We rigorously prove the aforementioned formula in two particular cases.

Existing Global Path Planning (GPP) algorithms predominantly presume planning in a static environment. This assumption immensely limits their applications to Unmanned Surface Vehicles (USVs) that typically navigate in dynamic environments. To address this limitation, we present OkayPlan, a GPP algorithm capable of generating safe and short paths in dynamic scenarios at a real-time executing speed (125 Hz on a desktop-class computer). Specifically, we approach the challenge of dynamic obstacle avoidance by formulating the path planning problem as an obstacle kinematics augmented optimization problem, which can be efficiently resolved through a PSO-based optimizer at a real-time speed. Meanwhile, a Dynamic Prioritized Initialization (DPI) mechanism that adaptively initializes potential solutions for the optimization problem is established to further ameliorate the solution quality. Additionally, a relaxation strategy that facilitates the autonomous tuning of OkayPlan's hyperparameters in dynamic environments is devised. Comparative experiments involving canonical and contemporary GPP algorithms, along with ablation studies, have been conducted to substantiate the efficacy of our approach. Results indicate that OkayPlan outstrips existing methods in terms of path safety, length optimality, and computational efficiency, establishing it as a potent GPP technique for dynamic environments. The video and code associated with this paper are accessible at https://github.com/XinJingHao/OkayPlan.

In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. Through the integration of a parallel asynchronous model-based search approach with a simulation framework to simulate spiking architectures, we are able to efficiently explore the configuration space of neuromorphic architectures and identify the subset of conditions leading to the highest performance in a targeted application. We have demonstrated this approach on an exemplar case of real time, on-chip learning application. Our results indicate that we can effectively use optimization approaches to optimize complex architectures, therefore providing a viable pathway towards application-driven codesign.

This is going to be a soapbox article. "Working with AI," Thomas H. Davenport and Steven M. Miller, is an MIT Press book that has some good points but overall loses out. As the headline states, this book has a nice spread of case studies, but the higher point they try to make is not correct. Let's start with the good. As mentioned, the first two-thirds of this book is a series of fairly short case studies that show the breadth of artificial intelligence (AI) adoption across the business world.

The City of Ottawa has announced a new plan to pilot Microsoft's Power Virtual Agent to increase the accessibility of its 311 services. Built in collaboration with Microsoft, Ottawa will begin piloting its new 311 AI chatbot in the first quarter of 2020. Users will be able to enter questions about the city's services and receive immediate answers in a conversational format. "We will progressively cover more and more topics over time," said Marc Rene de Cotret, director of service transformation for the City of Ottawa, to IT World Canada. "Eventually, I could see us migrate into more task-based things like maybe filling out an application for your permit or license."

One of the most frequently-used phrases at business events these days is "the future of work." It's increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they're already present in many organizations for many different jobs. The situation brings to mind the William Gibson comment, "The future is already here--it's just not evenly distributed." The job and incumbent described below is an example of this phenomenon.

With the increasing availability of AI-based decision support, there is an increasing need for their certification by both AI manufacturers and notified bodies, as well as the pragmatic (real-world) validation of these systems. Therefore, there is the need for meaningful and informative ways to assess the performance of AI systems in clinical practice. Common metrics (like accuracy scores and areas under the ROC curve) have known problems and they do not take into account important information about the preferences of clinicians and the needs of their specialist practice, like the likelihood and impact of errors and the complexity of cases. In this paper, we present a new accuracy measure, the H-accuracy (Ha), which we claim is more informative in the medical domain (and others of similar needs) for the elements it encompasses. We also provide proof that the H-accuracy is a generalization of the balanced accuracy and establish a relation between the H-accuracy and the Net Benefit. Finally, we illustrate an experimentation in two user studies to show the descriptive power of the Ha score and how complementary and differently informative measures can be derived from its formulation (a Python script to compute Ha is also made available).

Doctors can perfect their craft playing Level Ex medical video games, and even earn continuing education credits towards maintaining their licenses. Can playing video games be a prescription for good health? Two to three times a week, the UCSF/Stanford-trained internist and founder of the Turntable Health primary care clinic, is on his smartphone playing video games. "People who are good at video games are actually good at some aspects of clinical medicine." Instead, ZDoggMD, as he's known by his pseudonym as a producer of healthcare videos and live shows, is among the 400,000 medical professionals practicing the craft of medicine through a series of games from Level Ex, a Chicago videogames developer whose titles are specially designed for doctors, med students and other healthcare providers.