Does AI have the power to control the spread of infection of COVID-19, discover cures and vaccines, and aid in the treatment of the critically ill? Or should AI practitioners step back and let the epidemiologists, clinicians, and microbiologists manage the response? Can we trust AI to guide decision making? Do we have access to the data we need and how can we share it whilst balancing patient privacy? We have assembled a world class panel of AI and medical experts to tackle our biggest crisis.

Artificial intelligence's emergence into the mainstream of enterprise computing raises significant issues -- strategic, cultural, and operational -- for businesses everywhere. What's clear is that enterprises have crossed a tipping point in their adoption of AI. A recent O'Reilly survey shows that AI is well on the road to ubiquity in businesses throughout the world. The key finding from the study was that there are now more AI-using enterprises -- in other words, those that have AI in production, revenue-generating apps -- than organizations that are simply evaluating AI. Taken together, organizations that have AI in production or in evaluation constitute 85% of companies surveyed. This represents a significant uptick in AI adoption from the prior year's O'Reilly survey, which found that just 27% of organizations were in the in-production adoption phase while twice as many -- 54% -- were still evaluating AI.

Given the outsized hold Artificial Intelligence (AI) technology has acquired on public imagination of late, it comes as no surprise that many are wondering what AI can do for the public health crisis wrought by the COVID-19 coronavirus. A casual search of AI and COVID-19 already returns a plethora of news stories, many of them speculative. While AI technology is not ready to help with the magical discovery of a new vaccine, there are important ways it can assist in this fight. Controlling epidemics is, in large part, based on laborious contact tracing and using that information to predict the spread. We live in a time in which we constantly leave digital footprints through our daily life and interactions.

Automation Anywhere, a global leader in Robotic Process Automation (RPA), announced the launch of Bot Security, the industry's first security program to set the standard for securing software bots that enable business continuity. The magnitude of the coronavirus (Covid-19) outbreak has organizations around the world looking to technologies like RPA and intelligent automation to help mitigate disruptions and advance public health, keep global supply chains moving and governments afloat. Today, the company introduced a flexible, multi-tiered framework to certify that bots built by customers, partners, and publishers of bots on Bot Store – the world's largest intelligent automation marketplace with more than 850 pre-built bots – are pre-certified and trusted to scale RPA more rapidly and securely. With Bot Security, users downloading ready-to-deploy intelligent software bots no longer have to compromise on security as they build RPA solutions to access critical data, track the virus' spread and direct citizens to vital information from trusted sources. Automation Anywhere leads the industry as the first vendor to offer a web-based, cloud-native RPA platform that is System and Organization Controls (SOC) 2 Type 1 certified.

Mice move their ears, cheeks and eyes to convey emotion.Credit: Getty Researchers have used a machine-learning algorithm to decipher the seemingly inscrutable facial expressions of laboratory mice. The work could have implications for pinpointing neurons in the human brain that encode particular expressions. Their study "is an important first step" in understanding some of the mysterious aspects of emotions and how they manifest in the brain, says neuroscientist David Anderson at the California Institute of Technology in Pasadena. Nearly 150 years ago, Charles Darwin proposed that facial expressions in animals might provide a window onto their emotions, as they do in humans. But researchers have only recently gained the tools -- such as powerful microscopes, cameras and genetic techniques -- to reliably capture and analyse facial movement, and investigate how emotions arise in the brain.

Fear is spreading on social media as people share their thoughts on the deadly coronavirus and the impact of the efforts to combat it. Italian-based artificial intelligence company Expert System has been searching through tens of thousands of social media posts to track feelings towards COVID-19. They used a range of natural language systems to capture the emotional view of different English language social media posts related to the pandemic. The team plan to publish a daily update showing the changing attitudes and emotions surrounding the spread of the virus and efforts to slow it down. For the fourth day in a row fear has been the most dominant emotion expressed in posts, with all negative views increasing across the English-language world.

However, one issue that still persists is how to avoid printing objects that don't meet expectations and thus can't be used, leading to a waste in materials and resources. Scientists at the University of Southern California's (USC's) Viterbi School of Engineering has come up with what they think is a solution to the problem with a new machine-learning-based way to ensure more accuracy when it comes to 3D-printing jobs. Researchers from the Daniel J. Epstein Department of Industrial and Systems Engineering developed a new set of algorithms and a software tool called PrintFixer that they said can improve 3D-printing accuracy by 50 percent or more. The team, led by Qiang Huang, associate professor of industrial and systems engineering and chemical engineering and materials science, hopes the technology can help make additive manufacturing processes more economical and sustainable by eliminating wasteful processes, he said. "It can actually take industry eight iterative builds to get one part correct, for various reasons," said Qiang, who led the research.

Codebase Ventures Inc (CSE:CODE) (OTCQB:BKLLF) reported that Love Hemp, a CBD supplier in the UK, and a subsidiary of World High Life Plc, in which the company is invested, has seen record monthly sales via its retail presence and e-commerce site. In an update on the early-stage investor's holdings, Codebase also told investors it is "actively pursuing" pharmaceutical opportunities that could have a positive impact on the current global coronavirus pandemic. World High Life is focused on backing or acquiring companies operating in the CBD wellness and medicinal cannabis industry and its wholly-owned subsidiary, Love Hemp is a leading CBD supplier in the United Kingdom. Elsewhere, the firm said its Arcology investment - an AI (artificial intelligence) blockchain ecosystem - is advancing its presence among dAPP developers by launching a project on GitHub, the world's largest source code sharing platform. DApp stands for decentralized application and such an app has its backend code running on a decentralized peer-to-peer network.

Researchers at the Center for Nanoscale Materials (CNM), a U.S. Department of Energy (DOE) Office of Science User Facility located at the DOE's Argonne National Laboratory, have invented a machine-learning based algorithm for quantitatively characterizing, in three dimensions, materials with features as small as nanometers. Researchers can apply this pivotal discovery to the analysis of most structural materials of interest to industry. "What makes our algorithm unique is that if you start with a material for which you know essentially nothing about the microstructure, it will, within seconds, tell the user the exact microstructure in all three dimensions," said Subramanian Sankaranarayanan, group leader of the CNM theory and modeling group and an associate professor in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago. "For example, with data analyzed by our 3D tool," said Henry Chan, CNM postdoctoral researcher and lead author of the study, "users can detect faults and cracks and potentially predict the lifetimes under different stresses and strains for all kinds of structural materials." Most structural materials are polycrystalline, meaning a sample used for purposes of analysis can contain millions of grains.

The emergence of the novel coronavirus has left the world in turmoil. COVID-19, the disease caused by the virus, has reached virtually every corner of the world, with the number of cases exceeding a million and the number of deaths more than 50,000 worldwide. It is a situation that will affect us all in one way or another. With the imposition of lockdowns, limitations of movement, the closure of borders and other measures to contain the virus, the operating environment of law enforcement agencies and those security services tasked with protecting the public from harm has suddenly become ever more complex. They find themselves thrust into the middle of an unparalleled situation, playing a critical role in halting the spread of the virus and preserving public safety and social order in the process.