“The Department of Health and Human Services’ chief information officer said his agency has the first functioning, recurrent neural network in the federal government, and it’s using the machine-learning technology to help officials make acquisition plans.”
“HHS’s network is the first “that I know of,” though the intelligence community “is doing some of this,” Jose Arrieta said at the Professional Services Council Tech Trends Conference on Monday.”
“The network cost about $175,000, took four weeks to build and now reads unstructured datasets and offers insights, the CIO said. Recurrent neural networks take the concept further than traditional neural networks, adding loops that help them develop a “memory” from previous inputs of information.”
“There was a strong incentive for HHS to try the technology: Drafting acquisition plans is a time-consuming process, and the department procures a broad range of projects and services…”
“The department is applying the technology to its forthcoming blockchain-based acquisition portal, HHS Accelerate. It has about 42,000 statements of work, and 9,000 were run through natural language processing. Every word in every sentence was scored with a number because predictive analytics doesn’t work on text…” Read the full article here.
Source: HHS touts first recurrent neural network for making acquisition plans – By Dave Nyczepir, September 17, 2019. FedScoop.
