A United Arab Emirates (UAE) academic lab today launched an artificial intelligence world model and agent, two large language models (LLMs) and a new research center in Silicon Valley as it ramps up its investment in the cutting-edge field. The UAE's Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) revealed an AI world model called PAN, which can be used to build physically realistic simulations for testing and honing the performance of AI agents. Eric Xing, President and Professor of MBZUAI and a leading AI researcher, revealed the models and lab at the Computer History Museum in Mountain View, California today. The UAE has made big investments in AI in recent years under the guidance of Sheikh Tahnoun bin Zayed al Nahyan, the nation's tech-savvy national security advisor and younger brother of president Mohamed bin Zayed Al Nahyan. Xing says the UAE's new center in Sunnyvale, California, will help the nation tap into the world's most concentrated source of AI knowledge and talent.

In nature, symmetry governs regularities, while symmetry breaking brings texture. In artificial neural networks, symmetry has been a central design principle to efficiently capture regularities in the world, but the role of symmetry breaking is not well understood. Here, we develop a theoretical framework to study the geometry of learning dynamics in neural networks, and reveal a key mechanism of explicit symmetry breaking behind the efficiency and stability of modern neural networks. To build this understanding, we model the discrete learning dynamics of gradient descent using a continuous-time Lagrangian formulation, in which the learning rule corresponds to the kinetic energy and the loss function corresponds to the potential energy. Then, we identify kinetic symmetry breaking (KSB), the condition when the kinetic energy explicitly breaks the symmetry of the potential function. We generalize Noether's theorem known in physics to take into account KSB and derive the resulting motion of the Noether charge: Noether's Learning Dynamics (NLD). Finally, we apply NLD to neural networks with normalization layers and reveal how KSB introduces a mechanism of implicit adaptive optimization, establishing an analogy between learning dynamics induced by normalization layers and RMSProp. Overall, through the lens of Lagrangian mechanics, we have established a theoretical foundation to discover geometric design principles for the learning dynamics of neural networks.

The da Vinci Research Kit (dVRK, also known as dVRK Classic) is an open-source teleoperated surgical robotic system whose hardware is obtained from the first generation da Vinci Surgical System (Intuitive, Sunnyvale, CA, USA). The dVRK has greatly facilitated research in robot-assisted surgery over the past decade and helped researchers address multiple major challenges in this domain. Recently, the dVRK-Si system, a new version of the dVRK which uses mechanical components from the da Vinci Si Surgical System, became available to the community. The major difference between the first generation da Vinci and the da Vinci Si is in the structural upgrade of the Patient Side Manipulator (PSM). Because of this upgrade, the gravity of the dVRK-Si PSM can no longer be ignored as in the dVRK Classic. The high gravity offset may lead to relatively low control accuracy and longer response time. In addition, although substantial progress has been made in addressing the dynamic model identification problem for the dVRK Classic, further research is required on model-based control for the dVRK-Si, due to differences in mechanical components and the demand for enhanced control performance. To address these problems, in this work, we present (1) a novel full kinematic model of the dVRK-Si PSM, and (2) a gravity compensation approach based on the dynamic model identification.

Cerebras demonstrated how its AI inference can be 10 to 20 times faster than conventional cloud AI inference services. The market for serving up predictions from generative artificial intelligence, what's known as inference, is big business, with OpenAI reportedly on course to collect 3.4 billion in revenue this year serving up predictions by ChatGPT. With a pie that big for inference, there is plenty of room for challengers. On Tuesday, AI chip maker Cerebras Systems of Sunnyvale, California, debuted its AI inference service, which it claims is the fastest in the world and, in many cases, ten to twenty times faster than systems built using the dominant technology, Nvidia's H100 "Hopper" graphics processing unit, or GPU. "We have never seen a technology market growing this fast," said Cebrebras cofounder and CEO Andrew Feldman in a press conference in San Francisco.

Telesurgical robotic systems provide a well established form of assistance in the operating theater, with evidence of growing uptake in recent years. Until now, the da Vinci surgical system (Intuitive Surgical Inc, Sunnyvale, California) has been the most widely adopted robot of this kind, with more than 6,700 systems in current clinical use worldwide [1]. To accelerate research on robotic-assisted surgery, the retired first-generation da Vinci robots have been redeployed for research use as "da Vinci Research Kits" (dVRKs), which have been distributed to research institutions around the world to support both training and research in the sector. In the past ten years, a great amount of research on the dVRK has been carried out across a vast range of research topics. During this extensive and distributed process, common technical issues have been identified that are buried deep within the dVRK research and development architecture, and were found to be common among dVRK user feedback, regardless of the breadth and disparity of research directions identified. This paper gathers and analyzes the most significant of these, with a focus on the technical constraints of the first-generation dVRK, which both existing and prospective users should be aware of before embarking onto dVRK-related research. The hope is that this review will aid users in identifying and addressing common limitations of the systems promptly, thus helping to accelerate progress in the field.

What you will be doing: In-car testing execution: Plan, coordinate, and complete testing requirements in your region. Ensure smooth flow of operations, handle schedule, coordinate drivers and test operators. Drive process improvement: Build issue reports, deliver feedback, run execution risks, test new tool features. Own operational processes, build and track critical metrics. Report, track, and improve Fleet efficiency: lead the fleet efficiency across car, garage, people and execution.

With new partnership, Replit's 20M developers now get access to Google Cloud services, infrastructure, and foundation models, further reducing the time from idea to live software on Replit SUNNYVALE, Calif., March 28, 2023 -- Leading cloud software development platform Replit today announced a new strategic partnership with Google Cloud. Under the new partnership, Replit developers will now get access to Google Cloud infrastructure, services, and foundation models via Ghostwriter, Replit's software development AI, while Google Cloud and Workspace developers will get access to Replit's collaborative code editing platform. The collaboration will accelerate the creation of generative AI applications and underscores Google Cloud's commitment to nurturing the most open ecosystem for generative AI. For Replit, already 20 million developers strong, this partnership with Google Cloud is its next move in realizing its mission to empower the next 1 billion software creators. AI is changing software development.

The Bosch Group operates in most countries in the world. With over 400,000 associates, a career at Bosch offers a chance to grow an exceptional career in an environment that values diversity, initiative, and a drive for results. If you are interested in working on the cutting-edge of technology, working at Bosch Research is the place for you! We are committed to quality at Bosch. Our environment celebrates diversity and promotes career progression.

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The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania and Cambridge, Massachusetts is a part of the global Bosch Group (www.bosch.com), The Research and Technology Center North America (RTC-NA) is dedicated to providing technologies and system solutions for various Bosch business fields, primarily in the field of artificial intelligence (for example, human-assisted AI, natural language processing, robotics, 3D perception, and AI platform), energy technologies, internet technologies, circuit design, semiconductors and wireless, as well as advanced MEMS design. Our global research on human-machine intelligence focuses on Big Data Visual Analytics, Explainable AI (XAI), Audio Analytics, Natural Language Processing, Knowledge Engineering, XR/AR/MR, 3D Perception, and Cloud Robotics. We develop intelligent and trustworthy AIoT solutions to enable inspiring UX for Bosch products and services in application areas such as autonomous driving, driver assistance systems (ADAS), robotics, smart manufacturing, health care, smart home and building solutions. As a part of our global research unit, our Mixed Reality and Autonomous System group is responsible for shaping the future user experience of Bosch products by developing cutting-edge technologies and prototype systems in the field of mixed reality and robotics, including object detection, segmentation, tracking and pose estimation, 3D reconstruction and understanding, visual localization, sensor fusion, reinforcement learning and adaptive robot control.