A similar process is done on the target images to mask out the faces and intelligently put your own in their place -- and voila! It's not limited to hairstyles, either: put yourself in a movie, a location, a painting -- as long as there's a similarly positioned face to swap yours with, the software can do it. Kemelmacher-Shlizerman has also created systems that do automated age progression, something that can be useful in missing persons cases. "This is a first step in trying to imagine how a missing person's appearance might change over time."

Ever wonder what you would look like with long, wavy hair? I think you'd look great. But how can you try on a few looks without spending a fortune at the salon, or hours in photoshop? All you need is a selfie and Dreambit, the face-swapping search engine. The system analyzes the picture of your face and determines how to intelligently crop it to leave nothing but your face.

The problem of content discovery and recommendation is very common in many machine learning applications: social networks, news aggregators and search engines are constantly updating and tweaking their algorithms to give individual users a unique experience. Personalization engines suggest relevant content with the objective of maximizing a specific metric. For example: a news website might want to increase the number of clicks in a session; on the other hand, for an e-commerce app it is very important to identify visitors that are more likely to buy a product in order to target them with special offers. In this post I will explore some techniques that can be used to generate recommendations and predictions using the amazingly fast Vowpal Wabbit library. Make sure that you have installed scikit-learn and Vowpal Wabbit's Prerequisite Software.

Object queries are essential in information seeking and decision making in vast areas of applications. However, a query may involve complex conditions on objects and sets, which can be arbitrarily nested and aliased. The objects and sets involved as well as the demand---the given parameter values of interest---can change arbitrarily. How to implement object queries efficiently under all possible updates, and furthermore to provide complexity guarantees? This paper describes an automatic method. The method allows powerful queries to be written completely declaratively. It transforms demand as well as all objects and sets into relations. Most importantly, it defines invariants for not only the query results, but also all auxiliary values about the objects and sets involved, including those for propagating demand, and incrementally maintains all of them. Implementation and experiments with problems from a variety of application areas, including distributed algorithms and probabilistic queries, confirm the analyzed complexities, trade-offs, and significant improvements over prior work.

Hadoop, therefore, doesn't have what it requires to be considered a data warehouse. Obviously, Hadoop vendors will probably be working more difficult to improve security of information access, restrict permissions, and address a broader array of data protection issues. The two major goals of the initiative should happen to increase performance and provide a rich series of SQL features like analytic functions, query optimization, and standard data types including timestamp etc.. An increasing community of Hadoop vendors provide a byzantine selection of solutions. The opportunity would be to monetise huge levels of data using tools which weren't previously offered.

At the strategy conference of Baidu cloud computing on Wednesday, Baidu launched three intelligent cloud platforms. They will integrate with pre-existing cloud services for its open cloud platforms to help enterprises increase working efficiency. Baidu founder and CEO Robin Li said that Baidu has been a de facto search engine company from the very beginning, but that the company was bound to move into cloud technology, as efficient web searching is made possible via the cloud. Li said that Baidu used to consider cloud computing as too simple a technology and would rather focus on building its web search engine, but then some recent changes happened: On the one hand, the days are gone when economic development is accelerated by a cheap labor force, and companies today must survive using technological innovations and higher efficiency. On the other hand, cloud technology has been making breakthroughs, and it is no longer merely about storage and computing.

Microsoft today announced the general availability of the Azure SQL Data Warehouse, an elastic data warehouse as a service with enterprise-class features. It is a fully managed DW as a Service that you can provision in minutes and scale up to 60 times larger in seconds. With Azure SQL Data Warehouse, storage and compute scale independently. You can dynamically deploy, grow, shrink, and even pause compute, taking advantage of best-in-class price/performance. Also, SQL Data Warehouse uses the power and familiarity of T-SQL to let you easily integrate query results across relational data in your data warehouse and non-relational data in Azure blob storage.

Social Content is Clearly Overtaking Traditional Journalism Despite constant warnings from hardcore journalists and reporters alike, it seems that the people now prefer using the social media to remain up-to-date with the latest and greatest in international news, rather than relying on traditional news sources like the New York Times or The Telegraph. There is an obvious reason behind this increase. Almost everyone who knows how to use the internet probably has an account on Facebook and Twitter, and thanks to recent improvements in both the platforms, sharing and accessing latest content on the fly is easier than ever. As such, matters of importance and attention seem to be showing up on our Facebook and Twitter feeds far before they appear on any of the traditional news websites, causing people to flock to them instead. A number of reputed online news sources, including the Huffington Post, are now taking steps to ensure that content, especially news, can be shared across their platforms just as easily as it is on Facebook and Twitter.

Natural Language Processing seems to have become somewhat of a commodity in recent years. More than a few companies have sprung up that offer basic NLP capabilities through a cloud API. If you'd like to know whether a text carries a positive or negative message, or what people or companies it mentions, you can just send it to one of these black boxes, and receive the answer in less than a second. Superficially, all these NLP APIs look more or less the same. Textrazor, AlchemyAPI, Aylien, MeaningCloud and Lexalytics all offer similar services (named entity recognition, sentiment analysis, keyword extraction, topic identification, etc.), and do so through similar interfaces.

Today was a day for the history books. The UK has voted to leave the European Union and opened a deep crack in the heart of Europe. As a consequence of this result, Prime Minister David Cameron will step down by October urging for a fresh leadership. At this point nobody knows the repercussions of these results. Will the Brexit hurt the economy of the UK and ignite a new recession?