Sphero, the company behind robotic toys like the BB-8 robot and educational robotics kits, announced today that it's spinning its public safety division into a new company, dubbed Company Six. It plans to commercialize robots and AI software for first responders, government, defense and "those who work in dangerous situations." While Sphero didn't say that Company Six will make robots for police, it sounds like the new company could be headed in that direction. "Our team is excited to build critically-needed robotic hardware and advanced software solutions that help first responders and people with dangerous jobs," said Company Six CEO Jim Booth, formerly Sphero's COO. Sphero has brought four million robots to market, including programmable tank robots, and it's experience in mobility could come in handy.

UK prime minister Boris Johnson told MPs today that he is confident that the government will have recruited 25,000 coronavirus contact tracers by the start of June, which he says will provide the capacity to trace the contacts of 10,000 new coronavirus cases per day. Johnson said 24,000 contact tracers have already been recruited. In April, health secretary Matt Hancock said the government hoped to recruit 18,000 contact tracers by mid-May, to coincide with the planned release of the NHS covid-19 contact tracing app. But the widespread release of the app, currently being trialled on the Isle of Wight, has now been delayed until June. There are also ongoing concerns about privacy. In a recent report, security researchers wrote that there should be a legal requirement that all data collected by the app is deleted at the end of the coronavirus crisis, rather than being anonymised or repurposed.

For patients with COVID-19, terrifying shortness of breath can set in virtually overnight. In many cases, it's caused by an aggressive pneumonia infection in the lungs, which fills them with thick fluid and robs the body of life-giving oxygen. Detecting these severe cases early on is essential for treating them successfully. At the moment, however, the only way to tell whether a patient's pneumonia is caused by the coronavirus is by examining X-ray and CT scans of the chest--and as cases rack up worldwide, radiologists are being deluged with images, creating a backlog that may delay critical decisions about care. One solution, said Karen Panetta, may involve taking some of that workload away from humans.

Artificial Intelligence (AI) and machine learning are the kind of buzzwords that generate a great deal of interest; they are tossed all the time around. When a huge set of data is involved, it seems like a nightmare to have to interpret it all by hand. It is the kind of work one might describe as repetitive and boring. Not to mention the fact that a lot of staring at the computer will take you to figure out what you set out to learn. The best thing about machines and technology is that it never gets tired-unlike humans. Also, it's easier designed to notice trends.

One of the defining aspects of COVID-19 is its disproportionate impact on underserved communities and the harsh spotlight it shines on existing social equity issues around the world. From access to quality education, jobs or affordable healthcare, COVID-19 is magnifying virtually every inequality in our communities. Never has there been a more important time to capture the moment to create the solutions the world needs to make a positive and lasting contribution to the social inequity issues of our generation. Solutions will come from all corners and technology innovators will need to play their part. Building on Microsoft's long-standing efforts to ensure technology fulfills its promise to address the world's biggest challenges, Microsoft joined efforts with Giving Tech Labs to unleash the power of public interest technology.

We use machine learning methods to examine the power of Treasury term spreads and other financial market and macroeconomic variables to forecast US recessions, vis-à-vis probit regression. In particular we propose a novel strategy for conducting cross-validation on classifiers trained with macro/financial panel data of low frequency and compare the results to those obtained from standard k-folds cross-validation. Consistent with the existing literature we find that, in the time series setting, forecast accuracy estimates derived from k-folds are biased optimistically, and cross-validation strategies which eliminate data "peeking" produce lower, and perhaps more realistic, estimates of forecast accuracy. That is, while a k-folds cross-validation indicates tha t the forecast accuracy of tree methods dominates that of neural networks, which in turn dominates that of probit regression, the more conservative cross-validation strategy we propose indicates the exact opposite, and that probit regression should be preferred over machine learning methods, at least in the context of the present problem. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods outperform many alternative classification algorithms and we discuss some possible reasons for our result.

Zebra Medical Vision, the deep-learning medical imaging analytics company, announced on Monday that it secured its 5th FDA clearance, this time for an AI solution that identifies findings suggestive of compression fractures in scans. The Israeli firm said the FDA gave 510(k) clearance for its Vertebral Compression Fractures (VCF) product that enables clinicians to place patients at risk of osteoporosis "in treatment pathways to prevent potentially life-changing fractures," Zebra Medical said in a statement. The solution can be applied to abdominal or chest CT scan performed for any clinical indication, the company says. Founded in 2014 by Eyal Toledano, Eyal Gura, and Elad Benjamin, Zebra uses AI to read medical scans and automatically detect anomalies. Through its development and use of different algorithms, Zebra Medical has been able to identify visual symptoms for diseases such as breast cancer, osteoporosis, and fatty liver, as well as conditions such as aneurysms and brain bleeds.

Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts' opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts' causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being \emph{compatible}, and show how compatible causal models can be merged. We then use it as the basis for combining experts' causal judgments. We also provide a definition of decomposition for causal models to cater for cases when models are incompatible. We illustrate our approach on a number of real-life examples.

Moog Inc. has automated the evaluation of copper (Cu) alloy grain size using a deep-learning convolutional neural network (CNN). The proof-of-concept automated image acquisition and batch-wise image processing offers the potential for significantly reduced labor, improved accuracy of grain evaluation, and decreased overall turnaround times for approving Cu alloy bar stock for use in flight critical aircraft hardware. A classification accuracy of 91.1% on individual sub-images of the Cu alloy coupons was achieved. Process development included minimizing the variation in acquired image color, brightness, and resolution to create a dataset with 12300 sub-images, and then optimizing the CNN hyperparameters on this dataset using statistical design of experiments (DoE). Over the development of the automated Cu alloy grain size evaluation, a degree of "explainability" in the artificial intelligence (XAI) output was realized, based on the decomposition of the large raw images into many smaller dataset sub-images, through the ability to explain the CNN ensemble image output via inspection of the classification results from the individual smaller sub-images.

Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices, but recent reports suggest that these systems may produce biased results, discriminating against people in certain demographic groups. Diagnosing and understanding the underlying true causes of model biases, however, are challenging tasks because modern computer vision systems rely on complex black-box models whose behaviors are hard to decode. We propose to use an encoder-decoder network developed for image attribute manipulation to synthesize facial images varying in the dimensions of gender and race while keeping other signals intact. We use these synthesized images to measure counterfactual fairness of commercial computer vision classifiers by examining the degree to which these classifiers are affected by gender and racial cues controlled in the images, e.g., feminine faces may elicit higher scores for the concept of nurse and lower scores for STEM-related concepts. We also report the skewed gender representations in an online search service on profession-related keywords, which may explain the origin of the biases encoded in the models.