The great success of deep learning shows that its technology contains profound truth, and understanding its internal mechanism not only has important implications for the development of its technology and effective application in various fields, but also provides meaningful insights into the understanding of human brain mechanism. At present, most of the theoretical research on deep learning is based on mathematics. This dissertation proposes that the neural network of deep learning is a physical system, examines deep learning from three different perspectives: microscopic, macroscopic, and physical world views, answers multiple theoretical puzzles in deep learning by using physics principles. For example, from the perspective of quantum mechanics and statistical physics, this dissertation presents the calculation methods for convolution calculation, pooling, normalization, and Restricted Boltzmann Machine, as well as the selection of cost functions, explains why deep learning must be deep, what characteristics are learned in deep learning, why Convolutional Neural Networks do not have to be trained layer by layer, and the limitations of deep learning, etc., and proposes the theoretical direction and basis for the further development of deep learning now and in the future. The brilliance of physics flashes in deep learning, we try to establish the deep learning technology based on the scientific theory of physics. Opening the black box of deep learning

A lectionary, according to Wikipedia, is a listing of scripture readings for Christian or Judaic worship on a given day. The Roman Catholic Lectionary will contain a list of readings for a specific day that are on a 3-year cycle. Here is an example:

Supplier’s Declaration of Conformity (SDoC) The accuracy and reliability of machine learning algorithms are an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety, security, and provenance, are also critical elements to engender consumers’ trust in a service. In this paper, we propose a supplier’s declaration of conformity (SDoC) for AI services to help increase trust in AI services. An SDoC is a transparent, standardized, but often not legally required, document used in many industries and sectors to describe the lineage of a product along with the safety and performance testing it has undergone. We envision an SDoC for AI services to contain purpose, performance, safety, security, and provenance information to be completed and voluntarily released by AI service providers for examination by consumers. Importantly, it conveys product-level rather than component-level functional testing. We suggest a set of declaration items tailored to AI and provide examples for two fictitious AI services. …

By Yaroslav Kuflinski, AI/ML Observer at Iflexion

The restricted Boltzmann machine is a network of stochastic units with undirected interactions between pairs of visible and hidden units. This model was popularized as a building block of deep learning architectures and has continued to play an important role in applied and theoretical machine learning. Restricted Boltzmann machines carry a rich structure, with connections to geometry, applied algebra, probability, statistics, machine learning, and other areas. The analysis of these models is attractive in its own right and also as a platform to combine and generalize mathematical tools for graphical models with hidden variables. This article gives an introduction to the mathematical analysis of restricted Boltzmann machines, reviews recent results on the geometry of the sets of probability distributions representable by these models, and suggests a few directions for further investigation. Restricted Boltzmann Machines: Introduction and Review

Jag Bhalla points us to this article, “Differences in exam performance between pupils attending selective and non-selective schools mirror the genetic differences between them,” by Emily Smith-Woolley, Jean-Baptiste Pingault, Saskia Selzam, Kaili Rimfeld, Eva Krapohl, Sophie von Stumm, Kathryn Asbury, Philip Dale, Toby Young, Rebecca Allen, Yulia Kovas, and Robert Plomin, along with this response by Eric Turkheimer.

Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange

Abstract You can find the data used in this blog post here: https://github.com/b-rodrigues/elections_lux

Model Selection using Multi-Objective Optimization

A new minor release of RcppRedis arrived on CRAN earlier today. RcppRedis is one of several packages to connect R to the fabulous Redis in-memory datastructure store (and much more). RcppRedis does not pretend to be feature complete, but it may do some things faster than the other interfaces, and also offers an optional coupling with MessagePack binary (de)serialization via RcppMsgPack. The package has carried production loads for several years now.