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Most subfields of computer science have an interface layer via
which applications communicate with the infrastructure, and this is
key to their success (e.g., the Internet in networking, the
relational model in databases, etc.). So far this interface layer
has been missing in AI. First-order logic and probabilistic
graphical models each have some of the necessary features, but a
viable interface layer requires combining both. Markov logic is a
powerful new language that accomplishes this by attaching weights
to first-order formulas and treating them as templates for features
of Markov random fields. Most statistical models in wide use are
special cases of Markov logic, and first-order logic is its
infinite-weight limit. Inference algorithms for Markov logic
combine ideas from satisfiability, Markov chain Monte Carlo, belief
propagation, and resolution. Learning algorithms make use of
conditional likelihood, convex optimization, and inductive logic
programming. Markov logic has been successfully applied to problems
in information extraction and integration, natural language
processing, robot mapping, social networks, computational biology,
and others, and is the basis of the open-source Alchemy system.
Table of Contents: Introduction / Markov Logic / Inference /
Learning / Extensions / Applications / Conclusion
Recommended by Bill Gates A thought-provoking and wide-ranging
exploration of machine learning and the race to build computer
intelligences as flexible as our own In the world's top research
labs and universities, the race is on to invent the ultimate
learning algorithm: one capable of discovering any knowledge from
data, and doing anything we want, before we even ask. In The Master
Algorithm, Pedro Domingos lifts the veil to give us a peek inside
the learning machines that power Google, Amazon, and your
smartphone. He assembles a blueprint for the future universal
learner--the Master Algorithm--and discusses what it will mean for
business, science, and society. If data-ism is today's philosophy,
this book is its bible.
'Pedro Domingos demystifies machine learning and shows how wondrous
and exciting the future will be' Walter Isaacson, author of Steve
Jobs Society is changing, one learning algorithm at a time, from
search engines to online dating, personalized medicine to
predicting the stock market. But learning algorithms are not just
about Big Data - these algorithms take raw data and make it useful
by creating more algorithms. This is something new under the sun: a
technology that builds itself. In The Master Algorithm, Pedro
Domingos reveals how machine learning is remaking business,
politics, science and war. And he takes us on an awe-inspiring
quest to find 'The Master Algorithm' - a universal learner capable
of deriving all knowledge from data.
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