OpenAI researcher Jason Wei is joining Meta's new superintelligence lab, according to multiple sources familiar with the matter. Wei worked on OpenAI's o3 and deep research models, according to his personal website. He joined OpenAI in 2023 after a stint at Google, where he worked on chain-of-thought research, which involves training an AI model to process complex queries step-by-step. At OpenAI, Wei became a self-described "diehard" for reinforcement learning, a method of training or refining an AI model with positive or negative feedback. It's become a promising area of AI research--one that several of the researchers Meta has hired for its superintelligence team specialize in.

Robotic dentistry is becoming a reality. Your dentist may remind you to brush, floss and mouthwash – but what is the "right" order to do it? While all steps of oral hygiene can benefit dental health, Dr. Mike Wei, DDS, of New York City, shared with Fox News Digital that he'd recommend the below order. Starting with floss helps to remove food debris and plaque between the teeth and along the gumline, which a toothbrush "may not reach effectively," according to Wei. Dr. Ellie Phillips (not pictured) recommends using xylitol gum and mints to promote healthy salivary flow.

Researchers have designed a robotic "artificial skin" that is as unique as the team's animal inspiration--the platypus. Created by collaborators between China's Tsinghua University and the Beijing Institute of Nanoenergy and Nanosystems, the dual-sensory system can interpret information not just from direct physical touch, but also through detecting electrostatic changes in the air around it. The platypus is famously recognized for its wide range of zoological oddities. Over millions of years, the egg-laying mammal has evolved a duck bill, webbed feet tipped with tiny, venomous talons, as well as a flat, beaver-like tail. But not all of its notable attributes are physical--the creature also relies on a highly attuned sensory system capable of identifying both mechanical inputs like touch, and electrical shifts in its nearby environment.

Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets.

As scientists make strides in finding answers about COVID-19, artificial intelligence has aided one Michigan State University researcher and his team in finding answers about the new omicron variant. The MSU researchers report omicron and other variants are evolving increased infectivity and antibody resistance according to an artificial intelligence model. Therefore, new vaccines and antibody therapies are needed, the researchers say. Understanding how SARS-CoV-2 evolves is essential to predicting vaccine breakthrough and designing mutation-proof vaccines and monoclonal antibody treatments. In a recent study in American Chemical Society Infectious Diseases, Guowei Wei, professor in MSU's Departments of Mathematics as well as Electrical and Computer Engineering, and colleagues, analyzed almost 1.5 million SARS-CoV-2 genome sequences taken from people with COVID-19.

The vote mechanism is widely utilized to rank answers in community-based question answering sites. In generating a vote, a user's attention is influenced by the answer position and appearance, in addition to real answer quality. Previously, these biases are ignored. As a result, the top answers obtained from this mechanism are not reliable, if the number of votes for the active question is not sufficient. In this paper, we solve this problem by analyzing two kinds of biases; position bias and appearance bias. We identify the existence of these biases and propose a joint click model for dealing with both of them. Our experiments in real data demonstrate how the ranking performance of the proposed model outperforms traditional methods with biases ignored by 15.1% in precision@1, and 11.7% in the mean reciprocal rank. A case study on a manually labeled dataset futher supports the effectiveness of the proposed model.

We consider the problem of covering an environment with a robot when the robot has limited energy budget. The environment is represented as a polygon with a grid, whose resolution is proportional to the robot size, imposed on it. There is a single charging station in the environment. At each time step, the robot can move from one grid cell to an adjacent one.The energy consumption when moving in the environment is assumed to be uniform and proportional to the distance traveled. Our goal is to minimize both the total distance and the number of visits to the charging station. We present a coverage path planning algorithm which has O(ln D) approxima-tion factor for both objectives, where D is the distance of thefurthest cell in the environment measured on the grid.

A George Washington University School of Engineering and Applied Science professor is working on an interdisciplinary research project funded by NASA that aims to design and develop a safety management system for electric autonomous aircraft. Peng Wei, an assistant professor in the Department of Mechanical and Aerospace Engineering, researches control, optimization, machine learning and artificial intelligence (AI) in air transportation and aviation. His lab builds flight deck and ground-based automation and decision support tools to improve and ensure safety for emerging aircraft types and flight operations. While a lot of the innovation in AI and machine learning applications has been focused on revolutionizing the internet and digital connectivity, Dr. Wei is part of a group of researchers focused on expanding those benefits into transforming air transportation for physical connectivity and future mobility. Dr. Wei is the principal investigator of a new three-year, $2.5 million NASA System-Wide Safety grant project.

Although growing numbers of organisations are working with artificial intelligence (AI) software in some shape or form, very few are generating significant financial benefits when rolling it out in a serious way, according to new research. A study conducted by the MIT Sloan Management Review and management consulting firm the Boston Consulting Group revealed that as many as 57% of the 3,000 managers, executives and academics questioned were currently either piloting or deploying the technology. A further 59% had devised an AI strategy and 70% believed they understood how the software could generate business value. Despite this situation, the report, Expanding AI's impact with organizational learning, indicated that just one in 10 organisations were deriving significant financial value from the technology. When exploring the reasons why, researchers found that simply getting the basics right – that is, having an appropriate strategy with the right supporting data, technology and skills in place – was not enough.

A novel machine learning model developed by researchers at Michigan State University suggests that mutations to the SARS-CoV-2 genome have made the virus more infectious. The model, developed by lead researcher Guowei Wei, professor in the departments of Mathematics and Biochemistry and Molecular Biology, analyzed SARS-CoV-2 genotyping from more than 20,000 viral genome samples. The researchers analyzed mutations to the spike protein--a protein primarily responsible for facilitating infection--and found that five of the six known virus subtypes are now more infectious. As with any virus, many mutations are ultimately benign, posing little to no risk to infected patients. Some mutations even reduce infectiousness.