US lawmakers have asked the Government Accountability Office to examine how face recognition technology is being used by companies and law enforcement agencies. The questioners: A group of Democrats from both the House of Representatives and the Senate sent a letter to the GAO asking to examine which agencies are using the technology, and what safeguards the industry has in place. Some form of government regulation could eventually be imposed. Eye spies: There is growing concern that unfettered use of facial recognition could enable greater government surveillance and automate discrimination. Some companies also appear concerned.

Amazon's face surveillance technology is the target of growing opposition nationwide, and today, there are 28 more causes for concern. In a test the ACLU recently conducted of the facial recognition tool, called "Rekognition," the software incorrectly matched 28 members of Congress, identifying them as other people who have been arrested for a crime. The members of Congress who were falsely matched with the mugshot database we used in the test include Republicans and Democrats, men and women, and legislators of all ages, from all across the country. Our test used AmazonRekognition to compare images of members of Congress with a database of mugshots. The results included 28 incorrect matches.

There is a treasure trove of litigation data that for years was virtually inaccessible. While court rulings and filings were available and individual documents could be accessed and viewed, the technology needed to search and analyze the data and provide useful, actionable information simply did not exist. The recent maturation of the foundational technologies needed to support machine learning have made advanced data analytics and sophisticated language processing possible on a scale never before seen. Using these tools, massive amounts of data can be sifted through, organized and analyzed in mere seconds. These capabilities are particularly useful in the litigation arena.

From relentless automation to algorithmic bias and human rights abuses, artificial intelligence (AI) has a laundry list of well-known potential costs and risks that do not bode well for the future of inclusion. Take Amazon's recent foray into mass surveillance, for example. Designed by Amazon Web Services, Amazon Rekognition is a deep learning AI that makes it easier for businesses to add image and video analysis to their applications. Common use cases include searchable image and video libraries, face-based user verification, and unsafe content detection. But documents obtained by the American Civil Liberties Union (ACLU) of Northern California show that Amazon is now supplying its facial recognition technology to governments and law enforcement agencies, as well. Last month, a coalition of over 70 civil liberties and human rights organizations signed a letter that calls on Amazon to stop powering the government's surveillance infrastructure.

"We create open platforms and share our technology because it helps new ideas get out faster," Pichai said. Then he namechecked TensorFlow, the machine learning software Google developed and uses internally. The company open sourced the code in 2015, and it has since been downloaded more than 15 million times. "We created TensorFlow to make it possible for anyone to use AI," Pichai said. Such homilies to openness have become standard from the large tech companies competing intensely to develop AI technology.

LOS ANGELES – Law enforcement officials are looking into whether a New York man arrested in California for killing a woman he met on a dating app may have killed others. Danueal Drayton was arrested in Los Angeles last week and charged with raping and strangling a woman. Two law enforcement officials told The Associated Press on Monday that Drayton talked about killing at least five others in Connecticut and New York. Investigators are trying to determine whether his claims are true. The officials said Drayton did not admit killing Samantha Stewart, a nurse found dead in her Queens apartment, though police believe he's responsible for her murder.

Humans should get'out of the loop' of artificial intelligence systems, UTS roboticist Professor Mary-Anne Williams argued last week at an Australian Human Rights Commission technology conference in Sydney. AI needn't consult a flesh and blood individual even when making life or death decisions, said Williams, director of The Magic Lab at the university's Centre of Artificial Intelligence. The rise of autonomous weapons systems which operate without human control is being campaigned against around the world. "States must draw the line now against unchecked autonomy in weapon systems by ensuring that the decision to take human life is never delegated to a machine," the Campaign to Stop Killer Robots states. In Australia, 122 AI experts last year signed a letter to Prime Minister Malcolm Turnbull urging him to "take a firm global stand" against weapons systems that remove "meaningful human control" when selecting targets and deploying lethal force.

Artificial Intelligence (AI) is a much-bandied about term by both product vendors and reporters. The former is trying to sell you on their product, the latter uses the topic to increase readership. It often works because advisors are insecure about the term. Generally speaking, we fear AI; we're not sure what it means exactly, but we're pretty sure it will replace us some day. Today we start a three-part blog series that is intended to dispel these fears and shed some light on the topic.

Answer Set Programming (ASP) is a well-known declarative problem solving approach based on nonmonotonic logic programs, which has been successfully applied to a wide range of applications in artificial intelligence and beyond. To address the needs of modern applications, HEX-programs were introduced as an extension of ASP with external atoms for accessing information outside programs via an API style bi-directional interface mechanism. To evaluate such programs, conflict-driving learning algorithms for SAT and ASP solving have been extended in order to capture the semantics of external atoms. However, a drawback of the state-of-the-art approach is that external atoms are only evaluated under complete assignments (i.e., input to the external source) while in practice, their values often can be determined already based on partial assignments alone (i.e., from incomplete input to the external source). This prevents early backtracking in case of conflicts, and hinders more efficient evaluation of HEX-programs. We thus extend the notion of external atoms to allow for three-valued evaluation under partial assignments, while the two-valued semantics of the overall HEX-formalism remains unchanged. This paves the way for three enhancements: first, to evaluate external sources at any point during model search, which can trigger learning knowledge about the source behavior and/or early backtracking in the spirit of theory propagation in SAT modulo theories (SMT). Second, to optimize the knowledge learned in terms of so-called nogoods, which roughly speaking are impossible input-output configurations. Shrinking nogoods to their relevant input part leads to more effective search space pruning. And third, to make a necessary minimality check of candidate answer sets more efficient by exploiting early external evaluation calls. As this check usually accounts for a large share of the total runtime, optimization is here particularly important. We further present an experimental evaluation of an implementation of a novel HEX-algorithm that incorporates these enhancements using a benchmark suite. Our results demonstrate a clear efficiency gain over the state-of-the-art HEX-solver for the benchmarks, and provide insights regarding the most effective combinations of solver configurations.

London-based CUBE, was founded in 2011 and claims to offer a RegTech platform that can help businesses cut regulatory costs and minimize risk of non-compliance. The 94-employee company claims their platform can assist in predicting compliance risk, automating AML, Know Your Customer (KYC) and Cyber/information security processes. CUBE states that the platform uses machine learning to help enterprises to automatically keep track of global regulatory data and prompt alerts by detecting regulatory changes that pose a compliance risk. The company claims it has created a regulatory'data lake' that covers the regulations for financial services organizations across the globe. We, however, could find no evidence of how extensive their database is.