An artificial intelligence-enabled synthetic data generator that converts clinical data of any kind into equivalent, mock versions that don't expose sensitive patient-identifying details is being put to use as a component of the National Institutes of Health-steered National COVID Cohort Collaborative, or N3C effort. "The NIH's N3C initiative is a result of the urgent need for understanding of COVID both to develop better patient care and understand the impacts on individuals and the health system as a whole," Dr. Michael D. Lesh told Nextgov this week. Lesh--the co-founder and CEO of Syntegra, the company behind the synthetic data engine--shed light on how the tool works, and a new partnership between the business, NIH and the Bill and Melinda Gates Foundation that underpins this fresh endeavor. In June 2020, not long after the novel coronavirus pandemic disrupted nearly every aspect of American life, NIH launched N3C to accelerate COVID-19 research and new medical breakthroughs. The collaborative pursuit, according to a June press release, intends to systematically capture relevant data from participating health care providers across the country, aggregate that data into accessible formats, and in-turn help approved users harness research insights from that harmonized information, via the NCATS N3C Data Enclave.

BGL Corporate Solutions, Australia's leading supplier of SMSF administration and ASIC corporate compliance solutions, have released BGL SmartDocs, a new AI-powered document reader. SmartDocs is an AI-powered document reader that extracts data from images and PDFs (including scanned documents and photos) and converts the data into digital information. BGL SmartDocs helps fill data feed gaps, driving zero-touch processing and adding new efficiencies by linking source documents to BGL's Accounting Workpapers. "We have been running BGL SmartDocs in BETA for over 6 months," said BGL's Managing Director, Ron Lesh. "Turning paper into data has been a long term problem for accountants and advisors – BGL SmartDocs solves this problem."

The recognition of the goal a user is pursing when interacting with a software application is a crucial task for an interface agent as it serves as a context for making opportune interventions to provide assistance to the user. The prediction of the user goal must be fast and a goal recognizer must be able to make early predictions with few observations of the user actions. In this work we propose an approach to automatically build an intention model from a plan corpus using Variable Order Markov models. We claim that following our approach, an interface agent will be capable of accurately ranking the most probable user goals in a time linear to the number of goals modeled.