US Special Operations Command Employs AI and Machine Learning to Improve Operations

December 11, 2020 — In today’s digital environment, winning wars requires more than “boots on the ground.” It also requires computer algorithms and artificial intelligence.

The United States’ Special Operations Command is currently playing a critical role advancing the employment of AI and machine learning in the fight against the country’s current and future advisories, through Project Maven.

To discuss the initiatives taking place as part of the project, General Richard Clarke, who currently serves as the Commander of USSOCOM, and Richard Shultz, who has served as a security consultant to various U.S. government agencies since the mid-1980s, joined the Hudson Institute for a virtual discussion on Monday.

Among other objectives, Project Maven aims to develop and integrate computer-vision algorithms needed to help military and civilian analysts encumbered by the sheer volume of full-motion video data that the Department of Defense collects every day in support of counterinsurgency and counter terrorism operation, according to Clarke.

When troops carry out militarized site exploration, or military raids, they bring back copious amounts of computers, papers, and hard drives, filled with potential evidence. In order to manage enormous quantities of information in real time to achieve strategic objectives, the Algorithmic Warfare Cross-Function task force, launched in April 2017, began utilizing AI to help.

“We had to find a way to put all of this data into a common database,” said Clarke. “Over the last few years, humans were tasked with sorting through this content — watching every video, and reading every detainee report. A human cannot sort and shift through this data quickly and deeply enough,” he said.

AI and machine learning have demonstrated that algorithmic warfare can aid military operations.

Project Maven initiatives helped “increase the frequency of raid operations from 20 raids a month to 300 raids a month,” said Schultz. “AI technology increases both the number of decisions that can be made, and the scale. Faster more effective decisions on your part, are going to give enemies more issues.”

Project Maven initiatives have increased the accuracy of bomb targeting. “Instead of hundreds of people working on these initiatives, today it is tens of people,” said Clarke.

AI has also been used to rival adversary propaganda. “I now spend over 70 percent of my time in the information environment. If we don’t influence a population first, ISIS will get information out more quickly,” said Clarke.

AI and machine learning tools, enable USSOCOM to understand “what an enemy is sending and receiving, what are false narratives, what are bots, and more,” the detection of which allows decision makers to make faster, and more accurate, calls.

Military use of machine learning for precision raids and bomb strikes naturally raises concerns. In 2018, more than 3,000 Google employees signed a petition in protest against the company’s involvement with Project Maven.

In an open letter addressed to CEO Sundar Pichai, Google employees expressed concern that the U.S. military could weaponize AI and apply the technology towards refining drone strikes and other kinds of lethal attacks. “We believe that Google should not be in the business of war,” the letter read.

Published at Sat, 12 Dec 2020 01:52:30 +0000

20 key health system AI tool rollouts in 2020

Katie Adams – Friday, December 11th, 2020 | Email

Artificial intelligence has proven itself as a technology that can make significant improvements to workflow efficiency in hospitals, with many hospitals adopting AI tools so that clinicians can have more time to spend treating patients.

Below are 20 hospital and health system launches of artificial intelligence tools Becker’s Hospital Review reported in 2020:

Advocate Lutheran General Hospital in Park Ridge, Ill., began using Viz.ai, an AI-powered software that can recognize and respond to stroke within minutes.
Syracuse, N.Y.-based Crouse Health also implemented Viz.ai’s stroke detection software.
Los Angeles-based Huntington Hospital also deployed Viz.ai’s software.
Allentown, Pa.-based Lehigh Valley Health Network adopted Viz.ai’s software as well.
Phoenix-based Banner Health partnered with Buoy Health to deploy its AI-powered digital triage tool to help patients navigate their care options online.
Cincinnati-based Bon Secours Mercy Health entered a partnership with Lirio, beginning with a direct investment in the AI software startup’s behavior change platform.
Sacramento, Calif.-based Sutter Health implemented AI technology to analyze imaging data and physicians’ notes to improve the health system’s care quality and outcomes in diagnostic imaging.
Peoria, Ill.-based OSF HealthCare began using patient engagement software startup Gyant’s AI-powered virtual clinical assistant to aid healthcare providers as they offer patients COVID-19 screening for and education about the disease caused by the novel coronavirus.
Rochester, Minn.-based Mayo Clinic teamed up with the state’s health department to create an AI-powered tool that can identify zones of greater COVID-19 transmission in southern Minnesota.
Mayo Clinic also began using AI software from U.K. health tech company Ultromics to analyze echocardiograms of COVID-19 patients to identify how the virus affects the cardiovascular system.
Mayo Clinic also entered a collaboration with Diagnostic Robotics to implement its AI platform that predicts patients’ hospitalization risk and triages them to appropriate care.
UC San Diego Health developed and applied an AI algorithm to more than 2,000 lung X-ray images, helping radiologists more quickly identify signs of early pneumonia in COVID-19 patients.
Mount Sinai Health System in New York City began using an AI algorithm that can quickly detect COVID-19 based on how lungs appear in CT scans along with patient data such as age, sex, symptoms, bloodwork and potential contact with the virus.
Mount Sinai Health System researchers also deployed machine learning-powered models that identify high risk and likelihood of mortality in COVID-19 patients.
La Crosse, Wis.-based Gundersen Health System invested in AI technology that would help collect insurer payments and lower administrative errors as well as speed up the time it takes to receive outstanding payments.
University Hospitals Cleveland Medical Center began using GE Healthcare’s AI-powered imaging technology daily to identify patients’ collapsed lungs.
UC San Diego Health partnered with Amazon Web Services to deploy an AI imaging algorithm to detect COVID-19 in 10 days.
Norfolk, Va.-based Sentara Healthcare launched a new AI system that allows providers to better predict which patients are most likely to develop sepsis.
Houston Methodist Hospital began using an AI-powered risk calculator that predicts the likelihood a patient will develop breast cancer and recommends whether a biopsy should be performed.
Altamonte Springs, Fla.-based AdventHealth started using AI to build a patient registry biobank to deliver targeted treatment for COVID-19 patients.