Three Carnegie Mellon University research teams have received funding through the Program on Fairness in Artificial Intelligence, which the National Science Foundation sponsors in partnership with Amazon. The program supports computational research focused on fairness in AI, with the goal of building trustworthy AI systems that can be deployed to tackle grand challenges facing society. "There have been increasing concerns over biases in AI systems, for example computer vision algorithms working worse for Blacks than for other races, or ads for higher paying jobs only being shown to men," said Jason Hong, a professor in the Human-Computer Interaction Institute (HCII). "Machine learning researchers are developing new tools and techniques to improve fairness from a quantitative perspective, but there are still many blind spots that defy pure quantification." The CMU projects address new methods for detecting bias, translating fairness goals into public policy and increasing the diversity of people able to use systems that recognize human speech.

AI and autonomy are two closely linked concepts. While AI refers to computer-controlled systems, autonomy describes how independently they can act from human operators. NATO has long understood the need to prioritize technological integration with AI and autonomy and published its first AI strategy in October 2021. Notable AI projects include the Chinese Brain Project's mind-controlled drones, and Russia's MiG system, which recommends maneuvers to fighter pilots. Amongst Allied capabilities, the United States Project Maven utilizes target recognition systems to identify hostile combatants and platforms, and the British army's Battlegroup Command and Control Trainer runs conflict simulations.

Beginning in 2013, the Dutch government used an algorithm to wreak havoc in the lives of 25,000 parents. The software was meant to predict which people were most likely to commit childcare-benefit fraud, but the government did not wait for proof before penalizing families and demanding that they pay back years of allowances. Families were flagged on the basis of'risk factors' such as having a low income or dual nationality. As a result, tens of thousands were needlessly impoverished, and more than 1,000 children were placed in foster care. From New York City to California and the European Union, many artificial intelligence (AI) regulations are in the works.

Companies are quickly adopting cybersecurity products and systems that incorporate artificial intelligence (AI) and machine learning, but the technology comes with significant challenges, and it can't replace human analysts, experts say. In a Wakefield Research survey published this week, for example, almost half of IT security professionals (46%) said their AI-based systems create too many false positives to handle, 44% complained that critical events are not properly flagged, and 41% do not know what to do with AI outputs. In total, 89% of companies reported challenges with cybersecurity solutions that claimed to have AI capabilities. Not all AI-based projects are created equal, as some technology is more mature, says Gunter Ollmann, chief security officer at Devo, which sponsored the survey. "When they talk about rolling out AI for cybersecurity ... those are the projects that are commonly failing," he says.

Scientists from China's Zhejiang University have unveiled a drone swarm capable of navigating through a dense bamboo forest without human guidance. The group of 10 palm-sized drones communicate with one another to stay in formation, sharing data collected by on-board depth-sensing cameras to map their surroundings. This method means that if the path in front of one drone is blocked, it can use information collected by its neighbors to plot a new route. The researchers note that this technique can also be used by the swarm to track a human walking through the same environment. If one drone loses sight of the target, others are able to pick up the trail.

The panel on'The Five' sounds off on DHS chief's defense of new bureaucracy The Biden administration announced the establishment of the Disinformation Governance Board (DGB) last week to be created within the Homeland Security Department (DHS), aiming to counter "misinformation related to homeland security." There are many unknowns about DGB. For example, we don't know how the members of DGB will be selected, what kind of power it will have, and how it defines misinformation. But the early signs are not promising. The vaguely defined roles and authorities of DGB have alarmed Americans, and many see the agency as the "Ministry of Truth" that George Orwell warned us about in his dystopian novel "1984."

Euphemisms have not received much attention in natural language processing, despite being an important element of polite and figurative language. Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not. Nevertheless, the first step to tackling the issue is to collect and analyze examples of euphemisms. We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus. Additionally, we present a subcorpus of texts where these PETs are not being used euphemistically, which may be useful for future applications. We also discuss the results of multiple analyses run on the corpus. Firstly, we find that sentiment analysis on the euphemistic texts supports that PETs generally decrease negative and offensive sentiment. Secondly, we observe cases of disagreement in an annotation task, where humans are asked to label PETs as euphemistic or not in a subset of our corpus text examples. We attribute the disagreement to a variety of potential reasons, including if the PET was a commonly accepted term (CAT).

In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this paper, we provide a principled framework for incorporating action recommendations from online suboptimal advisors in multi-agent settings. We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning based algorithms: ADMIRAL - Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed point guarantees regarding their learning in general-sum stochastic games. Furthermore, extensive experiments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors.

We learn new skills by repetition and reinforcement learning. Through trial and error, we repeat actions leading to good outcomes, try to avoid bad outcomes and seek to improve those in between. Researchers are now designing algorithms based on a form of artificial intelligence that uses reinforcement learning. They are applying them to automate chemical synthesis, drug discovery and even play games like chess and Go. Scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a reinforcement learning algorithm for yet another application.

The Inspection and Maintenance (I&M) Industry represents a large economic activity spanning across multiple sectors such as energy, oil & gas, water supply, transport, civil engineering, and infrastructure. RIMA project aims at bringing together Digital Innovation Hubs and Facilitators operating under a common network that allow them to join forces and competences in promoting I&M robotics in Europe. The BIS Research projects' analysis of the Inspection and Maintenance Robot Industry forecasts that the I&M market will grow at a significant CAGR of 12.73% on the basis of value from 2020 to 2025. In 2019, Europe dominated the 40% of the global inspection and maintenance robot market (BIS322A, Mar 2020). Although the European Union hosts most of the I&M robotics offer – being France, Germany, and Spain (and U.K. until 2021 Brexit) the leading manufacturing countries, there is still a bottleneck connecting this offer to the market and high potential applications.