Artificial Intelligence is a central topic in the computer science curriculum. From the year 2011 a project-based learning methodology based on computer games has been designed and implemented into the intelligence artificial course at the University of the Bio-Bio. The project aims to develop software-controlled agents (bots) which are programmed by using heuristic algorithms seen during the course. This methodology allows us to obtain good learning results, however several challenges have been founded during its implementation. In this paper we show how linguistic descriptions of data can help to provide students and teachers with technical and personalized feedback about the learned algorithms. Algorithm behavior profile and a new Turing test for computer games bots based on linguistic modelling of complex phenomena are also proposed in order to deal with such challenges. In order to show and explore the possibilities of this new technology, a web platform has been designed and implemented by one of authors and its incorporation in the process of assessment allows us to improve the teaching learning process.

Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images. Even though it has recently been shown that GAN training converges, GAN models often end up in local Nash equilibria that are associated with mode collapse or otherwise fail to model the target distribution. We introduce Coulomb GANs, which pose the GAN learning problem as a potential field of charged particles, where generated samples are attracted to training set samples but repel each other. The discriminator learns a potential field while the generator decreases the energy by moving its samples along the vector (force) field determined by the gradient of the potential field. Through decreasing the energy, the GAN model learns to generate samples according to the whole target distribution and does not only cover some of its modes. We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution. We show the efficacy of Coulomb GANs on a variety of image datasets. On LSUN and celebA, Coulomb GANs set a new state of the art and produce a previously unseen variety of different samples.

Support vector machine (SVM) is one of the most well-known supervised classification methods that has been extensively used in such fields as disease diagnosis, text categorization, and fraud detection. Training nonlinear SVM classifier (such as Gaussian kernel based) requires solving convex quadratic programming (QP) model whose running time can be prohibitive for large-scale instances without using specialized acceleration techniques such as sampling, boosting, and hierarchical training. Another typical reason of increased running time is complex data sets (e.g., when the data is noisy, imbalanced, or incomplete) that require using model selection techniques for finding the best model parameters. The motivation behind this work was extensive applied experience with hard, large-scale, industrial (not necessarily highly heterogeneous) data sets for which fast linear SVMs produced extremely low quality results (as well as many other fast methods), and various nonlinear SVMs exhibited a strong trade off between running time and quality. It has been noticed in multiple works that many different real-world data sets have a strong underlying multiscale (in some works called hierarchical) structure [35, 31, 37, 66] that can be discovered through careful definitions of coarse-grained resolutions.

Welcome to the third chapter of ML lessons from Ng's experience! Yes, this one is the continuation of the series entirely based on a recent course by Andrew Ng on Coursera. Although this post can be an independent learning, reading the previous two articles will only help understand this one better. Here are the links to the first and second articles in the series. When trying to solve a new machine learning problem (one which does not have too many online resources available already), Andrew Ng advises to build you first system real quick and then iterate on it.

Kangaroo Kapital is the largest credit card company in Australia. Animals across the continent use Kangaroo Kapital credit cards to make all of their daily purchases, racking up points in the company's reward system. Since Australian animals have traditionally not worn much clothing, the challenges of carrying around cash are substantial. Only having to keep track of a single credit card is a big help for your average working wallaby. But, since no clothes means no pockets, even keeping track of one credit card can be problematic.

One in five jobs in British cities is likely to be displaced by 2030 because of automation and globalisation, a new report predicts. Retail, customer service and warehouse jobs are among those most at threat of being lost, said Centre for Cities. The think tank said struggling cities in the North and Midlands were more exposed to job losses than wealthier cities in the South, compounding the North/South divide. Cities including Mansfield, Sunderland and Wakefield could see two out of five jobs lost, while Oxford and Cambridge face losing 13%, the study found. The report said the changes would lead to jobs being created as well as lost, but in Northern and Midlands' cities they would largely be in low-skilled occupations.

Recently 2017 came to a glittering end and as we look back through the lens of technology, the winner was probably Artificial Intelligence aka AI. It received tremendous success as much as some of the industry leaders commented that 2017 was the'Year of AI'. This write-up is an attempt to collate the achievements under the academic and industry. Starting off with academics, the sheer volume of papers published is increasing every year. To give you some statistics, in 2017 it was 9 times more than 1996.

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.

ABSTRACT The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance optimization techniques for human-only or sensor-only networks are quite mature, HuMaINs require novel signal processing and machine learning solutions. In this paper, we present an overview of the HuMaINs architecture with a focus on three main issues that include architecture design, inference algorithms including security/privacy challenges, and application areas/use cases. Index Terms-- human-in-the-loop systems, behavioral signal processing, self-driving cars, health care informatics, intelligent tutoring systems 1. INTRODUCTION In traditional economics, cognitive psychology, and artificial intelligence (AI) literature, the problem-solving or inference process is described in terms of searching a problem space, which consists of various states of the problem, starting with the initial state and ending at the goal state which one would like to reach [1]. Each path from the initial state represents a possible strategy which can be used.

We present theoretical guarantees for an alternating minimization algorithm for the dictionary learning/sparse coding problem. The dictionary learning problem is to factorize vector samples $y^{1},y^{2},\ldots, y^{n}$ into an appropriate basis (dictionary) $A^*$ and sparse vectors $x^{1*},\ldots,x^{n*}$. Our algorithm is a simple alternating minimization procedure that switches between $\ell_1$ minimization and gradient descent in alternate steps. Dictionary learning and specifically alternating minimization algorithms for dictionary learning are well studied both theoretically and empirically. However, in contrast to previous theoretical analyses for this problem, we replace the condition on the operator norm (that is, the largest magnitude singular value) of the true underlying dictionary $A^*$ with a condition on the matrix infinity norm (that is, the largest magnitude term). This not only allows us to get convergence rates for the error of the estimated dictionary measured in the matrix infinity norm, but also ensures that a random initialization will provably converge to the global optimum. Our guarantees are under a reasonable generative model that allows for dictionaries with growing operator norms, and can handle an arbitrary level of overcompleteness, while having sparsity that is information theoretically optimal. We also establish upper bounds on the sample complexity of our algorithm.