The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Why would you need a robot with a ten-inch screen, camera, sensors, and a bunch of other gadgets to go around your home and make Wall-E noises? Because Amazon thinks it might be useful in the future. Astro, Amazon's latest innovation, looks a lot like an Echo Show on wheels. It packs a lot of interesting technology and shows just how far deep learning, sensor technology, and mobile robots have come.

Having flexed its muscles in predicting kidney injury, toppling Go champions and solving 50-year-old science problems, artificial intelligence company DeepMind is now dipping its toes in weather forecasting. The company's latest tool is designed to predict oncoming precipitation through what's known as nowcasting, and the vast majority of meteorologists found it to be more accurate than current methods in early testing. The science of precipitation nowcasting focuses on predicting rain within the next one to two hours, and is of real importance in areas such as outdoor events, aviation and emergency planning. DeepMind set out to develop a machine-learning tool that can bring a new level of precision to these efforts, by making use of high-precision radar data that tracks precipitation every five minutes at a 1-km (0.62-mile) resolution. It did so by using a generative modeling approach, which analyzes the past 20 minutes of observed radar and then makes predictions for the upcoming 90 minutes.

U.S. researchers developed an AI system that helps classifying buildings with wildfire damage by relying solely on post-fire images with 92% accuracy. Wildfires are increasing in frequency and intensity as climate change becomes more pronounced and visible. These are now causing disruptions in urban areas people left homes and their lives behind. Now, they will have to wait anxiously to know the state of their homes and the damage that they will need to fix. Researchers at Stanford University and the California Polytechnic State University (Cal Poly) have developed an Artificial Intelligence (AI) algorithm system called DamageMap that is a damage classifier; it helps identify building damages within minutes of a catastrophe by studying aerial photographs.

Research interest in Conversational AI has experienced a massive growth over the last few years and several recent advancements have enabled systems to produce rich and varied turns in conversations similar to humans. However, this apparent creativity is also creating a real challenge in the objective evaluation of such systems as authors are becoming reliant on crowd worker opinions as the primary measurement of success and, so far, few papers are reporting all that is necessary for others to compare against in their own crowd experiments. This challenge is not unique to ConvAI, but demonstrates as AI systems mature in more "human" tasks that involve creativity and variation, evaluation strategies need to mature with them. Conversational AI, or ConvAI as it has been abbreviated, is a sub-field of artificial intelligence (AI) where the goal is to build an autonomous agent that is capable of maintaining natural discourse with a human over some interface such as text or speech. The purpose may be to help humans perform tasks as a virtual/digital assistant, provide a natural language interface to another system as in information retrieval or navigation systems, or simply to converse like one would with an open domain chatbot.

Engaging students through interactive activities, discussions, feedback and AI-enhanced technologies resulted in improved academic performance compared to traditional lectures, lessons or readings, faculty from Carnegie Mellon University's Human-Computer Interaction Institute concluded after collecting research into active learning. The research also found that effective active learning methods use not only hands-on and minds-on approaches, but also hearts-on, providing increased emotional and social support. Interest in active learning grew as the COVID-19 pandemic challenged educators to find new ways to engage students. Schools and teachers incorporated new technologies to adapt, while students faced negative psychological effects of isolation, restlessness and inattention brought on by quarantine and remote learning. The pandemic made it clear that traditional approaches to education may not be the best way to learn, but questions persisted about what active learning is and how best to use it to teach and engage and excite students.

Juan Carlos Augusto, Philippe Lalanda, Massimo Mecella Intelligent Environments are populated with numerous devices and have multiple occupants, inherently exhibit increasingly intelligent behaviour, support consistent functionality and human-centric operation (humans, as opposed to mere users, have increased requirements from a system, including, for example, intuitive interaction, protection of privacy, fault-tolerance etc.), and provide optimized resource usage. The development of Intelligent Environments is considered the first and primary step towards the realization of the Ambient Intelligence vision and requires input from research and contributions from several scientific and engineering disciplines, including computer science, software engineering, artificial intelligence, architecture, social sciences, art and design. The series of IE conferences have been consistently creating a unique blend of researchers in these disciplines, fostering cross-disciplinary discussions, debate and collaborations.

Most emerging infectious diseases of humans (like COVID-19) are zoonotic – caused by viruses originating from other animal species. Identifying high-risk viruses earlier can improve research and surveillance priorities. A study published in PLOS Biology on September 28th by Nardus Mollentze, Simon Babayan, and Daniel Streicker at University of Glasgow, United Kingdom suggests that machine learning (a type of artificial intelligence) using viral genomes may predict the likelihood that any animal-infecting virus will infect humans, given biologically relevant exposure. Identifying zoonotic diseases prior to emergence is a major challenge because only a small minority of the estimated 1.67 million animal viruses are able to infect humans. To develop machine learning models using viral genome sequences, the researchers first compiled a dataset of 861 virus species from 36 families.

Karl Aberer, Ebrahim Bagheri, Marya Bazzi, Rumi Chunara, Ziv Epstein, Fabian Flöck, Adriana Iamnitchi, Diana Inkpen, Maurice Jakesch, Kyraki Kalimeri, Elena Kochkina, Ugur Kursuncu, Maria Liakata, Yelena Mejova, George Mohler, Daniela Paolotti, Jérémie Rappaz, Manon Revel, Horacio Saggion, Indira Sen, Panayiotis Smeros, Katrin Weller, Sanjaya Wijeratne, Christopher C. Yang, Fattane Zarrinkalam The Association for the Advancement of Artificial Intelligence’s 2021 International Conference on Web and Social Media was held virtually from June 8-10, 2021. There were 8 workshops in the program: Data for the Wellbeing of Most Vulnerable, Emoji 2021: International Workshop on Emoji Understanding and Applications in Social Media, Information Credibility and Alternative Realities in Troubled Democracies, International Workshop on Cyber Social Threats (CySoc 2021), International Workshop on Social Sensing (SocialSens 2021): Special Edition on Information Operations on Social Media, Participatory Development of Quality Guidelines for Social Media Research: A Structured, Hands-on Design Workshop, Mediate 2021: News Media and Computational Journalism, Mining Actionable Insights from Social Networks: Special Edition on Healthcare Social Analytics.