismic signals are some of the sources. Ultimately my purpose is “musification.” Pattern and correlation are sensed “in time.”

Each project has its particular challenges when incorporating extra-musical data. Static structures, for example DNA sequences I’ve worked with, lack a temporal dimension. The solution in that case
was to create an exploratory interaction, returning to primitives – how we knock on different materials to make sounds that tell us about the material. The sound of one “klunk” vs. a different “klank.” Slowed down and made into room echoes, the same DNA structure can become a space. We perceive the structure of spaces extremely well by ear (as well as visually). Beyond potential practical applications, this exploration links two very lively fields: synthetic biology and computer music.

I am keen on finding new materials for my music and new cultures of music in the broadest sense. We might make music by manipulating (microscopic) biological cultures. Via digital translations, can we take our sensibilities into the much slower biodynamics of yeasts? I've explored temporal contrasts in other pieces e.g., Oxygen Flute and Tomato Quintet (with Greg Niemeyer), shown in the Figure 1. These pieces often include a “temporal gear shift” to combine human musical time scales with and non-human time scales. Seven Airs is a musification of seven air quality measurements collected from locations around the 35th Parallel in California.

I’ve been in a “bring me your data” phase as a composer for several years. Each collaboration has required unique solutions. Out of this grows an emerging practice and tool kit. I have my own version of the Turing test for artificial music, which is ready to tour.

Laurie Frick — Quantify-me
I’m playing with an updated version of Alan Turing’s question, “Can Machines Think” and have been testing how self-tracking ‘machines’ can measure and decode our behavior – the profound connection between underlying rhythms of our inner selves, the way we unconsciously slice our time, and the patterns of activities we undertake.  Soon, sensors will be built into our clothes. We’ll know our variable heart rate, temperature spikes, stress levels, exact stages of sleep, food ingested and expended, and our exact movement through the day.

I imagine a future where everything can be passively measured and significantly added to my daily regimen in order to build a patterned vocabulary and grammar for self-tracking. Steps walked, calories expended, weight, sleep, time-online, GPS location, daily mood as color, micro-journal of food ingested are part of my daily tracking – simple and easy to collect using sensors and gadgets that point toward a time when complete self-surveillance will be the norm.

Each work and installation I make is an experiment to find the exact resonant rhythm, which tracks the underlying spaces and neural patterns of my mind. Emerging theory in neuroscience suggests that we each have an innate neural pattern.  To test this idea, I’m measuring self-tracking data, formulating a pattern language that conveys up-to-date records – reflections of ourselves.

What if walls in our homes could display ambient patterns of how we’re doing, and we could respond to those measurements, subtly adjusting our behavior? It could be soothing to see patterned portraiture constructed from our personal measurements, and even possibly motivating. How much we move, what we eat and how we sleep can tell us how we feel, a language built from pattern that more viscerally and directly communicates this data.  I start from hand-built patterns to find the subliminal punch in the more primal and primitive parts of the brain, later translating analog to digital work.  Numbers are abstract concepts, but our brains recognize pattern intuitively.

My hypothesis is that these measurements can be felt when we visualize them.  I’m creating patterns to prototype and build a model of how self-tracking data could be visualized.  Eventually software algorithms will automatically translate your daily experience, text you cruised by online, color of novel objects captured from a wearable camera — reconfiguring all into patterns that map your movement and bio data.

Peter Foucault – Encoding/Decoding
Attraction/Repulsion is an interactive drawing installation where small, sensor-driven robots are influenced by audience–artist participation to create a large-scale, abstract composition.

This future of intelligence scenario could shed light on how evolution’s inventive powers can be replicated in the way we use technology. Evolution achieves strikingly well-adapted results via random mutation, pattern recognition and environmental selection. Art is a vehicle to explore evolutionary dynamics. A modified Toy Robot with an affixed pen is pulled this way and that via sensors, activated by audience participation. The Robot traces his path using tracks of an attached pen to map where he’s been on the surface of blank white paper. 

Cross-disciplinary collaborations extend this project, including experiments with video, sound and performance artists, whose outside influences provide new ways to guide the robot to create a composition that translates their input into a map of past interactions. With each iteration of the project, circumstances and collaborators change, producing a different and unique drawing each time, evoking futures of intelligence where collaborations between professionals in many fields will be essential to explore and shape new possibilities across science and art, where each contributor has a unique role in the ideas, technologies and solutions.

Free play of uncoordinated, distributed agent controllers (the Audience) and the Robot generates a chaotic, but potential-filled, record of the Robot’s activity, where many possibilities are implicit, waiting to emerge. I then work as an artist with this raw material, using my pattern recognition capacity to explore what the Robot markings could potentially mean, molding from emergent potential a visual message, enabling it to emerge from chaos. If one future of intelligence lies in the collaboration of randomness with pattern recognition, mechanism with mind, that is the future my work explores.

With each presentation, a new drawing is created collectively through audience participation. The drawings are then re-visited in the studio where I add additional hand-drawn elements, a decoding process that further translates each work on paper into a finished composition. Recent work has also included USGS maps, connecting drawing with space/geography, collectively mapped through both audience–artist participation.

Shona Kitchen – "intelligence for what?"
Turing predicted that machines could become intelligent. Today he might be amazed at the humble devices that have primitive intelligence, from digital cameras detecting human faces to online cookies collecting our shopping patterns across dozens of web sites, predicting our next impulse to buy. Pervasive intelligence crops up in new places, from automotive headlights that automatically activate in darkness, to social network trackers on match-making sites.

Dreaming FIDS, an artwork for the San Jose International Airport, demonstrates how commoditizing computer-based intelligence can turn up in unexpected places with unpredicted applications. This fish tank has submerged surveillance equipment inside. Cameras, LCD monitors, and two host computers perform integrated “surveillance” on the fish in the tank and the fish-watchers outside of the tank. Fish, suspected of “suspicious” behavior, are photographed. Still images of the offenders are stored. In this playful take on how security cameras have infiltrated our world, the fish entertain, but they also ask us to rethink what “intelligence” means today, and what it might mean in the future.

Seana McNamara – Layered Mind
Art-making is like archeology, digging for meaning. But where the archeologist digs deeper and deeper to unearth the past, digital art adds layer upon layer, projecting images and ideas into the future, a method of “effective search.” To create the digital image Ice Angel, I started by drawing a pencil portrait in which a woman’s face appears twice, one the distorted reflection of the other.

That first experiment sparked my fascination with transformations as a process of search. The transitional experiment that preceded my shift to abstract images started from a painted portrait in a jungle setting (not shown), which evolved into a tiling of digital images about the disintegration of self in the process of search, called The Birdwatchers.

Art-making is like myth, tapping into the archetypes we all share, taking us on mental journeys through story-telling. As I create individual images, they flow, one into another. Each image reacts to the previous in a running animation of changes, telling a visual story. . . . I use images that lack physical depth or perspective to allude to loss of perception and new imagination. As I crystallize sensation, creating images that flow from one to another, each symbol and form repeating, changing in size and orientation, I want visual onomatopoeias that echo my process of art making cannibalizing old work to create new work, warping, layering, sketching, and erasing. The four images in the abstract tiling, shown left, called Azure Sky, illustrate how thematic experiments can be a process of search. Other tilings include Anima and Stumped. Working digitally, I stretch, play and transform images in ways that would be impossible if I avoided the computer. Images are pounded into their new forms, fighting among themselves. The weakest are consumed as raw materials for other images. The strongest snap into place, each layer and color seamlessly fitting, instantly right. Meaning emerges, not just with the topmost layer, but through the play of transparency and opacity that allows me to return to past layers, inviting them back into my digital world, or hiding them.

Unlike narrative story or traditional painting, where the linearity of time is critical, digital art is made in a hyper-reality where time is relational. Digital art-making is akin to free association because digital art dissolves linear time. The digital artist can at any moment change the order of layers, or return to a previous layer to pick up a lost thread that has again become relevant, moving forward, or jumping back, or shifting levels of translucency, or reordering layers at any point. This flexibility in producing digital art allows a different kind of thought process and attracts me to digital art as a non-linear process of “effective search.”

Todd Siler – Upside Downside
Could Alan Turing imagine we’d turn on our ultimate “universal thinking machine”: the World Wide Web, ingeniously networked to support crowdsourcing invention communities? Would he delight in our perpetual demonstrations of the power and benefits of realizing human ingenuity by applying one humbling and inspiring principle of common sense: “None of us is as smart as all of us”? Perhaps Turing would single-handedly invent the next “Black Swan” to transform all technology-enabled learning tools into personal “Creative Operating Systems,” each customized to fit our favorite multi-terabyte mobile devices, designed with nanotechnology to optimize their functionality a thousand-fold. By simply tapping the button on our mobiles, we could all think like geniuses, “metaphorming” every piece of information we direct our search engines to hunt for and gather. We could transform all this information into new, personally meaningful anduseful things.

My images are digital creations and manipulations from scratch, transforming real and virtual or imaginary imagery in experimental ways with unpredictable outcomes. I imagine a world where each of us can access our “Turing-side”: think, create and perform with our own unique genius. Imagine a wildly inventive world where we could exercise our intuition to dream, discover, innovate — a blowout of higher awareness, a Big Bang of Consciousness. Leonardo da Vinci, Alan Turing, and other geniuses might return to comment on “the art of science and science of art” — ArtScience. We search for insights into Nature’s creations, which inspired da Vinci and Turing’s boldest innovations: from the helicopter to hydraulic constructs for waterways to water-powered gristmills to the ultimate thinking machine. Can our collective future be augmented by super-sensitive and intuitively intelligent machines that teach us empathy and ingenuity? Can we arrive at the edge of new knowledge, and then wisely apply our collaborative intelligence to create a sustainable future?

Decoding an Experiment
Digital artists, as early adopters of technology, were the ideal group to seed a collaborative intelligence experiment that will expand to scientists, entrepreneurs, and others, all envisioning “futures of intelligence.” Like Turing, whose work can be seen through both technical and artistic lenses, collaborative intelligence as a technical challenge characterizes multi-agent, distributed, systems where each agent is uniquely positioned with autonomy to contribute to a collaborative problem-solving network that aggregates and integrates diverse input toward a coherent result. As an artistic design challenge, to engage diverse problem-solvers, with differing capacities for pattern recognition and interpretation requires not only computing capacity to process large amounts of data but also human capacity for pattern recognition.

David Dunkley-Gyimah filmed Tahrir Memento (2010) of Egypt’s uprising — the I-reality of a new journalism that captures many points of view. MIT scientists have shown how the brain/ mind empties itself to gather new information. If a consumer camera is soon mass-produced that captures brains' electrical signals, thoughts as seen here, and cameras reading data, such as pupil dilation, will reveal other clues about a person's mental state. Then "Congressman what were you thinking?" will be a question we don't even need to ask. Capturing intelligence will become our next reality.

Whereas collective intelligence identifies similarities in aggregates to predict better, collaborative intelligence identifies disparities among agents to innovate better. As in nature, each individual organism (or in this case individual artist) plays a unique role in an ecosystem. Collaborative intelligence offers one potential future of intelligence that harnesses networks of humans and smart devices to address cross-disciplinary challenges, from envisioning ambitious projects to preparing for global climate change, where contributors with different expertise and priorities must collaborate to achieve a breakthrough solution.

ART: Artists Redefine Turing is an experiment where each artist brings his or her unique capacity for pattern recognition to answer a simple question: How does your work comment on the Centenary of Alan Turing and potential futures of intelligence? Draft responses were posted so artists could be inspired by each other. The experiment avoided consensus, converging toward coherent diversity that inspires new concepts for potential futures of intelligence – open-ended and open-sourced – as implied when Dyson wrote, “Turing’s model of universal computation was one-dimensional: a string of symbols encoded on a tape. Von Neumann’s implementation of Turing’s model was two-dimensional: the address matrix underlying all computers in use today. The landscape is now three-dimensional, yet the entire Internet can still be viewed as a common tape shared by a multitude of Turing’s Universal Machines. Turing proved that code is in one sense like life in that there is no systematic way to tell, by looking at a code, what that code will do. If so, then at the macro scale, it is impossible to predict where the digital universe is going.”

Berkeley, G. [1710] 2008. A Treatise Concerning the Principles of Human Knowledge. Forgotten Books.
Chen, T.-H., Hsu, J. J., Zhao, X., Guo, C., Wong, M., Huang, Y., Li, Z., Garfinkel, A., Ho, C.-M., Tintut, Y., Demer, L. L. 2012. Left-Right Symmetry Breaking in Tissue Morphogenesis via Cytoskeletal Mechanics. Circulation Research, doi: 10.1161/circresaha.111.25592.
Dyson, George 2012. Turing’s Cathedral: The Origins of the Digital Universe. NY: Pantheon/ Vintage USA; London: Allen Lane/Penguin UK
Gill, Z. 2011a. Collaborative Intelligence and effective complexity. ICCS 2011. International Conference on Complex Systems. Proceedings.
Gill, Z. 2011b. Algorithmic implications of evo-devo debates. GECCO 2011. Genetic and Evolutionary Computation Conference Proceedings. 12-16 July Dublin, Ireland.
Howe, J. 2006. The Rise of Crowdsourcing. Wired. June.
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Turing, A.M. 1936 – 7. On computable numbers with an application to the entscheidungsproblem. Proc. Lond. Math. Soc. Ser. 2.42:230 – 265.
_______. 1939. ‘Systems of logic defined by ordinals’, Proc. Lond. Math. Soc., Ser. 2, 45: 161-228; also in (Davis 1965) and in (Gandy and Yates 2001). This was Turing's Ph.D. thesis, Princeton University (1938).
_______. 1946. Proposed electronic calculator, report for National Physical Laboratory, Teddington; published in A. M. Turing's ACE report of 1946 and other papers, eds. B. E. Carpenter and R. W. Doran, Cambridge, Mass.: MIT Press (1986); also in Ince 1992.
_______. 1947. ‘Lecture to the London Mathematical Society on 20 February 1947’, in A. M. Turing's ACE report of 1946 and other papers, eds. B. E. Carpenter and R. W. Doran, Cambridge, Mass.: MIT Press (1986); also in Ince 1992.
_______. 1948. ‘Intelligent machinery’, report for National Physical Laboratory, in Machine Intelligence 7, eds. B. Meltzer and D. Michie (1969); also in Ince 1992.
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Miu Ling Lam - Turing1


Miu Ling Lam – Turing 4c

Miu Ling Lam – Turing 3 Miu Ling Lam – from her Turing Series

Chris Chafe – Tomato Quintet

Chris Chafe – Tomato Quintet (with Greg Niemeyer) and Oxygen
Chris Chafe & Greg Niemeyer – Tomato Quintet (above, left) & Oxygen Flute

Chris Chafe & Greg Niemeyer – Seven Airs
Chris Chafe & Greg NiemeyerSeven Airs

Laurie Frick – Daily Activities for April

Laurie Frick – Night

Laurie Frick – White_White_White
Laurie Frick – Daily Diary (April), Night, and White_White_White (below)

Peter Foucault – Robot Audience CollaborationPeter Foucault - Robot Audience Drawing
Peter Foucault – Detail from Robot Audience DrawingPeter Foucault – Attraction/ Repulsion (installation, robotic drawing, detail)

Shona Kitchen – Dreaming FIDS

Shona Kitchen – Dreaming FIDS

Shona Kitchen – Dreaming FIDSShona Kitchen & Ben Hooker – Dreaming FIDs (photos by Daniel Brown)

Seana McNamara - Ice Angel

Seana McNamara - The Birdwatchers

Seana McNamara - Azure Sky
Seana McNamara – Ice Angel (top), Birdwatchers (middle), Azure Sky (below)

Todd Siler – Mind

Todd Siler – RevolutionsTodd Siler – Mind Icon – Upside (above) and Mind Icon – Downside (below)

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