We introduce a framework for the use of large language models (LLMs) in Building Understandable Messaging for Policy and Evidence Review (BUMPER). LLMs are proving capable of providing interfaces for understanding and synthesizing large databases of diverse media. This presents an exciting opportunity to supercharge the translation of scientific evidence into policy and action, thereby improving livelihoods around the world. However, these models also pose challenges related to access, trust-worthiness, and accountability. The BUMPER framework is built atop a scientific knowledge base (e.g., documentation, code, survey data) by the same scientists (e.g., individual contributor, lab, consortium). We focus on a solution that builds trustworthiness through transparency, scope-limiting, explicit-checks, and uncertainty measures. LLMs are rapidly being adopted and consequences are poorly understood. The framework addresses open questions regarding the reliability of LLMs and their use in high-stakes applications. We provide a worked example in health policy for a model designed to inform measles control programs. We argue that this framework can facilitate accessibility of and confidence in scientific evidence for policymakers, drive a focus on policy-relevance and translatability for researchers, and ultimately increase and accelerate the impact of scientific knowledge used for policy decisions.

This paper introduces the research effort of an undergraduate research team in realizing robot localization. More specifically, the undergraduate research team developed and tested wall-following software that allowed a ground robot Roombas to independently find their positions within a defined space. The software also allows a robot to send its localized position to other Roombas, so that each Roomba knows its relative location to realize robot cooperation.

Artificial intelligence has already beaten chess. Hell, the most sophisticated AI systems have a very good chance against top players in the incredibly complicated game of Go. But, in the uber-complicated car-based soccer game of Rocket League, can an AI do a boosted 360 aerial bicycle kick power shot from the midline? Can it pinch a ball off the side ramp so precisely it sails into the goal at 90 MPH? No, at least not yet, but AI can apparently dribble like a madman. For more than a week, players have been driven up the wall (sometimes literally, in game) by machine learning-based AI that's been hacked into games of Rocket League.

Bumper is a fast-growing payments start-up, and we're excited to begin the next phase of our growth having recently completed a successful Series A funding round. We're looking to hire a Senior Data Engineer We work with over 4,000 automotive retailers across Europe, helping drivers to pay for motoring costs, accessories and other services through a variety of different payment options. Our product is customer-centric and a market leading. Our HQ is in London along with offices in Sheffield and Ankara, but we are spreading our wings through a rapid growth phase with many of the leading brands across the continent. Bumper is looking for a Senior Data Engineer to be responsible for data engineering capability within the Data and BI tech.

Large efforts have focused on ensuring that the controllers for mobile service robots follow proxemics and other social rules to ensure both safe and socially acceptable distance to pedestrians. Nonetheless, involuntary contact may be unavoidable when the robot travels in crowded areas or when encountering adversarial pedestrians. Freezing the robot in response to contact might be detrimental to bystanders' safety and prevents it from achieving its task. Unavoidable contacts must hence be controlled to ensure the safe and smooth travelling of robots in pedestrian alleys. We present a force-limited and obstacle avoidance controller integrated into a time-invariant dynamical system (DS) in a closed-loop force controller that let the robot react instantaneously to contact or to the sudden appearance of pedestrians. Mitigating the risk of collision is done by modulating the velocity commands upon detecting a contact and by absorbing part of the contact force through active compliant control when the robot bumps inadvertently against a pedestrian. We evaluated our method with a personal mobility robot -- Qolo -- showing contact mitigation with passive and active compliance. We showed the robot able to overcome an adversarial pedestrian within 9 N of the set limit contact force for speeds under 1 m/s. Moreover, we evaluated integrated obstacle avoidance proving the ability to advance without incurring any other collision.

Nissan's "intelligent factory" hardly has any human workers. The robots do the work, including welding and mounting. They do the paint jobs and inspect their own paint jobs. "Up to now, people had to make production adjustments through experience, but now robots with artificial intelligence, analyzing collected data, are able to do it. The technology has developed to that level," Nissan Executive Vice President Hideyuki Sakamoto said during a tour of the production line for the Ariya sport-utility vehicle at its Tochigi plant Friday.

Nissan's "intelligent factory" hardly has any human workers. The robots do the work, including welding and mounting. They do the paint jobs and inspect their own paint jobs. "Up to now, people had to make production adjustments through experience, but now robots with artificial intelligence, analyzing collected data, are able to do it. The technology has developed to that level," Nissan Executive Vice President Hideyuki Sakamoto said during a tour of the production line for the Ariya sport-utility vehicle at its Tochigi plant Friday.

Waymo claimed last week that its autonomous vehicles are outperforming human drivers. In a report it compiled, between January 2019 and September 2020, the company's fleet of AVs logged 6.1 million miles in Phoenix, Arizona. Sixty-five thousand of those miles were without a safety driver behind the wheel. Waymo says that its fleet was not responsible for a single accident in that entire time. There were 18 minor accidents in which AVs were involved.

To butcher a quote from the great science fiction writer William Gibson, "Autonomous vehicles are already here – they're just not very evenly distributed." In other words, while autonomous vehicles may not be in your city, they might be in the city next door. Autonomous vehicles are primed for exponential growth, and all indicators point to that growth beginning to happen sooner rather than later. By 2040, we can expect our highways to be bumper to bumper with over 33 million self-driving vehicles. By 2040, we can expect our highways to be bumper to bumper with over 33 million self-driving vehicles.

Self-driving cars often use a combination of normal two-dimensional cameras and depth-sensing'LiDAR' units to recognize the world around them. However, others make use of visible light cameras that capture imagery of the roads and streets. They are trained with a wealth of information and vast databases of hundreds of thousands of clips which are processed using artificial intelligence to accurately identify people, signs and hazards. In LiDAR (light detection and ranging) scanning - which is used by Waymo - one or more lasers send out short pulses, which bounce back when they hit an obstacle. These sensors constantly scan the surrounding areas looking for information, acting as the'eyes' of the car.