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Federal agencies need stricter limits on facial recognition to protect privacy, says government watchdog

Washington Post - Technology News

Six agencies, including the U.S. Park Police and the FBI said they had used facial recognition on people who participated in protests after the killing of George Floyd by Minneapolis police officer in May 2020. The agencies said they only used it on people they suspected of breaking the law, according to the report. The U.S. Capitol Police used Clearview AI to conduct its investigation into the Jan. 6 attack on the Capitol. Customs and Border Protection and the State Department said they ran searches for Capitol rioters on their own databases at the request of other federal agencies.


New machine learning methods could improve environmental predictions

#artificialintelligence

Machine learning algorithms do a lot for us every day--send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to watch next. Now, we are increasingly using these same algorithms to make environmental predictions for us. A team of researchers from the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey recently published a new study on predicting flow and temperature in river networks in the 2021 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining (SDM21) proceedings. The study was funded by the National Science Foundation (NSF). The research demonstrates a new machine learning method where the algorithm is "taught" the rules of the physical world in order to make better predictions and steer the algorithm toward physically meaningful relationships between inputs and outputs.


New machine learning methods could improve environmental predictions

#artificialintelligence

IMAGE: A new machine-learning method developed by researchers at the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey will provide more accurate stream and river temperature predictions, even when... view more Machine learning algorithms do a lot for us every day--send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to watch next. Now, we are increasingly using these same algorithms to make environmental predictions for us. A team of researchers from the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey recently published a new study on predicting flow and temperature in river networks in the 2021 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining (SDM21) proceedings. The study was funded by the National Science Foundation (NSF). The research demonstrates a new machine learning method where the algorithm is "taught" the rules of the physical world in order to make better predictions and steer the algorithm toward physically meaningful relationships between inputs and outputs.


New machine learning methods could improve environmental predictions

#artificialintelligence

Machine learning algorithms do a lot for us every day--send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to watch next. Now, we are increasingly using these same algorithms to make environmental predictions for us. A team of researchers from the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey recently published a new study on predicting flow and temperature in river networks in the 2021 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining (SDM21) proceedings. The research demonstrates a new machine learning method where the algorithm is "taught" the rules of the physical world in order to make better predictions and steer the algorithm toward physically meaningful relationships between inputs and outputs. The study presents a model that can make more accurate river and stream temperature predictions, even when little data is available, which is the case in most rivers and streams.


lukasz-madon/awesome-remote-job

#artificialintelligence

Adeva partners with companies to scale engineering teams on-demand. AgentFire - Hyper local real estate websites powered by Wordpress. Aha! - Aha! is roadmapping software for PMs who want their mojo back. AirTreks - Multi-stop international flight planner with a distributed team. We are strategists, researchers, designers, and developers who craft custom digital experiences for publishers, nonprofit institutions, museums, and brands. ALICE empowers the world's best hotels to deliver a remarkable guest experience. Makes software that helps teachers make e-learning courses. AT&T - Nearly 20% of the eligible workforce works remotely. Authentic F & F - Independent design and technology studio based in Denver and Minnesota Aurity - 100% remote company, specializing in React and React Native.


AI Algorithm Aids Early Detection of Low Ejection Fraction

#artificialintelligence

FRIDAY, May 28, 2021 (HealthDay News) -- An artificial intelligence (AI) algorithm that uses data from electrocardiography can help increase the diagnosis of low ejection fraction (EF), according to a study published online May 6 in Nature Medicine. Xiaoxi Yao, Ph.D., from the Mayo Clinic in Rochester, Minnesota, and colleagues randomly assigned 120 primary care teams, including 358 clinicians, to intervention (access to AI results from the low ejection fraction algorithm developed by Mayo and licensed to Anumana Inc.; 181 clinicians) or control (usual care; 177 clinicians) in a pragmatic trial at 45 clinics and hospitals. A total of 22,641 adult patients with echocardiography performed as part of routine care were included (11,573 in the intervention group; 11,068 controls). The researchers found positive AI results, indicating a high likelihood of low EF, in 6.0 percent of patients in both arms. More echocardiograms were obtained for patients with positive results by clinicians in the intervention group (49.6 versus 38.1 percent), but echocardiogram use was similar in the overall cohort (19.2 versus 18.2 percent).


AI Algorithm Aids Early Detection Of Low Ejection Fraction - AI Summary

#artificialintelligence

Xiaoxi Yao, Ph.D., from the Mayo Clinic in Rochester, Minnesota, and colleagues randomly assigned 120 primary care teams, including 358 clinicians, to intervention (access to AI results from the low ejection fraction algorithm developed by Mayo and licensed to Anumana Inc.; 181 clinicians) or control (usual care; 177 clinicians) in a pragmatic trial at 45 clinics and hospitals. More echocardiograms were obtained for patients with positive results by clinicians in the intervention group (49.6 versus 38.1 percent), but echocardiogram use was similar in the overall cohort (19.2 versus 18.2 percent). The diagnosis of low EF was increased with the intervention in the overall cohort (2.1 versus 1.6 percent; odds ratio, 1.32) and among patients with positive results (19.5 versus 14.5 percent; odds ratio, 1.43). "The AI intervention increased the diagnosis of low ejection fraction overall by 32 percent relative to usual care.


Facial Recognition Software Results in Few Arrests, Raises Concerns

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At least 42 law enforcement agencies in Minnesota reportedly used Clearview AI facial recognition software, according to a Buzzfeed investigation. Questions about the softwares reliability and legal standing remain in limbo, according to law enforcement, artificial intelligence, and privacy experts. Clearview AI is a web-based platform that allows users to submit pictures for possible matches in a database of more than 3 billion images pulled from open source websites, including news sites and social media, according to the company's web page. The company also boasted of a 100% accuracy rate at one point, according to a document obtained by a public records request from Buzzfeed. However, questions about the software's reliability and legal standing remain in limbo, according to law enforcement and artificial intelligence and privacy experts.


Mayo Clinic, Others Use 'AI Factories' to Speed AI Development

WSJ.com: WSJD - Technology

Mayo Clinic and other organizations are using an assembly-line approach to artificial-intelligence development, where small teams use a common set of software tools and procedures to speed the production of AI applications while cutting costs. The Minnesota-based healthcare provider launched what it calls its AI factory in September and is now getting into full production, with about 60 projects under way, said James Buntrock, vice chair of the department of information technology at Mayo Clinic. "It's [a] more consistent process to produce algorithms," Mr. Buntrock said. One application in development aims to analyze medical images to identify and classify "biomarkers"--measurable medical signs, such as excess abdominal fat--which could help predict patient health. Mr. Buntrock declined to quantify the speed and cost savings of the AI factory effort, but said they are significant.


Your Doctor's Assistant is AI - AI Trends

#artificialintelligence

AI is being increasingly incorporated by doctors to transcribe, read, analyze, and make predictions based on notes and conversations between physicians and their patients. This opens up new possibilities for care and new concerns about privacy, according to a recent account from Axios. A big and largely invisible contribution AI can make is to capture a physician's written or spoken notes automatically. Spending hours entering data manually into electronic health records (EHRs) is not helpful to medical professionals close to burning out. A recent study from researchers at the University of New Mexico, outlined in EHR Intelligence, found that 13% of stress and burnout self-reported by physicians were directly correlated to EHRs.