"A chemical reaction is a highly complex system," explains Frederik Sandfort, PhD student at the Institute of Organic Chemistry and one of the lead authors of the publication. "In contrast to the prediction of properties of individual compounds, a reaction is the interaction of many molecules and thus a multidimensional problem," he adds. Moreover, there are no clearly defined "rules of the game" which, as in the case of modern chess computers, simplify the development of AI models. For this reason, previous approaches to accurately predicting reaction results such as yields or products are mostly based on a previously gained understanding of molecular properties. "The development of such models involves a great deal of effort. Moreover, the majority of them are highly specialized and cannot be transferred to other problems," Frederik Sandfort adds.

In most large organizations, artificial intelligence (AI) and machine learning (ML) are powering key business functions, from big data analytics and customer service to fraud detection and personalized marketing. Insights that AI and ML can produce are powerful, but it's often difficult, if not impossible, to explain how these algorithms arrived at them. This limitation could pose significant problems for compliance with the EU's General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and other laws governing data and privacy. Let's look at GDPR first. When an automated process such as AI or ML makes a decision about an individual based on personal data, GDPR requires the organization to supply an explanation if requested.

Facilitating customer self-service using automated contact channels, such as IVR and chatbots is a recognised method of reducing strain on contact centres during spikes in demand. For organizations that experience high levels of churn, as seen in the mobile telecoms, insurance and energy sectors, a seamless customer journey can make the difference between keeping them onboard for another year, or losing customers to your nearest competitor. To address this, many organizations have focused on using chatbots to answer common queries, so that customer service employees have time to handle more complex calls. However, when it comes to reducing churn, applying a more granular approach can yield major benefits. Deploying a range of chatbots, each suited to a specific task, can greatly enhance the customer experience and deliver substantial benefits to the business.

The White House has urged AI experts to analyze a dataset of 29,000 scholarly articles about coronavirus that could offer insights into how to manage the pandemic. These questions have been published on Kaggle, a machine learning community owned by Google. The entire COVID-19 Open Research Dataset (CORD-19) has been made available on SemanticScholar, a free, nonprofit, academic search engine. The collection will be updated whenever new research is published in archival services and peer-reviewed publications. "Decisive action from America's science and technology enterprise is critical to prevent, detect, treat, and develop solutions to COVID-19," said Michael Kratsios, the USA's Chief Technology Officer.

In late December last year, Dr. Li Wenliang began warning officials about a novel coronavirus in Wuhan, China, but was silenced by the police before tragically succumbing to the disease two months later. Meanwhile, almost simultaneously, a computer server halfway across the world started issuing worrying alerts of a potential new outbreak. The server runs software by BlueDot, a company based in San Francisco that uses AI to monitor infectious disease outbreaks for signs of early trouble. Not enough people listened to either human expertise or AI. Then cases skyrocketed in Wuhan and spread across the world, and people had to take note.

Service Objects, the leading provider of real-time global contact validation solutions, announced it is delivering enhanced results for contact data validation by coupling artificial intelligence (AI) capabilities with its extensive network of over 300 data sources. This combination makes these services the most complete and accurate contact validation APIs available today. Service Objects' APIs allow customers to validate global contact information within their software platforms. These services can verify a contacts' name, global address, phone, email address and device simultaneously against hundreds of authoritative data sources, all in less than a second. Service Objects' services work with a process of adaptive machine learning that continually improves their capabilities, leveraging the results of previous transactions.

On Christmas Eve, 1968, the astronauts aboard NASA's Apollo 8 spacecraft became the first humans to behold the entirety of Earth with their own eyes. That day, crew member Bill Anders took an iconic photograph called "Earthrise'' that captured our home world emerging from behind the Moon's horizon. "We came all this way to explore the Moon, and the most important thing is that we discovered the Earth," Anders famously said of his mission. More than 50 years later, Earth is being rediscovered from space once again, but this time it is through the "eyes" of satellites, supercomputers, and artificial intelligence (AI) networks. Geospatial science, a sprawling and multifaceted field dedicated to resolving ever-finer details about Earth and its systems, is poised to undergo an unprecedented growth spurt powered by this confluence of technologies across both the public and private sectors. "With the proliferation of satellite platforms, essentially this is something that's almost become impossible to keep a handle on because there are so many new systems being launched and developed by so many different actors globally," said Jonathan Chipman, director of Dartmouth College's Citrin Family GIS/Applied Spatial Analysis Laboratory, in a call. "It's just mind-boggling the amount of data that's now being collected from low-Earth orbit." The feeling of epiphanic connection with the planet experienced by astronauts gazing at Earth is known popularly as "the overview effect," a term coined by author Frank White in his book of the same name. The new geospatial view of Earth, however, may offer something closer to an "overwhelm effect," as our home world is imaged, valued, and monitored by millions of sensors on thousands of spacecraft orbiting Earth. How will we deal with the petabytes of Earth-observation data that may document the collapse of whole ecosystems or the wreckage of natural disasters? What will we do with geospatial information that predicts such dire outcomes but also demands nimble and dramatic changes to our lifestyles? It will take foresight to ensure that the deluge of information is managed in a way that equitably benefits communities and ecosystems around the world, and remains as accessible to the public as possible. "The biggest challenge will be in making sense of all these data," said Dawn Wright, chief scientist of the Environmental Systems Research Institute (Esri), a major geospatial software and data science company, in an email. "It is one thing to store, to distribute, even to analyze, but how do truly understand it?

While automation and digital technologies are disrupting the workplace as we traditionally know it, it has become imperative for organisations to start reskilling their workforce. Automation and artificial intelligence are not only disrupting the assembly lines but right across the so-called blue-collar jobs. This is mainly because in most instances, artificial intelligence (AI), is actually doing a better job than humans. For example, the use of virtual assistants in the workplace is growing. By 2021, Gartner predicts that 25 percent of digital workers will use a virtual employee assistant on a daily basis.

Over the last few years, artificial intelligence (AI) has been the enfant terrible of the business world: a technology full of unconventional and sometimes controversial behaviour that has shocked, provoked and enchanted audiences worldwide. But now it's time for AI to grow up. Businesses and consumers are tired of having the same debates around the hype vs reality of AI. In 2020, I see three opportunities for this to happen across responsibility, advocacy and regulation. As AI becomes more pervasive, we're likely to see those wronged by it inspired to take action.

Applying artificial intelligence to everything we're comfortable doing in banking is much easier than changing how we do things -- which would make the greatest use of AI. Few in financial services would argue that the future belongs to those institutions that harness data-driven machine intelligence to do more, better and faster. The insights and efficiencies needed to compete and thrive will come from AI-driven service personalization and optimization. But AI should do more than speed up a financial assembly line. As Ernst & Young stated in a report: "AI-driven financial health systems will become personal financial operating systems. Consumer finance will unbundle products and rebundle personalized and holistic value propositions based on life events."