Golan Levin, an associate professor of art at Carnegie Mellon University, suggested. I was looking at a satellite image of the school's campus in Pittsburgh, embedded in the home page of Levin's latest online project, Terrapattern. "What you should immediately see are all the most tennis-court-ish patches of Allegheny County," he said. With gratifying speed, the right-hand side of my screen filled with dozens and dozens of tennis courts--solo or in pairs or in clusters of six, white on green, purple on green, green on red. A confusingly painted parking lot ended up in the mix, too.

Search engines that aren't Google rarely have much that's interesting to offer to the average consumer. But Omnity, a new search engine aimed at researchers -- or even just students doing their homework -- offers some glimmers of something new that make it worth taking notice. Search, as we know it, is ripe for some sort of change, after all. Google is certainly working to bake search more fully into our cars, phones and other devices. Specialized search engines -- for flights, places to stay, even .gifs And then there are those AI bots being promised by Google, Facebook, Microsoft and others.

Ever since gerard salton of Cornell University developed the first computerized search engine (Salton's Magical Automatic Retriever of Text, or SMART) in the 1960s, search developers have spent decades essentially refining Salton's idea: take a query string, match it against a collection of documents, then calculate a set of relevant results and display them in a list. All of today's major Internet search engines--including Google, Amazon, and Bing--continue to follow Salton's basic blueprint. Yet as the Web has evolved from a loose-knit collection of academic papers to an ever-expanding digital universe of apps, catalogs, videos, and cat GIFs, users' expectations of search results have shifted. Today, many of us have less interest in sifting through a collection of documents than in getting something done: booking a flight, finding a job, buying a house, making an investment, or any number of other highly focused tasks. Meanwhile, the Web continues to expand at a dizzying pace.

If you're still stuck on the same old on-page SEO routine, it's time to wake up, smell the coffee, and figure out what's up in 2016. If you're waking up from 2010, you've got a long way to go, Rip Van Winkle. But even if you're poking into the SEO world from just a few months ago, you might be surprised at how much changed while you were away. We're always in need of "updated" information for SEO, and I thought now was a great time to release my updated checklist. I've organized this list into three main sections: Analytics help you gain an insight into your visitor demographics and understand your marketing better.

Lately, there's been a rumble pretty much everywhere about artificial intelligence, digital personal assistants, the Internet of Things, wearables and apps for everything. I've even written about what the rise of digital assistants means to search. There are some who claim that these new technologies will render search obsolete, passed over for the convenience and joy of an always-available digital world. I think they are wrong. Instead of looking at a search engine as an ad platform, we need to remember what it actually does for people.

In a previous post I wrote about how to build a'People Who Like This Also Like ...' feature for displaying lists of similar musicians. My goal was to show how simple Information Retrieval techniques can do a good job calculating lists of related artists. For instance, using BM25 distance on The Beatles shows the most similar artists being John Lennon and Paul McCartney. One interesting technique I didn't cover was using Matrix Factorization methods to reduce the dimensionality of the data before calculating the related artists. This kind of analysis can generate matches that are impossible to find with the techniques in my original post.

My name is Jonathan Armoza and I am a data science intern at Neustar and a PhD candidate in English Literature at New York University. My work focuses on the development of computational text mining and visualization methods in the emerging field of digital humanities. The era of big data has created the need to develop techniques and mechanisms to not only handle large datasets, but to understand them as well. Much of this influx of information is about people, places, and things. Although some of that data is anonymized, there are a number of reasons we might want to understand how to associate those real world "entities" with their data points.

Lately, there's been a rumble pretty much everywhere about artificial intelligence, digital personal assistants, the Internet of Things, wearables and apps for everything. I've even written about what the rise of digital assistants means to search. There are some who claim that these new technologies will render search obsolete, passed over for the convenience and joy of an always-available digital world. I think they are wrong. Instead of looking at a search engine as an ad platform, we need to remember what it actually does for people.

Google it today and you'll see that the European Commission has turned up the heat in its long-running probe into anti-competitive behaviour by the web's most popular search engine. EC competition chief, Margrethe Vestager, issued formal objections alleging that Google abuses its dominant position in the market of "general internet search". In particular, the EC claims that Google artificially boosts its own products in returning Google comparison shopping results in its service "Google Shopping", even if those products aren't the best or cheapest – the "most relevant", as the Commission puts it – for consumers. Since taking office in November 2014, Vestager has made the Google inquiry a top priority, signalling a willingness to consider court battles and hefty fines if Google and other digital giants don't fall into line with European competition law. In this, she has displayed a distinct shift from her predecessor, Joaquín Almunia, whose multiple attempts to achieve private settlement with Google fell apart a year ago, before descending into a political and economic boxing match.

Computers don't have the ability to understand English language, and to address this issue, this is where the Google's free AI software dubbed Parsey McParseface, based on SyntaxNet machine learning framework comes in. The search engine giant announced on Thursday that it will release a new piece of software that will understand written English. More interestingly, Google will release the AI software as open-source which means developers can freely use the software to train computer programs with ability to process natural language. Google says its latest innovation will be equipped with Natural Language Understanding (NLU) to parse written English sentences with up to 94 percent of accuracy. Google added that trained human linguists can only achieve up to 96 percent of accuracy. As an AI machine to achieve such milestone, it is a huge breakthrough in the field of artificial intelligence.