How machine intelligence is helping healthcare providers fight the pandemic. In times of crisis, help can come from the most unexpected places. We're seeing that right now in the innovative ways AI is being used to protect healthcare workers and aid the effort to overcome COVID-19. And we're just scratching the surface on the potential for AI to make the entire healthcare journey safer and more humane for nurses, doctors, and patients. At Tampa General Hospital in Florida, an AI-driven technology screens individuals for COVID-19 symptoms before they interact with hospital staff and patients.

Eyenuk, Inc., a global artificial intelligence (AI) medical technology and services company and the leader in real-world applications for AI Eye Screening, announced that it has successfully fulfilled the contract awarded by Public Health England (PHE) to use Eyenuk's EyeArt AI Eye Screening System to grade 60,000 patient image sets from 6 different National Health Service (NHS) Diabetic Eye Screening Programmes in England. Diabetic retinopathy (DR) is a vision-threatening complication of diabetes and a leading cause of preventable vision loss globally.1 In England, an estimated 4.6 million are living with diabetes, one-third of whom are at risk of developing DR. Diabetes has become a growing health concern as the number of people diagnosed with diabetes in the U.K. has more than doubled in the last 20 years.2 The U.K. has been leading the world in diabetic retinopathy screening, achieving patient uptake rates of over 80% (screening nearly 2.5 million diabetes patients annually),3 as compared with most parts of the world where typically less than half of diabetes patients receive annual eye screening.4 As a result, diabetic retinopathy is no longer the leading cause of blindness in the working age group in England.5

The government's Integrated Review comes at a time of considerable technological change. The UK has entered a'Fourth Industrial Revolution' (4IR), which promises to'fundamentally alter the way we live, work, and relate to one another'. This new era will be characterised by scientific breakthroughs in fields such as the Internet of Things, Blockchain, quantum computing, fifth-generation wireless technologies (5G), robotics, and artificial intelligence (AI), which together are expected to deliver transformational changes across almost every sector of the economy. Of particular note are recent developments in AI, specifically advances in the sub-field of machine learning. Progress over the last decade has been driven by an exponential growth in computing power, coupled with increased availability of vast datasets with which to train machine learning algorithms. While machine learning technology can be traced back to at least the 1950s, investment has increased substantially in recent years, and as a result AI is rapidly becoming ubiquitous across the economy.

The Patent Office recently launched a page for artificial intelligence information on its website. The page provides information on the Patent Office's AI initiatives, public notices and responses, AI-related events and outside resources. The website is a part of a broader effort by the Patent Office to engage with the innovation community and experts on issues relating to AI. Director Iancu stated the following: One of the agency's top priorities is to ensure that the United States maintains its leadership in innovation, especially in emerging technologies such as artificial intelligence (AI). To that end, the USPTO has been actively engaging with the innovation community and experts in AI to determine whether further guidance is needed to promote the predictability and reliability of intellectual property rights relating to AI technology and to encourage further innovation in and around this critical area. The Patent Office has solicited comments relating to AI issues and has received nearly 200 responses from individuals, corporations, associations, academia and others.

Customers of any type of business expect help instantly and access to their services in a growing number of ways. Banks are turning to chatbots to help deal with massive volumes of customer interactions. Conversational banking frees up agents for more complex issues, while the move to app-based and web banking sees customers more used to dealing with digital interfaces, of which chatbots and AI virtual assistants are just the latest step. Established banks and their challenger rivals are all keen to develop a conversational banking strategy. Those that have been experimenting for some years find themselves with key advantages over banks stepping fresh into the conversational customer service arena.

While general purpose chatbots haven't shaped user interfaces as radically as early advocates like Facebook may have hoped, when used in a more targeted capacity, they have shown promise in building closer relationships between consumers and brands and making critical enterprise workflows more streamlined. India's Yellow Messenger operates a conversational AI platform used by companies including Accenture, Flipkart and Grab to communicate with employees and customers. The startup is announcing new funding as they officially launch their chatbot platform stateside. The Bengaluru-based company tells TechCrunch they've closed a $20 million Series B led by Lightspeed Venture Partners. The startup previously raised funding from Lightspeed India Partners, which led the startup's Series A last year.

As the COVID-19 coronavirus outbreak continues to spread across the globe, companies and researchers are looking to use artificial intelligence as a way of addressing the challenges of the virus. Here are just some of the projects using AI to address the coronavirus outbreak. A number of research projects are using AI to identify drugs that were developed to fight other diseases but which could now be repurposed to take on coronavirus. By studying the molecular setup of existing drugs with AI, companies want to identify which ones might disrupt the way COVID-19 works. BenevolentAI, a London-based drug-discovery company, began turning its attentions towards the coronavirus problem in late January.

In particular, the machine learning advantage showed itself early on in the pandemic, spotting the first signs of pandemic risk and recommending derisking or even a stop to trading. And, as the spread of the virus gives the world a lethal demonstration of exactly what exponential growth looks like, we're all learning that an apparently insignificant delay in responding early on can make a catastrophic difference once the curve steepens. The US introduced social distancing on March 16; had it done so on March 2 instead, 90% of those now dead of Covid-19 would still be alive, epidemiologists estimate. Similar delays of a few days in response may have made the difference between European countries like Greece and Ireland suffering hundreds of deaths, and countries like the UK and Spain suffering thousands. Taiwan's health ministry, meanwhile, has reportedly credited part of the country's prompt response to the pandemic to an unlikely source: a December 2019 post on the virus on the country's unruly PTT online bulletin board.

This article about coronavirus and stock markets was written by Gabriel Plat, a Financial Analyst at I Know First. The year 2020 in the stock markets is what we call a roller coaster. Since the spread of the new Coronavirus outside China, we have seen stocks from different sectors and from different countries changing abruptly its trends. This can be seen clearly by the VIX Index chart below. This index is also called the "Fear Index" since it is a measure of the market expectation for volatility in the next 30 days.

In a new study published this week on the preprint server Arxiv.org, Both it and several baseline models will soon be available on GitHub. One of the most powerful techniques in machine learning -- reinforcement learning, which entails spurring software agents toward goals via rewards -- is also one of the most flawed. It's sample inefficient, meaning it requires a large number of compute cycles to complete, and without additional data to cover variations, it adapts poorly to environments that differ from the training environment. It's theorized that prior knowledge of tasks through structured language could be combined with reinforcement learning to mitigate its shortcomings, and BabyAI was designed to put this theory to the test.