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Artificial Intelligence: A Modern Approach
The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.Expand
Artificial intelligence - a modern approach, 2nd Edition
mathematical description; the agent program is a concrete implementation, running within some physical system. To illustrate these ideas, we use a very simple example—the vacuum-cleaner world shownExpand
Deep Learning with Dynamic Computation Graphs
TLDR
This work introduces a technique called dynamic batching, which not only batches together operations between different input graphs of dissimilar shape, but also between different nodes within a single input graph. Expand
The Unreasonable Effectiveness of Data
TLDR
A trillion-word corpus - along with other Web-derived corpora of millions, billions, or trillions of links, videos, images, tables, and user interactions - captures even very rare aspects of human behavior. Expand
Artificial Intelligence
We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given aExpand
Verbmobil: A Translation System for Face-to-Face Dialog
From the Publisher: The first experimental prototypes, with restricted capabilities, of Verbmobil, a system that provides simultaneous language translations, are anticipated by the year 2000. ThisExpand
Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp
From the Publisher: Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AIExpand
Artificial intelligence - a modern approach: the intelligent agent book
TLDR
In the second edition of Artificial Intelligence: A Modern Approach, every chapter has been extensively rewritten and significant new material has been introduced to cover areas such as constraint satisfaction, fast propositional inference, planning graphs, internet agents, exact probabilistic inference, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning and ethical aspects of AI. Expand
A modern, agent-oriented approach to introductory artificial intelligence
TLDR
The book tries to make the concepts of AI more concrete via two strategies: relating them to the student's existing knowledge, and using examples based on an agent operating in an environment. Expand
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