Data prepper Tamr Inc. will assist the U.S. Air Force in boosting utilization of its air assets under a five-year contract designed to use machine learning techniques to accelerate the flight certification process for new aircraft configurations. Those configurations include equipping front-line aircraft with new weapons, sensors and defenses such as electronic warfare pods. Tamr said the contract with the Air Force's Seek Eagle Office could be worth as much $60 million. The office based at Eglin Air Force Base, Fla., is responsible for integration new technologies into front-line aircraft. The Air Force office will use Tamr's machine learning platform to organize more than 30 years of aircraft performance studies dispersed across the organization.
PHILADELPHIA - To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning -- an approach first implemented by Google for keyboards' autocorrect functionality -- trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.
A former Google engineer has been sentenced to 18 months in prison after pleading guilty to stealing trade secrets before joining Uber's effort to build robotic vehicles for its ride-hailing service. The sentence handed down Tuesday by U.S. District Judge William Alsup came more than four months after former Google engineer Anthony Levandowski reached a plea agreement with the federal prosecutors who brought a criminal case against him last August. Levandowski, who helped steer Google's self-driving car project before landing at Uber, was also ordered to pay more than $850,000. Alsup had taken the unusual step of recommending the Justice Department open a criminal investigation into Levandowski while presiding over a high-profile civil trial between Uber and Waymo, a spinoff from a self-driving car project that Google began in 2007 after hiring Levandowski to be part of its team. Levandowski eventually became disillusioned with Google and left the company in early 2016 to start his own self-driving truck company, called Otto, which Uber eventually bought for $680 million. He wound up pleading guilty to one count, culminating in Tuesday's sentencing.
The U.S. Department of Education's Institute of Education Sciences has awarded the National Center for Research on Education Access and Choice (REACH) at Tulane University a $100,000 contract to collect data from approximately 150,000 school websites across the country to see how the nation's education system is responding to the coronavirus pandemic. The project, which will track traditional public schools, charter schools and private schools, aims to quickly answer questions that are critical for understanding how students are learning when school buildings are closed. Key questions include: how many schools are providing any kind of instructional support; which are delivering online instruction; what resources are they offering to students and how do students stay in contact with teachers? "This data will also help answer important questions about equity in the school system, showing how responses differ according to characteristics like spending levels, student demographics, internet access, and if there are differences based on whether it is a private, charter or traditional public school," said REACH National Director Douglas N. Harris, Schlieder Foundation Chair in Public Education and chair of economics at Tulane University School of Liberal Arts. REACH will work in cooperation with Nicholas Mattei, assistant professor of computer science at Tulane University School of Science and Engineering, to create a computer program that will collect data from every school and district website in the country.
This more than doubles the startup's total raised, and a spokesperson says it will be used to accelerate Sight's operations globally -- with a focus on the U.S. -- as Sight advances R&D for the detection of conditions like sepsis and cancer, as well as factors affecting COVID-19. Blood tests are generally unpleasant -- not to mention costly. On average, getting blood work done at a lab costs uninsured patients between $100 and $1,500. In the developing world, where the requisite equipment isn't always readily available, ancillary costs threaten to drive the price substantially higher. That's why Yossi Pollak, previously at Intel subsidiary Mobileye, and Daniel Levner, a former scientist at Harvard's Wyss Institute for Biologically Inspired Engineering, founded Sight Diagnostics in 2011.
CAMBRIDGE – COVID-19 has become a severe stress test for countries around the world. From supply-chain management and health-care capacity to regulatory reform and economic stimulus, the pandemic has mercilessly punished governments that did not – or could not – adapt quickly. From Latin America's lost decade in the 1980s to the more recent Greek crisis, there are plenty of painful reminders of what happens when countries cannot service their debts. A global debt crisis today would likely push millions of people into unemployment and fuel instability and violence around the world. The virus has also pulled back the curtain on one of this century's most important contests: the rivalry between the United States and China for supremacy in artificial intelligence (AI).
The world needs robots that make life better, not just ones that put people out of work. But business attitudes, government policy, and scientific priorities are geared toward replacing workers rather than complementing and enhancing their skills. That's the bottom line of a report by a task force at MIT that was released today. "It's super easy to make a business case for reducing head count. You can always light up a boardroom" by promising to replace people with robots, says David Autor, an MIT economist and co-chair of the task force, who gave an interview about the report.
Every time Congress holds a hearing about Silicon Valley companies, people mock the legislators for being out of their depth. Last week's effort by the antitrust subcommittee of the House Judiciary Committee was no exception. "The technological ignorance demonstrated by our elected officials ... was truly stunning," Shelly Palmer, CEO at the Palmer Group, a tech strategy advisory group, told USA Today. "People who are this clueless about the economic forces shaping our world should not be tasked with leading us into the age of AI," he said. "The data elite are playing a different game with a different set of rules. Apparently, Congress can't even find the ballpark."
We live in a world where we are constantly in contact with Artificial Intelligence, perhaps without even being aware. We live in a world where we are constantly in contact with Artificial Intelligence, perhaps without even being aware. It may not seem that way due to the stigma that Hollywood has put into our mind about what exactly Artificial Intelligence is (killer robots, omniscient software, etc.) but it's really a lot simpler than that. John McCarthy (2007) defined Artificial Intelligence as the science and engineering of making intelligent [having the computational ability to achieve goals in the world] machines. Right now, the main way in which these machines "learn" is through rote learning (trail and error) and drawing inferences. It is widely believed that "AI [artificial intelligence] will drive the human race" (Prime Minister Navendra Modi) and there is not true evidence for or against the contrary, but it is widely accepted that A.I. does and will have a extreme influence on day to day life.
Discussions about the application of artificial intelligence (AI) in healthcare often span multiple areas, most commonly about making more accurate diagnoses, identifying at-risk populations, and better understanding how individual patients will respond to medicines and treatment protocols. To date, there has been relatively little discussion about practical applications of AI to improve medication management across the care continuum, an area this article will address. What's the first thing that comes to mind when someone mentions prescription drugs in the United States? In poll after poll, the high and rising costs of medications are American voters' top healthcare-related issue. This concern is well founded.