Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Americans are mad as hell about high food prices. They hate paying more at the supermarket even more than they hate paying more at the pump. Food inflation was arguably their main reason for President Donald Trump's win, and Trump's failure to reverse it (while imposing tariffs that accelerate it) is arguably the main reason for his sinking approval ratings. Cost-conscious consumers have been clipping more coupons, dining out less, buying more generic brands, and generally changing their grocery shopping habits to save money.

Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis. NMR spectrometers allow scientists to characterize the structure of molecules, such as proteins, but it can take highly skilled human experts a significant amount of time to analyze that data. This new machine learning method can analyze the data much more quickly and just as accurately.

Imagine a world where a farmer's smart phone predicts the perfect day to harvest. Or a governor can dial up exactly how to enhance food security prior to a hurricane. It would take seamless access to a highly technical artificial intelligence (AI) infrastructure, but Ohio State's Dhabaleswar K. (DK) Panda is working to get us there. "If you look at AI, it's become very important but it is limited to only advanced technical people," said Panda, professor of computer science and engineering at Ohio State. "How do we take it to the masses? We want to create a plug-and-play AI that will be democratized so anybody can use it."

Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: Nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis. NMR spectrometers allow scientists to characterize the structure of molecules, such as proteins, but it can take highly skilled human experts a significant amount of time to analyze that data. This new machine learning method can analyze the data much more quickly and just as accurately.

A new 30-foot tower has sprouted on the edge of The Ohio State University Airport, but it has nothing to do with directing the thousands of planes that take off and land there each year. Instead, this tower is the focal point of an Ohio State research project that will explore using artificial intelligence and a variety of sensors to monitor environmental conditions on a minute-to-minute basis. A key part of the project is the use of machine learning to interpret the data as it is collected, said Tanya Berger-Wolf, director of Ohio State's Translational Data Analytics Institute (TDAI) and the leader of the project. "This is a unique opportunity for our researchers to help understand environmental conditions in urban areas, such as carbon emissions, noise and air pollution, and how it changes in real time," Berger-Wolf said. "We will use artificial intelligence and machine learning models to take all the complex information we collect and get insight out of the data, such as the impact of emissions from the airplanes on the local environment."

The intent of this work is to speed up drug repurposing, which is not a new concept -- think Botox injections, first approved to treat crossed eyes and now a migraine treatment and top cosmetic strategy to reduce the appearance of wrinkles. But getting to those new uses typically involves a mix of serendipity and time-consuming and expensive randomized clinical trials to ensure that a drug deemed effective for one disorder will be useful as a treatment for something else. The Ohio State University researchers created a framework that combines enormous patient care-related datasets with high-powered computation to arrive at repurposed drug candidates and the estimated effects of those existing medications on a defined set of outcomes. Though this study focused on proposed repurposing of drugs to prevent heart failure and stroke in patients with coronary artery disease, the framework is flexible -- and could be applied to most diseases. "This work shows how artificial intelligence can be used to'test' a drug on a patient, and speed up hypothesis generation and potentially speed up a clinical trial," said senior author Ping Zhang, assistant professor of computer science and engineering and biomedical informatics at Ohio State.

Researchers have used artificial intelligence to detect Vietnam War-era bomb craters in Cambodia from satellite images – with the hope that it can help find unexploded bombs. The new method increased true bomb crater detection by more than 160 percent over standard methods. The model, combined with declassified U.S. military records, suggests that 44 to 50 percent of the bombs in the area studied may remain unexploded. As of now, attempts to find and safely remove unexploded bombs and landmines – called demining – has not been as effective as needed in Cambodia, said Erin Lin, assistant professor of political science at The Ohio State University. She cites a recent UN-commissioned report that has criticized the Cambodian national clearance agency for presenting a picture of rapid progress by focusing on areas at minimal or no risk of having unexploded mines.

F.A.C.P. is a fulltime, practicing gastroenterologist and internal medicine physician. As an active holistic health practitioner in the field, Dr. Nandi is also the Chief Health Editor at WXYZ ABC Detroit. At the age of 16, he completed his high school education in Columbus, Ohio where he was awarded a full academic scholarship to The Ohio State University and University of Notre Dame. To remain closer to his family, he chose Ohio State. Partha graduated summa cum laude (Top 1% of the class), a member of Phi Beta Kappa honor society, with a Bachelors degree in chemistry and a minor in classical Greek civilization.

We thank Kaz Kulikowski and Priscilla Rasmussen of Rutgers Universitv for their areat helD in organization of the Workshop One of the particularly sat,isfying aspects of this Workshop was the attendance by a large number of graduate students and medical fellows active in AIM research; this was made possible by a generous grant, from AAAI Chris Putnam and OS17 AI graduate students worked very hard in t,aking care of a number of details. A nurnber of systems for medical decision making, experimenting with new ideas for knowledge organization and problem solving, have been built there. The College of Medicine has just started a center for research in knowledge-based systems in medicine. Thus, after a number of years when the Workshop had been hosted by the AIM groups of MIT, TJniversity of Pittsburgh, Rutgers, and Stanford, sometimes in conjunction with major AI and medical computiug conferences, holding the Workshop at Ohio State University was an indication of a broader base for research activities in AIM. This report gives an overview of the Workshop discussions, without any claim of being complete or even representative-a report, of this kind can only be an impressionistic account.

Lined up at a row of computers, five Ohio State University students stare intently at their screens amid the clatter of keyboards and mouse clicks. They're keeping in shape -- so to speak. Ohio State, along with 11 other colleges, is in the middle of the first-ever Big Ten tournament for League of Legends, one of the most popular e-sports in the world. That partnership -- between the country's oldest Division I athletic conference and League of Legends maker Riot Games -- shows how far e-sports have come into the mainstream. With a 2-0 record so far, though, Ohio State still has a lot to prove -- to their school, as much as to themselves.