A chilling drone video shows a hammerhead shark circling a seemingly oblivious swimmer off a Miami beach. The video was posted to Instagram by drone operator and photographer Jason McIntosh. The Miami Herald reports that the close encounter was captured off South Beach on Sunday. McIntosh captioned the video, "Hammer Time," and used MC Hammer's famous song "U Can't Touch This" as the soundtrack. The video has been viewed more than 29,000 times since it was posted last week.

Salesforce is backing an AI project called SharkEye which aims to save the lives of beachgoers from one of the sea's deadliest predators. Shark attacks are, fortunately, quite rare. However, they do happen and most cases are either fatal or cause life-changing injuries. Just last week, a fatal shark attack in Australia marked the eighth of the year--an almost 100-year record for the highest annual death toll. Once rare sightings in Southern California beaches are now becoming increasingly common as sharks are preferring the warmer waters close to shore.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Hollywood man who operated a drone that crashed into a police helicopter, forcing an emergency landing, is facing a federal charge. Andrew Rene Hernandez, 22, was arrested by FBI agents Thursday and charged with one count of unsafe operation of an unmanned aircraft, the Justice Department said. The criminal case is believed to be the first in the nation stemming from a drone collision.

Amazon has confirmed that it is laying off a number of people working on its internal drone delivery project. The Financial Times reported that the mega-retailer had opted to shrink its internal team in favor of using external contractors to complete the work. The report's anonymous sources said that executives were frustrated at the speed of progress, leading to the change in strategy. The first two companies to sign up are FACC Aerospace from Austria and Aernnova Aerospace from Spain, which are both component manufacturers. Reportedly, other businesses are expected to sign up in the near future, as Amazon tries to push Prime Air closer to reality.

FBI agents have arrested a Hollywood man, accusing him of recklessly operating a drone and crashing it into a Los Angeles Police Department helicopter earlier this year. The collision damaged the chopper's fuselage and required the LAPD pilot to make an emergency landing following the September encounter. The drone, which authorities say was operated by Andrew Rene Hernandez, then tumbled from the sky and crashed into a vehicle. Hernandez, 22, was arrested Thursday and charged with unsafe operation of an unmanned aircraft after an investigation by the FBI, the LAPD and the Federal Aviation Administration. The potentially deadly collision occurred Sept. 18 after Los Angeles police officers responding to a predawn burglary call at a Hollywood pharmacy requested air support.

Aerial vehicles are revolutionizing the way film-makers can capture shots of actors by composing novel aerial and dynamic viewpoints. However, despite great advancements in autonomous flight technology, generating expressive camera behaviors is still a challenge and requires non-technical users to edit a large number of unintuitive control parameters. In this work we develop a data-driven framework that enables editing of these complex camera positioning parameters in a semantic space (e.g. calm, enjoyable, establishing). First, we generate a database of video clips with a diverse range of shots in a photo-realistic simulator, and use hundreds of participants in a crowd-sourcing framework to obtain scores for a set of semantic descriptors for each clip. Next, we analyze correlations between descriptors and build a semantic control space based on cinematography guidelines and human perception studies. Finally, we learn a generative model that can map a set of desired semantic video descriptors into low-level camera trajectory parameters. We evaluate our system by demonstrating that our model successfully generates shots that are rated by participants as having the expected degrees of expression for each descriptor. We also show that our models generalize to different scenes in both simulation and real-world experiments. Supplementary video: https://youtu.be/6WX2yEUE9_k

A solar-powered autonomous drone scans for forest fires. A surgeon first operates on a digital heart before she picks up a scalpel. A global community bands together to print personal protection equipment to fight a pandemic. "The future is now," says Frédéric Vacher, head of innovation at Dassault Systèmes. And all of this is possible with cloud computing, artificial intelligence (AI), and a virtual 3D design shop, or as Dassault calls it, the 3DEXPERIENCE innovation lab. This open innovation laboratory embraces the concept of the social enterprise and merges collective intelligence with a cross-collaborative approach by building what Vacher calls "communities of people--passionate and willing to work together to accomplish a common objective." This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review's editorial staff. "It's not only software, it's not only cloud, but it's also a community of people's skills and services available for the marketplace," Vacher says. "Now, because technologies are more accessible, newcomers can also disrupt, and this is where we want to focus with the lab." And for Dassault Systèmes, there's unlimited real-world opportunities with the power of collective intelligence, especially when you are bringing together industry experts, health-care professionals, makers, and scientists to tackle covid-19. Vacher explains, "We created an open community, 'Open Covid-19,' to welcome any volunteer makers, engineers, and designers to help, because we saw at that time that many people were trying to do things but on their own, in their lab, in their country."

This experiment is a proving ground for a pivotal challenge in modern robotics: the ability to guarantee the safety and success of automated robots operating in novel environments. As engineers increasingly turn to machine learning methods to develop adaptable robots, new work by Princeton University researchers makes progress on such guarantees for robots in contexts with diverse types of obstacles and constraints. "Over the last decade or so, there's been a tremendous amount of excitement and progress around machine learning in the context of robotics, primarily because it allows you to handle rich sensory inputs," like those from a robot's camera, and map these complex inputs to actions, said Anirudha Majumdar, an assistant professor of mechanical and aerospace engineering at Princeton. However, robot control algorithms based on machine learning run the risk of overfitting to their training data, which can make algorithms less effective when they encounter inputs that differ from those they were trained on. Majumdar's Intelligent Robot Motion Lab addressed this challenge by expanding the suite of available tools for training robot control policies, and quantifying the likely success and safety of robots performing in novel environments.

But the president's aspirations have long run into resistance, as his own national security officials argued that abandonment of such troubled countries could have catastrophic consequences -- such as when the United States pulled out of Iraq at the end of 2011, leaving a vacuum that fostered the rise of the Islamic State in Iraq and Syria. Mr. Trump has also repeatedly pushed to withdraw from Syria, but several hundred U.S. troops remain stationed there, partly to protect coveted oil fields held by American-backed Syrian Kurdish allies from being seized by the government of President Bashar al-Assad of Syria. The current deliberations over withdrawals would not affect those in Syria, officials said. The plan under discussion to pull out of Somalia is said to not apply to U.S. forces stationed in nearby Kenya and Djibouti, where American drones that carry out airstrikes in Somalia are based, according to officials familiar with the internal deliberations who spoke on the condition of anonymity. Keeping those air bases would mean retaining the military's ability to use drones to attack militants with the Shabab, the Qaeda-linked terrorist group -- at least those deemed to pose a threat to American interests.

We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.