Building agents with large language models (LLMs) for computer control is a burgeoning research area, where the agent receives computer states and performs actions to complete complex tasks. Previous computer agents have demonstrated the benefits of in-context learning (ICL); however, their performance is hindered by several issues. First, the limited context length of LLMs and complex computer states restrict the number of exemplars, as a single webpage can consume the entire context. Second, the exemplars in current methods, such as high-level plans and multi-choice questions, cannot represent complete trajectories, leading to suboptimal performance in long-horizon tasks. Third, existing computer agents rely on task-specific exemplars and overlook the similarity among tasks, resulting in poor generalization to novel tasks. To address these challenges, we introduce Synapse, a computer agent featuring three key components: i) state abstraction, which filters out task-irrelevant information from raw states, allowing more exemplars within the limited context, ii) trajectory-as-exemplar prompting, which prompts the LLM with complete trajectories of the abstracted states and actions to improve multi-step decision-making, and iii) exemplar memory, which stores the embeddings of exemplars and retrieves them via similarity search for generalization to novel tasks. We evaluate Synapse on MiniWoB++, a standard task suite, and Mind2Web, a real-world website benchmark. In MiniWoB++, Synapse achieves a 99.2% average success rate (a 10% relative improvement) across 64 tasks using demonstrations from only 48 tasks. Notably, Synapse is the first ICL method to solve the book-flight task in MiniWoB++. Synapse also exhibits a 56% relative improvement in average step success rate over the previous state-of-the-art prompting scheme in Mind2Web.

Grown men wearing tights like to yell terrible things at Fred DeJesus. DeJesus is an umpire in the outer constellations of professional baseball, where he's been spat on and, once, challenged to a postgame fight in a parking lot. He was born in Bushwick, Brooklyn, to Puerto Rican parents, stands five feet three, and is shaped, in his chest protector, like a fire hydrant; he once ejected a player for saying that he suffered from "little-man syndrome." Two years ago, DeJesus became the first umpire in a regular-season game anywhere to use something called the Automated Ball-Strike System. Most players refer to it as the "robo-umpire."

FILE - In this May 13, 2018, file photo, MLB umpire Joe West, right, talks with a player in the ninth inning during a baseball game between the Arizona Diamondbacks and the Washington Nationals in Phoenix. West, who has umpired more than 5,000 big league games, said the 2016 TrackMan computer system test was far from perfect. NEW YORK – Get ready for strikes by robots. Computers will be used for ball/strike calls starting April 25 in the independent Atlantic League, where the distance between home and first will be shortened by 3 inches. The ground between the mound and home plate will lengthen by 2 feet for the second half of the season beginning July 12.

Get ready for strikes by robots. Computers will be used for ball/strike calls starting April 25 in the independent Atlantic League, where the distance between home and first will be shortened by 3 inches. The ground between the mound and home plate will lengthen by 2 feet for the second half of the season beginning July 12. The 60-foot-6-inch distance between the front of the pitching rubber and the back point of home plate has been standard since 1893, but Major League Baseball reached a three-year deal to experiment in the Atlantic League, an eight-team circuit that occasionally produces big leaguers. Infield defensive shifts will be limited.

Growth of the artificial intelligence market for healthcare is expected to reach $6.6 billion by 2021, according to Accenture, and the combined deployment of key clinical health AI applications could potentially create $150 billion in annual savings for the U.S. healthcare economy by 2026. In line with that growing trend, Rockville Centre, New York-based Precision Innovation Network, a physician-centric group purchasing organization focused on strengthening the patient-physician relationship by making it easier to practice, has tapped Splice Machine's big data and artificial intelligence data platform to develop its new Treatment Advisor application. The Treatment Advisor app will leverage multi-dimensional data – such as medical records, quantified measured performance obtained from digital devices, patient perspectives from questionnaires and demographic information – and use machine learning to help clinicians learn the trajectory of a disease and gather predictions for what may be the best treatment for each individual. It also will enable physicians to analyze the data to target disease-modifying therapies and better understand how a patient might feel – a patient-reported outcome – in the future. Splice Machine will allow Precision Innovation Network to apply machine learning to the data and gain insights that will help provide more precise medical treatment to patients, officials said.

James Peters, coauthor of "A Knowledge-Based Model of Audit Risk," is an assistant professor in the Department of Accounting, College of Business Administration, University of Oregon. Glenn D. Rennels coauthor of "Prose Generation from Expert Systems: An Applied Computational Linguistics Thomas Arcidiacono, the author of the review of An Artificial Intelligence Approach, " is a research affiliate in Approach to Legal Reasoning, is affiliated with the Artificial Intelligence Laboratory, the Medical Information Sciences Program, the New York Institute of Technology, Sunburst Center 203, Central Edwina L. Rissland, author of "Artificial Intelligence and Legal Reasoning: R. Peter Bonasso, author of "An Hermann Kaindl, author of "Minimaxing: A Discussion of the Field and Assessment of What AI Can Do for Theory and Practice", is a Gardner's Book," is an associate professor Battle Management--A Report of the software engineer in the position of of Computer and Information First AAAI Workshop on AI Applications "Gruppenleiter" at Siemens AG Science at the University of Massachusetts to Battle Management" is the osterreich, Program and System Engineering at Amherst and lecturer on department head of the Artificial Since 1984, he has been a lecturer law at the Harvard Law School. Operations division, 7525 Colshire research interests include planning Drive, Mclean, VA 22102. Vasant Dhar, coauthor of "A Knowledge-Based Model of Audit Risk," is Model of Audit Risk," is Peat Marwick Professor of Accounting, Kenneth D. Forbus is an assistant professor Perry Miller, coauthor of "Prose Generation of computer science at the University from Expert Systems: An Call toU-free 800-521-3044 Or mail inquiry to: University Microfilms International. Forbus's research interests Program, Yale University include qualitative reasoning, inference School of Medicine, 333 Cedar Street, engine design, analogical reasoning P.O.