We begin by congratulating Yixin Wang and David Blei for their thought-provoking article that opens up a new research frontier in the field of causal inference. The authors directly tackle the challenging question of how to infer causal effects of many treatments in the presence of unmeasured confounding. We expect their article to have a major impact by further advancing our understanding of this important methodological problem. This commentary has two goals. We then briefly consider three possible ways to address some of the limitations of the deconfounder method. We first discuss several advantages offered by the deconfounder method. We then examine the assumptions required by the method and discuss its limitations.

Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is demand for an autonomous cinematographer that can reason about both geometry and scene context in real-time. Existing approaches do not address all aspects of this problem; they either require high-precision motion-capture systems or GPS tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow artistic guidelines specified before flight. In this work, we address the problem in its entirety and propose a complete system for real-time aerial cinematography that for the first time combines: (1) vision-based target estimation; (2) 3D signed-distance mapping for occlusion estimation; (3) efficient trajectory optimization for long time-horizon camera motion; and (4) learning-based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in-depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at: https://youtu.be/ookhHnqmlaU.

Spooky jokes, eerie noises, and popular costume ideas--if you're looking for help celebrating Halloween on Thursday, Oct. 31, your smart speaker has your back. We've rounded up some of the top skills and ways Alexa and Google Assistant help you celebrate the creepiest day of the year. To enable these Halloween skills on your Echo, open the Amazon Alexa app, which is available for download on iOS and Android devices, tap "Skills and Games" in the menu, and search for the skill you want. For Google Home, you can find these actions in the Google Assistant app, which is also available for iOS and Android devices. From recipes ideas to trivia games, here are eight ways Alexa and Google Assistant can help you celebrate Halloween.

Concerns about AI's use remain, however, including security risks and biases it could introduce into hiring and society as a whole as well as bad decisions it might make due to poor underlying data quality. Here's a snapshot of the present and future of AI, told in 11 statistics: That's a 14 percent increase, more than the current economic output of China and India combined, a PwC study projects. Some $6.6 trillion of the boost will come from increased productivity, while $9.1 trillion will arrive as a result of increased economic consumption. PwC concludes that AI is "the biggest commercial opportunity in today's fast-changing economy." The biggest global winners will be China and North America.

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists, and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying use of the phrase. However, this is not the classical case of the public not understanding the scientists--here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us, enthralling us and frightening us in equal measure. There is a different narrative that one can tell about the current era.

They might be competing AI's from giant companies, but Alexa and Google still play nice together. On Tuesday, Facebook will release its second generation Portal, one of three new entrants into the "Video Display" wars to compete for your shopping dollar during the holidays. Facebook is trying again to take on Google and Amazon, striving to convince consumers they want a home unit for making video calls, looking at their photos on a digital photo frame, listening to music and watching video clips. Plus, Facebook is going further by bringing the concept to TVs, but not until November. Here's how the three compare: The original edition launched in fall 2018, to surprisingly strong reviews: Critics were surprised they liked it, considering Facebook's poor history with privacy.

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"One of the near-term opportunities is driven by the sheer amount of aerial and satellite data that's starting to be produced around the world," Reid agrees. "There's a fantastic company out of Christchurch called Orbica, who are specialists in what they call GeoAI – analysing geospatial data and aerial and satellite photography to extract features and effectively count the number of houses or vehicles or identify bodies of water from the air. These are things that used to take people days of manual analysis and now it can be run through an algorithm in seconds.

Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionising many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it?

"Machine learning is not magic," cautions Nick Cafferillo, chief data and technology officer at S&P Global. "It's about augmenting our thought processes …