The Intelligent Web: Search, smart algorithms, and big data

The Intelligent Web: Search, smart algorithms, and big data

Gautam Shroff

Language: English

Pages: 256

ISBN: 0199646716

Format: PDF / Kindle (mobi) / ePub


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.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

handy. To see how, let us first look at what the Google and other search engines did to change the mutual information equation between consumers and advertisers, thereby changing the fundamentals of online advertising and, for that matter, the entire media industry. An ideal scenario from the point of view of an advertiser would be to have to pay only when a consumer actually buys their product. In such a model the mutual information between advertising and outcome would be very high indeed.

have reductionist roots, or not. We shall neither speculate much on these matters nor attempt to describe the diverse philosophical debates and arguments on this subject. For those interested in a comprehensive history of the confluence of philosophy, psychology, neurology, and artificial intelligence often referred to as ‘cognitive science’, Margaret xix THE INTELLIGENT WEB Boden’s recent volume Mind as Machine: A History of Cognitive Science5 is an excellent reference. Equally important are

concern ourselves with exploring how today’s web-intelligence applications are able to mimic some aspects of intelligent behaviour. Additionally however, we shall also compare and contrast these immense engineering feats to the wondrous complexities that the human brain is able to grasp with such surprising ease, enabling each of us to so effortlessly ‘connect the dots’ and make sense of the world every single day. xxiii This page intentionally left blank 1 LOOK I n ‘A Scandal in

different, ‘nonmonotonic’, logics. The term ‘non-monotonic’ merely means that the number of facts known to be true can sometimes decrease over time, instead of monotonically increasing (or remaining the same) as in normal logic. Dealing with beliefs also leads us to mechanisms for dealing with uncertainties, such as those which Watson might need to handle as it tries to figure out the right answer to a Jeopardy! question. Beliefs and uncertainties are essential aspects of human reasoning. Perhaps

positive versus negative comments on Twitter. Given the evidence at hand, which could be features of an animal or words in a tweet, such a classifier would find the most probable explanation for such evidence amongst the available alternatives. The naive Bayes classifier computes the required likelihood probabilities during its training phase and uses them during classification to determine the most probable class, or explanation, given the evidence, which in this case is an object characterized

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