Greg Nichols covers robotics, AI, and AR/VR for ZDNet. A full-time journalist and author, he writes about tech, travel, crime, and the economy for global media outlets and reports from across the U. You might think you should look down toward the water to spot a sub. If you're in North Carolina, you'd be better off looking up. That terrible joke is brought to you by a drone delivery services company called Flytrex, which just announced a partnership with Jersey Mike's Subs.

Royal Mail is building a fleet of 500 drones to carry mail to remote communities all over the UK, including the Isles of Scilly and the Hebrides. The postal service, which has already conducted successful trials over Scotland and Cornwall, will create more than 50 new postal drone routes over the next three years as part of a new partnership with London company Windracers. Drones, or UAVs (uncrewed aerial vehicles), can help reduce carbon emissions and improve the reliability of island mail services, Royal Mail claims. They offer an alternative to currently-used delivery methods that can be affected by bad weather – ferries, conventional aircraft and land-based deliveries. They can also take off from any flat surface (sand, grass or tarmac) providing it is long enough.

In my third article about the ethics of artificial intelligence (AI), I look at operationalizing AI ethics. Human intelligence remains a key factor – to keep a watchful eye on potential biases. Amazon caused a stir in late 2018 with media reports that it had abandoned an AI-powered recruitment tool because it was biased against women. Conceived as a piece of in-house software that could sift through hundreds of CVs at lightspeed and accurately identify the best candidates for any open position, the application had acquired one bad habit: It had come to favor men over women for software developer jobs and other technical roles. It had learned from past data that more men applied for and held these positions, and it now misread male dominance in tech as a reflection of their superiority, not social imbalances.

To be truly useful, drones--that is, autonomous flying vehicles--will need to learn to navigate real-world weather and wind conditions. Right now, drones are either flown under controlled conditions, with no wind, or are operated by humans using remote controls. Drones have been taught to fly in formation in the open skies, but those flights are usually conducted under ideal conditions and circumstances. However, for drones to autonomously perform necessary but quotidian tasks, such as delivering packages or airlifting injured drivers from a traffic accident, drones must be able to adapt to wind conditions in real time--rolling with the punches, meteorologically speaking. To face this challenge, a team of engineers from Caltech has developed Neural-Fly, a deep-learning method that can help drones cope with new and unknown wind conditions in real time just by updating a few key parameters.

As an Applied Data Scientist at Civis, I implemented the latest data science research to solve real-world problems. We recently worked with a global tool manufacturing company to reduce churn among their most loyal customers. A newly proposed tool, called SHAP (SHapley Additive exPlanation) values, allowed us to build a complex time-series XGBoost model capable of making highly accurate predictions for which customers were at risk, while still allowing for an individual-level interpretation of the factors that made each of these customers more or less likely to churn. To understand why this is important, we need to take a closer look at the concepts of model accuracy and interpretability. Until recently, we always had to choose between an accurate model that was hard to interpret, or a simple model that was easy to explain but sacrificed some accuracy.

To be truly useful, drones--that is, autonomous flying vehicles--will need to learn to navigate real-world weather and wind conditions. Right now, drones are either flown under controlled conditions, with no wind, or are operated by humans using remote controls. Drones have been taught to fly in formation in the open skies, but those flights are usually conducted under ideal conditions and circumstances. However, for drones to autonomously perform necessary but quotidian tasks, such as delivering packages or airlifting injured drivers from a traffic accident, drones must be able to adapt to wind conditions in real time--rolling with the punches, meteorologically speaking. To face this challenge, a team of engineers from Caltech has developed Neural-Fly, a deep-learning method that can help drones cope with new and unknown wind conditions in real time just by updating a few key parameters.

In Her, the 2013 science fiction movie portraying the love between a human and an AI, the behaviors of AI can be explainable. As a result, she can help humans make decisions and even generate deep communication with humans. Unfortunately, these overly idealize scenarios can only exist in science fiction works for now. The explainable AI in practice are not up to standard neither in the aspect of technology nor in the aspect of experiences. Why is AI required to be explainable? Or a more fundamental question would be: why is AI considered as a black box?

The US is hoping to combat the growing criminal use of drones with new rules that will allow local law enforcement agencies and other organisations to have counter-drone systems. At present, legal restrictions in the US hamper efforts to tackle such activity. The Federal Aviation Administration bans anyone from interfering with an aircraft in flight, including drones, while the Federal Communications Commission forbids jamming radio signals, a common anti-drone technique. Only a few federal agencies are permitted to shoot down drones in extreme circumstances, such as threats to critical infrastructure. US police agencies have been clamouring for counter-drone systems in the face of increasing criminal and careless use of drones.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. The public cloud is growing rapidly and the market for the technology is expected to reach $1.3 trillion by 2025. The cloud has revolutionized the computing industry and enabled many applications, business models and enterprises, which otherwise wouldn't have been possible. Immediate availability, scalability, minimal capital expenditure and streamlined developer experience are its main advantages -- but it comes at a cost. Due to a lack of in-house infrastructure optimization capabilities, most enterprises stick to the cloud even after achieving certain maturity. To keep cloud spending under control, enterprises have built or acquired tools and services.

Greg Nichols covers robotics, AI, and AR/VR for ZDNet. A full-time journalist and author, he writes about tech, travel, crime, and the economy for global media outlets and reports from across the U. There can be little doubt that the FAA is paving the way for a framework governing the widespread operation of commercial drones in the U.S. In advance of a definitive ruling on whether commercial drones can operate beyond visual line of sight (BVLOS), the FAA has been busily granting case-by-case permission to drone operators for exactly that. One recent example, just announced, drone company American Robotics has added seven additional sites of operation approved by the FAA for its automated BVLOS drone technology, the Scout System. American Robotics has 10 operational sights across eight U.S. states.