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.

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction. QBC allows our AL algorithm to automatically sample regions of chemical space where the machine learned potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach we develop the COMP6 benchmark (publicly available on GitHub), which contains a diverse set of organic molecules. We show the use of our proposed AL technique develops a universal ANI potential (ANI-1x), which provides very accurate energy and force predictions on the entire COMP6 benchmark. This universal potential achieves a level of accuracy on par with the best ML potentials for single molecule or materials while remaining applicable to the general class of organic molecules comprised of the elements CHNO.

About this course: Leaders in business and non-profit organizations increasingly work across national borders and in multi-cultural environments. You may work regularly with customers or suppliers abroad, or be part of a globally dispersed cross-functional team, or an expatriate manager on an international assignment. You may be a member of a global online community, or a development aid worker collaborating with an international network of partner organizations. In all of these contexts, your effectiveness as a leader depends on how well you understand and are able to manage individual and collective behaviors in an intercultural context. In this course – together with a team of Bocconi expert faculty and Bocconi alumni – we'll explore the theory and practice of international and intercultural leadership and organizational behavior.

I have an upcoming interview that involves applying Deep Learning to Computer Vision problems. Though I have experience with deep learning I'm currently weak on the pure Computer Vision side of things. What are the topics that I should revise? What questions might be asked?

Bradbury taps into a human concern that surrounds the idea of Artificial Intelligence – if technology continues to develop at such a rate, humans will begin to become obsolete in our own homes and workplaces. Every week there's a new article telling us that robots can do our jobs better than we can, after all. But step back from the hysteria--the robots are not really coming to get us--and AI is already very much a part of our lives. Computers have been mimicking cognitive functions for many years: Deep Blue beat Kasparov in a chess match in 1996. We've all been helped (or hindered) by a chatbot online.

Neurons store and transmit information in the brain.Credit: CNRI/SPL Superconducting computing chips modelled after neurons can process information faster and more efficiently than the human brain. That achievement, described in Science Advances on 26 January1, is a key benchmark in the development of advanced computing devices designed to mimic biological systems. And it could open the door to more natural machine-learning software, although many hurdles remain before it could be used commercially. Artificial intelligence software has increasingly begun to imitate the brain. Algorithms such as Google's automatic image-classification and language-learning programs use networks of artificial neurons to perform complex tasks. But because conventional computer hardware was not designed to run brain-like algorithms, these machine-learning tasks require orders of magnitude more computing power than the human brain does.