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: 541

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

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Author: QuickRead

Publisher: QuickRead.com

ISBN:

Category: Study Aids

Page:

View: 685

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.

The Master Algorithm

Complementary opposition, any system, is the basis for identity, thus Conservation of the Circle unifies gravity with string theory, the standard model and general (universal) relativity, providing, then, the master algorithm in nature and ...

Author: Ilexa Yardley

Publisher: Createspace Independent Publishing Platform

ISBN: 9781523743971

Category:

Page: 24

View: 155

Zeno's paradox articulates a circle explaining why time moves backwards as we move forward. Complementary opposition, any system, is the basis for identity, thus Conservation of the Circle unifies gravity with string theory, the standard model and general (universal) relativity, providing, then, the master algorithm in nature and the grand unification theory in physics.

Neuronale Netze Selbst Programmieren

- Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Author: Tariq Rashid

Publisher:

ISBN: 9781492064046

Category:

Page: 232

View: 383

Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. - Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

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: 653


Hot Earth Hell the Master Algorithm

The MasterCoders each focus on their unique sub-specialty in our world system, from anthropologists, geneticists and climatologists ... to behavioral economists, generals and physicists ... from politicians, oceanographers and astronauts .. ...

Author: Dr Paul B Farrell

Publisher: Paul B. Farrell

ISBN: 9780692134733

Category:

Page: 172

View: 767

Physicist Stephen Hawking tells us, the future of our human civilization is in space! What about living here, now, till then? The truth is: Anyone can predict the future of our home Planet-Earth. And anyone can discover the secret of living here in peace and prosperity, now, today and every day. "Hot Earth Hell! The Master Algorithm" will give you all the technology you need, plus easy simple access to the massive database of the "33 MasterCoders" already decoding your future. The MasterCoders each focus on their unique sub-specialty in our world system, from anthropologists, geneticists and climatologists ... to behavioral economists, generals and physicists ... from politicians, oceanographers and astronauts ... to psychologists, evangelicals and agriculturalists ... from historians, biologists and geneticists ... to entrepreneurs, philosophers, philanthropists and many more ... and collectively, their unique "Master Algorithm" captures the big story, our world system, your story! "Hot Earth Hell! The Master Algorithm" is designed to help you understand our rapidly emerging world order, the collective consciousness of humanity, understand our home planet's 13.7 billion year history and the role of human civilization in it, and most important, your journey, living in a future of peace and prosperity, with a clear sense of what's coming, why it's meaningful, where we're going next. Bottom line: "Hot Earth Hell! The Master Algorithm" is a comprehensive blueprint for anyone searching for answers to the four most important questions facing all humans today: "The Questions Nobody's Asking ... #1. Who's Killing All 'Life' On Planet-Earth? ... #2. Can New Technology 'Save' The World in Time? ... #3. What's Next for the 10 Billion Humans Living in 2047? ... and #4. The Good News: The Secret To Living in Peace & Prosperity, on a Dying Planet?"

Learning Nested Differences of Intersection Closed Concept Classes

Abstract: "This paper introduces a new framework for contructing learning algorithms.

Author: David Helmbold

Publisher:

ISBN:

Category: Algorithms

Page: 39

View: 755

Abstract: "This paper introduces a new framework for contructing learning algorithms. Our methods involve master algorithms which use learning algorithms for intersection closed concept classes as subroutines. For example, we give a master algorithm capable of learning any concept class whose members can be expressed as nested differences (e.g. c[subscript 1] - (c[subscript 2] - (c[subscript 3] - (c[subscript 4] - c[subscript 5])))) of concepts from an intersection closed class. We show that our algorithms are optimal or nearly optimal with respect to several different criteria. These criteria include: the number of examples needed to produce a good hypothesis with high confidence, the worst case total number of mistake made, and the expected number of mistakes made in the first t trials."



An Introduction to Approaches and Modern Applications with Ensemble Learning

The purpose of this book is to provide insights into EL for readers not majoring in computer science or related subjects, introduce the latest development and applications of EL; in particular, share its practical applications in various ...

Author: Yi-Tung Chan

Publisher: Nova Science Publishers

ISBN: 9781536188462

Category: Computers

Page: 335

View: 292

From the successful application of deep learning (DL) in AlphaGo in 2012 to the recent advances in edge computing, artificial intelligence (AI) has continued to develop over the years. In the face of the current sweeping trend of AI, ensemble learning (EL) is expected to be further applied to DL and AI for developing higher-level ensemble systems in the future. Moreover, it could become an important step for achieving "The Master Algorithm" proposed by Prof. Pedro Domingos. In light of this, EL will continue to make a significant contribution to future development. The purpose of this book is to provide insights into EL for readers not majoring in computer science or related subjects, introduce the latest development and applications of EL; in particular, share its practical applications in various fields. Accordingly, this book intends to present theoretical parts relating to mathematics and computing in a simple and concise manner. The examples and practical use of EL have been used to explain methods that utilize EL to solve readers'' issues in their fields, which demonstrates the essence of EL for practical applications. While many AI and ML books are available on the market, most require a certain level of mathematical and machine learning (ML) knowledge. Complicated theories of mathematics and computation may be intimidating for people without a background in computer science and engineering, such as biological and medical researchers. It would be unfortunate if they were to miss the opportunity to use EL as a practical tool to solve data analysis problems at hand. Moreover, EL is usually introduced in the later or advanced chapters of AI and ML books. Beginners in ML, or readers without a technical background, are likely to be frustrated by mathematical or technical terms that only appear occasionally in the book or be anxious about complicated mathematical and computational theories related to classification algorithms. It would be regrettable if they were intimidated, and therefore, missed the opportunity to learn and use EL. From a practical perspective, existing classification techniques, such as decision trees with the C4.5 algorithm, support vector machines, and neural networks are now relatively mature and have been proven to be effective. For readers without a technical background, it is not essential to understand the complicated mathematical and computational theories behind the above techniques. Instead, it is recommended to grasp the logic and meaning of parameters in these classification algorithms and directly conduct tests using EL. Learning through practice can help readers to establish computational thinking. It is the best approach to learning EL, ML, AI, and DL.Furthermore, this book provides references and recommended reading for each technique to satisfy the curiosity of some readers with regard to mathematical theories and algorithms so that they can acquire further knowledge and answer their questions. Finally, the hope is that readers can be made aware, through practical use of EL, that they can build a robust ensemble system and solve problems in their areas without having to learn the absolute details of specific ML algorithms and mathematics behind the algorithms.This book provides insights into EL from worldwide experts and scholars in various fields. This book extensively introduces and discusses the application of EL in various fields and the current and future research directions of its novel applications. It also reviews some of the more popular areas in which EL has received widespread attention in recent years in the ML and AI. Each chapter opens with an introduction to ML and EL techniques, and then, analyzes the applications of EL in different fields, such as signal and image processing, medical care, education, geology, and agriculture. More than two experts and scholars in related fields acted as reviewers for the peer review of each chapter. It is hoped that these applications in various fields can inspire readers to use EL in practice.

Parallel Optimization

To formally present the parallel branch - and - bound algorithm for the clustering
problem ( PGROUPS ) , we divide the algorithm into two parts : the Master
algorithm and the Slave algorithm . The communication between the two
processes is ...

Author: Teodor Gabriel Crainic

Publisher:

ISBN:

Category: Algorithms

Page: 324

View: 491


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: 581


The Weighted Majority Algorithm

In this paper we investigate the situation where we are given a pool of prediction
algorithms that make varying numbers of mistakes . We aim to design a master
algorithm that uses the predictions of the pool to make its own prediction . Ideally
 ...

Author: Nick Littlestone

Publisher:

ISBN:

Category: Algorithms

Page: 16

View: 970

For example, given a sequence of trials, if there is an algorithm in the pool A that makes at most m mistakes then the Weighted Majority Algorithm will make at most c(log [absolute value of A] + m) mistakes on that sequence, where c is fixed constant."

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 ( Lk ) be the class of ...

Author: David Helmbold

Publisher:

ISBN:

Category: Algorithms

Page: 14

View: 384

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 of the Third Annual Workshop on Computational Learning Theory

Using the results obtained in a companion paper ( HSW90 ) , we can use
Algorithm A in the construction of a master algorithm which learns nested
differences of lattices . Let DIFF ' ( ck ) be the class of concepts of the form 11 - (
12 – ( 13 - ( Ap ...

Author: ACM Special Interest Group for Automata and Computability Theory

Publisher: Morgan Kaufmann

ISBN: 9781558601468

Category: Computers

Page: 395

View: 266



1995 IEEE 14th Annual International Phoenix Conference on Computers and Communications

5000 0 20 120 140 40 60 80 100 Number of nodes in graph Figure 2 : The
behavior of the B & B sequential algorithm . Execution begins by starting up a
process called master . This master in its turn starts up one process at every
processor in ...

Author: IEEE Communications Society

Publisher: Institute of Electrical & Electronics Engineers(IEEE)

ISBN: 9780780324930

Category: Computer networks

Page: 742

View: 224


Machine Learning

For example the patterns might change and different subalgorithms may predict
better for different segments of the on - line sequence of patterns . We seek to
design master algorithms that " track ” the best expert in the sense that they incur
 ...

Author: Armand Prieditis

Publisher: Morgan Kaufmann

ISBN:

Category: Computers

Page: 591

View: 438

Machine Learning Proceedings 1995.