We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for suggesting a single evaluation point based on previous observations, while far fewer are known for selecting batches of points to evaluate in parallel. The few batch selection schemes that have been studied all resort to greedy methods to compute an optimal batch. To the best of our knowledge, PPES is the first non-greedy batch Bayesian optimization strategy. We demonstrate the benefit of this approach in optimization performance on both synthetic and real world applications, including problems in machine learning, rocket science and robotics.

We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for suggesting a single evaluation point based on previous observations, while far fewer are known for selecting batches of points to evaluate in parallel. The few batch selection schemes that have been studied all resort to greedy methods to compute an optimal batch. To the best of our knowledge, PPES is the first nongreedy batch Bayesian optimization strategy. We demonstrate the benefit of this approach in optimization performance on both synthetic and real world applications, including problems in machine learning, rocket science and robotics.

When it comes to keeping your workforce safe, providing proper personal protective equipment is just the start. Workplace safety programs of the future, however, will bring risk mitigation and incident prevention to the next level – including the ability to stop hazardous events from happening in the first place. With the help of artificial intelligence and workflow-integrated computer vision technology such as Benchmark Digital's new Benchmark ESG SuperVisionAI, the future is now. We've collaborated with world-class computer vision technology companies such as SparkCognition, Intenseye and 3MotionAI to harness the power of AI-analyzed live video streaming and photos to integrate the data they capture with the Benchmark solution platform to generate reports and alerts in real time for the ultimate safe work environment. The AI monitors and recognizes hazards as work happens, giving site leaders full visibility over key details such as PPE usage, potential hazards and day-to-day habits through a simple camera lens.

Most of the existing works on pedestrian pose estimation do not consider estimating the pose of an occluded pedestrian, as the annotations of the occluded parts are not available in relevant automotive datasets. For example, CityPersons, a well-known dataset for pedestrian detection in automotive scenes does not provide pose annotations, whereas MS-COCO, a non-automotive dataset, contains human pose estimation. In this work, we propose a multi-task framework to extract pedestrian features through detection and instance segmentation tasks performed separately on these two distributions. Thereafter, an encoder learns pose specific features using an unsupervised instance-level domain adaptation method for the pedestrian instances from both distributions. The proposed framework has improved state-of-the-art performances of pose estimation, pedestrian detection, and instance segmentation.

Not long after the 1918 Spanish flu pandemic, Czech writer Karel Čapek first introduced the term "robot" to describe artificial people in his 1921 sci-fi play R.U.R. While we have not yet created the highly intelligent humanoid robots imagined by Čapek, the robots most commonly used today are complex systems that work alongside humans, assisting with an ever-expanding set of tasks. In a piece in Nature Machine Intelligence, Johns Hopkins researchers discuss how the coronavirus pandemic has driven unexpected innovations in automation, while at the same time revealing bottlenecks to deploying robotic systems in health care settings. They contend that advances in human-robot interaction--such as improving robots' capabilities to feel, touch, and decide--will determine if the robots of tomorrow will help hospitals stay ahead of the next pandemic. In the commentary, the team identifies three ways robots have greatly enhanced patient care and provider safety during COVID-19: minimizing contact between infected patients and care providers, reducing the need for PPE, and giving providers more time to focus on critical tasks.

Between May and July 2020, there have been 30 recorded industrial accidents in India, killing at least 75 workers. These figures, along with regular reports of similar incidents around the country and the world, bring into sharp focus the need to enhance industrial safety on site. While safety has always been a primary concern for manufacturing and logistics organisations, forward-looking companies have, in recent years, tried to leverage emerging technologies to monitor and improve safety protocols and training methods on site. With the possibility of using machine learning to detect and flag violations, artificial intelligence (AI) can help organisations around the world make strides in reducing the incidence of injuries and fatalities at work. One crucial aspect of industrial safety is the need for people on the site to wear personal protective equipment (PPE).

A former NHS surgeon has created an AI-powered "virtual patient" which helps to keep skills sharp during a time when most in-person training is on hold. Dr Alex Young is a trained orthopaedic and trauma surgeon who founded Virti and set out to use emerging technologies to provide immersive training for both new healthcare professionals and experienced ones looking to hone their skills. COVID-19 has put most in-person training on hold to minimise transmission risks. Hospitals and universities across the UK and US are now using the virtual patient as a replacement--including our fantastic local medics and surgeons at the Bristol NHS Foundation Trust. The virtual patient uses Natural Language Processing (NLP) and'narrative branching' to allow medics to roleplay lifelike clinical scenarios.

The following image shows an example input image and its corresponding output from the DetectProtectiveEquipment as seen on the Amazon Rekognition PPE detection console. In this example, we supply face cover as the required PPE and 80% as the required minimum confidence threshold as part of summarizationattributes. We receive a summarization result that indicates that there are four persons in the image that are wearing face covers at a confidence score of over 80% [person identifiers 0, 1,2, 3]. It also provides the full fidelity API response in the per-person results. Note that this feature doesn't perform facial recognition or facial comparison and can't identify the detected persons.

Drone firm Zipline has been given the go-ahead to deliver medical supplies and personal protective equipment to hospitals in North Carolina. The firm will be allowed to use drones on two specified routes after the Federal Aviation Administration granted it an emergency waiver. It is the first time the FAA has allowed beyond-line-of-sight drone deliveries in the US. Experts say the pandemic could help ease some drone-flight regulations. Zipline, which has been negotiating with the FAA, wants to expand to other hospitals and eventually offer deliveries to people's homes.

We develop \textit{parallel predictive entropy search} (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a \textit{batch} of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for suggesting a single evaluation point based on previous observations, while far fewer are known for selecting batches of points to evaluate in parallel. The few batch selection schemes that have been studied all resort to greedy methods to compute an optimal batch. To the best of our knowledge, PPES is the first non-greedy batch Bayesian optimization strategy. We demonstrate the benefit of this approach in optimization performance on both synthetic and real world applications, including problems in machine learning, rocket science and robotics.