A robot from the Artificial Intelligence and Intelligent Systems (AIIS) laboratory of Italy's ... [ ] National Interuniversity Consortium for Computer Science (CINI) is displayed at the 7th edition of the Maker Faire 2019, the greatest European event on innovation, on October 18, 2019 in Rome. The enormous leaps in technology and HR have created many opportunities for companies to rethink how they address processes around human resources. The potential for efficiency and speed is unlike anything considered, and at a time when there is more choice than ever to draw talent from a global pool. Still, there is a greater need to ensure successful recruitment and retention. As organizations grapple with moving towards the next normal after successive lockdowns, they also need to continue a proactive approach to handling diversity challenges in the workplace.

Nearly two thousand government bodies, including police departments and public schools, have been using Clearview AI without oversight. Buzzfeed News reports that employees from 1,803 public bodies used the controversial facial-recognition platform without authorization from bosses. Reporters contacted a number of agency heads, many of which said they were unaware their employees were accessing the system. A database of searches, outlining which agencies were able to access the platform, and how many queries were made, was leaked to Buzzfeed by an anonymous source. It has published a version of the database online, enabling you to examine how many times each department has used the tool.

The concept of digital twin is not new. The technology has been around since the 1960s. NASA has been physically creating duplicate systems for its various space missions at ground level, to test its equipment in a virtual environment. An example of this is Apollo 13, for which a digital twin was developed by NASA to assess and simulate conditions on board. In the recent past, digital twin has become one of the most promising technological trends. It is estimated that the global Digital Twin technology will reach $48.2 billion by 2026, from $3.1 billion in 2020 and estimated to grow at a rate of 58% between 2021 and 2026 A digital twin is a digital representation of a physical object, process, or service.

Nasa has revealed that there are more hidden Easter eggs on the Marsperseverance rover in a perfectly timed announcement over the holiday weekend. The six-wheeled Perseverance rover was landed on Mars on 18 February, and has begun traversing the landscape understand the geology of Mars and seek signs of ancient life. The rover boasts several embellishments, including artwork, signs, and symbols on board thoughtfully chosen to reflect the significance of the machine. And while it was thought that all of the Easter eggs onboard had been officially revealed, CBS News reported that two more unique details were divulged on Easter Sunday. The first Easter egg is an individual 17-digit ID on the mission nameplate, reading "AONREHMELN1730055".

In this work, we propose a novel missile guidance algorithm that combines deep learning based trajectory prediction with nonlinear model predictive control. Although missile guidance and threat interception is a well-studied problem, existing algorithms' performance degrades significantly when the target is pulling high acceleration attack maneuvers while rapidly changing its direction. We argue that since most threats execute similar attack maneuvers, these nonlinear trajectory patterns can be processed with modern machine learning methods to build high accuracy trajectory prediction algorithms. We train a long short-term memory network (LSTM) based on a class of simulated structured agile attack patterns, then combine this predictor with quadratic programming based nonlinear model predictive control (NMPC). Our method, named nonlinear model based predictive control with target acceleration predictions (NMPC-TAP), significantly outperforms compared approaches in terms of miss distance, for the scenarios where the target/threat is executing agile maneuvers.

Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains. In many applications of GSP, multiple network structures are available, each of which captures different aspects of the same underlying phenomenon. To integrate these different data sources, graph alignment techniques attempt to find the best correspondence between vertices of two graphs. We consider a generalization of this problem, where there is no natural one-to-one mapping between vertices, but where there is correspondence between the community structures of each graph. Because we seek to learn structure at this higher community level, we refer to this problem as "coarse" graph alignment. To this end, we propose a novel regularized partial least squares method which both incorporates the observed graph structures and imposes sparsity in order to reflect the underlying block community structure. We provide efficient algorithms for our method and demonstrate its effectiveness in simulations.

Confidence in the regulatory environment is crucial to enable responsible AI innovation and foster the social acceptance of these powerful new technologies. One notable source of uncertainty is, however, that the existing legal liability system is unable to assign responsibility where a potentially harmful conduct and/or the harm itself are unforeseeable, yet some instantiations of AI and/or the harms they may trigger are not foreseeable in the legal sense. The unpredictability of how courts would handle such cases makes the risks involved in the investment and use of AI difficult to calculate with confidence, creating an environment that is not conducive to innovation and may deprive society of some benefits AI could provide. To tackle this problem, we propose to draw insights from financial regulatory best practices and establish a system of AI guarantee schemes. We envisage the system to form part of the broader market-structuring regulatory frameworks, with the primary function to provide a readily available, clear, and transparent funding mechanism to compensate claims that are either extremely hard or impossible to realize via conventional litigation. We propose it to be at least partially industry-funded. Funding arrangements should depend on whether it would pursue other potential policy goals aimed more broadly at controlling the trajectory of AI innovation to increase economic and social welfare worldwide. Because of the global relevance of the issue, rather than focusing on any particular legal system, we trace relevant developments across multiple jurisdictions and engage in a high-level, comparative conceptual debate around the suitability of the foreseeability concept to limit legal liability. The paper also refrains from confronting the intricacies of the case law of specific jurisdictions for now and—recognizing the importance of this task—leaves this to further research in support of the legal system’s incremental adaptation to the novel challenges of present and future AI technologies. This article appears in the special track on AI and Society.

Topic models such as the Structural Topic Model (STM) estimate latent topical clusters within text. An important step in many topic modeling applications is to explore relationships between the discovered topical structure and metadata associated with the text documents. Methods used to estimate such relationships must take into account that the topical structure is not directly observed, but instead being estimated itself. The authors of the STM, for instance, perform repeated OLS regressions of sampled topic proportions on metadata covariates by using a Monte Carlo sampling technique known as the method of composition. In this paper, we propose two improvements: first, we replace OLS with more appropriate Beta regression. Second, we suggest a fully Bayesian approach instead of the current blending of frequentist and Bayesian methods. We demonstrate our improved methodology by exploring relationships between Twitter posts by German members of parliament (MPs) and different metadata covariates.

A robot takeover is the plot of many science fiction films, but a video shared on Twitter has some believing the idea may not be too farfetched. A user shared a clip from Chinese-based Unitree robotics firm that shows a squadron of four-legged machines moving in unison. The AI-powered, canine-like robot, named, Aliengo, is designed with depth perception, high explosive sport performance and an advanced protection level – among other features. The short video shows dozens of robots crouched down on the floor, but then spring up to a squatting position, lean forward and then return to their original position - all at the same time. However, the video has sparked some funny, and terrifying responses, on Twitter with some likening to a scene from'The Terminator' and'Black Mirror.'

The relationship between bigdata and Artificial Intelligence has been at the center of the speech of the Acting Chairman of the Federal Trade Commission, Rebecca Kelly Slaughter, at the Forum for the Future of Privacy, reported in the document entitled "Protecting consumer privacy in times of crisis", on February 10, 2021. The starting point of her talk was this undoubtedly relevant observation: "…companies are collecting and using consumer data in illegal ways: we should require violators to reject not only the illegally obtained data, but also the benefits -- here, the algorithms -- generated by that data." Slaughter then proposes, downstream from recent cases involving photo apps or women's tech apps, that effective consumer notification of wrongdoing should be used, and have a greater impact, than FTC enforcement orders, which remain all but unknown. And there's a point to this strategy, which she explains in the next few lines: "The notice allows consumers to show their opinion and helps them better decide whether to recommend the service to others. Finally, and crucially, notice grants consumers the dignity of knowing what happened. There's a fundamental issue of fairness here: many people -- including those who need to know most -- won't know about the FTC's action against a company they're dealing with unless the company tells them. So, I'm going to push staff to include provisions requiring notice in orders about privacy and data security as a systematic matter."