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.

Deep neural networks are often not robust to semantically-irrelevant changes in the input. In this work we address the issue of robustness of state-of-the-art deep convolutional neural networks (CNNs) against commonly occurring distortions in the input such as photometric changes, or the addition of blur and noise. These changes in the input are often accounted for during training in the form of data augmentation. We have two major contributions: First, we propose a new regularization loss called feature-map augmentation (FMA) loss which can be used during finetuning to make a model robust to several distortions in the input. Second, we propose a new combined augmentations (CA) finetuning strategy, that results in a single model that is robust to several augmentation types at the same time in a data-efficient manner. We use the CA strategy to improve an existing state-of-the-art method called stability training (ST). Using CA, on an image classification task with distorted images, we achieve an accuracy improvement of on average 8.94% with FMA and 8.86% with ST absolute on CIFAR-10 and 8.04% with FMA and 8.27% with ST absolute on ImageNet, compared to 1.98% and 2.12%, respectively, with the well known data augmentation method, while keeping the clean baseline performance.

With the growing number of published functional magnetic resonance imaging (fMRI) studies, meta-analysis databases and models have become an integral part of brain mapping research. Coordinate-based meta-analysis (CBMA) databases are built by automatically extracting both coordinates of reported peak activations and term associations using natural language processing (NLP) techniques. Solving term-based queries on these databases make it possible to obtain statistical maps of the brain related to specific cognitive processes. However, with tools like Neurosynth, only singleterm queries lead to statistically reliable results. When solving richer queries, too few studies from the database contribute to the statistical estimations. We design a probabilistic domain-specific language (DSL) standing on Datalog and one of its probabilistic extensions, CP-Logic, for expressing and solving rich logic-based queries. We encode a CBMA database into a probabilistic program. Using the joint distribution of its Bayesian network translation, we show that solutions of queries on this program compute the right probability distributions of voxel activations. We explain how recent lifted query processing algorithms make it possible to scale to the size of large neuroimaging data, where state of the art knowledge compilation (KC) techniques fail to solve queries fast enough for practical applications. Finally, we introduce a method for relating studies to terms probabilistically, leading to better solutions for conjunctive queries on smaller databases. We demonstrate results for two-term conjunctive queries, both on simulated meta-analysis databases and on the widely-used Neurosynth database.

A young Egyptian engineer has invented a remote-control robot that can take patient's temperature, test for COVID-19 and even reprimand those not wearing a mask. With a human-like face and robotic arms, 'Cira-03' is capable of drawing blood and performing EKGs and x-rays, then display test results on a screen on its chest. Cira-03 tests patients for coronavirus by cupping their chin and then extending an arm with a swab into their mouth. While the goal was to limit exposure of healthcare workers, El-Komy also wanted to put patients at ease in a harrowing situation. 'I tried to make the robot seem more human, so that the patient doesn't fear it,' El-Komy said.