The face of SEO is changing, and it's mainly due to incredible research and development into artificial intelligence (AI) baked into search algorithms. A decade ago, search engine marketers could create a site with some keywords in specific places, drop some links around the web, and start ranking within a short amount of time. The web has grown tremendously since that time, and now these methods are no longer viable. Instead of focusing on keywords, search engine algorithms attempt to "understand" your content. The latest news of Google's new RankBrain algorithm is just one example of why the future of SEO will change Internet marketing. Before getting into the details of 2016 SEO, it helps to understand Google's market.

Tutorials, books, articles, data sets, certifications, you name it. All about data science, machine learning and related topics. You can find them with a simple keyword search: enter the keyword "free" in the DSC's search box, and here are the results. Below is a screenshot of the DSC search results page, for the keyword "free". It shows the top 6 results, out of dozens of highly relevant search results.

The way you add a bot is the same way you'd add a contact; indeed, the Add Contact screen in Skype now has two categories: "People" and "Bots." As of right now, the only Bots available to add on Skype are Bing Music, Bing News, Bing Images, Getty Images and Build Bot (which is mostly centered around the Build conference). They're mostly in Preview mode at the moment (in other words, they're still sort of in beta), but they do appear to be functional. As you might expect, the chat bots respond to very specific keywords. For example, with the Bing Music bot, typing in "Hello" would bring up Adele's song (a link to her YouTube video, it looks like) rather than polite small talk.

An Iranian charged with hacking the computer system that controlled a New York dam used a readily available Google search process to identify the vulnerable system, according to people familiar with the federal investigation. The process, known as "Google dorking," isn't as simple as an ordinary online search. Yet anyone with a computer and Internet access can perform it with a few special techniques. Federal authorities say it is...

We represent knowledge as integrity constraints in a formalization of probabilistic spatio-temporal knowledge bases. We start by defining the syntax and semantics of a formalization called PST knowledge bases. This definition generalizes an earlier version, called SPOT, which is a declarative framework for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because the exact value is unknown. We augment the previous definition by adding a type of non-atomic formula that expresses integrity constraints. The result is a highly expressive formalism for knowledge representation dealing with probabilistic spatio-temporal data. We obtain complexity results both for checking the consistency of PST knowledge bases and for answering queries in PST knowledge bases, and also specify tractable cases. All the domains in the PST framework are finite, but we extend our results also to arbitrarily large finite domains.

I am a newbie to Bigdata and would like to highlight some significant advantages if incorporated in a company's supply-chain management strategies, expecting the reader's views and suggestions. Because, in recent past I have developed a online supply-chain management systems in which sellers and customers are matched using an algorithm. It acted as a decision support system and I needed to dig deeper on the available data to get more insights over the data pattern (even for evaluating its correctness). We know that data is generated throughout the supply chain – So, for sure the manufacturers of consumer goods or services analyze key performance indicators to ensure the resources are delivering at its capacity. So, there is a need for efficient data storage/management and information retrieval techniques. Current technology can track and capture data effectively and efficiently, even in vast quantities.

The key points for RAKE is the parameters setting, and RAKE provides a method to select a proper parameters based on the train data.

Zittrain, J. Engineering an election – digital gerrymandering poses a threat to democracy.

Geoffrey Hinton, known as the godfather of deep learning -- the tech that helped Google's AlphaGo beat a master at Go -- said the most powerful machines are about a million times smarter than the human brain, and becoming more sophisticated each year. Hinton, who splits his time between working at Google and the University of Toronto, earned a PhD in AI from Edinburgh in 1978. While Google uses AI in its search engines to learn how to return smarter query results, Hinton predicts it still will take more than five years before machines possess human-level abilities. Start with search engines and move the technology into android-looking robotics. The robotics use many of the technologies required for smarter search engine queries, such as artificial intelligence and natural language processing.

Recent years have seen significant efforts in the area of Cross Language Information Retrieval (CLIR) for text retrieval. This work initially focused on formally published content, but more recently research has begun to concentrate on CLIR for informal social media content. However, despite the current expansion in online multimedia archives, there has been little work on CLIR for this content. While there has been some limited work on Cross-Language Video Retrieval (CLVR) for professional videos, such as documentaries or TV news broadcasts, there has to date, been no significant investigation of CLVR for the rapidly growing archives of informal user generated (UGC) content. Key differences between such UGC and professionally produced content are the nature and structure of the textual UGC metadata associated with it, as well as the form and quality of the content itself. In this setting, retrieval effectiveness may not only suffer from translation errors common to all CLIR tasks, but also recognition errors associated with the automatic speech recognition (ASR) systems used to transcribe the spoken content of the video and with the informality and inconsistency of the associated user-created metadata for each video. This work proposes and evaluates techniques to improve CLIR effectiveness of such noisy UGC content. Our experimental investigation shows that different sources of evidence, e.g. the content from different fields of the structured metadata, significantly affect CLIR effectiveness. Results from our experiments also show that each metadata field has a varying robustness to query expansion (QE) and hence can have a negative impact on the CLIR effectiveness. Our work proposes a novel adaptive QE technique that predicts the most reliable source for expansion and shows how this technique can be effective for improving the CLIR effectiveness for UGC content.