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- Michael Ullman's team at Georgetown University has found strong evidence suggesting that language is learned in brain systems that serve other purposes besides language acquisition (paper). Traditionally, it was thought that humans learned language from brain circuits designed specifically for language learning and that these circuits were unique to humans. New evidence suggests that language is learned in general-purpose brain systems that account for other tasks, such as memorizing a list or driving a car. Language learning was studied in the brain systems related to declarative and procedural memory. Declarative memory was responsible for language memorization, while procedural memory was responsible for grammar abilities. These brain systems can also be found in other animals, suggesting that the existence of these systems predates the existence of humans. It is still unknown how these systems evolved to support a system as complex as human language, but these findings hold promising research, educational, and clinical implications. (Contributed by Cristina Eckhardt)
- Ed Tyantov's summary of deep learning achievements of 2017
- Ryan Shrott's summary of deep learning
- Katherine Rowland ( this article in Aeon ) discusses how women carry three different cell populations in their bodies- their own, their mother's, and their child's. These foreign cells are called `microchimeric cells' and have shown to improve the outcome of future pregnancies for women, extend longevity, and improve disease status. Traditionally, Western societies have viewed the individual as a solitary organism that functions solely to ensure their own survival. Katherine points out that even on a genetic level, humans are interconnected with one another. It is possible that the evolutionary motives guiding humans are rooted in a collective good rather than focused on individual survival.
(Contributed by Cristina Eckhardt)
- Sharon Glotzer, a computational physicist at the University of Michigan, has found that entropy can be a guiding force in the emergence and organization of matter. The traditional view equated entropy with increasing disorder. Glotzer's research has found that entropy plays a primary role in the self-assembly of particles into complex structures. Glotzer isolated tetrahedra, a simple three-dimensional building block, to all forces other than entropy. Upon isolation, the tetrahedra began to self-organize into quasicrystal, a highly organized and complex spatial pattern. This self-assembly was guided by a set of design rules, which are common themes guiding how entropy manifests itself in the organization of matter. Using what she describes as "digital alchemy", Glotzer and her team have been able to reverse-engineer structures into their preliminary components through simulations using the design rules and self-organizing pattern of entropy.
(Contributed by Cristina Eckhardt)
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