Vikas Nanda has spent more than two decades studying the intricacies of proteins, the highly complex substances present in all living organisms. The Rutgers scientist has long contemplated how the unique patterns of amino acids that compose proteins determine whether they become anything from hemoglobin to collagen, as well as the subsequent, mysterious step of self-assembly where only certain proteins clump together to form even more complex substances. So, when scientists wanted to conduct an experiment pitting a human – one with a profound, intuitive understanding of protein design and self-assembly – against the predictive capabilities of an artificially intelligent computer program, Nanda, a researcher at the Center for Advanced Biotechnology and Medicine (CABM) at Rutgers, was one of those at the top of the list. Now, the results to see who – or what – could do a better job at predicting which protein sequences would combine most successfully are out. Nanda, along with researchers at Argonne National Laboratory in Illinois and colleagues from throughout the nation, reports in Nature Chemistry that the battle was close but decisive.

Dr. Steven Gustafson is Noonum's CTO and an AI scientist, passionate about solving hard problems while having fun and building great teams. When I began learning about artificial intelligence (AI) and machine learning (ML), it was from the perspective of trying to help very smart people do challenging tasks. This is opposed to learning about an AI algorithm and trying to see how far I could push it for the sake of science (which has its own merits) and then searching for an application in the real world. Some early examples of AI systems were for fire prevention on naval ships, predicting component failure causes in jet engines and classifying reasons for claim denial by health insurance companies. Some lessons I learned were that smart people solve these problems every day, so an AI system needs to augment their intelligence and not provide a poor replacement for it.

Jason Allen, a video game designer in Pueblo, Colorado, spent roughly 80 hours working on his entry to the Colorado State Fair's digital arts competition. Judges awarded him first place, which came with a $300 prize. But when Allen posted about his win on social media late last month, his artwork went viral--for all the wrong reasons. Allen's victory took a turn when he revealed online that he'd created his prize-winning art using Midjourney, an artificial intelligence program that can turn text descriptions into images. He says he also made that clear to state fair officials when he dropped off his submission, called Théâtre D'opéra Spatial.

An artificial intelligence algorithm soundly defeated an experienced U.S. F-16 fighter pilot in a simulation of an aerial dogfight, the Pentagon said. The event, called Alpha Dogfight Trials, was part of the Air Combat Evaluation program of the Pentagon's Defense Advanced Research Projects Agency. Designed to accelerate the development of artificial intelligence in combat situations, DARPA invited teams from eight defense contractors to fly F-16 fighter planes in competitive, simulated combat. The weeklong competition at the Applied Physics Lab of Johns Hopkins University in Baltimore led to the winner facing off against a live, experienced pilot. Heron Systems, a Maryland-based AI developer, was the last company standing, and on Thursday its algorithm quickly defeated a U.S. Air Force pilot identified only as "Banger" in five rounds of combat, all in less than two minutes.

They tell a story, which gives the photos context and additional emotional meaning. A paper published by Microsoft Research describes an image captioning system that mimics humans' unique style of visual storytelling. Companies like Microsoft, Google, and Facebook have spent years teaching computers to label the contents of images, but this new research takes it a step further by teaching a neural-network-based system to infer a story from several images. Someday it could be used to automatically generate descriptions for sets of images, or to bring humanlike language to other applications for artificial intelligence. "Rather than giving bland or vanilla descriptions of what's happening in the images, we put those into a larger narrative context," says Frank Ferraro, a Johns Hopkins University PhD student who coauthored the paper.