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".

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