The Lifecycle Of Software Objects Reddit
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The Lifecycle of Software Objects
What's the best way to create artificial intelligence? In 1950, Alan Turing wrote, "Many people think that a very abstract activity, like the playing of chess, would be best. It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English. This process could follow the normal teaching of a child. Things would be pointed out and named, etc. Again I do not know what the right answer is, but I think both approaches should be tried." The first approach has been tried many times in both science fiction and reality. In this new novella, at over 30,000 words, his longest work to date, Ted Chiang offers a detailed imagining of how the second approach might work within the contemporary landscape of startup companies, massively-multiplayer online gaming, and open-source software. It's a story of two people and the artificial intelligences they helped create, following them for more than a decade as they deal with the upgrades and obsolescence that are inevitable in the world of software. At the same time, it's an examination of the difference between processing power and intelligence, and of what it means to have a real relationship with an artificial entity.
China Mountain Zhang
'I am Zhang, alone with my light, and in that light I think for a moment that I am free.' Imagine a world where Chinese Marxism has vanquished the values of capitalism and Lenin is the prophet of choice. A cybernetic world where the new charioteers are flyers, human-powered kites dancing in the skies over New York in a brief grab at glory. A world where the opulence of Beijing marks a new cultural imperialism, as wealthy urbanites flirt with interactive death in illegal speakeasies, and where Arctic research stations and communes on Mars are haunted by their own fragile dangers. A world of fear and hope, of global disaster and slow healing, where progress can only be found in the cracks of a crumbling hegemony. This is the world of Zhang. An anti-hero who's still finding his way, treading a path through a totalitarian order - a path that just might make a difference.
Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala