What do you think?
Rate this book
Intelligent Web, The
As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion.
These are just basic examples of the growth of "Web intelligence", as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected. Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.
These are just basic examples of the growth of "Web intelligence", as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected. Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.
MP3 CD
First published January 1, 2013
22 people are currently reading
186 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 10 of 10 reviews
January 3, 2019
An expansive tour of the main machine learning systems and procedures responsible for much of the major web functionalities, including those directly involved in online ad auctions, recommendation engines, search, natural language understanding, and ranking/rating functionalities to name a few. The book is also historical in scope, explaining how key technologies and theories, like the theory of information, helped make the intelligent web possible.
However, what made this book stand out for me was how accurate and detailed it went into the subject matter, especially for a lay book. The reader is introduced to text vectorizing, categorical label encodings, hashing, rules learning, resampling, associative learning, and a host of other nitty-gritty tools for practical machine learning.
Further, the book delves into the main algorithms themselves, including collaborative filtering, TF-IDF, ACO, ANNs, clusterings etc. The reader almost gets practical knowledge from this reading, which is rare again for a lay book. Perhaps I'm biased cause of my daily use of these apparatus, but I felt the reading of this book was easily comprehensible while jogging, which few technically oriented text are, unfortunately. Part of this comprehension is achieved by minimizing direct calculations, and by focusing more on general descriptions of procedures or broad high-level stepwise explanations of the algorithms.
Actually, this book could serve as a decent review even to practitioners who may want to cast their often disjoint understanding of machine learning techniques under a unified, largely historical narrative.
The only negative is that the book was written in 2013, and therefore much of the GPU revolution with respect to deep neural networks is largely missing, which includes some outdated commentary on the difficulty of image and video learning with respect to self-driving vehicles. Although difficult from a human standpoint, engineering and computing have tamed this domain since the publication, which demonstrates the velocity of change in this field.
It definitely would be a good secondary light reference book. Highly recommend
However, what made this book stand out for me was how accurate and detailed it went into the subject matter, especially for a lay book. The reader is introduced to text vectorizing, categorical label encodings, hashing, rules learning, resampling, associative learning, and a host of other nitty-gritty tools for practical machine learning.
Further, the book delves into the main algorithms themselves, including collaborative filtering, TF-IDF, ACO, ANNs, clusterings etc. The reader almost gets practical knowledge from this reading, which is rare again for a lay book. Perhaps I'm biased cause of my daily use of these apparatus, but I felt the reading of this book was easily comprehensible while jogging, which few technically oriented text are, unfortunately. Part of this comprehension is achieved by minimizing direct calculations, and by focusing more on general descriptions of procedures or broad high-level stepwise explanations of the algorithms.
Actually, this book could serve as a decent review even to practitioners who may want to cast their often disjoint understanding of machine learning techniques under a unified, largely historical narrative.
The only negative is that the book was written in 2013, and therefore much of the GPU revolution with respect to deep neural networks is largely missing, which includes some outdated commentary on the difficulty of image and video learning with respect to self-driving vehicles. Although difficult from a human standpoint, engineering and computing have tamed this domain since the publication, which demonstrates the velocity of change in this field.
It definitely would be a good secondary light reference book. Highly recommend
March 29, 2014
There have been many recent successes in the field of artificial intelligence. Some, like the 2011 success of IBM’s computer Watson beating champion human players in the popular TV show Jeopardy!, are ‘grandstanding’. Others are seemingly less obvious, but no less impressive for that. Face recognition, a task normally associated with humans and a few ‘higher-animals’, is now incorporated as routine by Facebook and Google’s Picasa. The ability to recognise speech is not far behind, as any user of the Siri feature on Apple iPhones can attest to. The point to note is that artificial intelligence technologies are becoming more important, more inconspicuous and an integral part of mainstream computing.
In The Intelligent Web, Gautam Shroff successfully argues that many of the recent successes have come through the deployment of many known but disparate techniques working together. He further argues that these advances have come about by this deployment being on large volumes of ‘big data’; all of which has been made possible , and indeed driven by the internet and the world wide web. In other words, Shroff argues, rather than ‘traditional’ artificial intelligence, these successes are best described as ‘web intelligence’. Semantic arguments aside, Shroff presents his case logically and convincingly.
What Shroff argues is that the cumulative use of artificial intelligence techniques at web scale can result in behaviour that exhibits the very basic feature of human intelligence, to; ‘put two and two together’ or ‘connect the dots’. In the book he dissects the ability to connect the dots. He argue, quite convincingly, that this ability is composed of looking and experiencing the world around us, next is to listen to what is important and discard the irrelevant, then to connect different facts and derive new conclusions. Then is the ability to to make predictions about the future and finally go put these to good use to correct and control our actions.
The bulk of the book is chapters devoted to each of these steps. Shroff accomplishes this with great diligence and intelligence. each chapter is replete with explanation and useful, if not energising, examples. the epilogue acknowledges that tricky question regarding any discourse on artificial intelligence, ‘where do the goals come from?” “Obviously the answer” says Shroff, “is from us. The web intelligence systems themselves do not generate their overarching goals; these are built in, by their creators, i.e., us.”
The missing ingredient between these ‘artificial intelligence’ systems and us, as ‘natural intelligence’ systems is purpose. Shroff in his epilogue poses the thought that for the web intelligence systems of today to cross the chasm, integrate these six elements and become a mind is an achievable scientific task worthy of the effort. As he presents the problem it seems far more close than it did in Turing’s time, when even the possible path was mere whimsy.
At the same time this book is about the ‘weak’ artificial intelligence. Shroff avoids the deep philosophical waters of strong artificial intelligence; whether or not machines can be conscious or ‘truly intelligent’. The tenuous links are there for the reader to make if they choose. As presented by Shroff these links are more tenuous than the very worthwhile and entertaining Wetware: a computer in every living cell by Dennis Bray. Where Bray provocatively engages in the debate about the computational capabilities of protein networks (not too dissimilar from an electronic web network).
Despite the success of his arguments there is a certain cumbersome feel to Shroff’s writing. In the prologue of his book Shroff says how he “grew up reading and being deeply influenced by the popular science books of George Gamow” and “I hope to entertain and elucidate, in the spirit of Gamow.” This rather lofty goal Shroff does fall short on. This book requires attention and diligence to read. It does not effortlessly tackle a complex subject with the insightful ramble of Bray in Wetware, for example. It is worth the effort, but I doubt it will be remembered with the same ‘affection’ as, for example, Gamow’s Mr Tomkins in Wonderland.
This review first appeared here: http://dragonlaughing.tumblr.com/post...
In The Intelligent Web, Gautam Shroff successfully argues that many of the recent successes have come through the deployment of many known but disparate techniques working together. He further argues that these advances have come about by this deployment being on large volumes of ‘big data’; all of which has been made possible , and indeed driven by the internet and the world wide web. In other words, Shroff argues, rather than ‘traditional’ artificial intelligence, these successes are best described as ‘web intelligence’. Semantic arguments aside, Shroff presents his case logically and convincingly.
What Shroff argues is that the cumulative use of artificial intelligence techniques at web scale can result in behaviour that exhibits the very basic feature of human intelligence, to; ‘put two and two together’ or ‘connect the dots’. In the book he dissects the ability to connect the dots. He argue, quite convincingly, that this ability is composed of looking and experiencing the world around us, next is to listen to what is important and discard the irrelevant, then to connect different facts and derive new conclusions. Then is the ability to to make predictions about the future and finally go put these to good use to correct and control our actions.
The bulk of the book is chapters devoted to each of these steps. Shroff accomplishes this with great diligence and intelligence. each chapter is replete with explanation and useful, if not energising, examples. the epilogue acknowledges that tricky question regarding any discourse on artificial intelligence, ‘where do the goals come from?” “Obviously the answer” says Shroff, “is from us. The web intelligence systems themselves do not generate their overarching goals; these are built in, by their creators, i.e., us.”
The missing ingredient between these ‘artificial intelligence’ systems and us, as ‘natural intelligence’ systems is purpose. Shroff in his epilogue poses the thought that for the web intelligence systems of today to cross the chasm, integrate these six elements and become a mind is an achievable scientific task worthy of the effort. As he presents the problem it seems far more close than it did in Turing’s time, when even the possible path was mere whimsy.
At the same time this book is about the ‘weak’ artificial intelligence. Shroff avoids the deep philosophical waters of strong artificial intelligence; whether or not machines can be conscious or ‘truly intelligent’. The tenuous links are there for the reader to make if they choose. As presented by Shroff these links are more tenuous than the very worthwhile and entertaining Wetware: a computer in every living cell by Dennis Bray. Where Bray provocatively engages in the debate about the computational capabilities of protein networks (not too dissimilar from an electronic web network).
Despite the success of his arguments there is a certain cumbersome feel to Shroff’s writing. In the prologue of his book Shroff says how he “grew up reading and being deeply influenced by the popular science books of George Gamow” and “I hope to entertain and elucidate, in the spirit of Gamow.” This rather lofty goal Shroff does fall short on. This book requires attention and diligence to read. It does not effortlessly tackle a complex subject with the insightful ramble of Bray in Wetware, for example. It is worth the effort, but I doubt it will be remembered with the same ‘affection’ as, for example, Gamow’s Mr Tomkins in Wonderland.
This review first appeared here: http://dragonlaughing.tumblr.com/post...
Read
June 7, 2016It's not a technical book. It gives the reader a solid grounding in the historical grounding of many modern consumer facing web applications in combination with underlying principles such as Binary Search, Sparse Distributed Memory (SDM) & Locality Sensitive Hashing (LSH). Well-written and digestible but not entirely reductionist. Worthwhile for those intending to learn more about the mechanics of the web or existing professionals seeking a refresher.
October 25, 2016
* algorithms that make the web possible
* we expect an intelligent search, not just a keyword based one
* how much knowledge can be derived automatically from public info
not very well structured
* we expect an intelligent search, not just a keyword based one
* how much knowledge can be derived automatically from public info
not very well structured
August 21, 2014
it was nice
I learned a lot
yet the linguistic structure of this book was quite challenging to me
I learned a lot
yet the linguistic structure of this book was quite challenging to me
September 18, 2014
Focused on Big Data history, methods, current and future uses.
Good read if you have computer science and/or statistics background.
Good read if you have computer science and/or statistics background.
August 7, 2015
One of my favorite books! If you're into computer science and How the internet work or how relationships are formed on the WWW than read this book.
February 22, 2016
An interesting read, but it's more of a foundation in A.I. than a commentary on the development of an intelligent wwweb. The term "web" is applied in a more general sense.
February 15, 2016
Interesting, easy reading material for all "Big Data" begginers.
April 18, 2017
Interesting subject, challenging read. Too basic for experts, too expert for beginners.
Displaying 1 - 10 of 10 reviews