Creating accurate maps today is a painstaking, time-consuming manual process, even with access to satellite imagery and mapping software. Many regions -- particularly in the developing world -- remain largely unmapped. To help close this gap, Facebook AI researchers and engineers have developed a new method that uses deep learning and weakly supervised training to predict road networks from commercially available high-resolution satellite imagery. The resulting model sets a new bar for the state of the art for accuracy, and because it is able to accommodate regional differences in road networks, it can effectively predict roads around the globe. We are now sharing the details of our model and making data available to the global mapping community through Map With AI, a new set of specialized map-editing services and tools. Map With AI includes an editor interface, RapiD, which allows mapping experts to easily review, verify, and adjust the map as needed.

Tension fills and permeates the room as millions of eyes are hooked to every single moved performed on the 361 points of the 19x19 grid lines of the board. Their brains hopelessly trying to compute the best of the infinitude of possible positions. Yet the most brilliant mind at it belongs to the man the camera focuses on, the Go 18-time world champion, Lee Sedol. As the clock ticks and the game advances, Lee Sedol delights the public with expressions of surprise and concentration that move by move slowly turn into concern and exhaustion. He rests his head on his palm, rubs his forehead as if looking for a brilliant idea.

Cardiovascular disease is the leading cause of death globally. According to the World Health Organization, approximately 31% of all global deaths occur due to cardiovascular diseases (CV) diseases, with coronary heart disease being the most common type. Four out of five CV disease deaths are due to heart attacks and strokes. However, early detection, and preventive measures and management including lifestyle and diet modification and proper treatment for CV diseases can significantly reduce morbidity and mortality. Meanwhile, researchers are developing tools to help identify the probability of CV diseases, harnessing the power of artificial intelligence (AI).

Artificial intelligence is being increasingly used to classify employees, and there's a growing fear it might be used to classify people in other respects. These are only a small handful of the most well-known uses of artificial intelligence, yet there is one that, despite being on the margins for much of AI's recent history, is now threatening to grow significantly in prominence. This is AI's ability to classify and rank people, to separate them according to whether they're "good" or "bad" in relation to certain purposes. At the moment, Western civilization hasn't reached the point where AI-based systems are used en masse to categorize us according to whether we're likely to be "good" employees, "good" customers, "good" dates and "good" citizens. Nonetheless, all available indicators suggest that we're moving in this direction, and that this is regardless of whether Western nations consciously decide to construct the kinds of social credit system currently being developed by China.

There is thus a great need to define inputs, outputs, and their interactive relationships clearly. Inevitably, technologists would code fairness as a narrowly defined modular property of the machine learning system. However, fairness is not a well defined nor universally applicable concept, to begin with as it has to be understood amidst a particular social context. Abstracting away this context is thus an abstraction error. With the presence of this error, AI would have an ineffective, inaccurate and misguided interpretation and thus, quantification of fairness when it is introduced to varying societal systems.

You may have assumed that Artificial intelligence (AI) will replace humans in the healthcare sector down the line based on the headlines you have come across so far. There is no doubt that AI has contributed to its significant impact across every industry from the creation of a robot featuring communication skills like humans to grow more food with scarce resources, and healthcare is no exception. Trends suggest that medical practitioners will collaborate with AI to deliver more personalized care to patients; now, they can find their lives partly in the hands of machines working alongside doctors. AI has tremendous benefits for the healthcare sector; in fact, it is a miracle, but it is still struggling to grow from its infancy stage. The overall adoption of AI is low though machine-learning tools have become more sophisticated, and their use cases have expanded. The core strength of AI is not only whittling down the cost and quickly diagnosing life-threatening diseases, but also providing treatments for quick recuperation.

The following article is part of a series on Argonne National Laboratory's efforts to use the predictive power of artificial intelligence, specifically machine learning, to advance discoveries in a broad range of scientific disciplines. High-energy physics and cosmology seem worlds apart in terms of sheer scale, but the invisible components that comprise the field of one inform the composition and dynamics of the other -- collapsing stars, star-birthing nebulae and, perhaps, dark matter. For decades, the techniques by which researchers in both fields studied their domains seemed almost incompatible, as well. High-energy physics relied on accelerators and detectors to glean some insight from the energetic interactions of particles, while cosmologists gazed through all manner of telescopes to unveil the secrets of the universe. " … it would be interesting to know if image classification techniques from machine learning that have been used successfully by Google and Facebook can simplify or shorten the development of algorithms that identify particle signatures in our 3D detectors."

The face-recognition technology behind smartphones, self-driving cars and diagnostic imaging in healthcare has made massive strides of late. These examples all use solutions that make sense of objects in front of them, hence the term "computer vision" -- these computers are able to make sense of what they "see." During a recent Data Lab meetup at CompassRed in downtown Wilmington, Delaware, Chandra Kambhamettu, professor and director of the Video/Image Modeling and Synthesis Lab in the Department of Computer and Information Sciences at the University of Delaware, and Dave Wallin, manager of innovations at The Archer Group, offered a high-level explanation of how image technology works along with the deep learning technology that powers it. Much of the innovation in image recognition relies on deep learning technology, an advanced type of machine learning and artificial intelligence. Typical machine learning takes in data, pushes it through algorithms and then makes a prediction, making it appear that the computer is "thinking" and coming to its own conclusions.

Two Department of Energy Computational Science Graduate Fellowship recipients are leading an effort to address global climate change effects with machine-learning techniques. Priya Donti, a third-year fellow in computer science and public policy at Carnegie Mellon University, and Kelly Kochanski, a fourth-year fellow in Earth surface processes at the University of Colorado Boulder, are on the steering committee (Donti is co-chair) for Climate Change AI. The group's website says it is a coalition of "volunteers from academia and industry who believe in using machine learning, where it is relevant, to help tackle the climate crisis." Machine learning algorithms identify patterns in known data and use that information to make predictions or to classify previously unseen data. Machine learning is a key component of artificial intelligence (AI).

At the core of today's state-of-the-art Artificial Intelligence (AI) algorithms is the ability to learn complex patterns from a sample of data. In the manufacturing context, an example of a pattern might be the ways in which a set of parameters contained in that data, which are related to a process in a factory, vary together. When considering AI, it's important to understand what the data requirements are at the outset. The algorithm learns the patterns by being shown many examples of the parameter values in question--typically between a few thousand and several million. This data sample is a representation of the history of the factory process.