June 15, 2016

Your Strandbeests Want to Engage in Sodaplay

The strandbeest Animaris Ordis in an non-native environment (a visit to MIT).

Several years ago [1], I discovered the wonder that is Theo Jansen's Strandbeests (beach beasts in Dutch). Strandbeests are mechatronic creatures partially designed using evolutionary algorithms and built to roam the sands (or are at least demonstrated at the beach). Standbeests mimic the movement patterns of biological animals, despite having only approximations of limbs and joints. Some of these creatures even have a "stomach" without conventional animal muscles [2].

The wing- and bottle-propelled "stomach" of Animaris percipiere. COURTESY: Strandbeest.com.

A Strandbeest forelimb with each segment in its optimized proportions [3]. Jansen calls these "magic numbers", but in biological terms they more closely resemble allometric scaling.

While there is great artistic (kinetic sculpture) and scientific (biomechanical) value to the Strandbeest, it can also teach us a great deal about the ability of point masses to approximate biological movement. For the Strandbeests are reminiscent of another model of movement, this one being entirely digital. This model, Sodaplay, is actually a classic internet-based application first developed around the year 2000. Sodaconstructor allowed people to build animated creatures based on point physics and an approximation of muscle activity (via central pattern generation).

Simulated strandbeest on the move. COURTESY: YouTube user petabyte99.

In the sodaplay model, a mass-spring system is used to provide structure to the phenotype. Springs (connectors) are used to approximate muscles and connect point masses, which provide inertial responses to gravity and motion [4]. These connectors can be modulated as desired, going beyond the default sinusoidal response. In general, a networked mass-spring model can be used to examine the geometric effects of a phenotypic configuration. Depending on how the points are arranged, certain ranges of motion are possible. In the case of sodaplay, certain configurations can also lead to certain death (or collapse of the model due to gravitational conditions in the virtual environment).

An example of the Sodaconstructor (seemingly now defunct). Sodaplay models (for example, Daintywalker) are reliant upon the human expertise and perceptual selection [5] rather than natural selection. Nevertheless, this form of constructivist selection has results in nearly boundless innovation, and Sodarace allows humans to innovate against a genetic algorithm.

An approximation of quadrupedal gait in Strandbeests by tracing joint and end-effector movement. COURTESY: [6].

UPDATE (6/15):
A regular reader of this blog (Dr. Richard Gordon) provided an insight that the blog's commenting system was not able to post: "It seems to me that Strandbeasts and tensegrity structures are special cases of a broader class of objects, which may be instantiated by cytoskeleton and its motor and attachment proteins".

Indeed, there are some interesting linkages between biomechanical systems and tensegrity structures that have yet to be explored. In the case of Strandbeests, Theo Jansen has actually hit upon very different (but equally functional) biomechanical systems for "limb movement" and "stomach movement". While Strandbeests do not have biological muscle (and its associated biochemistry), nor the ability to produce isometric force, they can still produce powered movements.

As is the case with homoplastic traits (e.g. bird, bat, and insect wings), both purely mechanical and biomechanical system uses identical physical principles (e.g. levers and pulleys) to produce biologically realistic movements.

[1] Alicea, B.   Theo Jansen, Lord of the Strandbeests. Synthetic Daisies, May 28 (2012).

[2] Revisiting this post as well: Alicea, B.   On Rats (cardiomyocytes) and Jellyfish (bodies). Synthetic Daisies blog, August 22 (2012).

[3] Thor, P.   Project 3(Strandbeest). Wikiversity, December 10 (2012).

[4] McOwan, P.W. and Burton, E.J.   Sodarace: Continuing Adventures in Artificial Life. In "Artificial Life Models in Software". M. Komosinski and A. Adamatzky, eds. Chapter 3, 61-77. Springer (2009).

[5] Ostler, E.   Sodaplay Sodaconstructor. Mathematics and Computer Education, Spring (2002).

[6] Walking Strandbeests Dynamics. Online Technical Discussion Groups, Wolfram Community.

June 8, 2016

200K, 1K (or less) At A Time

While I have not been keeping up with my blogging habit over the last 18 months or so, Synthetic Daisies is still reaching milestones. As of today, we have reached 200,000 reads! While this took 7 years and 6 months (as of June 15), it is quite a milestone. A few historical points.

Frequency of posts over time

The bulk of posts (particularly the longer posts) were written during a period from late 2011 to early 2015. Some of them have had longer lifetimes than others, as you will see below.

Logos over time
2008-2012. Classic version

2012-2015. New design, pretentious slogan.

2016-present. Cleaned up new design.

Here is a reading list with some of the lesser-read but perhaps most interesting posts in the blog's history.

Network Science:
Six Degrees of the Alpha Male: breeding networks to understand population structure. August 22, 2014.

Fireside Science: Inspired by a visit to the Network's Frontier.... December 16, 2013.

Scientific Paradigm Network. February 8, 2015.

Academic Connectivity and the Future of Scientific Ideas. September 9, 2011.

Fireside Science: The Representation of Representations. June 21, 2014.

Book Reviews:
Review of "Arrival of the Fittest". March 9, 2015.

Metabiology and the Evolutionary Proof. January 11, 2013.

Review of "Intelligent Movement Machine". April 19, 2009.

Cognition, Biology, Technology, and Innovation:
Merging electronics and biology: the future of touch. November 1, 2012.

The "nature" of materials: evolution and biomimetics. December 26, 2011.

I, Automaton. September 16, 2013.

Evolution, Alife, and Complexity:
Artificial Life meets Geodynamics (EvoGeo). November 21, 2012.

Reflections on Chaos in Biological Evolution. May 25, 2013.

The Neuromechanics and Evolution of Very Slow Movements. April 18, 2012.

Systems Biology:
Modeling Processes with No Beginning, an Adaptive Middle, and No End. October 27, 2013.

Facilitated Variation (FV): a random (walk) tour. October 29, 2011.

"Reining" in Diabetes. January 10, 2011.

Game Theory and Complexity:
Games, Noise, and Science-related Obscure References. April 8, 2013.

Makin' Pha-ses. March 11, 2013.

Although the blog got off to a slow start, I learned a lot about "how to blog" (use interactive media to greater effect) over the course of time. Nevertheless, hooray for 200K!

May 26, 2016

Rectified and Ramifying Representations for the Purpose of Theoretical Expediency

One aim of the DevoWorm project is to take a tree structure (in this case a cell lineage tree from an embryo) and extract distributed structural information. This is done to find previously undiscovered patterns in early development (embryogenesis). One way in which this can be accomplished is by building undirected complex networks to represent the relationships between three-dimensional cellular position in a point model of the embryo. Indeed, rather than a branching tree, we are left with a much larger tree with a significant number of cycles. This allows us to examine previously undiscovered interactions between cells based on proximity (such as juxtacrine and paracrine signalling).

A tree with a cycle, indeed. Popular meme or research problem?

Now these ideas have been made concrete in the form of a poster and presentation that describe the methodology and results of representing approximations of cell nuclei in the embryo as a connected network. This work has been featured at the Network Frontiers Workshop (Northwestern University) and the Midwest Regenerative Medicine Meeting (Washington University, St. Louis). Here is the poster in slide form:

Notice how this approach is both geometrically vivid and extensible to different modes of development. The graphs and statistics were rendered in Gephi, and other computation was done in MATLAB and R. Our next steps include developing customized modules in Gephi for drawing differentiation trees, developing hybrid directed acyclic graph (DAG)/undirected network graph structures, and refining the network construction methodology.

We are also working on a methodology called the scalable interactome, which simply involves using graphs to visualize and extract information at multiple spatial and temporal scales. One current example of this is OneZoom explorer, which renders the tree of life in a fractal manner. This can be extended to exploring the fractal and complex geometric nature of the embryo itself.

A slightly different view of human evolution and rejection of human exceptionalism. COURTESY: OneZoom Tree of Life.

"Miscellaneous Polyhedra" by Carol Branch (no pun intended).

With that nod to complexity, I would be remiss if I did not mention the old SimCity dictum? A gratuitous image of fractals and reference to a Wil Wright easter egg is the perfect way to end this post. 

May 6, 2016

It's a bird! It's a plane! It's a simulated cognitive epiphenomenon!

Is it a robot, or is it a human? Sometimes, the conclusions overlap. The first picture refers to the profanity habit picked up by IBM’s Watson early in the course of its training. It must be disconcerting to hear the word “bullshit” in a synthetic voice (even if it’s Q*bert). A more recent example of this comes from Microsoft's ill-fated attempt at politically-correct AI, giving us more of a robo-fascist instead [1].

Speaking of artificial systems that reveals the less-promoted side of intelligent behavior, I have run across a number of references to the computational exploration of artificial pareidolia [2]. Having first-hand experience with this phenomenon on Facebook, it's nice to see people exploring this oft-maligned feature of human cognition using artificial intelligence.

In these two articles [3, 4], studying digital pareidolia means generating certain types of false-positive using facial recognition software. Whether this highly-restricted definition [5] qualifies as the study of neurological pareidolia, there are many shapes and patterns that have many of the features found in faces [6].

Another way to study digital pareidolia is to evolve faces from a series of overlapping shapes [7]. In this way, we can see exactly how machine learning algorithms come to define a face in both a holistic and feature-based sense. The faces themselves can be evolved using genetic algorithms to breed faces that self-assemble, as has been implemented in an interactive algorithm called Pareidoloop.

Converging to Mona Lisa (using a fitness function).

[1] the goal was not actually to build a politically-correct AI, only a Twitterbot that did not pick up the worst habits of humanity. The project has since been terminated.

[2] Geere, D. Pareidolic robot looks for faces in clouds. Wired UK, October 14 (2012).

[3] Rosen, R.J.   Pareidolia: a bizarre bug of the human mind emerges in computers. The Atlantic, August 7 (2012).

[4] Borenstein, G. Machine Pareidolia: hello little fella meets facetracker. Ideas for Dozens blog, January 14 (2012).

[5] One problem with this definition involves the restriction of the pareidolia phenomenon to faces. The other (and potentially more significant) problem is that biologically speaking, faces (and perhaps other objects) may be processed holistically rather than by evaluating sets of landmarks.

[6] For more reading on the topic, please see:

Taubert, J., Apthorp, D., Aagten-Murphy, D., and Alais, D. The role of holistic processing in face perception: evidence from the face inversion effect. Vision Research, 51(11), 1273-1278 (2011).

Goffaux, V. The discriminability of local cues determines the strength of holistic face processing. Vision Research, 64, 17-22 (2012). Goffaux lab explainer.

Richler, J.J. and Gauthier, I. A meta-analysis and review of holistic face processing. Psychological Bulletin, 140(5), 1281-1302 (2014).

[7] Johansson, R. Evolution of Mona Lisa. December 7 (2008).

April 30, 2016

Claude Shannon, posthumously 1100100

How do you model a centennial birthday, Dr. Shannon? COURTESY: Hackaday blog.

Claude Shannon, the so-called father of information theory, was born 100 years ago today [1]. This is a Google Doodle-worthy event, even though he died in 2001. Hence, internet rule #34' [2]: "if there exists a milestone, there's a Google Doodle for it".

April 30, 2016 Google Doodle.

Claude was also a juggler and an inventor of mechanical toys, hence the zeros and ones being juggled in the Doodle. A few years ago I wrote a post detailing this "mechanical zoo". Not a real zoo, mind you, but a collection of mechanical wonders far removed from his information theory work [3].

Spectrum, April 27.

[2] I made up Rule #34' as a less-provocative variant of existing Rule #34.

[3] his Master's thesis and Bell Systems Technical Journal paper (pdf) were milestones in the then- emerging academic field.