A shoddily tailored suit or a shrunken T-shirt may not be the most stylish, but wearing them is unlikely to hurt more than your reputation. An ill-fitting robotic exoskeleton on the battlefield or factory floor, however, could be a much bigger problem than a fashion faux pas. Exoskeletons, many of which are powered by springs or motors, can cause pain or injury if their joints are not aligned with the user's. To help manufacturers and consumers mitigate these risks, researchers at the National Institute of Standards and Technology (NIST) developed a new measurement method to test whether an exoskeleton and the person wearing it are moving smoothly and in harmony. In a new report, the researchers describe an optical tracking system (OTS) not unlike the motion capture techniques used by filmmakers to bring computer-generated characters to life.

An example of ML, specifically supervised ML, is the DoD's use of a computer vision algorithm to identify people and objects of interest in surveillance footage. The computer is fed a labeled or tagged dataset, e.g., images of high-profile individuals or armored security vehicles that contain notations of certain physical characteristics. The goal is for the computer's machine learning algorithm to learn these patterns, establish profiles, and be able to accurately identify these individuals or objects once it begins sifting through the footage.

Less than 20% of Europeans believe that current laws "efficiently regulate" artificial intelligence, and 56% have low trust in authorities to exert effective control over the technology, according to a new survey from the European Consumer Organisation (BEUC). The findings have important implications for the governance and design of AI-powered public services, emphasising the need to address citizens' fears over transparency, accountability, equity in decision-making, and the management of personal data. The BEUC surveyed 11,500 consumers in nine European countries: Belgium, Denmark, France, Germany, Italy, Poland, Portugal, Spain and Sweden. It found that while a large majority of respondents feel that artificial intelligence (AI) can be useful, most don't trust the technology and feel that current regulations do not protect them from the harms it can cause. It also found that 66% of respondents from Belgium, Italy, Portugal and Spain agree that AI can be hazardous and should be banned by authorities.

There are well-documented examples of AI systems making decisions that affect protected classes, such as housing assistance or unemployment benefits. AI can be used to screen resumes; banks apply AI models to grant individual consumers credit and set interest rates for them. Many small decisions, taken together, can have large effects, such as: AI-driven price discrimination could lead to certain groups in a society consistently paying more. But are there AI applications today that affect everyone, no matter their "class"? As I mentioned earlier, we are shifting our AI Ethics courses to more practical, useful techniques.

Earlier this year, the independent research organisation of which I am the Director, London-based Ada Lovelace Institute, hosted a panel at the world's largest AI conference, CogX, called The Ethics Panel to End All Ethics Panels. The title referenced both a tongue-in-cheek effort at self-promotion, and a very real need to put to bed the seemingly endless offering of panels, think-pieces, and government reports preoccupied with ruminating on the abstract ethical questions posed by AI and new data-driven technologies. We had grown impatient with conceptual debates and high-level principles. And we were not alone. It supersedes the two waves that came before it: the first wave, defined by principles and dominated by philosophers, and the second wave, led by computer scientists and geared towards technical fixes.

Washington – U.S. Secretary of Defense Mark Esper announced Wednesday an ambitious plan to expand the U.S. Navy with a range of unmanned and autonomous ships, submarines and aircraft to confront the growing maritime challenge from China. The Pentagon chief said a sweeping review of U.S. naval power dubbed "Future Forward" had laid out a "game-changer" plan that would expand the U.S. sea fleet to more than 355 ships, from the current 293. The plan, which requires adding tens of billions of dollars to the U.S. Navy's budget between now and 2045, is aimed at maintaining superiority over Chinese naval forces, seen as the primary threat to the United States. "The future fleet will be more balanced in its ability to deliver lethal effects from the air, from the sea, and from under the sea," Esper said in a speech at the Rand Corp. in California. The expansion will add "more and smaller" surface ships; more submarines; surface and subsurface vessels that are optionally manned, unmanned and autonomous; and a broad range of unmanned carrier-based aircraft.

Washington – NASA is considering approving by next April up to two planetary science missions from four proposals under review, including one to Venus that scientists involved in the project said could help determine whether or not that planet harbors life. An international research team on Monday described evidence of potential microbes residing in the harshly acidic Venusian clouds: traces of phosphine, a gas that on Earth is produced by bacteria inhabiting oxygen-free environments. It provided strong potential evidence of life beyond Earth. The U.S. space agency in February shortlisted four proposed missions that are now being reviewed by a NASA panel, two of which would involve robotic probes to Venus. One of those, called DAVINCI, would send a probe into the Venusian atmosphere.

From Ronald Reagan in 1984 to Bob Dole in 1996 and even Hillary Clinton in 2016, candidate health has become a common theme across recent U.S. presidential campaigns. The issue is poised to take on added significance this fall. No matter who wins, the U.S. is set to inaugurate its oldest president by a wide margin. The Trump campaign and its surrogates have seized on Democratic nominee Joe Biden's age and have been painting him as mentally unfit for the presidency. Videos of Biden falling asleep during an interview, misspeaking about the dangers of "Joe Biden's America" and appearing lost during a campaign event have bolstered the belief, particularly among Trump supporters, that Biden is in cognitive decline.

COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e. chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data utilization. The presented framework utilizes advanced web technologies to ensure reusability and reliability, and an inference engine for natural language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework's usage and benefits.

In this thesis we look into programming by example (PBE), which is about finding a program mapping given inputs to given outputs. PBE has traditionally seen a split between formal versus neural approaches, where formal approaches typically involve deductive techniques such as SAT solvers and types, while the neural approaches involve training on sample input-outputs with their corresponding program, typically using sequence-based machine learning techniques such as LSTMs [41]. As a result of this split, programming types had yet to be used in neural program synthesis techniques. We propose a way to incorporate programming types into a neural program synthesis approach for PBE. We introduce the Typed Neuro-Symbolic Program Synthesis (TNSPS) method based on this idea, and test it in the functional programming context to empirically verify type information may help improve generalization in neural synthesizers on limited-size datasets. Our TNSPS model builds upon the existing Neuro-Symbolic Program Synthesis (NSPS), a tree-based neural synthesizer combining info from input-output examples plus the current program, by further exposing information on types of those input-output examples, of the grammar production rules, as well as of the hole that we wish to expand in the program. We further explain how we generated a dataset within our domain, which uses a limited subset of Haskell as the synthesis language. Finally we discuss several topics of interest that may help take these ideas further. For reproducibility, we release our code publicly.