Machine learning could help predict and prevent lead poisoning in children (5:26 -- 13:54).

Amazon has announced a full range of new spherical Echo devices, new motorised smart display, a camera drone that flies around your house, a game-streaming service and more. In a streaming presentation, the firm showed off a smorgasbord of new devices from its various brands, including Ring, Eero Fire and Echo. The new standard Echo ditches its cylindrical shape for a fabric-covered ball design with Amazon's characteristic light-ring in the base to indicate when it is listening to you. It has a new 3in woofer and two tweeters with Dolby processing for stereo sound and automatic adjustment to the acoustics of your room. It also has Amazon's new AZ1 artificial intelligence chip for greater local processing of voice and other actions for increased privacy and speed.

Engadget, which had an opportunity to demo Luna, wrote that the technology worked "just fine," playing across a Fire TV, Mac and iPhone over the span of 45 minutes. "I started on Fire TV and was able to boot up the beefiest game in the store, Control, in a matter of seconds. It stuttered a bit throughout the opening scenes, but not enough to interrupt the cinematic flow," wrote Jessica Conditt of Engadget. "More often than not, gameplay was smooth, and none of the network interruptions that did appear were significant enough to break my experience."

Nearly all security cameras available today have some form of video analytics on board, according to Brian Baker, vice president, Americas, for Calipsa, a leading provider of deep learning-powered video analytics for false alarm reduction. But why is this the case? And what do facilities managers need to know about it? Video analytics powered by artificial intelligence promise smarter alerts that free your security staff from responding to false alarms, says Baker, a presenter at the 2020 GSX virtual tradeshow. But to find the right AI-backed analytics for your organization, it's first important to understand the basic concepts behind the technologies.

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On the morning of November 9, 2016, the world woke up to the shocking outcome of the U.S. Presidential election: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots--automated social media accounts mimicking humans--played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory.16,19 In the aftermath of the 2016 U.S. elections, the world started to realize the gravity of widespread deception in social media. Following Trump's exploit, we witnessed to the emergence of a strident dissonance between the multitude of efforts for detecting and removing bots, and the increasing effects these malicious actors seem to have on our societies.27,29 This paradox opens a burning question: What strategies should we enforce in order to stop this social bot pandemic? In these times--during the run-up to the 2020 U.S. elections--the question appears as more crucial than ever. Particularly so, also in light of the recent reported tampering of the electoral debate by thousands of AI-powered accounts.a What struck social, political, and economic analysts after 2016--deception and automation--has been a matter of study for computer scientists since at least 2010. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavors in social bot detection can also inform strategies for detecting and mitigating the effects of other--more recent--forms of online deception, such as strategic information operations and political trolls.

Search and recommendation systems, such as search engines, recruiting tools, online marketplaces, news, and social media, output ranked lists of content, products, and sometimes, people. Credit ratings, standardized tests, risk assessments output only a score, but are also used implicitly for ranking. Bias in such ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. In this paper, we study a trade-off between individual fairness and group fairness in ranking. We define individual fairness based on how close the predicted rank of each item is to its true rank, and prove a lower bound on the trade-off achievable for simultaneous individual and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous individual and group fairness guarantees comparable to the lower bound we prove. Our algorithm can be used to both pre-process training data as well as post-process the output of existing ranking algorithms. Our experimental results show that our algorithm performs better than the state-of-the-art fair learning to rank and fair post-processing baselines.

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

Between smartphones, IoT censors and automated alerts, the volume of data that flows through some 911 call centers on any given day has grown exponentially over the past decade. When a center is inundated with calls about a single incident, the ability to know which calls are related, and then dispatch the nearest and most-needed response units to their location, can be more than any one, manual call taker can handle. With this in mind, the Safety and Infrastructure division of the technology company Hexagon created an AI-based tool for recognizing related incidents in real time and helping call takers coordinate a response. A news release on Tuesday announced the software as HxGN OnCall Dispatch Smart Advisor, or Smart Advisor for short. The company's Strategic Product Manager Jack Williams told Government Technology that more than 18 months ago, he and some colleagues in the Safety and Infrastructure division wanted to see what public safety challenges might be solved with recent innovations in AI, machine learning and advanced statistics.

The National Security Commission on Artificial Intelligence ("the Commission) is publishing this notice to request comments from small- and …