The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements---also called features or inputs---to the so-called class label---or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to come to a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner.

Deep learning is having a large impact on the field of natural language processing. But, as a beginner, where do you start? Both deep learning and natural language processing are huge fields. What are the salient aspects of each field to focus on and which areas of NLP is deep learning having the most impact? In this post, you will discover a primer on deep learning for natural language processing.

The word2vec method based on skip-gram with negative sampling (Mikolov et al., 2013) [49] was published in 2013 and had a large impact on the field, mainly through its accompanying software package, which enabled efficient training of dense word representations and a straightforward integration into downstream models. In some respects, we have come far since then: Word embeddings have established themselves as an integral part of Natural Language Processing (NLP) models. In other aspects, we might as well be in 2013 as we have not found ways to pre-train word embeddings that have managed to supersede the original word2vec. This post will focus on the deficiencies of word embeddings and how recent approaches have tried to resolve them. If not otherwise stated, this post discusses pre-trained word embeddings, i.e. word representations that have been learned on a large corpus using word2vec and its variants.

In this semi-tutorial paper, we first review the information-theoretic approach to account for the computational costs incurred during the search for optimal actions in a sequential decision-making problem. The traditional (MDP) framework ignores computational limitations while searching for optimal policies, essentially assuming that the acting agent is perfectly rational and aims for exact optimality. Using the free-energy, a variational principle is introduced that accounts not only for the value of a policy alone, but also considers the cost of finding this optimal policy. The solution of the variational equations arising from this formulation can be obtained using familiar Bellman-like value iterations from dynamic programming (DP) and the Blahut-Arimoto (BA) algorithm from rate distortion theory. Finally, we demonstrate the utility of the approach for generating hierarchies of state abstractions that can be used to best exploit the available computational resources.

We could say our 30th annual list of the most transformative products and discoveries required trucks full of experts, hours of toil, and countless friendship-ending debates. That's true, but you just want the good stuff. A robot just made me french fries. Delicious, they cooked for four minutes less than the instructions dictated. One minute less, they'd've been soggy.

Intelligent machines have learned to read and write, recognize images, and predict dangerous mutations. But how does a machine learn to learn in the first place? The art of'learning to learn' (or meta-learning) is now widely recognized as a cornerstone of artificial intelligence research. Over the last few years, the idea of using data to learn the learning algorithms has gained momentum -- and massive computational resources and datasets have made it possible. In 2016, Nando de Freitas, a Senior Fellow in CIFAR's Learning in Machines & Brains program, demonstrated a novel approach to learning to learn.

On October 4th, roughly one year after the introduction of its branded line of hardware products, Google unveiled a second iteration of "Made by Google" hardware. This was a major product launch, but more than that, the presenters repeatedly hammered home Google's AI first messaging mantra with proof points in the form of a second generation branded product line built around AI and machine learning. The company's hardware strategy is clear. Google believes it is uniquely positioned to blend AI Software Hardware to deliver innovative products that will win in the marketplace, even if they are late to market. This second generation of Google hardware provides abundant proof that the company can bring uniquely differentiated features to existing product categories, and maybe even create some new ones.

Welcome to the Semantic Web - Chris Skinner's blog. Vincent Fournier/Gallerystock By Toby Walsh However you look at it, the future appears bleak. The world is under immense stress environmentally, economically and politically. The novelist who inspired Elon Musk. Elon Musk, the world's most restless entrepreneur, has embarked on yet another venture.

At the bottom of the background page, there is a more extensive list of resources on AI safety.

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.