This article is relevant to our recent discussions and Zak Stein’s (see Edward’s recent post) suggestion that great destabilizing events open gaps in which new structures can supplant older, disintegrating systems–with the inherent risks and opportunities.
In his new book, Range: Why Generalists Triumph in a Specialized World, David J. Epstein investigates the significant advantages of generalized cognitive skills for success in a complex world. We’ve heard and read many praises for narrow expertise in both humans and AIs (Watson, Alpha Go, etc.). In both humans and AIs, however, narrow+deep expertise does not translate to adaptiveness when reality presents novel challenges, as it does constantly.
As you ingest this highly readable, non-technical book, please add your observations to the comments below.
Caltech researchers have identified the brain mechanisms that enable primates to quickly identify specific faces. In a feat of efficiency, surprisingly few feature-recognition neurons are involved in a process that may be able to distinguish among billions of faces. Each neuron in the facial-recognition system specializes in noticing one feature, such as the width of the part in the observed person’s hair. If the person is bald or has no part, the part-width-recognizing neuron remains silent. A small number of such specialized-recognizer neurons feed their inputs to other layers (patches) that integrate a higher-level pattern (e.g., hair pattern), and these integrate at yet higher levels until there is a total face pattern. This process occurs nearly instantaneously and works regardless of the view angle (as long as some facial features are visible). Also, by cataloging which neurons perform which functions and then mapping these to a relatively small set of composite faces, researchers were able to tell which face a macaque (monkey) was looking at.
These findings seem to correlate closely with Ray Kurzweil’s (Google’s Chief Technology Officer) pattern-recognition theory of mind.
BMCAI library file (site members only)
Google and others are developing neural networks that learn to recognize and imitate patterns present in works of art, including music. The path to autonomous creativity is unclear. Current systems can imitate existing artworks, but cannot generate truly original works. Human prompting and configuration are required.
Google’s Magenta project’s neural network learned from 4,500 pieces of music before creating the following simple tune (drum track overlaid by a human):
Click Play button to listen->
Is it conceivable that AI may one day be able to synthesize new made-to-order creations by blending features from a catalog of existing works and styles? Imagine being able to specify, “Write me a new musical composition reminiscent of Rhapsody in Blue, but in the style of Lynyrd Skynyrd.
There is already at least one human who could instantly play Rhapsody in Blue in Skynyrd style, but even he does not (to my knowledge) create entirely original pieces.
Original article: https://www.technologyreview.com/s/601642/ok-computer-write-me-a-song/