Google is pulling another lever on its influential search engine in an effort to boost voter turnout in November's U.S. presidential election. Beginning Tuesday, Google will provide a summary box detailing state voting laws at the top of the search results whenever a user appears to be looking for that information. The breakdown will focus on the rules particular to the state where the search request originates unless a user asks for another location. Google is introducing the how-to-vote instructions a month after it unveiled a similar feature that explains how to register to vote in states across the U.S. The search giant said its campaign is driven by rabid public interest in the presidential race between Hillary Clinton and Donald Trump. As of last week, it said, the volume of search requests tied to the election, the candidates and key campaign issues had more than quadrupled compared to a similar point in the 2012 presidential race.

This image provided by Google shows on a mobile device a summary box detailing state voting laws at the top of the search results whenever a request indicates a user is looking for the information. Google begins the how to vote feature in Google Search on Tuesday, Aug. 16, 2016, pulling another lever on its influential search engine in an effort to boost voter turnout in November's U.S. presidential election.

Do machine-learning algorithms processing search engine queries bring on prejudice, discrimination and stereotyping in query results? Search results have been known to highlight these negative attributes in the past. Now researchers at Brazil's Universidade Federal de Minas Gerais suggest it could be true when it comes to female physical attractiveness in images available across the Web. The paper submitted to the International Conference on Social Informatics scheduled for publication analyzes how Google and Bing represent female beauty in their image search results, particularly when it comes to different age and racial groups. They then passed the more than 2,000 images through a program, which estimates subject age, race and gender with an estimated 90% accuracy.

It's possible using Dreambit, a face-swapping search engine that automatically analyzes any photo you upload and figures out how to crop it into images you search for. Your results incorporate your face, cropped and colorized and adapted to the images you receive. It sure does beat the heck out of doing it all manually. Created by computer vision researcher Ira Kemelmacher-Shlizerman at the University of Washington, Dreambit is an interesting tool that can and will be used for tons of silly applications, but the serious implications it has are infinite as well. In a press release, Shlizerman noted how the engine could be used in missing persons cases as it can adapt how victims in said cases can change their looks over time. Dreambit will be on display at SIGGRAPH next week, but unfortunately it's not available to the public just yet.

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.