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This program includes an introduction read by the author. No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals its turbulent history and the recent surge of apparent successes, grand hopes, and emerging fears that surround AI. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent - really - are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant methods of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought that led to recent achievements. She meets with fellow experts like Douglas Hofstadter, the cognitive scientist and Pulitzer Prize - winning author of the modern classic Gödel, Escher, Bach, who explains why he is "terrified" about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much farther it has to go. Interweaving stories about the science and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and approachable accounts of the most interesting and provocative modern work in AI, flavored with Mitchell's humor and personal observations. This frank, lively book will prove an indispensable guide to understanding today's AI, its quest for "human-level" intelligence, and its impacts on all of our futures. PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

Learn smart insights about how leveraging artificial intelligence for hospital operations benefit hospital administration to reduce costs while increasing revenues and improve functionalities to simplify the hospital workflow management.

Transformers have now become the defacto standard for NLP tasks. Originally developed for sequence transduction processes such as speech recognition, translation, and text to speech, transformers work by using convolutional neural networks together with attention models, making them much more efficient than previous architectures. And although transformers were developed for NLP, they've also been implemented in the fields of computer vision and music generation. However, for all their wide and varied uses, transformers are still very difficult to understand, which is why I wrote a detailed post describing how they work on a basic level. It covers the encoder and decoder architecture, and the whole dataflow through the different pieces of the neural network. In this post, we'll get deeper into looking at transformers by implementing our own English to German language translator.

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