Machine learning is transforming the way an Air Force office analyzes and certifies new flight configurations. The Air Force SEEK EAGLE Office sets standards for safe flight configurations by testing and looking at historical data to see how different stores--like a weapon system attached to an F-16--affect flight. A project AFSEO developed along with industry partners can now automate up to 80% of requests for analysis, according to the office's Chief Data Officer Donna Cotton. "The application is kind of like an eager junior engineer consulting a senior engineer," Cotton said. "It makes the straightforward calls without any input, but in the hard cases it walks into the senior engineer's office and says: 'Hey, I did a bunch of research and this is what I found out. Can you give me your opinion?'" Cotton spoke at a Tuesday webinar hosted by Tamr, one of the industry partners involved in the project.

An algorithm has confirmed the existence of 50 new planets in a world-first for machine learning in astronomy. Scientists from Warwick University made the discovery by analyzing data collected by space telescopes, such as NASA's Kepler and TESS. This passage produces a distinctive dip in light emerging from the star. However, this effect can also be caused by a binary star system, interference from other objects, or problems with the camera. The new system was designed to separate these false positives from observations of real planets.

It has worked its way into our daily lives, from voice assistants like Siri and Alexa to traffic apps that guide us around gridlock, cars that drive themselves and news stories that pop up on our social media feeds. Researchers in the University of Maryland's College of Computer, Mathematical, and Natural Sciences work at the forefront of machine learning technology, where computers analyze data to identify patterns and make decisions with minimal human intervention. These faculty members are using machine learning for applications that touch many aspects of our lives--from weather prediction and health care to transportation, finance and wildlife conservation. Along the way, they are advancing the science of exactly how computers learn. The shift from a cash economy to one reliant on electronic transactions has left many consumers feeling vulnerable to identity theft and bank fraud. And it's no wonder--in 2018, the Federal Trade Commission received over 440,000 reports of identity theft, largely from stolen credit card and social security numbers. For any consumer, that figure is concerning.

Increasingly, organizations across many industries and geographies are building and deploying machine learning models and incorporating artificial intelligence into a variety of their different products and offerings. However, as they put AI capabilities into systems that we interact with on a daily basis, it becomes increasingly important to make sure these systems are behaving in a way that's beneficial to the public. When creating AI systems organizations should also consider the ethical and moral implications to make sure that AI is being created for good intentions. Policymakers that want to understand and leverage AI's potential and impact need to take a holistic view of the issues. This includes things like intentions behind AI systems, as well as potential unintended consequences and actions of AI systems.

The healthcare industry is increasingly focusing on niche patient populations. Around half of FDA approvals in the past two years were for rare or orphan drugs that serve fewer than 200,000 patients in total in the US and 1 in 2,000 patients in Europe. By 2024, orphan drug sales are expected to capture one-fifth of worldwide prescription sales. However, finding these hard-to-reach patients is difficult and keeping them engaged over time even more so. Could machine learning platforms that deliver personalized experiences for patients and caregivers be part of the answer?

An artificial intelligence algorithm has defeated a human F-16 fighter pilot in a virtual dogfight simulation. The Aug. 20 event was the finale of the Pentagon research agency's AI air combat competition. The algorithm, developed by Heron Systems, easily defeated the fighter pilot in all five rounds that capped off a yearlong competition hosted by the Defense Advanced Research Projects Agency. The competition, called the AlphaDogfight Trials, was part of DARPA's Air Combat Evolution program, which is exploring automation in air-to-air combat and looking to improve human trust in AI systems. "It's easy to go down the wrong path of thinking that that is either A) definitive in some way as to what the future of [basic fighter maneuvers will be]; or B) that it is a bad outcome," said Justin Mock of DARPA, a fighter pilot and commentator for the trials.

His motto is "Aim high, fly-fight-win." But for a top U.S. Air Force fighter pilot and weapons school graduate, aiming high--and in one instance aiming low--wasn't enough to prevail against an AI opponent in a simulated competition last week. The Defense Advanced Research Project Agency (DARPA) sponsored the AlphaDogfight trials as part of its effort to use AI to help pilots in realtime combat and encourage developers to sign up for its Air Combat Evolution (ACE) program to design AI defense systems. The winning program, designed by a Maryland-based defense contractor Heron Systems, outmaneuvered its human opponent flawlessly in a five-round sweep. Encouragingly, 'Banger,' a District of Columbia Air National Guard pilot and recent Air Force Weapons School Instructor Course graduate with over 2,000 hours of experience flying F-16s, was able to last longer each round.

Precision medicine is a medical model, which proposes customization of the healthcare to a subgroup of patients, based on a genetics, lifestyle and environment. This technique allows doctors and researchers to prognosis treatment and prevention strategies for a specific disease which can work on a group of people. It is opposed to a one-size-fits-all approach, in which disease treatment and prevention techniques are advanced for the average individual with much less attention for the variations among individuals. There is an overlap between the terms "precision medication" and "personalized medicine." As per the National Research Council, "personalized medicine" is a traditional word with a meaning close to "precision medication."

The City of London in Canada is implementing an artificial intelligence (AI) tool it has developed internally to predict and prevent homelessness. The Chronic Homelessness Artificial Intelligence (CHAI) model uses machine learning to forecast the probability of an individual in the city's shelter system becoming chronically homeless within the next six months – that is, remaining in the shelter system for more than 180 days in a year. In July, 312 people in London were chronically homeless. The tool was developed in-house with support from a consultant, and could help other cities – particularly those in Canada – deploy similar systems quickly. The CHAI model grew out of London's adoption of the federal Homeless Individuals and Families Information System (HIFIS), which is designed to provide a clearer picture of homelessness in communities and support organisations to work collaboratively.

In July 2015, two founders of DeepMind, a division of Alphabet with a reputation for pushing the boundaries of artificial intelligence, were among the first to sign an open letter urging the world's governments to ban work on lethal AI weapons. Notable signatories included Stephen Hawking, Elon Musk, and Jack Dorsey. Last week, a technique popularized by DeepMind was adapted to control an autonomous F-16 fighter plane in a Pentagon-funded contest to show off the capabilities of AI systems. In the final stage of the event, a similar algorithm went head-to-head with a real F-16 pilot using a VR headset and simulator controls. The AI pilot won, 5-0.