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Containing Matters of Grotesque Gastronomy.

Bibliography:
Gregory, Sinda and McCaffery, Larry — “Not just a Gibson Clone: An Interview with Goro Masaki” https://web.archive.org/web/20070927045310/http://www.center…asaki.html.
Tatsumi, Takayuki — “Generations and Controversies — An Overview of Japanese Science Fiction, 1957–1997″, Science Fiction Studies, Vol. 27, No. 1 (Mar., 2000)

Flatland: A Romance of Many Dimensions is a satirical novella by the English schoolmaster Edwin Abbott Abbott, first published in 1884 by Seeley & Co. of London. Written pseudonymously by “A Square”, [ 1 ] the book used the fictional two-dimensional world of to comment on the hierarchy of Victorian culture, but the novella’s more enduring contribution is its examination of dimensions. [ 2 ]

A sequel, Sphereland, was written by Dionys Burger in 1957. Several films have been based on including the feature film Dudley Moore and the short films [ 3 ].

AIs have a big problem with truth and correctness – and human thinking appears to be a big part of that problem. A new generation of AI is now starting to take a much more experimental approach that could catapult machine learning way past humans.

Remember Deepmind’s AlphaGo? It represented a fundamental breakthrough in AI development, because it was one of the first game-playing AIs that took no human instruction and read no rules.

Instead, it used a technique called self-play reinforcement learning to build up its own understanding of the game. Pure trial and error across millions, even billions of virtual games, starting out more or less randomly pulling whatever levers were available, and attempting to learn from the results.

We present GameNGen, the first game engine powered entirely by a neural model that enables real-time interaction with a complex environment over long trajectories at high quality. GameNGen can interactively simulate the classic game DOOM at over 20 frames per second on a single TPU. Next frame prediction achieves a PSNR of 29.4, comparable to lossy JPEG compression. Human raters are only slightly better than random chance at distinguishing short clips of the game from clips of the simulation. GameNGen is trained in two phases: an RL-agent learns to play the game and the training sessions are recorded, and a diffusion model is trained to produce the next frame, conditioned on the sequence of past frames and actions. Conditioning augmentations enable stable auto-regressive generation over long trajectories.