This edition of Popular Algorithmics is an introduction to cactus graphs [1]. Wolfram Mathworld defines cactus graphs as a connected graph where any two graph cycles have no edges in common. This is similar to the leaves of a botanical cactus. Cactus graph topologies are an instance of a block graph, in which every connected component constitutes a clique or a cycle. The cactus representation can be used for a variety of applications, from modeling wireless networks to hierarchical tree building.

[1] Lima, M.   The Book of Trees: Visualizing Branches of Knowledge. Princeton Press (2014). 

Here is a video [1] by the complexity theorist Dirk Helbing about the possibility of a self-regulating society. Essentially, by combining big data with the principles of complexity would allow us to solve previously intractable problems. This includes more effective management of everything from massively parallel collective behaviors to very-rare events.

But controlling how big data is used can keep us from getting into trouble as well. Writing at Gigaom blog, Derrick Harris argues that the potentially catastrophic effects of AI taking over society (the downside of the singularity) can be avoided by keeping key data away from such systems. In this case, even hyper-complex AI systems based on deep learning can become positively self-regulating.

[1] The World after Big Data: Building the Self-Regulating Society. YouTube, August 14 (2014).

[2] Harris, D.   When data become dangerous: why Elon Musk is right and wrong about AI. Gigaom blog, August 4 (2014).

As a follow-up on last Saturday’s post (easter eggs in John Landis films), today’s post is a pointer to the fake movies of Seinfeld. A few years ago, Joey Paur of Geek Tyrant blog decided to engage in this thankless task (since the show was theoretically about nothing). Here is his list, which includes “Rochelle, Rochelle”, “Sack Lunch”, and the phase “flaming globes of Sigmond”. Part parody, part creative excursion, this was a major part of the Seinfeld metaverse.

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. 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.

[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).

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 [1] 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 [2] 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 [3].

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

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

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

Here are some interesting readings and visualizations related to science and technology. The first [1] 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 [2] 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] Hu, E.   A Fascinating Look Inside Those 1.1 Million Open-Internet Comments. All Tech Considered blog, August 12 (2014).

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

To Tumblr, Love Pixel Union