The idea of Robotics & Automation in Healthcare has been around for quite some time now. Our lifestyle is rapidly changing. As a result, the need for medical units is also rapidly increasing. Most of the times the doctors are overworked or hospitals are understaffed. According to a report published by the United States Census Bureau in 2016, a new demographic trend has emerged.

The minister for artificial intelligence at the German government has spoken about the European vision for AI, especially how to grow and gain trust from non-expert users. Prof. Dr. Ina Schieferdecker, a junior minister in Germany's Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF), who has artificial intelligence in her portfolio, recently attended an AI Camp in Berlin (or KI-Camp in German, for "künstliche Intelligenz"). She was interviewed there by DW (Deutsche Welle, Germany's answer to the BBC World Service) on how the German government and the European Union can help alleviate concerns about AI among ordinary users of the internet and information technologies. When addressing the question that AI is often seen as a "black box", and the demand for algorithms to be made transparent, Schieferdecker said she saw it differently. "I don't believe that everyone has to understand AI. Not everyone can understand it," she said.

Almost 200 face recognition algorithms--a majority in the industry--had worse performance on nonwhite faces, according to a landmark study. What they tested: The US National Institute of Standards and Technology (NIST) tested every algorithm on two of the most common tasks for face recognition. The first, known as "one-to-one" matching, involves matching a photo of someone to another photo of the same person in a database. This is used to unlock smartphones or check passports, for example. The second, known as "one-to-many" searching, involves determining whether a photo of someone has any match in a database.

WASHINGTON: How big a difference does it make when you reinforce foot troops with drones and ground robots? You get about a 10–fold increase in combat power, according to a recent Army wargame. "Their capabilities were awesome," said Army Capt. Philip Belanger, a Ranger Regiment and Stryker Brigade veteran who commanded a robot-reinforced platoon in nearly a dozen computer-simulated battles at the Fort Benning's Maneuver Battle Lab. "We reduced the risk to US forces to zero, basically, and still were able to accomplish the mission."

The classic Bribery problem is to find a minimal subset of voters who need to change their vote to make some preferred candidate win. Its important generalizations consider voters who are weighted and also have different prices. We provide an approximate solution for these problems for a broad family of scoring rules (which includes Borda, t-approval, and Dowdall), in the following sense: for constant weights and prices, if there exists a strategy which costs Ψ, we efficiently find a strategy which costs at most Ψ Õ( Ψ). An extension for non-constant weights and prices is also given. Our algorithm is based on a randomized reduction from these Bribery generalizations to weighted coalitional manipulation (WCM). To solve this WCM instance, we apply the Birkhoff-von Neumann (BvN) decomposition to a fractional manipulation matrix. This allows us to limit the size of the possible ballot search space reducing it from exponential to polynomial, while still obtaining good approximation guarantees. Finding a solution in the truncated search space yields a new algorithm for WCM, which is of independent interest.

Personalizing drug prescriptions in cancer care based on genomic information requires associating genomic markers with treatment effects. This is an unsolved challenge requiring genomic patient data in yet unavailable volumes as well as appropriate quantitative methods. We attempt to solve this challenge for an experimental proxy for which sufficient data is available: 42 drugs tested on 1018 cancer cell lines. Our goal is to develop a method to identify the drug that is most promising based on a cell line's genomic information. For this, we need to identify for each drug the machine learning method, choice of hyperparameters and genomic features for optimal predictive performance. We extensively compare combinations of gene sets (both curated and random), genetic features, and machine learning algorithms for all 42 drugs. For each drug, the best performing combination (considering only the curated gene sets) is selected. We use these top model parameters for each drug to build and demonstrate a Drug Recommendation System (Dr.S). Insights resulting from this analysis are formulated as best practices for developing drug recommendation systems. The complete software system, called the Cell Line Analyzer, is written in Python and available on github.

Most may at the time of writing associate EU with Brexit since the United Kingdom is pulling out of the union. The European Union and their member countries does together have a population of approximately 500 million and about $22.0 trillion GDP which places EU as the 2nd largest economic force in the world. Therefore by some measures it is an important area to keep track of, and the international strategy for EU relating to AI may be of interest. By summarising some of these policies in a pragmatic way I hope you as a reader understand that this is no substitute for reading the documents, rather an attempt to bring together a few key points. What I provide is of course not a complete picture, rather small excerpts from an ongoing discussion. Looking at the EU strategy it can be hard to understand where to start.

Artificial intelligence, or AI, was the buzz term across every sector in Singapore this year, and for good reason - the technology of using machines to simulate human intelligence has immense potential to transform lives for the better. In the private sector, AI was used to improve workflow processes in businesses as varied as e-commerce and hospitality. Hotels, for example, are increasingly deploying facial recognition systems, using AI technology to allow self-check-ins quickly and conveniently. Reportedly, trials using such systems at Ascott Orchard, Swissotel The Stamford and Grand Park City Hall can reduce check-in times by up to 70 per cent. National carrier Singapore Airlines uses AI to predict flight delays as well as handle customer feedback and queries more promptly.

DoD clearly has recognized artificial intelligence (AI) as the next game-changer in military competition, with the Pentagon and the services pouring money into numerous development programs. Indeed, mastering AI and machine learning will be crucial to the new way of war envisioned by Pentagon leadership: Multi-Domain Operations. But the US government may be shooting itself in the foot by overlooking a key problem: a lack of American AI specialists, argues Megan Lamberth co-author of "The American AI Century: A Blueprint for Action," a new report from the Center for New American Security. The United States is engaged in a global technology competition in artificial intelligence. But while the US government has shown commitment to developing AI systems that will positively transform the American economy and national security, the country has neglected its most important resource: talent.

PARIS – France's defense ministry announced Monday it had carried out its first armed drone strike, killing seven Islamic extremists in central Mali over the weekend. France joins a tiny group of countries that use armed drones, including the United States. The drone deployment came nearly one month after two French helicopters collided in Mali, killing 13 soldiers in the deadliest military loss for France in nearly four decades. A defense ministry statement said the drone strike took place Saturday while French President Emmanuel Macron was visiting neighboring Cote d'Ivoire, where France has a military base. Macron already had announced that French forces had killed 33 extremists that day.