ALIFE 2020

ALIFE 2020 THEME: New Frontiers in AI: What can ALife offer AI?

The fields of AI and ALife share a common history and a common approach: true understanding of living systems can only be demonstrated by successfully re-creating them using biology and/or technology. But mind and life are two of the most mysterious systems known to us, so re-creating them is exceedingly challenging, exciting and, at times, surprising. Recently, AI has "thrown down the gauntlet" by making large strides forward in understanding some aspects of intelligence, and intelligence is one of many phenomena expressed by living systems; this suggests that more big discoveries are yet to come. So, we invite you to join us, July 2020, to continue the search for answers to life, the universe, and everything.
Dates - July 13-18, 2020
Location - Virtual
Organizers - Vermont Complex Systems Center, University of Vermont
Hashtag - #ALIFE2020

Melanie Mitchell

Professor, Santa Fe Institute

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Melanie Mitchell is Professor at the Santa Fe Institute. She attended Brown University, where she majored in mathematics and did research in astronomy, and the University of Michigan, where she received a Ph.D. in computer science. Her dissertation, in collaboration with her advisor Douglas Hofstadter, was the development of Copycat, a computer program that makes analogies.

Mitchell has held faculty or professional positions at the University of Michigan, the Santa Fe Institute, Los Alamos National Laboratory, the OGI School of Science and Engineering, and Portland State University. She is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems, including Complexity: A Guided Tour (Oxford, 2009), which won the 2010 Phi Beta Kappa Science Book Award. Her newest book, Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux) will be published in October 2019.

Mitchell originated the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.

Research and Interests
Artificial intelligence, machine learning, computer vision, cognitive science, complex systems.

Sara Walker

Professor, School Of Earth and Space Exploration, Arizona State University

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Sara Walker is an astrobiologist and theoretical physicist interested in the origin of life and how to find life on other worlds. While there are many things to be solved, she is most interested in whether or not there are ‘laws of life’ - related to how information structures the physical world - that could universally describe life here on Earth and on other planets.

At Arizona State University she is Deputy Director of the Beyond Center for Fundamental Concepts in Science, Associate Director of the ASU-Santa Fe Institute Center for Biosocial Complex Systems and Associate Professor in the School of Earth and Space Exploration. She is also Co-founder of the astrobiology-themed social website SAGANet.org, and is a member of the Board of Directors of Blue Marble Space. She is active in public engagement in science, with appearances at the World Science Festival, and on "Through the Wormhole" and NPR's Science Friday

Rebecca Kramer-Bottiglio

John J. Lee Associate Professor of Mechanical Engineering & Materials Science, School of Engineering & Applied Science, Yale University

Research Interests:

• Soft robotics
• Stretchable electronics
• Responsive material actuators
• Soft material manufacturing
• Soft-bodied control

Michael Levin

Professor, Biology, Vannevar Bush Professorship, Biology, Distinguished Professor, The School of Arts and Sciences, and Professor, Biomedical Engineering

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Research Interests:

The capacity to generate a complex organism from the single cell of a fertilized egg is one of the most amazing qualities of multicellular animals. The processes involved in laying out a basic body plan and defining the structures that will ultimately be formed depend upon a constant flow of information between cells and tissues. The Levin laboratory studies the molecular mechanisms cells use to communicate with one another in the 4-dimensional dynamical system known as the developing embryo. Through experimental approaches and mathematical modeling, we examine the processes governing large-scale pattern formation and biological information storage during animal embryogenesis. Our investigations are directed toward understanding the mechanisms of signaling between cells and tissues that allows a biological system to reliably generate and maintain a complex morphology. We study these processes in the context of embryonic development and regeneration, with a particular focus on the biophysics of cell behavior. In contrast to other groups focusing on gene expression networks and biochemical signaling factors, we are pursuing, at a molecular level, the roles of endogenous voltages, pH gradients, and ion fluxes as epigenetic carriers of morphological information. Using gain- and loss-of-function techniques to specifically modulate cells' ion flow we have the ability to regulate large-scale morphogenetic events relevant to limb formation, eye induction, etc. We believe this information will result in important clinical advances through harnessing the biophysical controls of cell behavior.

Luis Zaman

Assistant Professor, Ecology & Evolutionary Biology and Complex Systems, University of Michigan

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I became interested in evolution because of an undergraduate computer science class. It's still amazing to me that we can bottle up evolution in an algorithm, and yet are still just scratching the surface of understanding the biodiversity and complexity it has produced.

One of the challenges is that evolution creates diversity and complexity, which then strongly influences further evolution. Untangling this feedback loop between what evolution produces and what then becomes selectively favorable motivates much of my work. Host-parasite coevolution is a prime instance of this complex feedback loop at what I consider the core of evolutionary biology.

Coming to evolutionary biology via computer science has left its marks on my academic interests. I study host-parasite coevolution using a mixture of computational and microbial experiments. I treat computer systems as another experimental system, much like E. coli and Elephants are two living systems that can be studied in surprisingly similar ways

Leroy (Lee) Cronin

Professor and Regius Chair of Chemistry, Digital Chemistry, University of Glasgow

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Lee Cronin was born in the UK and was fascinated with science and technology from an early age getting his first computer and chemistry set when he was 8 years old. This is when he first started thinking about programming chemistry and looking for inorganic aliens. He went to the University of York where he completed both a degree and PhD in Chemistry and then on to do post docs in Edinburgh and Germany before becoming a lecturer at the Universities of Birmingham, and then Glasgow where he has been since 2002 working up the ranks to become the Regius Professor of Chemistry in 2013 aged 39. He has one of the largest multidisciplinary chemistry-based research teams in the world, having raised over $35 M in grants and current income of $15 M. He has given over 300 international talks and has authored over 350 peer reviewed papers with recent work published in Nature, Science, and PNAS. He and his team are trying to make artificial life forms, find alien life, explore the digitization of chemistry, understand how information can be encoded into chemicals and construct chemical computers.

Joshua Bongard

Academic Conference Chair and Professor, Department of Computer Science, University of Vermont

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My research interests can be broken down into two broad questions:

"How can we automatically design a robot with little human intervention?"
Imagine a robot-making machine is sent to Mars, and settles on Meridiani Planum. The machine detects that the ground is littered with boulders between 10 centimeters and 1 meter high. Should the machine build a robot with wheels or legs? If the robot should be able to not only observe its surrounding, but also manipulate objects (like drilling into rocks), how many manipulators should it have? What should the manipulators look like? Determining what the most appropriate kind of robot is for a particular task is tricky.

My PhD research was concerned with using evolutionary computation -- a computational search process -- to repeatedly test out different robot designs in a virtual environment. The computer observes the relative performance of these virtual robots, and throws away the bad designs, and makes slight changes (mutations) to the better designs. The computer repeats this process thousands of times, and if it's lucky, produces a robot design that is well suited to its particular task. More specifically, my research focussed on improving this computerized evolutionary process in order to produce better robots: I implemented a system, called Artificial Ontogeny, that 'grows' a virtual egg into a fully formed virtual adult robot. The adult robot is then evaluated against the task.

This approach then combines biological growth with biological evolution; an individual robot can learn and adapt to its virtual surroundings over its lifetime, while the robot population evolves over generations similar to how organisms grow and adapt to their surroundings, while species adapt over evolutionary time. (New Scientist article about my work.)

"How can we automatically create a model of a physical system?"
Consider the following exchange:
Q1: "Is it classified as a vegetable, animal, other or unknown?" A1: "Other"
Q2: "Does it usually have four corners?" A2: "No"
Q3: "Does it have cash value?" A3: "No"
Q4: "Does it move?" A4: "Yes"
Q5: "Does it grow over time?" A5: "Sometimes"
Q6: "Is it warm blooded?" A6: "No"
Q7: "Can you find it in a house?" A7: "No"
Q8: "Is it located in space?" A8: "No"
Q9: "Can you play with it?" A9: "No"
Q10: "Is it useful?" A10: "Sometimes"
Q11: "Is it tall?" A11: "Sometimes"
Q12: "Is it white?" A12: "Yes"
Q13: "Can it bend without breaking?" A13: "Unknown"
Q14: "Is it shiny?" A14: "No"
Q15: "Is it annoying?" A15: "No"
Q16: "Is it a specific color?" A16: "Yes"
Q17: "Do you love it?" A17: "No"
Q18: "Are you thinking of a cloud?" A18: "Yes"
(Courtesy of 20Q.net)
How did the questioner figure out that the answerer was thinking of a cloud? The answerer in this case was a human; the questioner was a computer program. The ability of the program to guess what the person was thinking of lies in the ability to ask the right questions.

My second line of research is developing software that can 'ask the right questions' of physical systems like walking robots, biological organisms and manmade structures. In other words, we are developing software that generates useful experiments that, when carried out on the target system, reveal hidden, internal information about that system. The algorithm we are developing to perform this intelligent questioning is called the Estimation-Exploration Algorithm, or EEA.

Consider an example. In one application, we are trying to unravel gene regulation pathways in bacteria. We know how many genes a bacteria like Eschericia coli has, but we don't know how the genes influence each others behavior. We can, however, gain indirect information about how genes influence each other by presenting the bacteria with particular stimuli, like presenting them with a large amount of nutrients, and then recording how the protein levels in the bacteria changes over time as they digest the nutrients. These protein changes can then help us to construct a model of which genes influence which other genes.

So by performing intelligent tests on a physical system, we can accomplish two things: we can build a model of the physical system (automated inference), and, by using that model, get the physical system to perform desired behavior (automated synthesis), such as getting a robot to carry out some desired task. We are currently applying this algorithm to a number of problem domains, and extending the algorithm so that can infer the structure of, or synthesize behaviors for, increasingly complex systems.

Juniper L. Lovato

General Conference Chair, Assistant Professor, Department of Computer Science, and VCSI Leadership Steering Committee

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At the Vermont Complex Systems Institute, Juniper works across generations and geographical limits to make resources and knowledge on cutting-edge complexity science more accessible to those with a hunger and curiosity for learning and exploration. Juniper came to Burlington in 2018. She previously served as the Director of Education for the Santa Fe Institute, an independent complexity science research center. She is also a co-founder of MAKE Santa Fe, a not-for-profit community makerspace in Santa Fe, New Mexico. Juniper received her Master’s in the Western Classics from St. John’s College in 2013 where she completed a thesis on the nature of pleasure in work in Aristotle’s Nicomachean Ethics.

Laurent Hébert-Dufresne

Co-Organizer, Associate Professor, Department of Computer Science, and VCSI Leadership Steering Committee

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Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.

Radhakrishna Dasari

Technical Education Lead, Web3 Foundation

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Radha comes to CEMS from the State University of New York at Buffalo. He earned a master's degree in computer science in 2013, worked as a research intern at Huawei Media Lab in 2014 and served as a graduate instructor for three summer semesters 2016-2018. His areas of expertise include Computer Vision and Image Processing, Machine Learning and Multimedia Systems. Radha has three years of work experience in software engineering, prior to his graduate study.

Chloe Barnes

Volunteer and Minecraft Chair and Lecturer, Applied AI & Robotics, School of Computer Science and Digital Technologies, Aston University

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Lisa Soros

Roman Family Teaching and Research Fellow, Barnard College

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Lisa Soros is a Roman Family Teaching and Research Fellow at Barnard College. She was formerly a Postdoctoral Associate in the Game Innovation Lab at New York University, a PhD student in the Evolutionary Complexity Research group at the University of Central Florida, and an Assistant Professor at Champlain College. Her research focuses on understanding and engineering open-ended generative systems. She is also the Secretary for the International Society for Artificial Life.