At the National Heart Lung and Blood Institute (NHLBI), CIO Alastair Thomson says machine learning (ML) played a major role in COVID-related research. The National COVID Cohort Collaborative (NC3) clearly demonstrates the value of securely bringing together data in one place, where it can be analyzed by thousands of researchers. Through collaboration with various healthcare and cloud service providers, the NC3 and ML helped NIH identify potential participants for the RECOVER initiative. "The utility of this really became clear when NIH launched the RECOVER initiative, which is dealing with post-acute COVID syndrome, or long COVID," Thomson said at the annual AFCEA's Health IT Summit last week. "They were able to use machine learning with that data to identify the key characteristics, what we call a phenotype, for long COVID."

Information on cyber-related crimes, incidents, and conflicts is abundantly available in numerous open online sources. However, processing the large volumes and streams of data is a challenging task for the analysts and experts, and entails the need for newer methods and techniques. In this article we present and implement a novel knowledge graph and knowledge mining framework for extracting the relevant information from free-form text about incidents in the cyberdomain. The framework includes a machine learning based pipeline for generating graphs of organizations, countries, industries, products and attackers with a non-technical cyber-ontology. The extracted knowledge graph is utilized to estimate the incidence of cyberattacks on a given graph configuration. We use publicly available collections of real cyber-incident reports to test the efficacy of our methods. The knowledge extraction is found to be sufficiently accurate, and the graph-based threat estimation demonstrates a level of correlation with the actual records of attacks. In practical use, an analyst utilizing the presented framework can infer additional information from the current cyber-landscape in terms of risk to various entities and propagation of the risk heuristic between industries and countries.

Anyone who has been following the Robotic Process Automation (RPA) revolution that is transforming enterprises worldwide has also been hearing about how artificial intelligence (AI) can augment traditional RPA tools to do more than just RPA alone can achieve. You might even have noticed that some RPA software vendors -- Automation Anywhere is one of them -- are attempting to be more precise with their language. Rather than call our intelligent software robot (bot) product an AI-based solution, we say it is built around cognitive computing theories. But is that any clearer? Let's try and dispel some of it.

Founded in 2014, Groundspeed Analytics provides artificial intelligence (AI) and data science solutions to the global commercial property and casualty insurance industry, serving insurance brokers, carriers and managing general agents (MGAs) alike. The insurtech company plans to utilize the new funding to continue to expand its products and fooprint apart from growing its data science and engineering team from the current number of 40. Groundspeed Co-CEO Jeff Mason said: "We are thrilled to partner with Oak HC/FT as we embark on our next phase of growth. "Given Oak HC/FT's deep expertise in InsurTech and extensive network of industry leaders, we are confident that this new association will help Groundspeed aggressively expand our products and reach to help brokers and carriers unlock the tremendous value trapped in their unstructured information." Groundspeed Analytics said that it focuses on solving the toughest data challenge in the insurance industry which is to unlock the value of unstructured information. Based in Ann Arbor and Atlanta, Groundspeed Analytics claims to accelerate commercial insurance performance by deploying machine learning and AI. Its technology platform is said to automatically turn loss runs, exposure documents and policy files into actionable information, thereby helping users to identify profitable pools of risk and deliver better financial outcomes. The company's Groundspeed Adaptive Data Pipeline allows carrier, broker, and MGA clients to boost margins, identify underwriting profit pools, and improve customer experience. Groundspeed Co-CEO Andrew Robinson said: "Groundspeed's Adaptive Data Pipeline has proven to be incredibly powerful.

WatsonPaths: Scenario-Based Question Answering and Inference over Unstructured Information

Say that to a human airline agent nicely, and he or she will quickly disentangle your words and find flights that meet your criteria. Say it to the airline's automated reservations line, however, and all you're likely to get is a cheery digital voice intoning, "Sorry, I didn't catch that." Even assuming the airline's computers overcame the garbled words, background noise, and Boston accent to render the request into accurate text, no language-processing system has the computational firepower to make sense of your price and routing constraints, ignore irrelevancies like the fact that Saturday is your sister's birthday, and understand that if the party starts at 3:00 p.m., you're not interested in flights that arrive in Milwaukee at 4:00. If computers could understand and respond to such routine natural-language requests, the results would be win-win: airlines wouldn't need to hire so many agents, and consumers wouldn't have to struggle with the confusion of touch-tone interfaces that leave them furiously tapping the "0" button, vainly trying to reach a live operator. Futurists have been envisioning such a world since at least 1968, when 2001: A Space Odyssey's HAL 9000 became the archetypal voice-interactive computer.

The trend continues to grow in enterprises wanting to draw deeper and more actionable insights from their organization's data. The days of simply analyzing information sets for superficial trends and anomalies are a thing of the past -- businesses want to glean greater value across a range of functions. Unfortunately, manufacturers still tend to be reactive and focused on historical performance for these insights. Few manufacturers are able to be proactive in terms of recognizing the impact of the ever-changing external environment on product performance and customer satisfaction, preventing them from systematically drawing actionable insights from the massive amounts of structured and unstructured information at their disposal. These challenges are not unique to manufacturers -- consumer product companies, global business consultants and wealth management firms are also all vulnerable to operating at less than peak performance due to poor visibility into their most vexing business challenges.

The trend continues to grow in enterprises wanting to draw deeper and more actionable insights from their organization's data. The days of simply analyzing information sets for superficial trends and anomalies are a thing of the past -- businesses want to glean greater value across a range of functions. Unfortunately, manufacturers still tend to be reactive and focused on historical performance for these insights. Few manufacturers are able to be proactive in terms of recognizing the impact of the ever-changing external environment on product performance and customer satisfaction, preventing them from systematically drawing actionable insights from the massive amounts of structured and unstructured information at their disposal. These challenges are not unique to manufacturers -- consumer product companies, global business consultants and wealth management firms are also all vulnerable to operating at less than peak performance due to poor visibility into their most vexing business challenges.