As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called "concept learning", which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called "experience learning", which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent.

The Dragonfly Machine Learning Engine (MLE) provides the machine learning and data science capabilities included within OPNids. Data science and machine learning promise to counteract the dynamic threat environment created by growing network traffic and increasing threat actor sophistication. This post will provide an overview of the MLE engine itself, reasoning for why data science and cybersecurity go together, and some insight into using the MLE as part of the OPNids system. The Dragonfly MLE is available as part of OPNids. The Dragonfly MLE provides a powerful framework for deploying anomaly detection algorithms, threat intelligence lookups, and machine learning predictions within a network security infrastructure.

"Artificial intelligence" (AI) may evoke fears of robots writing their own software code and not taking orders from humans. The real AI, at least in present form, is delivering results in the business world. Technology companies are using powerful computers and advanced statistical models to accelerate their product development. Most are not calling these efforts AI but rather machine learning. As a form of AI, machine learning is making it possible to quickly find relevant patterns in data captured by Internet of Things (IoT) devices and sensors, explains Adam Kahn, vice president of fleets for Netradyne, which has a vision-based fleet safety system called Driveri ("driver eye").

This document aims to provide a review on learning with deep generative models (DGMs), which is an highly-active area in machine learning and more generally, artificial intelligence. This review is not meant to be a tutorial, but when necessary, we provide self-contained derivations for completeness. This review has two features. First, though there are different perspectives to classify DGMs, we choose to organize this review from the perspective of graphical modeling, because the learning methods for directed DGMs and undirected DGMs are fundamentally different. Second, we differentiate model definitions from model learning algorithms, since different learning algorithms can be applied to solve the learning problem on the same model, and an algorithm can be applied to learn different models. We thus separate model definition and model learning, with more emphasis on reviewing, differentiating and connecting different learning algorithms. We also discuss promising future research directions. This review is by no means comprehensive as the field is evolving rapidly. The authors apologize in advance for any missed papers and inaccuracies in descriptions. Corrections and comments are highly welcome.

Deep neural networks are the state-of-the-art methods for many real-world tasks, such as computer vision, natural language processing and speech recognition. For all its popularity, deep neural networks are also criticized for consuming a lot of memory and draining battery life of devices during training and inference. This makes it hard to deploy these models on mobile or embedded devices which have tight resource constraints. Quantization is recognized as one of the most effective approaches to satisfy the extreme memory requirements that deep neural network models demand. Instead of adopting 32-bit floating point format to represent weights, quantized representations store weights using more compact formats such as integers or even binary numbers. Despite a possible degradation in predictive performance, quantization provides a potential solution to greatly reduce the model size and the energy consumption. In this survey, we give a thorough review of different aspects of quantized neural networks. Current challenges and trends of quantized neural networks are also discussed.

Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges. This paper gives a selective overview on recent advance on high-dimensional factor models and their applications to statistics including Factor-Adjusted Robust Model selection (FarmSelect) and Factor-Adjusted Robust Multiple testing (FarmTest). We show that classical methods, especially principal component analysis (PCA), can be tailored to many new problems and provide powerful tools for statistical estimation and inference. We highlight PCA and its connections to matrix perturbation theory, robust statistics, random projection, false discovery rate, etc., and illustrate through several applications how insights from these fields yield solutions to modern challenges. We also present far-reaching connections between factor models and popular statistical learning problems, including network analysis and low-rank matrix recovery.

Modern machine learning algorithms have been adopted in a range of signal-processing applications spanning computer vision, natural language processing, and artificial intelligence. Many relevant problems involve subspace-structured features, orthogonality constrained or low-rank constrained objective functions, or subspace distances. These mathematical characteristics are expressed naturally using the Grassmann manifold. Unfortunately, this fact is not yet explored in many traditional learning algorithms. In the last few years, there have been growing interests in studying Grassmann manifold to tackle new learning problems. Such attempts have been reassured by substantial performance improvements in both classic learning and learning using deep neural networks. We term the former as shallow and the latter deep Grassmannian learning. The aim of this paper is to introduce the emerging area of Grassmannian learning by surveying common mathematical problems and primary solution approaches, and overviewing various applications. We hope to inspire practitioners in different fields to adopt the powerful tool of Grassmannian learning in their research.

Customer behavior and experience change over time and there is much organizations can do to use that change to their own advantage. The challenge this presents for businesses is to place the customer at the center of their organizational chart. Customers do not care about the people in the organization chart, nor do they care about hierarchy. They probably won't even be aware whether the person they are talking to is from the marketing or the sales department. All they do care about is how much emphasis you and your brand are placing on understanding them, their behavior, and most of all, their needs.

The report starts by an introduction about the company profiling and a comprehensive review about the strategy concept and the tools that can be used to assess and analyze strategy. Porter's Five Forces model is a powerful tool that combines five competitive forces which limit any industry's profit according to external factors. These forces are the threat of new entrants, the customer bargaining power, the supplier bargaining power, the substitution to an alternative product or service, and the intensity of competition among current rivals inside the industry. This market is expected to grow at XXX billion by the end of forecast period with XX.X% of CAGR. The future trends also introduced in the report which elaborates key factors of Global Artificial Intelligence in Medicine such as market opportunities, future market risk, benefit, loss and profit, customer perspective, Innovation, Short Term vs.