On Saturday, about 12,000 human athletes ran in a half marathon race in Beijing, but most of the attention was on a group of other, unconventional participants: 21 humanoid robots. The event's organizers, which included several branches of Beijing's municipal government, claim it's the first time humans and bipedal robots have run in the same race, though they jogged on separate tracks. Six of the robots successfully finished the course, but they were unable to keep up with the speed of the humans. The fastest robot, Tiangong Ultra, developed by Chinese robotics company UBTech in collaboration with the Beijing Humanoid Robot Innovation Center, finished the race in two hours and 40 minutes after assistants changed its batteries three times and it fell down once. The slowest time allowed for human runners in the race was 3 hours and 10 minutes, and Tiangong Ultra was the only robot that barely qualified for a human participation award.

Generating high-quality stories spanning thousands of tokens requires competency across a variety of skills, from tracking plot and character arcs to keeping a consistent and engaging style. Due to the difficulty of sourcing labeled datasets and precise quality measurements, most work using large language models (LLMs) for long-form story generation uses combinations of hand-designed prompting techniques to elicit author-like behavior. This is a manual process that is highly dependent on the specific story-generation task. Motivated by the recent success of applying RL with Verifiable Rewards to domains like math and coding, we propose a general story-generation task (Next-Chapter Prediction) and a reward formulation (Verified Rewards via Completion Likelihood Improvement) that allows us to use an unlabeled book dataset as a learning signal for reasoning. We learn to reason over a story's condensed information and generate a detailed plan for the next chapter. Our reasoning is evaluated via the chapters it helps a story-generator create, and compared against non-trained and supervised finetuning (SFT) baselines. Pairwise human judgments reveal the chapters our learned reasoning produces are preferred across almost all metrics, and the effect is more pronounced in Scifi and Fantasy genres.

Robust network design, which aims to guarantee network availability under various failure scenarios while optimizing performance/cost objectives, has received significant attention. Existing approaches often rely on model-based mixed-integer optimization that is hard to scale or employ deep learning to solve specific engineering problems yet with limited generalizability. In this paper, we show that failure evaluation provides a common kernel to improve the tractability and scalability of existing solutions. By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design. FERN represents rich problem inputs as a graph and captures both local and global views by attentively performing feature extraction from the graph. It enables a broad range of robust network design problems, including robust network validation, network upgrade optimization, and fault-tolerant traffic engineering that are discussed in this paper, to be recasted with respect to the common kernel and thus computed efficiently using neural networks and over a small set of critical failure scenarios. Extensive experiments on real-world network topologies show that FERN can efficiently and accurately identify key failure scenarios for both OSPF and optimal routing scheme, and generalizes well to different topologies and input traffic patterns. It can speed up multiple robust network design problems by more than 80x, 200x, 10x, respectively with negligible performance gap.

CORVALLIS, Ore. – Cassie the robot, invented at the Oregon State University College of Engineering and produced by OSU spinout company Agility Robotics, has established a Guinness World Record for the fastest 100 meters by a bipedal robot. Cassie clocked the historic time of 24.73 seconds at OSU's Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls. The 100-meter record builds on earlier achievements by the robot, including traversing 5 kilometers in 2021 in just over 53 minutes. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge. Cassie was developed under the direction of Oregon State robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Defense Advanced Research Projects Agency, or DARPA.

Cassie the robot, invented at the Oregon State University College of Engineering and produced by OSU spinout company Agility Robotics, has established a Guinness World Record for the fastest 100 metres by a bipedal robot. Cassie clocked the historic time of 24.73 seconds at OSU's Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls. The 100-metre record builds on earlier achievements by the robot, including traversing five kilometres in 2021 in just over 53 minutes. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge. Cassie was developed under the direction of Oregon State robotics professor Jonathan Hurst.

CORVALLIS, Ore. – Cassie the robot, invented at the Oregon State University College of Engineering and produced by OSU spinout company Agility Robotics, has established a Guinness World Record for the fastest 100 meters by a bipedal robot. Cassie clocked the historic time of 24.73 seconds at OSU's Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls. The run can also be seen on YouTube.) The 100-meter record builds on earlier achievements by the robot, including traversing 5 kilometers in 2021 in just over 53 minutes. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge.

When researchers at Oregon State University created new tools to evaluate the decision-making algorithms of an advanced artificial intelligence system, study participants assigned to use them did, indeed, find flaws in the AI's reasoning. But once investigators instructed participants to use the tools in a more structured and rigorous way, the number of bugs they discovered increased markedly. "That surprised us a bit, and it showed that having good tools for visualizing and interfacing with AI systems is important, but it's only part of the story," said Alan Fern, professor of computer science at Oregon State. Since 2017, Fern has led a team of eight computer scientists funded by a four-year, $7.1 million grant from the Defense Advanced Research Projects Agency to develop explainable artificial intelligence, or XAI -- algorithms through which humans can understand, build trust in, and manage the emerging generation of artificial intelligence systems. Dramatic advancements in the artificial neural networks, or ANNs, at the heart of advanced AI have created a wave of powerful applications for transportation, defense, security, medicine, and other fields.

Although it took a few years for professional sports to catch up with the worlds of finance, medicine, and research, data analysis is now fundamental to many aspects of athletic endeavor (and business). Now, with datasets growing from the statistical observations of scouts and coaches to huge caches of information from wearables and training ground sensors, artificial intelligence (AI) is putting all of this data to work in professional sports. This article provides key examples of AI being used to power elite performance in the world of sports. In recent years, much research has highlighted the possibility of coaches and athletes employing AI to analyze their data and help them make decisions. A recent study looked at how neural networks (AI "brains") applied to weight training data can improve performance.

Ever wonder why virtual assistant Siri can easily tell you what the square root of 1,558 is in an instant but can't answer the question "what happens to an egg when you drop it on the ground?" Artificial intelligence (A.I.) interfaces on devices like Apple's iPhone or Amazon's Alexa often fall flat on what many people consider to be basic questions, but can be speedy and accurate in their responses to complicated math problems. That's because modern A.I. currently lacks common sense. "What people who don't work in A.I. everyday don't realize is just how primitive what we call'A.I.' is nowadays," machine-learning researcher Alan Fern of Oregon State University's College of Engineering told KOIN 6 News. "We have A.I.s that do very specialized, specific things, specific tasks, but they're not general purpose. They can't interact in general ways because they don't have the common sense that you need to do that."

TALLAHASSEE, FLORIDA – Florida could turn to the sky to fight Burmese pythons on the ground under a bill a Senate committee unanimously approved Monday to allow two state agencies to use drones in the effort to eradicate invasive plants and animals. The bill would create an exception to a current law that prohibits law enforcement from using drones to gather information and bans state agencies from using drones to gather images on private land. It would allow the Florida Fish and Wildlife Conservation Commission and the Florida Forest Service to fly drones to manage and eradicate invasion species on public lands. Sen. Ben Albritton said he has been told that drones equipped with lidar, which stands for "light detection and ranging," might be able to identify pythons. "As you know, chasing those nasty critters down there in the Everglades is a difficult task," Albritton said.