Modern health care has been reinvigorated by the widespread adoption of artificial intelligence. From speeding image analysis for radiology to advancing precision medicine for personalized care, AI has countless applications, but can it rise to the challenge in the fight against Covid-19? Researchers from the Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), now housed within the MIT Stephen A. Schwarzman College of Computing, say the ongoing public health crisis provides ample opportunities for leveraging AI technologies, such as accelerating the search for effective therapeutics and drugs that can treat the disease, and are actively working to translate this potential to success. When Covid-19 began to spread worldwide, Jameel Clinic's community of machine learning and life science researchers redirected their work and began exploring how they can collaborate on the search for solutions by tapping into their collective knowledge and expertise. The ensuing discussions led to the launch of AI Cures, an initiative dedicated to developing machine learning methods for finding promising antiviral molecules for Covid-19 and other emerging pathogens, and to lower the barrier for people from varied backgrounds to get involved by inviting them to contribute to the effort.

New developments in AI could spur a massive democratization of access to services and work opportunities, improving the lives of millions of people around the world and creating new commercial opportunities for businesses. Yet they also raise the specter of potential new social divides and biases, sparking a public backlash and regulatory risk for businesses. For the U.S. and other advanced economies, which are increasingly fractured along income, racial, gender, and regional lines, these questions of equality are taking on a new urgency. Will advances in AI usher in an era of greater inclusiveness, increased fairness, and widening access to healthcare, education, and other public services? Or will they instead lead to new inequalities, new biases, and new exclusions?

Artificial intelligence (AI) is certainly the'flavor of the month' and has become a part of our daily lives. However, there is one area that, until now, hasn't been involved in AI… As ubiquitous as artificial intelligence has become in modern life -- from boosting the understanding of the cosmos to surfacing entertaining videos on a phone -- AI hasn't yet found its way into orbit. That is until September 2, when an experimental satellite about the size of a cereal box was ejected from a rocket's dispenser along with 45 other similarly small satellites. The satellite, named PhiSat-1, is now soaring at over 17,000 mph (27,500 kmh) in sun-synchronous orbit about 329 miles (530 km) overhead. PhiSat-1 contains a new hyperspectral-thermal camera and onboard AI processing from an Intel Movidius Myriad 2 Vision Processing Unit (VPU) -- the same chip inside many smart cameras and even a $99 selfie taken by a drone on Earth.

In July 2019, Guillermo Federico Ibarrola was heading home on the subway when he was stopped by Buenos Aires police. The authorities told Ibarrola that he was being detained for an armed robbery that had happened three years ago in a city about 400 miles away. He said he had never even been to the city where he was accused of committing the crime. On the sixth day in police custody, he was suddenly released. The police officers offered Ibarrola coffee and dinner, and a bus ticket back home. As it turned out, a "Guillermo Ibarrola" had potentially committed a crime, but it wasn't this Guillermo Ibarrola.

Production of news content is growing at an astonishing rate. To help manage and monitor the sheer amount of text, there is an increasing need to develop efficient methods that can provide insights into emerging content areas, and stratify unstructured corpora of text into `topics' that stem intrinsically from content similarity. Here we present an unsupervised framework that brings together powerful vector embeddings from natural language processing with tools from multiscale graph partitioning that can reveal natural partitions at different resolutions without making a priori assumptions about the number of clusters in the corpus. We show the advantages of graph-based clustering through end-to-end comparisons with other popular clustering and topic modelling methods, and also evaluate different text vector embeddings, from classic Bag-of-Words to Doc2Vec to the recent transformers based model Bert. This comparative work is showcased through an analysis of a corpus of US news coverage during the presidential election year of 2016.

Non-Intrusive Load Monitoring (NILM) aims to predict the status or consumption of domestic appliances in a household only by knowing the aggregated power load. NILM can be formulated as regression problem or most often as a classification problem. Most datasets gathered by smart meters allow to define naturally a regression problem, but the corresponding classification problem is a derived one, since it requires a conversion from the power signal to the status of each device by a thresholding method. We treat three different thresholding methods to perform this task, discussing their differences on various devices from the UK-DALE dataset. We analyze the performance of deep learning state-of-the-art architectures on both the regression and classification problems, introducing criteria to select the most convenient thresholding method.

If we didn't have enough to worry about--Covid-19, a nation divided, massive job losses and civil unrest--now we have to be concerned that robots will take our jobs. The World Economic Forum (WEF) concluded in a recent report that "a new generation of smart machines, fueled by rapid advances in artificial intelligence (AI) and robotics, could potentially replace a large proportion of existing human jobs." Robotics and AI will cause a serious "double-disruption," as the coronavirus pandemic pushed companies to fast-track the deployment of new technologies to slash costs, enhance productivity and be less reliant on real-life people. Millions of people have lost their jobs due to the effects of the Covid-19 pandemic and now the machines will take away even more jobs from workers, according to the WEF. The organization cites that automation will supplant about 85 million jobs by 2025.

The tragedy happened to the AirFrance 447 more than 10 years ago, in 2009. The flight took off in Rio de Janeiro and was planned to land in Paris. It suddenly disappeared in the middle of the Atlantic ocean without any warning. Immediately, rescuers reached the zone and what they found were just some wreckage and corpse. All 228 people onboard died in the crash.

Locked down singles in Britain looking for love on Tinder, one of the world's most popular dating apps, can now video chat with their matches. Tinder had announced it is rolling out its'Face to Face' feature to its global customer base today. To prevent creeps and weirdos exploiting the feature to berate or harass their matches, video calling only becomes available when both parties opt in. It is designed to be used to compliment and boost conversation once a spark has been established. Tinder had announced it is rolling out its'Face to Face' feature today to its users around the world.

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