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ARLINGTON, Va. U.S. military researchers are asking industry to develop artificial intelligence (AI) and machine learning technologies for difficult jobs like combat medical triage, which refers to sorting wounded warfighters according to their need for medical attention.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement (HR001122S0031) this week for the In the Moment (ITM) project.

DARPA researchers are asking industry to develop algorithmic decision-makers that can help humans with decision-making in difficult domains like combat medical triage.

Difficult domains are where trusted decision-makers disagree; no right answer exists; and uncertainty, time-pressure, resource limitations, and conflicting values create significant decision-making challenges. Other examples include first response and disaster relief.

Related: Top technology challenges this decade for the warfighter

The DARPA ITM project focuses on two areas: small unit triage in austere environments, and mass casualty triage. ITM seeks to develop techniques that enable building, evaluating, and fielding trusted algorithmic decision-makers for mission-critical operations where there is no right answer and, consequently, ground truth does not exist.

Researchers are looking for capabilities that:

-- quantify algorithmic decision-makers with key decision-making attributes of trusted humans;

-- incorporate key human decision-maker attributes into more human-aligned, trusted algorithms;

-- enable the evaluation of human-aligned algorithms in difficult domains where humans disagree and there is no right outcome; and

Difficult decisions occur when the decision-maker is confronted with challenges that include too many or too few options, too much or too little information, uncertainty about the consequences of decisions, and uncertainty about the value of foreseeable outcomes.

ITM seeks to develop AI and machine learning algorithms based on key human attributes as the basis for trust in algorithmic decision-makers, as well as a computational framework for key human attributes and an alignment score match the algorithmic decision-maker to key human decision-makers.

Related: Simulation and mission rehearsal relies on state-of-the-art computing

ITM is interested in the notion of trust, or the willingness of a human to delegate difficult decision-making to AI computers. The project also will focus on human-off-the-loop, algorithmic decision-making in difficult domains to understand the limits of such a computational framework.

ITM is 3.5-year, two-phase program that focuses on four technical areas: decision-maker characterization; human-aligned algorithms; evaluation; and policy and practice.

Decision-maker characterization seeks to develop technologies that identify and model key decision-making attributes of trusted humans to produce a quantitative decision-maker alignment score.

Human-aligned algorithms should be able to balance situational information with a preference for key decision-maker attributes. Evaluation will assess the willingness of humans to delegate difficult decisions to AI computers.

Related: The next 'new frontier' of artificial intelligence

Policy and practice will develop recommendations for how military leaders can update policies to take advantage of AI and machine learning in combat medical triage.

Companies interested should upload abstracts by 30 March 2022, and proposals by 17 May 2022 to the DARPA BAA website at https://baa.darpa.mil/.

Email questions or concerns to Matt Turek, the DARPA ITM program manager, at ITM@darpa.mil. More information is online at https://sam.gov/opp/baae2217401748dbaeb89a08044d6998/view.

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Military researchers to apply artificial intelligence (AI) and machine learning to combat medical triage - Military & Aerospace Electronics

Dr. Steven Waldren, vice president and chief informatics officer at the American Academy of Family Physicians, right, and Dr. Kamel Sadek, director of informatics at Village Medical, speak at the HIMSS22 conference in Orlando.

Photo: Jeff Lagasse/Healthcare Finance News

ORLANDO, Fla. Even before COVID-19 made the business of healthcare a nightmare for countless physicians and clinicians, burnout was a prevalent issue. And even the slow, still-ongoing emergence into normalcy hasn't been enough to ease this trend: Clerical burdens, including clinical documentation, are a major contributor.

But for primary care physicians in particular, a new class of technology, including AI-powered digital assistants, is improving their capacity and capability, while reducing their administrative and cognitive burden.

Dr. Steven Waldren, vice president and chief informatics officer at the American Academy of Family Physicians, cited data showing that the average patient visit to a PCP takes about 18 minutes, and of that time, 27% is dedicated to face-to-face time with a patient. Forty-nine percent is consumed by EHR and desk work.

"A lot of the technology is not really designed to connect with patients," said Waldren. "Physicians and nurses don't necessarily give EHRs a passing grade."

The three main challenges to primary care, he said, are clerical burden, value-based payments and AI and machine learning, which fundamentally change what it means to be a physician. But innovations in the marketplace can be applied to the clinical setting, said Waldren, and if they focus on initial contact, comprehensiveness, and coordinated and continuous care, practices can improve both costs and quality while tackling the burnout issue.

In 2018, AAFP sought to do just that, developing processes to assess the value of AI-based solutions for primary care. The group called them "innovation labs," with the goal of gauging the value of emerging technology solutions.

"We have a lot of innovators in family medicine and primary care who are doing great things," said Waldren. "We try to help our doctors find a doctor like them and a practice like theirs. They have to find peers who have been there, done that and been successful. If you don't do that you're going to have a hard time doing this."

The innovation labs identify products that address the challenges of EHRs and clerical burden, and they're geared to be adaptable and usable by family physicians.

To reduce EHR burden, the labs pinpointed a voice-assisted AI assistant for documentation. Instead of the typical voice recognition technology that simply transcribes a doctor's dictations onto a screen, the program understands commands and can respond to them, removing dictation from the equation entirely.

In testing this particular piece of technology, the labs gathered 132 members from small practices across 40 states, with all reporting much higher satisfaction. That's largely because of a 50% reduction in documentation time.

"It's geared toward those with documentation burden," said Waldren. "Its key features arte integration into the EHR, mobility and it's affordable and adaptable at about $200 per month."

For the problem of clerical burden, the labs identified technology that resulted in a 70% decrease in physician prep time, and a 38% increase in RAF scores. Physicians ranked it 9.6 out of 10.

One of those physicians, Dr. Kamel Sadek, director of informatics at Village Medical, joined Waldren on the stage and testified to the solution's efficacy.

"It's been a year since we tested the product," said Sadek. "We just adopted it, and within 10 days, we had 230 physicians who wanted to start using the product. We're on the onboarding process as we speak. Many physicians want to use the dictation. So far it's been a very, very successful pilot study and adoption.

"You're not connected to the computers," he said. "You can use your phone, iPad, laptop, youcan use it from your car, or while your kid is playing soccer. It's bidirectional. That gives you the brief information of what you've done, and based on that you can finish the note."

What convinced Sadek to utilize this approach was the efficacy and the practical aspect of being able to dictate from anywhere, to say nothing of reducing wasted time.

"With all of these things we have to right now in the notes, with value-based care, you have to monitor a lot of things that come from different departments," said Sadek. "In order to perform well, you have to look at all these things."

According to Waldren, health IT hasn't been very consumer-friendly to date. But newer apps are being built by companies that have experience in the more user-friendly consumer space, so the situation is beginning to improve. The best part is that they don't require a large up-front investment or complicated integration.

"One of the biggest challenges of getting a solution in is our doctors are so burned out," said Waldren, "and they don't want to look at something and decide if this is something that's going to work."

Twitter:@JELagasseEmail the writer:jeff.lagasse@himssmedia.com

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Machine learning, AI can help ease the trend of physician burnout - Healthcare Finance News

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We have been overhyping deep learning for too long. Its time to start embracing it

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. It is focused on improving the AI process of having machines learn things. The core of deep learning lies in fast enough computers and enough data to train large neural networks. Deep learning became the focus of a hype cycle. Many companies use deep learning and advanced artificial intelligence to solve problems and their product services.

But deep learning is overhyped for too long a period to revert back. Meanwhile, media outlets often carried stories about artificial intelligence and deep learning that were misinformed. They were written by people who did not have a proper understanding of how the technology works. Many experts believe that DL is overhyped. Other prominent experts admit that deep learning has hit a wall, and this includes some of the researchers who were among the pioneers of deep learning and were involved in some of the most important achievements of the field.

Some people say that deep learning is just another name for machine learning, but its not correct. Deep learning is a subset of machine learning. People should stop trying to make ML/DL the solution to problems that might be more easily resolved by simple math. ML techniques have been in use for a long time, but deep learning is far superior to its peers.

An ML project needs data and a robust pipeline to support the data flows. And most of all, it needs high-quality labels. This last point highlights the need to get to know data. To label, it needs to understand the data to some degree. All of this needs to happen before starting throwing random data into a deep learning algorithm and praying for results.

As such, it would help to stop overselling the future of deep learning, machine learning, and artificial intelligence and instead, focus on the present need to better integrate human ingenuity with brute-force and machine-driven pattern matching.

Deep learning is essentially a way to do pattern matching at scale. Most importantly, deep learning has had limited success in particular areas only. These areas include reinforcement learning, adversarial models, and anomaly detection.

Some experts believe reinforcement learning involves developing AI models without providing them with a huge amount of labeled data. While deep reinforcement learning is one of the more interesting areas of AI research, it has limited success in solving real-world problems.

There have been several efforts to harden deep learning models against adversarial attacks, but so far, there has been limited success. Part of the challenge stems from the fact that artificial neural networks are very complex and hard to interpret.

Conclusion: It is important to remain tempered in our expectations of deep learning. As the world seemingly scrambles for The Master Algorithm one must keep in mind that deep learning is not machine learning; its a subset. While deep neural networks have their place, they wont solve all of humanitys woes. While deep learning is making waves, and deservedly so, keep in mind that it is but another effective tool to be used in appropriate situations. Even so, people will have opinions running the gamut from it being overhyped, to being the solution to every problem they will ever experience, to somewhere more moderate in between.

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Is Deep Learning a Real Big Thing! Or is it Overhyped Among Users - Analytics Insight

WASHINGTON, March 16, 2022 (GLOBE NEWSWIRE) -- The American government is beginning to focus on fusion in early 2022, recognizing the potential of this clean, extremely abundant energy source. With China currently in the lead with this science, keeping the American economic and strategic edge throughout the twenty-first century depends strongly on catching up to and surpassing this major rival's capabilities. The algorithms and machine learning solutions developed by Kronos Fusion Energy Defense Systems match up to the fusion energy goals to be laid out in the White House's March 17 summit. Kronos' technology offers an effective way of bringing the administration's plans swiftly and efficiently into reality.

Kronos' neural networks and sophisticated simulations operate on quantum computers. This combination enables analysis of data in multiple dimensions at immense speed, rather than following just a single thread of data at a time. The network can, therefore, learn from its mistakes, increasing its predictive accuracy on the fly. This enables turning wide-ranging research data into innovative design solutions meeting the White House and Department of Energy's objectives in a practical tokamak, or reactor, design.

Besides designing the next generation of tokamaks, Kronos says its algorithms can reduce, and eventually eliminate, the instability that has prevented the construction of successful large fusion reactors up to the current day. Its machine learning can predict plasma disruptions and instability, then engage safety measures, such as temporarily cooling the plasma, preventing damage to the tokamak's machinery. The Kronos simulation system can achieve almost 95% accuracy in disruption prediction within 30 milliseconds and may achieve 99% this year. These predictive levels greatly reduce the risk of the reactor damaging itself through runaway plasma processes.

Affordability is another advantage Kronos' simulations bring to any near-future U.S. tokamak reactor development program. In the midst of inflation and other significant economic upheavals generated by the Ukraine conflict, petroleum disruptions, and supply chain issues, among other causes, controlling costs is likely to be an important consideration. Reducing fusion development expense will help make a rapid timetable more viable, enabling bringing fusion energy's benefits to the U.S. faster.

Kronos' simulations are well suited not only to allow the USA to leap ahead of the current fusion baseline, but to build a superior tokamak at a lesser cost. The initial reactor is projected to be 17% to 20% cheaper than competing systems. The building and operation of this reactor will give Kronos' quantum computers a wealth of new data to input into the simulations, cutting costs by an extra 10% for subsequent tokamaks constructed after the first.

Given the Department of Energy's urgent call to develop America's fusion capabilities for the future, Kronos is in the right place at the right time to jumpstart the program with its algorithms and simulation systems. The company's Fusion Energy Commercialization Center will provide a central hub where powerful quantum computing at the heart of the project can be put to use.

For further information:

Kronos Fusion Energy1122 Colorado StAustin, TX 78701https://www.kronosfusionenergy.com/PR Contact - Erin Pendleton - pr@kronosfusionenergy.com

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How Fusion Energy Algorithms and Machine Learning Simulations Are Key to the White House's Goals for Commercializing Fusion Energy - GlobeNewswire