August 18, 2014

Maps, Models, and Concepts, August edition

Walcome back, Maps, Models, and Concepts series! In this edition, with content cross-posted to Tumbld Thoughts, we take a tour of Artificial Intelligence reconsidered (I) and the visualization of Economic History (II). Enjoy!

I. Can you haz intelligent behavior, internet bot?

Here are a few recent readings on the modeling and simulation of intelligence, broadly defined. The first two [1, 2] are part of a series by Beau Cronin on alternative ways to model intelligence. How do we produce "better" (e.g. more intuitive, or more human) artificial intelligence? Perhaps it is the model that counts, or perhaps it is the definition of intelligence itself. 

COURTESY: Figure 3 in [3].

The authors of [3] take the former view, and present a review on how various computational architectures can produce intelligent outputs. One example demonstrates how hierarchical Bayesian models (HBMs) can be used to acquire intuitive theories for various knowledge domains. But one can also use biologically-based architectural models to produce intelligent behavior. In [4], it is shown that fabrication and cell culture techniques can produce outputs similar to purely computational connectionist models.

COURTESY: Figure 2 in [4].

II. Did it begin with a bang, a boom, or a bust?

Aha! The moment of economic creation was not at 1650 after all! Conventional economic theory sometimes gives the impression that economists are creationists in spirit. Many historical graphs [5] only offer useful information back to the year 1650. Around 1650 or so, most economic indicators enter their exponential phase, which renders graphical information about previous eras incomparable.

But economist and modeler Max Roser [6] offers a historical view of global GDP going back 2,000 years. His "Our World in Data" website is an attempt to characterize global economics and other social phenomena as a series of visualizations. This includes maps (spatial distributions) and charts that make long-term comparisons more than a series of bad graphs. If John Maynard Keynes were to look at these data, he might say: in the long run, we are all wealthier [7].

[1] Cronin, B.   In search of a model for modeling intelligence. O'Reilly Radar blog, July 24 (2014).

[2] Cronin, B.   AI's dueling definitions. O'Reilly Radar blog, July 17 (2014).

[3] Tenenbaum, J.B., Kemp, C., Griffiths, T.L., and Goodman, N.D.   How to Grow a Mind: Statistics, Structure, and Abstraction. Science, 331, 1279-1285 (2014).

[4] Tang-Schomera, M.D., White, J.D., Tien, L.W., Schmitt, L.I., Valentin, T.M., Graziano, D.J., Hopkins, A.M., Omenetto, F.G., Haydon, P.G., and Kaplan, D.L.   Bioengineered functional brain-like cortical tissue. PNAS, 10:1073/pnas.1324214111 (2014).

[5] The bottom three pictures are courtesy of: Roser, M.   GDP Growth Over the Very Long Run. Our World in Data (2014).

[6] Matthews, D.   The world economy since 1 AD, in a single chart. Vox blog, August 15 (2014).

[7] Based on the quote "in the long run, we are all dead".

August 14, 2014

Dynamic Digital Diversity (in two parts)

This content is cross-posted to Tumbld Thoughts. A series of readings (in two parts) on trends in digital technology and the nature of internet use. 

I. The Digital Monoculture and its Discontents, the Digital Hellscape and its Malcontents

The first reading list is on alternatives for consumption and use of internet [1] and virtual worlds [2]. Where one type of person sees corporatist monoculture as the norm, others see new opportunities. How do we achieve a more mindful computing environment? In the case of reading [1], mindful means greater balance between the deluge of information and the ability to reflect upon it. Discover the possibilities courtesy of an insightful techno-buzzword salad on topics such as ubiquitous information and disconnectionists.

The second reading [2] confuses the "post-apocalyptic" for "eschewing the corporate". Today's Second Life is like what would happen to Burning Man if all of the hipsters and Silicon Valley types stopped going. People are doing a lot of interesting things under the radar of the hype machine. An interesting article notwithstanding.

II. What do People of the Internet and the Sciences Want?

Here are some interesting readings and visualizations related to science and technology. The first [3] is a network analysis of comments received by the FCC in response to preserving net neutrality. Interestingly, this analysis allows us to assess the uniqueness of each major argument (and how one side of the argument tended to be suspiciously more homogeneous). The second visualization [4] is a survey of how scientists use social media to advance their research. This includes now only how these tools are used, but which tools are most popular. 

[1] McFedries, P.    Mindful computing. IEEE Spectrum, July 25 (2014).

One version of a "post-apocalyptic hellscape".

[3] Hu, E.   A Fascinating Look Inside Those 1.1 Million Open-Internet Comments. All Tech Considered blog, August 12 (2014).

[4] Van Noorden, R.   Online collaboration: Scientists and the social network. Nature News, August 13 (2014).

August 4, 2014

The Ukraine is Strong (for Synthetic Daisies)!

This post was written using Ubuntu 13.10 (Saucy Salamander), GIMP 2.6 and Blogilo. No Bitcoins (or their open-source alternatives) were transacted in its creation.

According to the game of Risk, the Ukraine is weak. But for Synthetic Daisies blog and as an example of viral content, the Ukraine is strong! In the past few days, a Synthetic Daisies (and Fireside Science) blog post called "Bitcoin Angst with an Annotated Blogroll" has gone viral in the Ukraine.

The associated pictures demonstrate how one can simply but effectively triangulate viral content from basic analytic data. This is also confirmed by the number of Pageviews made by users with alternative browsers and operating systems, which is either a Ukraine thing, a Bitcoin community thing, or both. In any case, keep up the diffusion!

July 30, 2014

Fireside Science: Incredible, Evo-Developmental, and Aestastical Readings!

Here is yet another set of features from my micro-blog Tumbld Thoughts, although this time they will be cross-posted to Fireside Science. Also at Fireside Science is a short feature on my Orthogonal Research initiative. Among these three features are publications, articles, and videos from my reading queue, serving up some Summertime (Summer is Aestas in Latin) inspiration. 

I. Incredible Technologies!

Real phenomena, incredible videos. Here is a reading list on resources on how film and animation are used to advance science and science fiction alike. Here they are in no particular order: 

Gibney, E.   Model Universe Recreates Evolution of the Cosmos. Nature News, May 7 (2014).
A Virtual Universe. Nature Video, May 7 (2014).

Creating Gollum. Nature Video, December 11 (2013).

Letteri, J.   Computer Animation: Digital heroes and computer-generated worlds. Nature, 504, 214-216 (2013).

Laser pulse shooting through a bottle and visualized at a trillion frames per second. Camera Culture Group YouTube Channel, December 11 (2011).

Hardesty, L.   Trillion Frame-per-Second Video., December 13 (2011).

Ramesh Raskar: imaging at a trillion frames per second. Femto-photography TED Talk, July 26 (2012).

Preston, E.   How Animals See the World., Issue 11, March 20 (2014).

How Animals See the World. BuzzFeed Video YouTube Channel, July 5 (2012).

In June, a Synthetic Daisies post from 2013 was re-published on the science and futurism site Machines Like Us. The post, entitled "Perceptual time and the evolution of informational investment", is a cross-disciplinary foray into comparative animal cognition, the evolution of the brain, and the evolution of technology. 

Evo-Developmental Findings (new)!

Phylogenetic representation of sex-determination mechanism. From Reading [3]

Here are some evolution-related links from my reading queue. Topics: morphological transformations [1], colinearity in gene expression [2], and sex determination [3].

The first two readings [1,2] place pattern formation in development in an evolutionary context, while the third [3] is a brand new paper on the phylogeny, genetic mechanisms, and dispelling of common myths involved with sex determination.

III. Aestastical Readings (on Open Science)!


Welcome to the long tail of science. This tour will consist of three readings: two on the sharing of "dark data", and one on measuring "inequality" of citation rates. In [4, 5], the authors introduce us to the concept of dark data. When a paper is published, the finished product typically includes only a small proportion of data generated to create the publication (Supplemental Figures notwithstanding). Thus, dark data is the data that are not used, ranging from superfluous analyses to unreported experiments and even negative results. With the advent of open science, however, all of these data are potentially available to both secondary analysis and presentation as something other than a formal journal paper. The authors of [5] contemplate the potential usefulness of sharing these data.

Dark data and data integration meet yet again. This time, however, the outcome might be maximally informative. From reading [5].

In the third paper [6], John Ioannidis and colleagues contemplate patterns in citation data that reveal a Pareto/Power Law structure. That is, about 1% of all authors in the Scopus database produce a large share of all published scientific papers. This might be related to the social hierarchies of scientific laboratories, as well as publishing consistency and career longetivity. But not to worry -- if you occupy the long-tail, there could be many reasons for this, not all of which are harmful to one's career.

[1] Arthur, W.   D'Arcy Thompson and the Theory of Transformations. Nature Reviews Genetics, 7, 401-406 (2006).

[2] Rodrigues, A.R. and Tabin, C.J.   Deserts and Waves in Gene Expression. Science, 340, 1181-1182 (2013).

[3] Bachtrog and the Tree of Sex Consortium   Sex Determination: Why So Many Ways of Doing It? PLoS Biology, 12(7), e1001899 (2014).

[4] Wallis, J.C., Rolando, E., and Borgman, C.L.   If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS One, 8(7), e67332 (2013).

[5] Heidorn, P.B.   Shedding Light on the Dark Data in the Long Tail of Science. Library Trends, 57(2), 280-299 (2008).

[6] Ioannidis, J.P.A., Boyack, K.W., and Klavans, R.   Estimates of the Continuously Publishing Core in the Scientific Workforce. PLoS One, 9(7), e101698 (2014).

July 25, 2014

One Evolutionary Trajectory, Many Processes

Two months ago, I wrote a post on how we might think more deeply about human biological variation. This post also involved a discussion about the recent Nicholas Wade book (A Troublesome Inheritance), which has engendered its own rancor on the internet [1]. In the second part of the post, I discussed some potential ways we can more effectively model human variation. This was decidedly exploratory, the intent of which was to follow-up on the discussion. In this post, I will discuss the need for taking cultural evolution and other factors into account when interpreting genetic variation.

To understand where I am coming from here, consider the difference between population genetics and behavioral ecology approaches. In population genetics, the concern is over observing the patterns of standing variation in a population. We discuss topics such as allele frequencies, admixture, and the mechanisms of genetic differentiation. But populations also behave, and in behavioral ecology topics such as sexual selection, foraging patterns, and strategic behaviors are also taken into account.

While there is indeed implicit overlap between these two fields in the literature, there is little direct theoretical synthesis in this direction. For example, if one takes species concepts into account [2], we can see the issue rear its head: one can apply a host of species concepts which explain both the behavioral and genealogical dynamics of a population, but a unified conceptual framework (e.g. one that is not contradictory) is elusive.

Yet even when only taking behavioral dynamics into account, there are a multitude of factors that make direct comparisons between populations difficult. Species differ in both their sociality and acquisition of culture. This differentiation is even more profound in terms of how culture has shaped a species' ability to adaptively radiate and persist over multiple generations. Humans are not only an intensely eusocial species, but also fall into the latter category of being shaped by culture as much as by environmental selection.

One might simply refer to this as "cultural selection", but a better approach is to model the process of genealogical and cultural (or social) evolution as nominally separate but interrelated processes. In "Playing the Long Game of Human Biological Variation", I advocated for the use of dual process models. Such models treat the same population as being subject to two or more distinct processes simultaneously. In a Synthetic Daisies post re-published at Humanity+ [3], I introduced a dual process Artificial Life-based model that integrates genealogical dynamics and biogeographic processes (specifically changes in geomorphology).

There are a good number of examples of dual process models in the literature which integrate cultural and biological evolution. A good starting point is the work of Richerson, Boyd, McElreath, and Henrich [4, 5], who use a dual inheritance model (DIT) with similar genetical and cultural inheritance mechanisms. While this does not distinguish between the mode transmission for genetical units (genealogy) and cultural units (social learning), it does allow for their dynamics to differ within a population.

This provides us with a conceptual expression of "nature" not being equivalent to "nurture", even though we end up in a place similar to the species concept example. But this does not necessarily solve a key issue; namely, that culture and genetics do not simply have the potential to follow divergent trajectories. Culture might also provide a coherent and context-dependent evolutionary constraint [6] which can influence "fast" human evolution [7]. Specifically, culture might influence genetic evolution indirectly through evolutionary constraints (EC) on admixture, migration, local environmental genetic polymorphisms, and demographic fluctuations [8].

One example of a dual process model (in this case, an example from niche construction). COURTESY: Niche Construction page, Semiotics Encyclopedia Online.

Notice that this is quite a bit different than claims of genetics influencing cultural evolution, or culture acting as a multiplier of genetic differences. In fact, the effect is not a feedback or other type of causal mechanism at all, but rather an incongruence [9]. Evolutionary incongruence (EI) occurs when the evolutionary trajectory of the genome and the cultural environment do not lead in the same direction [8].

For example, even though you might possess a genotype that makes you very unfit for a certain environment, possessing a cultural adaptation on top of this genotype might make you fit enough (or even very fit). EI and EC can also determine more general outcomes in a dual process model. In the case of humans, where culture enables humans to survive in environments beyond what is enabled by genes alone, EI is much more dominant than EC. You can still find genetic variants that result from adaptation to a specific local environment, but they are not the determining factor in survival. In a very different context, for example in the case of a solitary species, EC might dominate over EI.

In summary, accounting for variation within and between human groups might best be done using a sophisticated theoretical framework. This framework includes 1) the use of a dual process model that represents cultural and genetic evolutionary processes, and 2) the identification of how culture contributes to genetic variation, namely either through constraint (which enables feedback between genes and culture) or incongruence (where the variation contributed by genetic and cultural evolutionary processes point in different directions). In the case of eusocial species that possess culture (example: Homo sapiens), incoherence will be predominant, although constraint can drive forward local genetic adaptation when needed.

 Examples of eusocial (left) and solitary (right) species.

In a future post, I will explore another theme in the original "Long Game" blog post, namely the idea that panmixia might not be the best way to assess the absence of population subdivision. Instead of using a traditional population genetics model, using scale-free networks to represent the null hypothesis might give us a more profound theory and more realistic results. Look forward to it.

[1] I'm not particularly interested in ideological debates. But be aware that this post will be largely theoretical and perhaps a bit too speculative. That's the way theoretical advances are made!

[2] Wheeler, Q. and Meier, R.   Species Concepts and Phylogenetic Theory: a debate. Columbia University Press (2000).

[3] Alicea, B.   Artificial Life meets Geodynamics (EvoGeo). Humanity+ Magazine, December 7 (2012).

[4] Richerson, P.J. and Boyd, R.   Not By Genes Alone: How Culture Transformed Human Evolution. University of Chicago Press (2005).

[5] McElreath, R. and Henrich, J.   Dual inheritance theory: the evolution of human cultural capacities and cultural evolution. In "Oxford Handbook of Evolutionary Psychology", R. Dunbar and L. Barrett eds., Oxford University Press (2007).

[6] Boyd, R. and Richerson, P.   The cultural transmission of acquired variation: effects on genetic fitness. Journal of Theoretical Biology, 100, 567-596 (1983).

[7]  Hawks, J., Wang, E.T., Cochran, G.M., Harpending, H.C., and Moyzis, R.K.   Recent acceleration of human adaptive evolution. PNAS, 104(52), 20753–20758 (2007).

[8] NOTE: The terms and abbreviations for evolutionary constraint (EC) and evolutionary incongruence (EI) are of my own coinage.

[9] Laland K. and Brown, G.   Sense and Nonsense: Evolutionary Perspectives on Human Behavior. Oxford: Oxford University Press (2002).