Common Sense, the Turing Test, and the Quest for Real AI


Hector J. Levesque - 2017
    AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example. In this book, Hector Levesque shifts the conversation to -good old fashioned artificial intelligence, - which is based not on heaps of data but on understanding commonsense intelligence. This kind of artificial intelligence is equipped to handle situations that depart from previous patterns -- as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there's no more soy milk.Levesque considers the role of language in learning. He argues that a computer program that passes the famous Turing Test could be a mindless zombie, and he proposes another way to test for intelligence -- the Winograd Schema Test, developed by Levesque and his colleagues. -If our goal is to understand intelligent behavior, we had better understand the difference between making it and faking it, - he observes. He identifies a possible mechanism behind common sense and the capacity to call on background knowledge: the ability to represent objects of thought symbolically. As AI migrates more and more into everyday life, we should worry if systems without common sense are making decisions where common sense is needed.

Computing machinery and intelligence


Alan Turing - 1950
    The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public.Published in Mind 49: page 433-460.(Source: Wikipedia)

Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS


John K. Kruschke - 2010
    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

Data Structures and Algorithms in Python


Michael T. Goodrich - 2012
     Data Structures and Algorithms in Python is the first mainstream object-oriented book available for the Python data structures course. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++.

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)


Jiawei Han - 2000
    Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business dataUpdates that incorporate input from readers, changes in the field, and more material on statistics and machine learningDozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projectsComplete classroom support for instructors at www.mkp.com/datamining2e companion site

Problem Solving 101: A Simple Book for Smart People


Ken Watanabe - 2007
    His goal was to help shift the focus in Japanese education from memorization to critical thinking, by adapting some of the techniques he had learned as an elite McKinsey consultant.He was amazed to discover that adults were hungry for his fun and easy guide to problem solving and decision making. The book became a surprise Japanese bestseller, with more than 370,000 in print after six months. Now American businesspeople can also use it to master some powerful skills.Watanabe uses sample scenarios to illustrate his techniques, which include logic trees and matrixes. A rock band figures out how to drive up concert attendance. An aspiring animator budgets for a new computer purchase. Students decide which high school they will attend.Illustrated with diagrams and quirky drawings, the book is simple enough for a middleschooler to understand but sophisticated enough for business leaders to apply to their most challenging problems.

Two Scoops of Django: Best Practices for Django 1.6


Daniel Roy Greenfeld - 2014
    

A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going


Michael Wooldridge - 2021
    As an AI researcher with 25 years of experience, professor Mike Wooldridge has learned to be obsessively cautious about such claims, while still promoting an intense optimism about the future of the field. There have been genuine scientific breakthroughs that have made AI systems possible in the past decade that the founders of the field would have hailed as miraculous. Driverless cars and automated translation tools are just two examples of AI technologies that have become a practical, everyday reality in the past few years, and which will have a huge impact on our world.While the dream of conscious machines remains, Professor Wooldridge believes, a distant prospect, the floodgates for AI have opened. Wooldridge's A Brief History of Artificial Intelligence is an exciting romp through the history of this groundbreaking field--a one-stop-shop for AI's past, present, and world-changing future.

Analyzing the Analyzers


Harlan Harris - 2013
    

The Formula: How Algorithms Solve all our Problems … and Create More


Luke Dormehl - 2014
    What if everything in life could be reduced to a simple formula? What if numbers were able to tell us which partners we were best matched with – not just in terms of attractiveness, but for a long-term committed marriage? Or if they could say which films would be the biggest hits at the box office, and what changes could be made to those films to make them even more successful? Or even who out of us is likely to commit certain crimes, and when? This may sound like the world of science-fiction, but in fact it is just the tip of the iceberg in a world that is increasingly ruled by complex algorithms and neural networks.In The Formula, Luke Dormehl takes you inside the world of numbers, asking how we came to believe in the all-conquering power of algorithms; introducing the mathematicians, artificial intelligence experts and Silicon Valley entrepreneurs who are shaping this brave new world, and ultimately asking how we survive in an era where numbers can sometimes seem to create as many problems as they solve.

Probability And Statistics For Engineers And Scientists


Ronald E. Walpole - 1978
     Offers extensively updated coverage, new problem sets, and chapter-ending material to enhance the book’s relevance to today’s engineers and scientists. Includes new problem sets demonstrating updated applications to engineering as well as biological, physical, and computer science. Emphasizes key ideas as well as the risks and hazards associated with practical application of the material. Includes new material on topics including: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; “dummy” variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting. Introduces Bayesian statistics, including its applications to many fields. For those interested in learning more about probability and statistics.

How Linux Works: What Every Superuser Should Know


Brian Ward - 2004
    Some books try to give you copy-and-paste instructions for how to deal with every single system issue that may arise, but How Linux Works actually shows you how the Linux system functions so that you can come up with your own solutions. After a guided tour of filesystems, the boot sequence, system management basics, and networking, author Brian Ward delves into open-ended topics such as development tools, custom kernels, and buying hardware, all from an administrator's point of view. With a mixture of background theory and real-world examples, this book shows both "how" to administer Linux, and "why" each particular technique works, so that you will know how to make Linux work for you.

Thinking in CSS


Aravind Shenoy - 2014
    Instead of wandering through loads of theory, we will understand CSS more practically so that we can design a webpage using CSS. We have used Notepad for the examples in this book. Alternatively, you can also use Notepad++ or any advanced editor. All that you need to do is copy the code and paste it into Notepad. Upon execution, you will get the output as depicted in the screenshots. Screenshots are provided for each sample code. Coding gets better with practice. The examples in this book are compatible with almost every browser. Instead of using the verbatim code, you can modify the code and see the change in the output, thereby understanding the subtle nuances of CSS. By the end of the book, with practice, you can achieve better things and get more acquainted with CSS.

High Performance Python: Practical Performant Programming for Humans


Micha Gorelick - 2013
    Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python's implementation.How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more.Get a better grasp of NumPy, Cython, and profilersLearn how Python abstracts the underlying computer architectureUse profiling to find bottlenecks in CPU time and memory usageWrite efficient programs by choosing appropriate data structuresSpeed up matrix and vector computationsUse tools to compile Python down to machine codeManage multiple I/O and computational operations concurrentlyConvert multiprocessing code to run on local or remote clustersDeploy code faster using tools like Docker

Good Math: A Geek's Guide to the Beauty of Numbers, Logic, and Computation


Mark C. Chu-Carroll - 2013
    There is joy and beauty in mathematics, and in more than two dozen essays drawn from his popular “Good Math” blog, you’ll find concepts, proofs, and examples that are often surprising, counterintuitive, or just plain weird.Mark begins his journey with the basics of numbers, with an entertaining trip through the integers and the natural, rational, irrational, and transcendental numbers. The voyage continues with a look at some of the oddest numbers in mathematics, including zero, the golden ratio, imaginary numbers, Roman numerals, and Egyptian and continuing fractions. After a deep dive into modern logic, including an introduction to linear logic and the logic-savvy Prolog language, the trip concludes with a tour of modern set theory and the advances and paradoxes of modern mechanical computing.If your high school or college math courses left you grasping for the inner meaning behind the numbers, Mark’s book will both entertain and enlighten you.