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How Google's hot air balloon surprised its creators

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They had spent many months honing an algorithm designed to steer an unmanned hot air balloon all the way from Puerto Rico to Peru. The balloon, controlled by its machine mind, kept veering off course. Salvatore Candido of Google's now-defunct Project Loon venture, which aimed to bring internet access to remote areas via the balloons, couldn't explain the craft's trajectory. His colleagues manually took control of the system and put it back on track. It was only later that they realised what was happening.


Skylum launches Luminar AI, its AI photo editor – TechCrunch

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Over the course of the last few years, Skylum made a name for itself with a set of photo-editing apps like Aurora HDR and Luminar. With Luminar AI, it is now launching a brand-new photo editor, starting at $79. The new application, available as a standalone product for Mac and Windows and as a plug-in for Lightroom and Photos for MacOS, was built from the ground up and offers many of the traditional photo-editing features you're probably familiar with from the likes of Lightroom. The focus, though, is on its new AI-based tools, with a special focus on editing landscapes (and skies in general) and portrait shoots. In total, Skylum added 13 AI features to the application.


Robots Can Encourage People To Take Greater Risks

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London: Even as the scale of interaction between humans and technology increases, a new research has shown that people tend to take more risks when prodded by a robot. The research, published in the journal Cyberpsychology, Behavior, and Social Networking, showed that robots can encourage people to take greater risks in a simulated gambling scenario than they would if there was nothing to influence their behaviours. The researcher now believe that further studies are needed to see whether similar results would emerge from human interaction with other artificial intelligence (AI) systems, such as digital assistants or on-screen avatars. "On the one hand, our results might raise alarms about the prospect of robots causing harm by increasing risky behaviour," said Yaniv Hanoch, Associate Professor in Risk Management at the University of Southampton in Britain. "On the other hand, our data points to the possibility of using robots and AI in preventive programmes, such as anti-smoking campaigns in schools, and with hard to reach populations, such as addicts."


Alphabet's Loon hands the reins of its internet air balloons to self-learning AI

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Alphabet's Loon, the team responsible for beaming internet down to Earth from stratospheric helium balloons, has achieved a new milestone: its navigation system is no longer run by human-designed software. Instead, the company's internet balloons are steered around the globe by an artificial intelligence -- in particular, a set of algorithms both written and executed by a deep reinforcement learning-based flight control system that is more efficient and adept than the older, human-made one. The system is now managing Loon's fleet of balloons over Kenya, where Loon launched its first commercial internet service in July after testing its fleet in a series of disaster relief initiatives and other test environments for much of the last decade. Similar to how researchers have achieved breakthrough AI advances in teaching computers to play sophisticated video games and helping software learn how to manipulate robotic hands in lifelike ways, reinforcement learning is a technique that allows software to teach itself skills through trial and error. Obviously, such repetition is not possible in the real world when dealing with high-altitude balloons that are costly to operate and even more costly to repair in the event they crash.


[R] Autonomous navigation of stratospheric balloons using reinforcement learning -- From Google Loon

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Efficiently navigating a superpressure balloon in the stratosphere1 requires the integration of a multitude of cues, such as wind speed and solar elevation, and the process is complicated by forecast errors and sparse wind measurements. Coupled with the need to make decisions in real time, these factors rule out the use of conventional control techniques2,3. Here we describe the use of reinforcement learning4,5 to create a high-performing flight controller. Our algorithm uses data augmentation6,7 and a self-correcting design to overcome the key technical challenge of reinforcement learning from imperfect data, which has proved to be a major obstacle to its application to physical systems8. We deployed our controller to station Loon superpressure balloons at multiple locations across the globe, including a 39-day controlled experiment over the Pacific Ocean.


Google's AI can keep Loon balloons flying for over 300 days in a row

New Scientist

Huge stratospheric balloons that act as floating cell towers in remote areas can stay in the air for hundreds of days thanks to an artificially intelligent pilot created by Google and Loon. Loon, a subsidiary of Google's parent company Alphabet, produces tennis-court-sized balloons that are filled with helium and sent into the stratosphere. Keeping these huge balloons in a fixed position is difficult as they can get blown off course. Now, researchers at Loon and Google have joined forces to create an AI controller that can counter the harsh winds of the stratosphere by releasing air to descend or adding it to ascend, riding atmospheric currents in the desired direction. The two firms used an AI technique called deep reinforcement learning to train the balloon's controllers.


Alphabet's Loon deploys new AI-powered navigation system to balloon fleet

ZDNet

Loon, the former Google X project and now independent Alphabet company, says it has built and deployed a new AI-powered navigation system that leverages reinforcement learning (RL) to steer balloons more accurately and efficiently through the stratosphere. Developed in cooperation with the Google AI team in Montreal, Loon said the new navigation system is capable of teaching itself how to navigate balloons better than the original balloon navigation system, which was built by human engineers over the last decade. During a head-to-head comparison of the human designed system and the reinforcement learning system, conducted over 39 days above the Pacific Ocean, Loon said the new navigation system kept a balloon over a defined location for longer periods of time while also using less power. The RL system also came up with complex navigational maneuvers that had not seen before. The reinforcement learning system is now live across Loon's fleet of stratospheric internet balloons, which are currently floating above Kenya in eastern Africa.


New AI-Based Navigation Helps Loon's Balloons Hover in Place

WIRED

High-flying balloons are bringing broadband connectivity to remote nations and post-disaster zones where cell towers have been knocked out. These "super-pressure" helium-filled polyethylene bags float 65,000 feet up in the stratosphere, above commercial planes, hurricanes, and pretty much anything else. But keeping a fleet of tennis-court-sized, internet-blasting balloons hovering over one spot has been a tricky engineering problem, just like keeping a boat floating in one place on a fast-moving river. Now researchers at Google spinoff Loon have figured out how to use a form of artificial intelligence to allow the balloon's onboard controller to predict wind speed and direction at various heights, then use that information to raise and lower the balloon accordingly. The new AI-powered navigation system opens the possibility of using stationary balloons to monitor animal migrations, the effects of climate change, or illegal cross-border wildlife or human trafficking from a relatively inexpensive platform for months at a time.


Autonomous balloons take flight with artificial intelligence

Nature

Project Loon is using balloons such as this to set up an aerial wireless network for telecommunications.Credit: Loon The goal of an autonomous machine is to achieve an objective by making decisions while negotiating a dynamic environment. Given complete knowledge of a system's current state, artificial intelligence and machine learning can excel at this, and even outperform humans at certain tasks -- for example, when playing arcade and turn-based board games1. But beyond the idealized world of games, real-world deployment of automated machines is hampered by environments that can be noisy and chaotic, and which are not adequately observed. The difficulty of devising long-term strategies from incomplete data can also hinder the operation of independent AI agents in real-world challenges. Writing in Nature, Bellemare et al.2 describe a way forward by demonstrating that stratospheric balloons, guided by AI, can pursue a long-term strategy for positioning themselves about a location on the Equator, even when precise knowledge of buffeting winds is not known.


This AI task-based app hopes to improve dementia care

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Mindset takes people through three "fun" cognitive tasks with the aim of creating a significant dementia database and one day screen for the syndrome. A group of UK medical students have released medical app Mindset which hopes to become the "world's largest dementia AI initiative". The brain syndrome – which can cause memory loss and changes in behaviour – is a significant and growing problem, particularly for people over the age of 65. It is thought that around 62% of individuals suffering from dementia are undiagnosed. By 2050, the number of people who suffer from the condition is expected to triple, mostly because of an aging population.