Goal recognition is the problem of recognizing the intended goal of autonomous agents or humans by observing their behavior in an environment. Over the past years, most existing approaches to goal and plan recognition have been ignoring the need to deal with imperfections regarding the domain model that formalizes the environment where autonomous agents behave. In this thesis, we introduce the problem of goal recognition over imperfect domain models, and develop solution approaches that explicitly deal with two distinct types of imperfect domains models: (1) incomplete discrete domain models that have possible, rather than known, preconditions and effects in action descriptions; and (2) approximate continuous domain models, where the transition function is approximated from past observations and not well-defined. We develop novel goal recognition approaches over imperfect domains models by leveraging and adapting existing recognition approaches from the literature. Experiments and evaluation over these two types of imperfect domains models show that our novel goal recognition approaches are accurate in comparison to baseline approaches from the literature, at several levels of observability and imperfections.

As countries around the world are gradually reopening following lockdowns, government authorities are using surveillance drones in an attempt to enforce social distancing rules. In India, police are using AI-equipped drones developed by US start-up Skylark Labs to monitor evening curfews and the distance between people who are outside during the day. The drones are being flown in six cities in the northern state of Punjab, and are also being trialled in the southern city of Bangalore, says Skylark Labs CEO Amarjot Singh. Each drone is fitted with a camera and an AI that can detect humans within a range of 150 metres to 1 kilometre. If it spots people it can send an alert to police in the district located nearest to the sighting.

We provide an insight into the open data resources pertinent to the study of the spread of Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behaviour, regional mortality rates, and effectiveness of government measures. Open data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, at a world scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 data-sets at a country-wide level (i.e. China, Italy, Spain, France, Germany, U.S., etc.). In an attempt to facilitate the rapid response to the study of the seasonal behaviour of Covid-19, we enumerate the main open resources in terms of weather and climate variables. CONCO-Team: The authors of this paper belong to the CONtrol COvid-19 Team, which is composed of different researches from universities of Spain, Italy, France, Germany, United Kingdom and Argentina. The main goal of CONCO-Team is to develop data-driven methods for the better understanding and control of the pandemic.

Google is using its speech recognition tech to help kids read. Google on Thursday shared early access to its Read Along app for Android, which is designed to help kids 5 years and older learn to read. The app provides verbal and visual feedback as children read stories aloud. Read Along is one of several online platforms meant to keep students engaged and learning as schools remain closed amid the COVID-19 pandemic. Read Along features an in-app reading buddy named Diya, who uses Google's text-to-speech and speech recognition technologies to determine if a child who's reading is struggling.

NEW YORK (Reuters) - After a week or so sick in bed in their New York City apartment in March, members of the Johnson-Baruch family were convinced they had been stricken by the novel coronavirus. Subsequent test results left them with more questions than answers. Tests both for the virus itself and for the antibodies the immune system produces to fight the infection are becoming more widely available, but they are not perfect. For Maree Johnson-Baruch, her husband, Jason Baruch, and their two teenage daughters, their experience ran the gamut. They all became sick around the same time with the same symptoms.

Facebook has launched a new chatbot that it claims is able to demonstrate empathy, knowledge and personality. "Blender" was trained using available public domain conversations which included 1.5 billion examples of human exchanges. The social media giant said 49% of people preferred interactions with the chatbot, compared with another human. But experts say training the artificial intelligence (AI) using a platform such as Reddit has its drawbacks. Numerous issues arose during longer conversations.

The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learning methods, especially deep and reinforcement learning, which have created high expectations for industrial, commercial and social value. Second, the emergence of concern for creating trusted AI systems, including the creation of regulatory principles to ensure transparency and trust of AI systems.These two threads have created a kind of "perfect storm" of research activity, all eager to create and deliver it any set of tools and techniques to address the XAI demand. As some surveys of current XAI suggest, there is yet to appear a principled framework that respects the literature of explainability in the history of science, and which provides a basis for the development of a framework for transparent XAI. Here we intend to provide a strategic inventory of XAI requirements, demonstrate their connection to a history of XAI ideas, and synthesize those ideas into a simple framework to calibrate five successive levels of XAI.

Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks. Yet, due to its black-box nature, it is inherently difficult to understand which aspects of the input data drive the decisions of the network. There are various real-world scenarios in which humans need to make actionable decisions based on the output DNNs. Such decision support systems can be found in critical domains, such as legislation, law enforcement, etc. It is important that the humans making high-level decisions can be sure that the DNN decisions are driven by combinations of data features that are appropriate in the context of the deployment of the decision support system and that the decisions made are legally or ethically defensible. Due to the incredible pace at which DNN technology is being developed, the development of new methods and studies on explaining the decision-making process of DNNs has blossomed into an active research field. A practitioner beginning to study explainable deep learning may be intimidated by the plethora of orthogonal directions the field is taking. This complexity is further exacerbated by the general confusion that exists in defining what it means to be able to explain the actions of a deep learning system and to evaluate a system's "ability to explain". To alleviate this problem, this article offers a "field guide" to deep learning explainability for those uninitiated in the field. The field guide: i) Discusses the traits of a deep learning system that researchers enhance in explainability research, ii) places explainability in the context of other related deep learning research areas, and iii) introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning. The guide is designed as an easy-to-digest starting point for those just embarking in the field.

Washington, DC (CNN Business)Tesla has said its latest version of Autopilot, its autonomous driving software, is able to stop at traffic lights. But some Tesla drivers are learning it doesn't just stop at red lights, it appears to slow down for green lights, too. Last Friday, Tesla drivers first reported receiving a software update that included "Traffic Light and Stop Sign Control," which is designed to slowdown and stop the vehicle for visible traffic lights or stop signs. Tesla (TSLA) describes the software as being in "beta," meaning it's unfinished and still officially in testing. It's designed to gradually improve as the artificial intelligence that powers it learns from the data that's being collected as Tesla cars drive on public roads, according to a notification in Tesla vehicles when the system is first activated.

Urology fellow, Jeremy Fallot, and nurse, Shauna Harnedy, assist in robotic surgery by Ruban Thanigasalam (out of view) in Sydney, Australia.Credit: Ken Leanfore for Nature Loved by surgeons and patients alike for its ease of use and faster recovery times, the da Vinci surgical robot is less invasive than conventional procedures, and lacks the awkwardness of laparoscopic (keyhole) surgery. But the robot's US$2-million price tag and negligible effect on cancer outcomes is sparking concern that it's crowding out more affordable treatments. There are more than 5,500 da Vinci robots globally, manufactured by California-based tech giant, Intuitive. The system is used in a range of surgical procedures, but its biggest impact has been in urology, where it has a market monopoly on robot-assisted radical prostatectomies (RARP), the removal of the prostate and surrounding tissues to treat localized cancer. Uptake in the United States, Europe, Australia, China and Japan for performing this procedure has been rapid.