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Artificial Intelligence is Transforming The Web

Thursday, February 4th, 2016

As Cade Metz writes in a great article in Wired, AI technology is transforming the web – and the world.

As Metz writes, “yesterday, the 46-year-old Google veteran who oversees its search engine, Amit Singhal, announced his retirement. And in short order, Google revealed that Singhal’s rather enormous shoes would be filled by a man named John Giannandrea. On one level, these are just two guys doing something new with their lives. But you can also view the pair as the ideal metaphor for a momentous shift in the way things work inside Google—and across the tech world as a whole.

Giannandrea, you see, oversees Google’s work in artificial intelligence. This includes deep neural networks, networks of hardware and software that approximate the web of neurons in the human brain. By analyzing vast amounts of digital data, these neural nets can learn all sorts of useful tasks, like identifying photos, recognizing commands spoken into a smartphone, and, as it turns out, responding to Internet search queries. In some cases, they can learn a task so well that they outperform humans. They can do it better. They can do it faster. And they can do it at a much larger scale. If AI is the future of Google Search, it’s the future of so much more.

This approach, called deep learning, is rapidly reinventing so many of the Internet’s most popular services, from Facebook to Twitter to Skype. Over the past year, it has also reinvented Google Search, where the company generates most of its revenue. Early in 2015, as Bloomberg recently reported, Google began rolling out a deep learning system called RankBrain that helps generate responses to search queries. As of October, RankBrain played a role in “a very large fraction” of the millions of queries that go through the search engine with each passing second.

As Bloomberg says, it was Singhal who approved the roll-out of RankBrain. And before that, he and his team may have explored other, simpler forms of machine learning. But for a time, some say, he represented a steadfast resistance to the use of machine learning inside Google Search. In the past, Google relied mostly on algorithms that followed a strict set of rules set by humans. The concern—as described by some former Google employees—was that it was more difficult to understand why neural nets behaved the way it did, and more difficult to tweak their behavior.

These concerns still hover over the world of machine learning. The truth is that even the experts don’t completely understand how neural nets work. But they do work. If you feed enough photos of a platypus into a neural net, it can learn to identify a platypus. If you show it enough computer malware code, it can learn to recognize a virus. If you give it enough raw language—words or phrases that people might type into a search engine—it can learn to understand search queries and help respond to them. In some cases, it can handle queries better than algorithmic rules hand-coded by human engineers. Artificial intelligence is the future of Google Search, and if it’s the future of Google Search, it’s the future of so much more.”

A perceptive look at the future we all share; read the entire article here.

Digital Detox

Saturday, December 5th, 2015

From Gillian Brockell and The Washington Post, click here or on this image for five tips to avoid digital overload.

Facebook Wants to Know What You Don’t Post

Thursday, February 13th, 2014

Even if you decide not to post something, Facebook wants to know what you were thinking.

As I wrote in my book Streaming: Movies, Media and Instant Access, Facebook has been from the beginning little more than a data mining operation, which simply tries to collect as much information on all of its subscribers as it possibly can, and then use this information for profit. And they keep upping the ante with every passing day. As Casey Johnston reported in Ars Technica on December 16, 2013, in addition to the material you actually post on Facebook, the site also wants to know about the stuff you type in, and then think better of, and decide not to post.

As Johnston writes, “Facebook released a study last week indicating that the company is moving into a new type of data collection in earnest: the things we do not say. For an analysis of self-censorship, two researchers at Facebook collected information on all of the statuses that five million users wrote out but did not post during the summer of 2012. Facebook is not shy about the information it collects on its users. Certain phrasings in its data use policy have indicated before that it may be collecting information about what doesn’t happen, like friend requests that are never accepted.

Capturing the failures of Facebook interactions would, in theory, allow the company to figure out how to mitigate them and turn them into ’successes.’ Adam Kramer, a data scientist at Facebook, and Sauvik Das, a summer Facebook intern, tracked two things for the study: the HTML form element where users enter original status updates or upload content and the comment box that allows them to add to the discussion of things other people have posted. Over the course of those 17 days, 71 percent of the users typed out a status, a comment, or both but did not submit it.

On average, they held back on 4.52 statuses and 3.2 comments. In addition to that information, Das and Kramer took note of the users’ demographic information, ‘behavioral features,’ and information on each user’s ’social graph’ like the average number of friends of friends or the user’s ‘political ideology’ in relation to their friends’ beliefs. They used this information to study three cross sections with self-censorship: how the user’s political stance differs from the audience, the user’s political stance and how homogenous the audience is, and the user’s gender related to the gender diversity of their network.”

This is exactly what Herbert Marcuse was predicting, as early as 1964; see my earlier post.

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About the Author

Wheeler Winston Dixon

Wheeler Winston Dixon, Ryan Professor of Film Studies at the University of Nebraska-Lincoln, is an internationally recognized scholar and writer of film history, theory and criticism. He is the author of thirty books and more than 100 articles on film, and appears regularly in national media outlets discussing film and culture trends. Frame by Frame is a collection of his thoughts on a number of those topics. All comments by Dixon on this blog are his own opinions.

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