Artificial Intelligence and the Semantic Web

If you are in the Baby Boomer era, you grew up watching all kinds of Sci-Fi shows about futuristic robots, crazy technological inventions, and space-time travel. It seems so ironic to me how quickly “make believe” has caught up with reality.  We as humans have been advancing for one hundred thousand years, but in the last 2 decades, technology has exploded exponentially.

The movie Back To The Future has surpassed the date of October 2015 that Marty McFly made his leap ahead in time to save his and his family’s existence. Space Odyssey 2001, an epic movie where HAL, their on board space computer, takes control of the ship, is now part of history, and the movie 1984 with Big Brother and the totalitarian society has long come and gone.

With many of these older Sci-Fi films, artificial intelligence is a recurring theme.   Amazingly, AI has been around for longer than Marty McFly. The term artificial intelligence was first coined in 1956 at Dartmouth College. However, achieving artificial intelligence was more difficult than coming up with the name. After a rocky start with up and down funding, it wasn’t until the 1990s that research began really moving forward.

Infographic:  History of Artificial Intelligence
A timeline of developments in computers and robotics.

Source: LiveScience

Fast forward to February 2016 when Google’s own Amit Singhal, head of the search engine department, decided to announce his retirement from the company. Google chose to replace him with John Giannandrea who was previously in charge of Google’s artificial intelligence research. Now the word is that Google will be heavily integrating AI into the way its search engine functions.

Since early 2015, Google was using a deep learning system called RankBrain to help generate responses to a small part of the search queries, but Singhal was known for having a resistance to machine learning and preferred using computerized algorithms to do the work instead. It looks as though change is on the horizon now that Singhal is gone and their artificial intelligence guru has taken his place, as you can read about in this article by


AI Is Transforming Google Search. The Rest of the Web Is Next


YESTERDAY, THE 46-YEAR-OLD Google veteran who oversees the company’s 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.Artificial Intelligence

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.

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Artificial Intelligence and the Semantic Web is courtesy of


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