Readings

How to Survive in a Science Fictional Universe (Charles Yu) – Not the book you think it is. This was a fantastic read, a meditation on loss and time and memory and connections. Sure there is a time travel, but it’s more about what that means psychologically than technologically. Highly recommended.

Carter Beats The Devil (Glen David Gold) – A fictionalized biography of stage magician Charles Joseph Carter, this is a real page-turner, especially if you like the attention to historical detail. I quite enjoyed it, despite the rather abrupt ending.

Daemonomania (John Crowley) – Book Three of the Aegypt cycle. This one was slow going and hard to get into, but it really picks up in the middle. The costume party towards the end of the book is all worth it, as is the mission to rescue Rosie’s daughter Sam from the cultish Powerhouse. Crowley may be accused of injecting the mystic into the mundane in an artificial way, but I think what this book did was show how desperate circumstances amplify and distort our perceptions to make events seem mysterious or magical.

Tango (Slawomir Mrozek) – a somewhat absurdist allegory about reactionary tendencies. The setting is a rather bohemian family living in a house in disarray, with the father Stromil putting on ridiculous art shows (“experiments”) and ignoring the fact that his wife is cheating on him with a roustabout, Eddie. All of this is very frustrating to Arthur, his son, who longs for a return to the old classical ways. He gets his way by holding them to higher standards at gunpoint, but then realizes the futility of it all. I imagine it’s funnier performed with more dramaturgical context. It takes place between the wars, so that clearly has something to do with it, but my play-reading chops are not up to snuff to say anything insightful.

Complexity and Information (J. F. Traub and A. G. Werschulz) – a gentle, if scattered, introduction to information based complexity, which I had heard about but didn’t really know too much about. It somehow feels “old fashioned” to me (perhaps that’s the machine learning kool-aid speaking), with comparisons to Turing machines and so on. But the central question of how to appropriately estimate integrals from samples is pretty interesting, given my recent forays into using MCMC.

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