 | |
| Author | Pedro Domingos |
|---|---|
| Country | United States |
| Language | English |
| Subject | Artificial intelligence |
| Genre | Philosophy, popular science |
| Publisher | Basic Books |
Publication date | September 22, 2015 |
| Media type | Print, e-book, audiobook |
| Pages | 352 pp. |
| ISBN | 978-0465065707 |
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos released in 2015. Domingos wrote the book in order to generate interest from people outside the field.
The book outlines five approaches of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes' theorem and analogical modelling. The author explains these tribes to the reader by referring to more understandable processes of logic, connections made in the brain, natural selection, probability and similarity judgments. Throughout the book, it is suggested that each different tribe has the potential to contribute to a unifying "master algorithm".
Towards the end of the book the author pictures a "master algorithm" in the near future, where machine learning algorithms asymptotically grow to a perfect understanding of how the world and people in it work. [1] Although the algorithm doesn't yet exist, he briefly reviews his own invention of the Markov logic network. [2]
In 2016 Bill Gates recommended the book, alongside Nick Bostrom's Superintelligence , as one of two books everyone should read to understand AI. [3] In 2018 the book was noted to be on Chinese Communist Party general secretary Xi Jinping's bookshelf. [4]
A computer science educator stated in Times Higher Education that the examples are clear and accessible. [5] In contrast, The Economist agreed Domingos "does a good job" but complained that he "constantly invents metaphors that grate or confuse". [6] Kirkus Reviews praised the book, stating that "Readers unfamiliar with logic and computer theory will have a difficult time, but those who persist will discover fascinating insights." [7]
A New Scientist review called it "compelling but rather unquestioning". [8]
Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is a field of study in computer science that develops and studies intelligent machines. "AI" may also refer to the machines themselves.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance. Machine learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine, where it is too costly to develop algorithms to perform the needed tasks.
In the history of artificial intelligence, neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 70s and was a subject of discussion until the middle 80s.
Geoffrey Everest Hinton is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. From 2013 to 2023, he divided his time working for Google and the University of Toronto, before publicly announcing his departure from Google in May 2023, citing concerns about the risks of artificial intelligence (AI) technology. In 2017, he co-founded and became the chief scientific advisor of the Vector Institute in Toronto.
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A Markov logic network (MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, enabling uncertain inference. Markov logic networks generalize first-order logic, in the sense that, in a certain limit, all unsatisfiable statements have a probability of zero, and all tautologies have probability one.
The following outline is provided as an overview of and topical guide to artificial intelligence:
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Brett King is an Australian futurist, author, and co-founder of Moven, originally a New York-based mobile banking startup. He is regarded as an influencer in financial services globally, and his book Augmented was cited by Chinese leader Xi Jinping as recommended reading on artificial intelligence. His book Bank 4.0 was awarded Top Book by a Foreign Author in Russia for the year 2019, as judged by an independent panel audited by PricewaterhouseCoopers. He was inducted into the Fintech Hall of Fame in 2020 for his contribution to the industry.
DeepMind Technologies Limited, doing business as Google DeepMind, is a British-American artificial intelligence research laboratory which serves as a subsidiary of Google. Founded in the UK in 2010, it was acquired by Google in 2014, The company is based in London, with research centres in Canada, France, Germany and the United States.
Pedro Domingos is a Professor Emeritus of computer science and engineering at the University of Washington. He is a researcher in machine learning known for Markov logic network enabling uncertain inference.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
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Thomas G. Dietterich is emeritus professor of computer science at Oregon State University. He is one of the pioneers of the field of machine learning. He served as executive editor of Machine Learning (journal) (1992–98) and helped co-found the Journal of Machine Learning Research. In response to the media's attention on the dangers of artificial intelligence, Dietterich has been quoted for an academic perspective to a broad range of media outlets including National Public Radio, Business Insider, Microsoft Research, CNET, and The Wall Street Journal.
Explainable AI (XAI), often known as Interpretable AI, or Explainable Machine Learning (XML), either refers to an AI system over which it is possible for humans to retain intellectual oversight, or to the methods to achieve this. The main focus is usually on the reasoning behind the decisions or predictions made by the AI which are made more understandable and transparent. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.
Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.
fast.ai is a non-profit research group focused on deep learning and artificial intelligence. It was founded in 2016 by Jeremy Howard and Rachel Thomas with the goal of democratizing deep learning. They do this by providing a massive open online course (MOOC) named "Practical Deep Learning for Coders," which has no other prerequisites except for knowledge of the programming language Python.
Thomas L. Griffiths is an Australian academic who is the Henry R. Luce Professor of Information Technology, Consciousness, and Culture at Princeton University. He studies human decision-making and its connection to problem-solving methods in computation. His book with Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions, was named one of the "Best Books of 2016" by MIT Technology Review.
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