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Micro aerial vehicles (MAVs) could have numerous useful applications, for instance, assisting humans in completing warehouse inventories or search and rescue missions. While many companies worldwide have already started producing and using MAVs, some of these flying robots still have considerable limitations.

To work most effectively, MAVs should be supported by an efficient pose estimation system. This is a system or method that can calculate a drone’s position and attitude, which can then be used to control its flight, adjust its speed and aid its navigation while it is operating autonomously and when controlled remotely.

Researchers at Huazhong University of Science and Technology in China have recently developed a new system for the pose estimation of MAVs in indoor environments. Their new approach, outlined in a paper pre-published on arXIv and set to be published in IEEE Transactions on Industrial Electronics, leverages existing WiFi infrastructure to enable more effective navigation in small and agile drones.

A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry.

3D Tetris: Chip placement, also known as chip floor planning, is a complex three-dimensional design problem. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan.

Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years. But as machine-learning algorithms have rapidly advanced, the need for new chip architectures has also accelerated. In recent years, several algorithms for optimizing chip floor planning have sought to speed up the design process, but they’ve been limited in their ability to optimize across multiple goals, including the chip’s power draw, computational performance, and area.

Amazon CEO Jeff Bezos and the World Health Organization’s director-general are trading ideas on how to get the COVID-19 pandemic under control, using tools ranging from Amazon Web Services’ firepower in cloud computing and artificial intelligence to distribution channels for coronavirus test kits.

Bezos recapped today’s talk with Director-General Tedros Adhanom Ghebreyesus in an Instagram post, featuring a screengrab of Bezos’ videoconference view with the billionaire’s own visage in the upper right corner of the frame:

Derek Haoyang Li, the founder of Squirrel AI Learning, is a serial entrepreneur who co-founded two publicly listed companies, and one of the companies has a market cap of $200 million. Squirrel AI Learning is the leading AI + education innovator and unicorn at the forefront of the K12 AI revolution. Within three years of its product release, Squirrel AI Learning has established more than 2,600+ learning centers in China and hosted the first series of human-vs-AI competitions in the Asia-Pacific region that proved the AI’s success. Squirrel AI Learning is recognized by Deloitte as one of the top 10 global AI enterprises with high growth. Squirrel AI Learning was also included in MIT Technology Review’s TR50 Smartest Companies in China list. Stanford Graduate School of Business has also published a case study on Squirrel AI Learning.

Four years ago, mathematician Vlad Voroninski saw an opportunity to remove some of the bottlenecks in the development of autonomous vehicle technology thanks to breakthroughs in deep learning.

Now, Helm.ai, the startup he co-founded in 2016 with Tudor Achim, is coming out of stealth with an announcement that it has raised $13 million in a seed round that includes investment from A.Capital Ventures, Amplo, Binnacle Partners, Sound Ventures, Fontinalis Partners and SV Angel. More than a dozen angel investors also participated, including Berggruen Holdings founder Nicolas Berggruen, Quora co-founders Charlie Cheever and Adam D’Angelo, professional NBA player Kevin Durant, Gen. David Petraeus, Matician co-founder and CEO Navneet Dalal, Quiet Capital managing partner Lee Linden and Robinhood co-founder Vladimir Tenev, among others.

Helm.ai will put the $13 million in seed funding toward advanced engineering and R&D and hiring more employees, as well as locking in and fulfilling deals with customers.