The area under the ROC curve is a common measure that is often used to rank the relative performance of different binary classifiers. However, as has been also previously noted, it can be a measure that ill-captures the benefits of different classifiers when either the true class values or misclassification costs are highly unbalanced between the two classes. We introduce a third dimension to capture these costs, and lift the ROC curve to a ROC surface in a natural way. We study both this surface and introduce the VOROS, the volume over this ROC surface, as a 3D generalization of the 2D area under the ROC curve. For problems where there are only bounds on the expected costs or class imbalances, we restrict consideration to the volume of the appropriate subregion of the ROC surface. We show how the VOROS can better capture the costs of different classifiers on both a classical and a modern example dataset.

Digital marketing firm Voro is using ChatGPT, the popular new artificial intelligence program, to "supercharge" content creation for clients. Before ChatGPT, content had been "incredibly expensive" to create -- especially individualized content necessary for search engine visibility, said Chris Gauron, partner and CEO at the Minneapolis firm. Voro has created an artificial intelligence-assisted, but human-edited, process that increases speed at the same time it lowers cost. ChatGPT's power and potential have fueled explosive growth, reaching 100 million users in just two months. Reports that it passed exams in four University of Minnesota law courses, at the Wharton School of Business and the exam to become a licensed physician have only heightened interest globally.

For people constrained to picture based communication, the expression of interest in a question answering (QA) or information retrieval (IR)scenario is highly limited. Traditionally, alternative and augmentative communication (AAC) methods (such as gestures and communication boards) are utilised. But only few systems allow users to produce whole utterances or sentences that consist of multiple words; work to generate them automatically is a promising direction in the big data context.In this paper, we provide a dedicated access method for the open-domain QA and IR context. We propose a method for the user to search for additional symbols to be added to the communication board in real-time while using access to big data sources and context based filtering when the desired symbol is missing. The user can select a symbol that is associated with the desired concept and the system searches for images on the Internet - here, in Wikipedia - with the purpose of retrieving an appropriate symbol or picture. Querying for candidates is performed by estimating semantic relatedness between text fragments using explicit semantic analysis (ESA).

In the race to develop fully autonomous vehicles, Israeli start-Up Lirhot Systems says they "see" the road ahead and assess potential hazards. While most leading industry actors have relied on and heavily invested in laser-based LiDAR (light detection and ranging) three-dimensional sensors for self-driving navigation, Tesla CEO Elon Musk has been the primary – and vocal – proponent of navigation based on using inexpensive cameras and radar. While developers continue to argue among themselves regarding the pros and cons of the two systems, Rehovot-based robotic vision start-up Lirhot Systems says it has developed a third method of navigation: a camera-like sensor inspired by insect navigation. "In nature, you have bugs and insects that navigate in a specific way, and we're copying that to enable autonomous vehicles to see," Lirhot CEO Shlomi Voro, an applied physicist with dozens of patents in the field of quantum physics, told The Jerusalem Post. "We were inspired by the heads of bees, their artificial intelligence-like neural network, size, accuracy of navigation, and how they see the world through their five eyes – two for vision and three for navigation."