CHICAGO--(BUSINESS WIRE)--Donnelley Financial Solutions (NYSE: DFIN), a leading risk and compliance company, today announced a strategic partnership with Galvanize, an award-winning, cloud-based security, risk management, compliance and audit software company. Galvanize was recently named a leader in The Forrester Wave: Governance, Risk, And Compliance Platforms for the first-quarter 2020. "The partnership with Galvanize accelerates DFIN's position as a leading risk and compliance company with a comprehensive portfolio of technology-enabled solutions that meet the needs of clients globally," said Craig Clay, president of Global Capital Markets at DFIN. "In these unprecedented times, our partnership with Galvanize gives us the ability to help our clients identify areas of revenue leakage, using Galvanize's analytics engine powered by ACL Robotics. The combination of cloud-based tools and automation with ACL Robotics has revolutionized internal control and audit management. Our clients will benefit from the HighBond software platform, a complete GRC solution that includes industry-leading internal controls / SOX compliance, operational audit and enterprise risk management software solutions."

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.

When the U.S. began locking down to slow the spread of the coronavirus, Amazon, grocery stores, and wholesale stores like Costco saw an enormous uptick in consumers wanting to buy a few select items. On Amazon, during the week of April 12th to 18th, the top ten search queries were face masks and N95 masks, hand sanitizer, paper products like paper towels and toilet paper, and sanitizing solutions like Lysol spray and Clorox wipes. So many people bought face masks that April's new #1 selling product on Amazon was "Face Mask, Pack of 50". This trend occurred across every single consumer- and business-facing industry and vertical. Consumers started behaving erratically literally overnight, and they haven't stopped behaving abnormally, creating a massive problem for companies who employ artificial intelligence (AI) and machine learning models.

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. COVID-19 disruption has left enterprises with no choice but to reassess digital transformation investments and roadmaps. While less important projects are delayed, transformation projects involving AI and automation are receiving a lot of attention right now. In just the last 60 days, the adoption of varying levels of AI technologies across the enterprise surged with an incredible sense of urgency.

Artificial intelligence (AI) may be an aid to interpreting ECG results, helping healthcare staff to diagnose diseases that affect the heart. Researchers at Uppsala University and heart specialists in Brazil have developed an AI that automatically diagnoses atrial fibrillation and five other common ECG abnormalities just as well as a cardiologist. The study has been published in Nature Communications. An electrocardiogram (ECG) is a simple test that can be used to check the heart's rhythm and electrical activity. The results are shown on a graph that can reveal various conditions that affect the heart.

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.

The last time I traveled along Stadium Way I was headed to a Dodger game, but on Monday afternoon I drove to the fire training center near the ballpark for a much less enjoyable experience. Just a cotton swab and a five-minute drive-through, with results to follow in a few days. I was conflicted about being tested, for two reasons. First, while we definitely needed to ramp up testing back at the beginning of this crisis, I'm wondering if the county has now gone overboard in offering free testing to all residents, whether or not they have symptoms. Second, I'm pretty sure that my minor allergy-like symptoms are just that: allergies.

Yesterday, Microsoft's Build 2020 developer conference kicked off (remotely), and we saw the first results of Microsoft's billion-dollar investment in OpenAI, a company co-founded by Elon Musk. Microsoft announced it has developed an Azure-hosted supercomputer built expressly for testing OpenAI's large-scale artificial intelligence models. While we've seen many AI implementations focused on single tasks, like recognizing specific objects in images or translating languages, a new wave of research focuses on massive models that can perform multiple tasks at once. As Microsoft notes, that can include moderating game streams or potentially generating code after exploring GitHub. Realistically, these large-scale models can actually make AI a lot more useful for consumers and developers alike.

From May 22, Didi's ride-hailing drivers in Latin America will need to take a selfie with mask on to pass the AI verification, and from June they will need to report their body temperature to the phone app and upload photos of daily vehicle disinfection works to the phone application.