The Master Algorithm

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.

Author: Pedro Domingos

Publisher: Penguin UK

ISBN: 0241004551

Category: Science

Page: 352

View: 456

A spell-binding quest for the one algorithm capable of deriving all knowledge from data, including a cure for cancer 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.

The Master Algorithm by Pedro Domingos Summary

Master. Algorithm. and. Your. Digital. Mirror. If you're thinking there can't be that
many companies with data on you, then let's take a look at all of the places where
your data is recorded: your emails, office documents, texts, tweets, Facebook ...

Author: QuickRead

Publisher: QuickRead.com

ISBN:

Category: Study Aids

Page:

View: 798

Do you want more free book summaries like this? Download our app for free at https://www.QuickRead.com/App and get access to hundreds of free book and audiobook summaries. How the Quest For the Ultimate Learning Machine Will Remake Our World. According to Pedro Domingos, one of the greatest mysteries of the universe is not how it begins or ends, or what infinitesimal threads it’s woven from, it’s what goes on in a small child’s mind: how a pound of gray jelly can grow into a seat of consciousness. Even more astonishing is how little role parents play in teaching the brain to go through this transformation, as it largely does it all on its own. Today, scientists, computer engineers, and more are working towards a machine that can do exactly what the human brain does: learn. With all the technology of today, machines may one day even become smarter than the human brain. Computers can learn from large sets of data that we may not even realize is getting collected. This means that our future can be run by technology, changing the way we live and interact with each other. As you read, you’ll learn how machines will one day be like the human brain, how there is no such thing as a perfect algorithm, and how a Master Algorithm is on its way to being created.

Learning Integer Lattices

7 Nested Differences Using the results obtained in a companion paper [HSW90],
we can apply Algorithm A in the construction of a number of master algorithms
which learn nested differences of lattices. Let DIFF(C*) be the class of concepts of
 ...

Author: David Helmbold

Publisher:

ISBN:

Category: Algorithms

Page: 14

View: 624

Abstract: "We consider the problem of learning of an integer lattice of Z[superscript k] in an on-line fashion. That is, the learning algorithm is given a sequence of k-tuples of integers and predicts for each tuple in the sequence whether it lies in a hidden target lattice of Z[superscript k]. The goal of the algorithm is to minimize the number of prediction mistakes. We give an efficient learning algorithm with an absolute mistake bound of [formula], where n is the maximum component of any tuple seen.

Proceedings

Our approach provides an access prediction algorithm that not only adapts to the
workload , but learns to avoid making predictions for specific files or data that are
unlikely to yield a successful successor prediction . the master algorithm can ...

Author:

Publisher:

ISBN:

Category: Computer networks

Page:

View: 319


Adaptive Caching by Experts

A master algorithm combines the predictions of experts to make its own
prediction . The master maintains a weight vector w = { W1 , ... , wn } E PN , where
PN is the probability simplex in N dimensions . The weight Wn represents the
master ...

Author: Robert B. Gramacy

Publisher:

ISBN:

Category: Algorithms

Page: 130

View: 815


Communicating Process Architectures 2017 2018

1 2 TERRA Contol Laws Plant 20 sim model 3 FMU FMU .xml and .so 4 5
Simulation Results CPP coded Master Algorithm LUNA Execution Framework
libLUNA.a Compile/Link Executable Co simulation Figure 5. A co-simulation
design flow.

Author: J. Bækgaard Pedersen

Publisher: IOS Press

ISBN: 161499949X

Category: Computers

Page: 612

View: 500

Concurrent and parallel systems are intrinsic to the technology which underpins almost every aspect of our lives today. This book presents the combined post-proceedings for two important conferences on concurrent and parallel systems: Communicating Process Architectures 2017, held in Sliema, Malta, in August 2017, and Communicating Process Architectures 2018, held in Dresden, Germany, in August 2018. CPA 2017: Fifteen papers were accepted for presentation and publication, they cover topics including mathematical theory, programming languages, design and support tools, verification, and multicore infrastructure and applications ranging from supercomputing to embedded. A workshop on domain-specific concurrency skeletons and the abstracts of eight fringe presentations reporting on new ideas, work in progress or interesting thoughts associated with concurrency are also included in these proceedings. CPA 2018: Eighteen papers were accepted for presentation and publication, they cover topics including mathematical theory, design and programming language and support tools, verification, multicore run-time infrastructure, and applications at all levels from supercomputing to embedded. A workshop on translating CSP-based languages to common programming languages and the abstracts of four fringe presentations on work in progress, new ideas, as well as demonstrations and concerns that certain common practices in concurrency are harmful are also included in these proceedings. The book will be of interest to all those whose work involves concurrent and parallel systems.

Algorithmic Learning Theory

The weighted majority prediction model can be generalized to the case where it
is allowed to hedge in predictions : The master algorithm and the experts are
allowed to output values in [ 0 , 1 ] rather than binary values 0 or 1 . In this paper
we ...

Author:

Publisher:

ISBN:

Category: Computer algorithms

Page:

View: 758


Computational Learning Theory

In each trial a master algorithm receives predictions from a large set of n experts .
Its goal is to predict almost as well as the best sequence of such experts chosen
off - line by partitioning the training sequence into k + 1 sections and then ...

Author:

Publisher:

ISBN:

Category: Artificial intelligence

Page:

View: 129


SIAM Journal on Computing

Using the results obtained in a companion paper ( 14 ) , we can apply Algorithm
A in the construction of a number of master algorithms that learn nested
differences of lattices . Let DIFF ( ck ) be the class of concepts of the form 11 - ( 12
– ( 13 – ...

Author: Society for Industrial and Applied Mathematics

Publisher:

ISBN:

Category: Electronic data processing

Page:

View: 259


Discretization Strategies for Semi infinite Optimization

This fact is reflected in the problem SSPE : abandonment test ( 5.2.5b ) in the
master algorithm below . The master algorithm below , constructs a problem
SSPE ; and applies the PPP minimax ni - 1 algorithm to it until it finds a point xi
such that ...

Author: Limin He

Publisher:

ISBN:

Category:

Page: 262

View: 668


Using Multiple Experts to Perform File Prediction

These experts can be simple static predictions, heuristics, or possibly other
machine learning algorithms. Most commonly, the experts are low cost algorithms
for making predictions. The master algorithm has no a priori knowledge about
which ...

Author: Karl S. Brandt

Publisher:

ISBN:

Category: Cache memory

Page: 122

View: 152





International Conference on Control and its Applications 23 25 March 1981

To check (approximately) feasibility the algorithms employ another finite
approximation At to A at each iteration: ... A master Algorithm for solving PI can
now be stated: Master Algorithm 1 Data: tejj}; Aq, a finite subset of A Step 0: Set k
= 0.

Author: Institution of Electrical Engineers. Computing & Control Division

Publisher:

ISBN:

Category: Technology & Engineering

Page: 387

View: 579


Parallel Optimization

If a Slave process has finished evaluating its subproblem , it broadcasts an idle
message to the Master process that it ... bound algorithm for the clustering
problem ( PGROUPS ) , we divide the algorithm into two parts : the Master
algorithm and ...

Author: Teodor Gabriel Crainic

Publisher:

ISBN:

Category: Algorithms

Page: 324

View: 590



Memorandum

As a result , the master algorithms presented in [ 7 ] cannot be implemented
efficiently for such problems . In [ 7 ] we find also a master algorithm for solving
finite dimensional optimization problems when both the cost function value and
its ...

Author:

Publisher:

ISBN:

Category: Electric engineering

Page:

View: 508


OFDMA for Broadband Wireless Access

2 Master algorithm For simplicity , let us consider one new user triggering the
resource allocation . In a similar way to the method described in Section 5 . 3 . 2 ,
also here the complete RRM algorithm ( also referred to as the “ master
algorithm ) ...

Author: Sławomir Pietrzyk

Publisher: Artech House Publishers

ISBN:

Category: Technology & Engineering

Page: 250

View: 296

Discussing OFDMA radio resource management in the context of broadband wireless access systems such as WiMAX, this unique resource serves as an excellent reference for OFDMA system design work and provides expert guidance on emerging enhancements to WiMAX technology.

Mastering Machine Learning Algorithms

By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

Author: Giuseppe Bonaccorso

Publisher: Packt Publishing Ltd

ISBN: 1838821910

Category: Computers

Page: 798

View: 622

Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications Book Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learn Understand the characteristics of a machine learning algorithm Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains Learn how regression works in time-series analysis and risk prediction Create, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANs Who this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.