Policy-based design, also known as policy-based class design or policy-based programming, is the term used in Modern C++ Design for a design approach based on an idiom for C++ known as policies. It has been described as a compile-time variant of the strategy pattern, and has connections with C++ template metaprogramming. It was first popularized in C++ by Andrei Alexandrescu with Modern C++ Design and with his column Generic<Programming> in the C/C++ Users Journal, and it is currently closely associated with C++ and D as it requires a compiler with highly robust support for templates, which was not common before about 2003. Previous examples of this design approach, based on parameterized generic code, include parametric modules (functors) of the ML languages,and C++ allocators for memory management policy. The central idiom in policy-based design is a class template (called the host class), taking several type parameters as input, which are instantiated with types selected by th
Artificial empathy (AE) or computational empathy is the development of AI systems − such as companion robot or virtual agents − that are able to detect and respond to human emotions in an empathic way.According to scientists, although the technology can be perceived as scary or threatening by many people, it could also have a significant advantage over humans in professions which are traditionally involved in emotional role-playing such as the health care sector.From the care-giver perspective for instance, performing emotional labor above and beyond the requirements of paid labor often results in chronic stress or burnout, and the development of a feeling of being desensitized to patients. However, it is argued that the emotional role-playing between the care-receiver and a robot can actually have a more positive outcome in terms of creating the conditions of less fear and concern for one's own predicament best exemplified by the phrase: "if it is just a robot taking care of
Nvidia DGX is a line of Nvidia produced servers and workstations which specialize in using GPGPU to accelerate deep learning applications. DGX-1 DGX-1 servers feature 8 GPUs based on the Pascal or Volta daughter cards with HBM 2 memory, connected by an NVLink mesh network. The product line is intended to bridge the gap between GPUs and AI accelerators in that the device has specific features specializing it for deep learning workloads.The initial Pascal based DGX-1 delivered 170 teraflops of half precision processing,while the Volta-based upgrade increased this to 960 teraflops. DGX-2 The successor of the Nvidia DGX-1 is the Nvidia DGX-2, which uses 16 32GB V100 (second generation) cards in a single unit. This increases performance of up to 2 Petaflops with 512GB of shared memory for tackling larger problems and uses NVSwitch to speed up internal communication. Additionally, there is a higher performance version of the DGX-2, the DGX-2H with a notable difference being the replacement o
Comments
Post a Comment