Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with the capability of high-end film studios. Current approaches either only handle off-line trajectory generation, or offer strategies that reason over short time horizons and simplistic representations for obstacles, which result in jerky movement and low real-life applicability. In this work we develop a method for aerial filming that is able to trade off shot smoothness, occlusion, and cinematography guidelines in a principled manner, even under noisy actor predictions. We present a novel algorithm for real-time covariant gradient descent that we use to efficiently find the desired trajectories by optimizing a set of cost functions. Experimental results show that our approach creates attractive shots, avoiding obstacles and occlusion 65 times over 1.25 hours of flight time, re-planning at 5 Hz with a 10 s time horizon. We robustly film human actors, cars and bicycles performing different motion among obstacles, using various shot types.

A new AI can make anybody look like a dance superstar by giving them the moves of a professional dancer. The creepy robot uses the movements of top dancers captured from a video to animate your body in a creepy'DeepFake'-style video. Experts behind the technology said it allows amateurs to'twirl like ballerinas', or perfectly imitate their favourite pop stars. A new AI can make anybody look like a dance superstar by giving them the moves of a professional dancer. The creepy robot uses the movements of top dancers (left) captured from a video to animate your body (right images) in a creepy'DeepFake'-style video The AI, developed by researchers at the University of California at Berkeley, first copies the moves of a professional dancer from a source video.

Experts from scores of countries are meeting to discuss ways to define and deal with "killer robots" _ futuristic weapons systems that could conduct war without human intervention.

The gadgets we think of as home essentials have changed beyond recognition in the past few decades. In 1970, just 35 per cent of homes had a landline telephone and there were no mobiles. Now, technologies that were once the stuff of science fiction are part of our daily lives: take Amazon's Alexa – inspired by the talking computer on the Starship Enterprise in Star Trek. Today, millions of British households own these'smart speakers' that allow voice commands to control everything from TVs to lighting systems. Indeed, as of this year, one in five households will be'smart', with devices that are wirelessly connected to the internet, and controlled via smartphone or a hub.

AI and robotics are going to shape our future. Next there are 10 issues that professionals and researchers need to address in order to desing intelligent systems that help humanity. The flow of misinformation together with our natural inability of perceiving reality based on evidence (a phenomenon called confirmation bias) is a threat to having an informed democracy. Russian hackers influencing the US elections, Brexit campaign and Catalonia crisis are examples of how social media can massively spread misinformation and fake news. It is an open question how institutions are going to address this threat. The scientific revolution in the 18th century and the industrial revolution in the 19th marked a complete change in society.

In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we compute well-studied content-based features on over 60K news articles from 4 communities on reddit.com. We train and test models over three different time periods between 2015 and 2017 to demonstrate which features degrade in performance the most due to concept drift. Our models can classify news articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0 ROC AUC. However, while we can predict the community-specific popularity of news articles with high accuracy, practitioners should approach these models carefully. Predictions are both community-pair dependent and feature group dependent. Moreover, these feature groups generalize over time differently, with some only degrading slightly over time, but others degrading greatly. Therefore, we recommend that community-interest predictions are done in a hierarchical structure, where multiple binary classifiers can be used to separate community pairs, rather than a traditional multi-class model. Second, these models should be retrained over time based on accuracy goals and the availability of training data.

Abstract--How can we enable users to heavily specify criteria for database queries in a user-friendly way? This paper describes a general framework of a conversational bot that extracts meaningful information from user's sentences, that asks subsequent questions to complete missing information, and that adjusts its questions and information-extraction parameters for later conversations depending on users' behavior. Additionally, we provide a comparison of existing tools and give novel techniques to implement such framework. Finally, we exemplify the framework with a bot to query movies in a database, whose code is available for Microsoft employees. Consider the problem of recommending movies to users: it is a longstanding problem in data science that has implied a variety of techniques [1][2], ranging from Conditional Random Fields (for language understanding) to Collaborative Filtering techniques (for recommendation based on users' feedback on watched movies).

HIROSHIMA--Students here are recreating the lives of people devastated by the atomic bombing on Aug. 6, 1945, by vividly colorizing photos taken before the city was leveled by the nuclear attack. Students from Hiroshima Jogakuin Senior High School combine artificial intelligence technology and interviews with atomic bomb survivors to produce realistic coloring of black-and-white photos provided by hibakusha. Monochrome pictures snapped before and during World War II are automatically colorized with artificial intelligence. The processed photos are shown to hibakusha so colors can be manually adjusted based on their accounts. Converting one black-and-white photo into color takes from one week to several months, and 140 pictures have been colorized since November.

A fully functioning Apple-1 being auctioned by Boston-based RR Auction in September is one of only 60 or so remaining of the original 200 that were designed and built by Steve Jobs and Steve Wozniak in 1976 and 1977.

Abstract: We propose an $O(N\cdot M)$ sorting algorithm by Machine Learning method, which shows a huge potential sorting big data. This sorting algorithm can be applied to parallel sorting and is suitable for GPU or TPU acceleration. Furthermore, we discuss the application of this algorithm to sparse hash table.