For Dr. Mary-Anne Hartley, a medical doctor and researcher in EPFL's intelligent Global Health group (iGH), 2020 has been relentless. "It's not a relaxing time to study infectious diseases," she explained. Since the beginning of the COVID-19 pandemic, Dr. Hartley's research team has been working non-stop with nearby Swiss university hospitals on two major projects. Using artificial intelligence (AI), they have developed new algorithms that, with data from ultrasound images and auscultation (chest/lung) sounds, can accurately diagnose the novel coronavirus in patients and predict how ill they are likely to become. "We've named the new deep learning algorithms DeepChest--using lung ultrasound images--and DeepBreath--using breath sounds from a digital stethoscope. This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising," said Professor Jaggi.

Alphabet's Loon has been using algorithmic processes to optimize the flight of its stratospheric balloons for years now -- and setting records for time spent aloft as a result. But the company is now deploying a new navigation system that has the potential to be much better, and it's using true reinforcement-learning AI to teach itself to optimize navigation better than humans ever could. Loon developed the new reinforcement-learning system, which it says is the first to be used in an actual product aerospace context, with its Alphabet colleagues at Google AI in Montreal over the past couple of years. Unlike its past algorithmic navigation software, this one is devised entirely by machine -- a machine that's able to calculate the optimal navigation path for the balloons much more quickly than the human-made system could, and with much more efficiency, meaning the balloons use much less power to travel the same or greater distances than before. How does Loon know it's better?

Although Apple's latest A14 Bionic chip enabled the iPhone 12 family and iPad Air tablets to deliver impressive performance improvements, Qualcomm is making clear that the next generation of Android devices will rely heavily on advanced AI and computer vision processors to retake the performance lead. Teased yesterday at Qualcomm's virtual Tech Summit, the Snapdragon 888 is getting a full reveal today, and the year-over-year gains are impressive, notably including the largest jump in AI performance in Snapdragon history. The Snapdragon 888's debut is significant for technical decision makers because the chip will power most if not all of 2021's flagship Android phones, which collectively represent a large share of the over two billion computers sold globally each year. Moreover, the 888's increasing reliance on AI processing demonstrates how machine learning's role is now critical in advancing all areas of computing, ranging from how devices work when they're fully on to what they're quietly doing when not in active use. From a high-level perspective, the Snapdragon 888 is a sequel to last year's flagship 865 chips, leveraging 5-nanometer process technology and tighter integration with 5G and AI chips to deliver performance and power efficiency gains.

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.

New technologies like artificial intelligence, machine learning, robotics, big data, and networks are expected to revolutionize production processes, but they could also have a major impact on developing economies. The opportunities and potential sources of growth that, for example, the United States and China enjoyed during their early stages of economic development are remarkably different from what Cambodia and Tanzania are facing in today's world. Our recent staff research finds that new technology risks widening the gap between rich and poor countries by shifting more investment to advanced economies where automation is already established. This could in turn have negative consequences for jobs in developing countries by threatening to replace rather than complement their growing labor force, which has traditionally provided an advantage to less developed economies. To prevent this growing divergence, policymakers in developing economies will need to take actions to raise productivity and improve skills among workers.

New technologies like artificial intelligence, machine learning, robotics, big data, and networks are expected to revolutionize production processes, but they could also have a major impact on developing economies. The opportunities and potential sources of growth that, for example, the United States and China enjoyed during their early stages of economic development are remarkably different from what Cambodia and Tanzania are facing in today's world. Our recent staff research finds that new technology risks widening the gap between rich and poor countries by shifting more investment to advanced economies where automation is already established. This could in turn have negative consequences for jobs in developing countries by threatening to replace rather than complement their growing labor force, which has traditionally provided an advantage to less developed economies. To prevent this growing divergence, policymakers in developing economies will need to take actions to raise productivity and improve skills among workers.

Alphabet's Loon has shifted to a different type of navigation system for its internet-beaming balloons. Rather than relying on algorithms designed by humans, the balloons are using an artificial intelligence system Loon developed with Google AI over the last few years. A reinforcement learning (RL) system is now in charge of navigation for a fleet of balloons over Kenya, where Loon switched on its first commercial service earlier this year. Loon says this is the first use of an RL model in "a production aerospace system." It also noted the "development is exciting because it shows that reinforcement learning can be applied to real-world use cases."

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.

Hyderabad, India: Millets have sometimes been hailed as the next quinoa but researchers collating a global database of millet products have found this ancient grain to be orchestrating a silent food revolution that could see quinoa outstripped. The "Millet Finder", launched today, discovered a surge in the use of millets, with over a thousand modern convenient products in a very wide range, across all the inhabited continents. Launched today at FoodTec Expo by the International Crops Research Institute of the Semi-Arid Tropics (ICRISAT) and the ICAR-Indian Institute of Millets Research (IIMR), the "Millet Finder" will help users find over 500 products across 30 countries. Another 500 products are identified and set to be included and mapped by end of the year by the Smart Food team at ICRISAT, who created the database and will continue growing it. "Unless there is a consumer driven demand and movement to diversify diets, farms cannot diversify and agriculture cannot be sustainable. By diversifying staples, we can have a major impact on diets, farms and the environment. ICRISAT strongly believes in creating awareness and helping consumers make informed choices while keeping their health and the environment in view. In that respect, millets check every box," said Dr Jacqueline d'Arros Hughes, Director General, ICRISAT, and Chair, Smart Food Executive Council.

This is the forth and final post in our series of blog posts focusing on the field of Natural Language Processing! In our first post, we saw that the application of neural networks for building language models was a major turning point in the NLP timeline, and in our second post we explored the significance of Word Embeddings in advancing the field. In our third post, we described the language model and how to build your own language-generating model in Keras! We are finally ready to tackle sequential processing, attention, and the Transformer! In their highly-memorable paper titled "Attention Is All You Need", Google Brain researchers introduced the Transformer, a new type of encoder-decoder model that relies solely on attention for sequence-to-sequence modelling.