A meteorite has been successfully recovered using drones and a machine-learning algorithm for the first time. The development marks a step forward in improving the process of searching Earth's surface for extraterrestrial rocks. Typically, when a meteorite falls to Earth, it takes a team of five or more people to visually search the ground around the fall zone to find it. While the process can be successful, it is costly and time-consuming.

Turbine maintenance is an expensive, high-risk task. According to a recent analysis from the news website, wind farm owners are expected to spend more than $40 billion on operations and maintenance over a decade. Another recent study finds by using drone-based inspection instead of traditional rope-based inspection, you can reduce the operational costs by 70% and further decrease revenue lost due to downtime by up to 90%. This blog post will present how drones, machine learning (ML), and Internet of Things (IoT) can be utilized on the edge and the cloud to make turbine maintenance safer and more cost effective. First, we trained the machine learning model on the cloud to detect hazards on the turbine blades, including corrosion, wear, and icing.

Walking rows of soybeans in the mid-summer heat is an exhausting but essential chore in breeding new cultivars. Researchers brave the heat daily during crucial parts of the growing season to look for plants showing desirable traits, such as early pod maturity. But without a way to automate detection of these traits, breeders can't test as many plots as they'd like in a given year, elongating the time it takes to bring new cultivars to market. In a new study from the University of Illinois, researchers predict soybean maturity date within two days using drone images and artificial intelligence, greatly reducing the need for boots on the ground. "Assessing pod maturity is very time consuming and prone to errors. It's a scoring system based on the color of the pod, so it is also subject to human bias," says Nicolas Martin, assistant professor in the Department of Crop Sciences at Illinois and co-author on the study.

Experts have got together to discuss the future of home automation and reveal their predictions for the future of home automation. According to futurologists, around 90 per cent of household chores will be automated thanks to robots, drones and AI by 2040. These will be carried out by drones, robots and virtual AI butlers that will help with laundry, dusting and even making the bed, they claim. Kings College Professor Mischa Dohler and futurologist Dr Ian Pearson created a report with consumer site comparethemarket.com to predict how homes will look in two decades time. Experts have got together to discuss the future of home automation and revealed their predictions for the future of home automation.

The business opportunities from the use of drones and AI are there now, and make positive "bot-tom-line" contributions, but many companies are not ready to take advantage of them, either be-cause they don't have the processes in place or do not have the talent. The PwC 2019 Global Risk, Internal Audit and Compliance Survey showed that 80 per cent of respondents "do not plan to use drones in the next two years" or are "unsure" about whether they will do so. Likewise, over half (52 per cent) of respondents feel the same way about artificial intelligence. Two-thirds (63 per cent) do not have a programme in place to attract digital talent. However, they are open to the idea of using these technologies, especially from the point of view of improved customer services and employee experiences.

Earlier, the students spent two days in workshops where they were versed in computer learning and object recognition with Microsoft's Custom Vision solution and Azure cloud computing platform. After forming 15 teams, they were assigned a DJI Phantom 4 Pro drone. They were given flying lessons and also tasked with training it to recognize different objects using AI. On competition day, each team was set a common challenge: maneuver their pre-trained drone over an area where different pieces of fruit had been placed, then operate the craft's camera so that AI technology could recognize each as an apple, a pear, a pomelo, a banana, or a mango. The teams were ranked and scored on the number accurately identified as well as physical drone flying skills and completion time.

The spate of natural disasters which have hit the U.S. has demonstrated that there is much that needs to be done when it comes to rescuing efforts, preventive measures, and other associated issues. Many tech companies such as Google, Facebook and Microsoft have put in large-scale efforts in order to help people be safe during natural disasters, but the relevance of technology in such situations has been rapidly increasing. Technology can play a large role during natural disasters as has been demonstrated in Hurricanes Harvey and Irma. Experts from IEEE, the world's biggest technical professional organizations have in an email to the International Business Times, provided their opinions on the use of technology in natural disasters. Dr. Massoud Amin, Professor of Electrical and Computer Engineering at the University of Minnesota, provided IBT with his take on one of the most important aspects of technology during natural disasters, stating that,"It [technology] enables better proactive planning, prepositioning of the assets and assists with more real-time recovery and restoration. Everything starts with intelligence and situational awareness, and closing the loops on those opportunities and challenges."

The technology, known as SharkSpotter, uses an algorithm to detect sharks in a live video feed recorded in real time by a drone (known as the Little Ripper Livesaver) flying above the water. Using a world-first algorithm, developed using artificial intelligence and deep neural networks, SharkSpotter is able to distinguish sharks from dolphins, rays and other marine animals, and even surfers. Thanks to an onboard megaphone, the drone can also warn swimmers about what's lurking in the water before they've even seen the threat. Watch the video here: What do you think about this. Let us know in the comments.

Whether or not shark attacks are a major problem in Australia (spoiler alert: they're not), the Australian government has devoted an enormous amount of resources into trying to mitigate the risk of sharks near popular beaches. They've tried nets to keep the sharks out, they've tried electronic gadgets to dissuade them, and they've tried lots of different ways of killing them, without much in the way of evidence that any of it is particularly effective. After six months of trials, the latest and most robot-y idea is about to be implemented: drones will start patrolling some Australian beaches next month, using cameras and some AI-backed image analysis software to spot lurking sharks much better than humans can. We can manage a 20-30 percent accuracy rate, which means both identifying other things as sharks (kinda bad) and misidentifying sharks as other things (way worse). As with many tasks of this kind, a machine learning system does much better: once it's been trained on labeled aerial videos of sharks, whales, dolphins, surfers, swimmers, boats, and whatever else, the software is 90 percent accurate at telling humans to panic because there's a shark somewhere.

Drones have the potential to play a big role in protecting endangered species, with a number of trials being conducted to investigate how small aerial surveillance aircraft can be used to combat poaching. The latest effort involves the use of artificial intelligence software to quickly identify poachers and animals in drone footage, in an attempt to better protect elephants and rhinos. Developed by Neurala, the software will be used by the Lindbergh Foundation in its efforts to combat poaching. It's designed to keep an eye on video as it's streamed back to researchers from drones in the field and identify animals, vehicles and poachers in real time without any human input. The software can analyze regular or infrared footage, so works with video taken day or night.