Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Is Real AI Superintelligence the Fundamental Solution of Human Problems? RAIS is like a scientific modelling makes a particular part or feature of the world to automatically understand, define, quantify, visualize, or simulate by referencing to its encoded/programmed world's data/information/knowledge base. The RAIS is to run the Master Algorithm of Reality and Mentality as Descriptive, Deductive, Intuitive, Inductive, Exploratory, Explainable, Predictive and Prescriptive (DDIIEEPP) Platform. In all, the RAI program implies radically innovative approaches and paradigmatic shifts in fundamental knowledge fields and advanced technology domains, as reality and mentality, causality, science, technology and statistics, AI and ML, data and intelligence, information and knowledge, AI software and hardware, cyberspace and intelligent robotics. The RAIS Platform could compute the real world as a whole and in parts [e.g., the causal nexus of various human domains, such as fire technology and human civilizations; globalization and political power; climate change and consumption; economic growth and ecological destruction; future economy, unemployment and global pandemic; wealth and corruption, perspectives on the world's future, etc.].

This article offers an overview of the main first-order ethical questions raised by robots and Artificial Intelligence (RAIs) under five broad rubrics: functionality, inherent significance, rights and responsibilities, side-effects, and threats. The

In theory, importance sampling speeds up stochastic gradient algorithms for supervised learning by prioritizing training examples. In practice, the cost of computing importances greatly limits the impact of importance sampling. We propose a robust, approximate importance sampling procedure (RAIS) for stochastic gradient de- scent. By approximating the ideal sampling distribution using robust optimization, RAIS provides much of the benefit of exact importance sampling with drastically reduced overhead. Empirically, we find RAIS-SGD and standard SGD follow similar learning curves, but RAIS moves faster through these paths, achieving speed-ups of at least 20% and sometimes much more.

In theory, importance sampling speeds up stochastic gradient algorithms for supervised learning by prioritizing training examples. In practice, the cost of computing importances greatly limits the impact of importance sampling. We propose a robust, approximate importance sampling procedure (RAIS) for stochastic gradient de- scent. By approximating the ideal sampling distribution using robust optimization, RAIS provides much of the benefit of exact importance sampling with drastically reduced overhead. Empirically, we find RAIS-SGD and standard SGD follow similar learning curves, but RAIS moves faster through these paths, achieving speed-ups of at least 20% and sometimes much more.