June 2020 Venture capitalists sell themselves as the top of the heap in Silicon Valley. They are the talent spotters, the cowboys, the risk takers; they support people willing to buck the system and, they say, deserve to be richly rewarded and lightly taxed for doing so. This largely white, largely male corner of finance has backed software companies that grow fast and generate large amounts of money for a shrinking number of Americans--companies like Google, Facebook, Uber, and Airbnb. But they don't create many jobs for ordinary people, especially compared with the companies or industries they disrupt. And things have been slowing down.

I found that the reality of drone delivery is still far from ideal, and people may be turned away by the steep learning curve. But at the same time, it was an exciting experience--the prospect of routine drone delivery feels more realistic than it's ever been. Meituan currently operates more than a hundred drones from five delivery hubs (or launchpads) in the city. Together, they completed over 100,000 orders in 2022. While the platform itself can deliver basically anything, from dinner to medicine to fresh flowers to electronic devices, the drones are mostly used for food and drinks.

The news: Brain signals can be used to detect how much pain a person is experiencing, which could overhaul how we treat certain chronic pain conditions, a new study has suggested. Electrodes implanted in the brains of four people living with chronic pain helped researchers to track their individual levels of pain, and to train an AI model to accurately predict its severity. Why it's important: Because we still don't really understand how chronic pain affects the brain, it's very difficult to treat. This research is the first time a human's chronic-pain-related brain signals have been recorded, and it could aid the development of personalized therapies for the most severe forms of pain. Meta's new AI models can recognize and produce speech for more than 1,000 languages What's happened: Meta has built AI models that can recognize and produce speech for more than 1,000 languages--a tenfold increase on what's currently available.

The drone delivery service I was trying out is operated by Meituan, China's most popular food delivery platform. In 2022, the company engaged some 6 million gig delivery workers to deliver billions of orders. But the company has also been developing drone delivery since 2017. And in Shenzhen, a southern city that's home to a mature drone supply chain, Meituan has been regularly operating such delivery routes for the last year and a half. Many big corporations have had their eyes on drone delivery: Amazon first proposed doing it in 2013, but its progress has been limited by regulations and a lack of demand.

Abstract-- We present a novel controller for fixed-wing UAVs that enables autonomous soaring in an orographic wind field, extending flight endurance. Our method identifies soaring regions and addresses position control challenges by introducing a target gradient line (TGL) on which the UAV achieves an equilibrium soaring position, where sink rate and updraft are balanced. We also demonstrate a single degree of control freedom in a soaring position through manipulation of the TGL. I. INTRODUCTION UAVs have benefited from advancements in battery technology and miniaturization of avionics, which resulted in an increase in their endurance and range. However, the full potential of UAV applications remains limited by reduced flight time.

A California-based company is developing a new drone for delivery services. Despite the increasing popularity of delivery services including the use of drones, it has been challenging for companies to keep these services profitable. This is because of the high costs associated with operating drones. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER Factors weighing in include cost of the drone itself, maintenance, and pilot training. On top of that, delivering through drones is tightly regulated by aviation authorities, which can add to the expense and difficulty of running these services.

Iranian President Ebrahim Raisi has appointed a veteran commander with the Islamic Revolutionary Guard Corps (IRGC) as the country's new security chief. Ali Akbar Ahmadian, 62, was named on Monday as the new secretary of the Supreme National Security Council (SNSC), replacing Ali Shamkhani, who held the post for close to a decade. Ahmadian takes the reins of the SNSC at a time of rapidly accelerating diplomatic regional efforts facilitated by his predecessor, including the re-establishment of ties with rival Saudi Arabia after a China-brokered agreement in March. Iran's relations with the West, however, remain sour. A landmark 2015 nuclear deal with world powers remains in limbo, while Iran has been accused of supplying Russia with armed drones for the war in Ukraine and tensions have steadily risen following nationwide protests that erupted across the country in September last year.

Lebanon's Hezbollah movement has carried out military exercises near the country's southern border with Israel in a show of its military power. About 200 Hezbollah fighters used live ammunition and an attack drone to take part in the exercises on Sunday in Aaramta, 20km (12 miles) north of the Israeli border. The drills took place ahead of the anniversary of Israel's withdrawal from southern Lebanon on May 25, 2000. It was the largest demonstration of Hezbollah's military might in years. Hezbollah fighters carried out simulated raids involving sniper and drone attacks against Israeli targets as part of the exercise.

Optical flow estimation is crucial for autonomous navigation and localization of unmanned aerial vehicles (UAV). On micro and nano UAVs, real-time calculation of the optical flow is run on low power and resource-constrained microcontroller units (MCUs). Thus, lightweight algorithms for optical flow have been proposed targeting real-time execution on traditional single-core MCUs. This paper introduces an efficient parallelization strategy for optical flow computation targeting new-generation multicore low power RISC-V based microcontroller units. Our approach enables higher frame rates at lower clock speeds. It has been implemented and evaluated on the eight-core cluster of a commercial octa-core MCU (GAP8) reaching a parallelization speedup factor of 7.21 allowing for a frame rate of 500 frames per second when running on a 50 MHz clock frequency. The proposed parallel algorithm significantly boosts the camera frame rate on micro unmanned aerial vehicles, which enables higher flight speeds: the maximum flight speed can be doubled, while using less than a third of the clock frequency of previous single-core implementations.

Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, utilizing consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred onboard where controllers have the task of tracking the uploaded guidance profile. Here we argue that end-to-end neural guidance and control architectures (here called G&CNets) allow transferring onboard the burden of acting upon these optimality principles. In this way, the sensor information is transformed in real time into optimal plans thus increasing the mission autonomy and robustness. We discuss the main results obtained in training such neural architectures in simulation for interplanetary transfers, landings and close proximity operations, highlighting the successful learning of optimality principles by the neural model. We then suggest drone racing as an ideal gym environment to test these architectures on real robotic platforms, thus increasing confidence in their utilization on future space exploration missions. Drone racing shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought, but it also entails different levels of uncertainties and unmodelled effects. Furthermore, the success of G&CNets on extremely resource-restricted drones illustrates their potential to bring real-time optimal control within reach of a wider variety of robotic systems, both in space and on Earth.