For International Girls in ICT Day, and a few weeks ahead of the AI for Good Global Summit, ITU News caught up with Sarah Porter, CEO and Founder of InspiredMinds, World Summit AI, Intelligent Health, and Ada-AI, a non-profit dedicated to ensuring AI benefits all. Sarah is a humanitarian first-response trauma medic, ambassador for the Royal Marsden hospital in London and speaker for the United Nations on Lethal Autonomous Weapons. When I saw the story that a team of young girls from Afghanistan had against all odds made a robot but were then refused their visa [to attend an international robotics contest], it made me realise just how fortunate the Global North is with their right to free education in many disciplines including Science, Technology Engineering and Mathematics (STEM), unlimited access to wifi, and opportunities to learn how to code. The rapid progression of Artificial Intelligence (AI) by the wealthy corporations risks excluding the sectors of society that need it the most. Not only are women a minority in STEM education and in the tech teams building AI, the Global South is under-represented.

The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-media Neural Cognitive Computing (CNCC) framework was proposed based on MNCC, and formal description and analysis for CNCC was given. It would effectively improve the performance of semantic processing for multimedia information, and has far-reaching significance for exploration and realization brain-inspired computing. Keywords Deep learning·cognitive computing·brain-inspired computing·cross-media neural cognitive computing·multimedia neural cognitive computing 1 Introduction The brain-inspired computing (BIC) is the integration of neural cognitive science and information technology. It would realize state-of-the-art computing system which has advanced in energy consumption, computing ability and efficiency.

Springer Nature, the publisher of Scientific American and the venerable scientific journal Nature, intends to stride into the white-hot field of machine learning in early 2019 with a new journal called Nature Machine Intelligence. But the community of machine learning researchers, which prides itself on publishing to open-access journals, was immediately put off by the idea of a closed-access journal that requires academic credentials to read. Thomas Dietterich, the former executive editor of the journal Machine Learning and an emeritus professor of computer science at Oregon State University, posted a pledge not to submit, review or edit for Nature Machine Intelligence, and invited other researchers in the field to sign the pledge as well. At the time of writing, the boycott had accumulated more than 2,400 signatures by employees of Google, Facebook, IBM, Harvard, MIT and a cross-section of other prominent institutions--as well as many of the biggest names in artificial intelligence research including neural network pioneers Yann LeCun and Yoshua Bengio and Google Brain co-founder Jeff Dean. "We write the papers, we copyedit the papers, we typeset the papers, and we review the papers," Dietterich told Motherboard in an email.

In this special guest feature, Geoff Tudor, VP and GM of Cloud Data Services at Panzura, believes AI poses both opportunities and risks in the automation of the datacenter. This article provides an overview regarding the impact of AI in the datacenter, and how companies can prepare their storage infrastructure for these technologies. Geoff has over 22 years experience in storage, broadband, and networking. As Chief Cloud Strategist at Hewlett Packard Enterprise, Geoff led CxO engagements for Fortune 100 private cloud opportunities resulting in 10X growth to over $1B in revenues while positioning HPE as the #1 private cloud infrastructure supplier globally. Geoff holds an MBA from The University of Texas at Austin, a BA from Tulane University, and is a patent-holder in satellite communications. Artificial intelligence (AI) with machine learning (ML) capabilities offers the promise of increased efficiency in data centers.

Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be extended to hierarchical RL, generative adversarial networks and decentralised optimisation. In all these settings the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable training or undesired final results. We present Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in the environment. The LOLA learning rule includes an additional term that accounts for the impact of one agent's policy on the anticipated parameter update of the other agents. Preliminary results show that the encounter of two LOLA agents leads to the emergence of tit-for-tat and therefore cooperation in the iterated prisoners' dilemma, while independent learning does not. In this domain, LOLA also receives higher payouts compared to a naive learner, and is robust against exploitation by higher order gradient-based methods. Applied to repeated matching pennies, LOLA agents converge to the Nash equilibrium. In a round robin tournament we show that LOLA agents can successfully shape the learning of a range of multi-agent learning algorithms from literature, resulting in the highest average returns on the IPD. We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL. This method thus scales to large parameter and input spaces and nonlinear function approximators. We also apply LOLA to a grid world task with an embedded social dilemma using deep recurrent policies and opponent modelling. Again, by explicitly considering the learning of the other agent, LOLA agents learn to cooperate out of self-interest.

Inspired by the matching of supply to demand in logistical problems, the optimal transport (or Monge--Kantorovich) problem involves the matching of probability distributions defined over a geometric domain such as a surface or manifold. In its most obvious discretization, optimal transport becomes a large-scale linear program, which typically is infeasible to solve efficiently on triangle meshes, graphs, point clouds, and other domains encountered in graphics and machine learning. Recent breakthroughs in numerical optimal transport, however, enable scalability to orders-of-magnitude larger problems, solvable in a fraction of a second. Here, we discuss advances in numerical optimal transport that leverage understanding of both discrete and smooth aspects of the problem. State-of-the-art techniques in discrete optimal transport combine insight from partial differential equations (PDE) with convex analysis to reformulate, discretize, and optimize transportation problems. The end result is a set of theoretically-justified models suitable for domains with thousands or millions of vertices. Since numerical optimal transport is a relatively new discipline, special emphasis is placed on identifying and explaining open problems in need of mathematical insight and additional research.

Statistical models (SMs) include ordinary regression, Bayesian regression, semiparametric models, generalized additive models, longitudinal models, time-to-event models, penalized regression, and others. Penalized regression includes ridge regression, lasso, and elastic net. Contrary to what some machine learning (ML) researchers believe, SMs easily allow for complexity (nonlinearity and second-order interactions) and an unlimited number of candidate features (if penalized maximum likelihood estimation or Bayesian models are used). It is especially easy, using regression splines, to allow every continuous predictor to have a smooth nonlinear effect. ML is taken to mean an algorithmic approach that does not use traditional identified statistical parameters, and for which a preconceived structure is not imposed on the relationships between predictors and outcomes. ML usually does not attempt to isolate the effect of any single variable.

Journal of Parallel and Distributed Computing 104, 19-35.

In the last years, deep learning algorithms have highly revolutionized several areas including speech, image and natural language processing. The specific field of Machine Translation (MT) has not remained invariant. Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture. Among the different neural networks, research works use feedforward neural networks, recurrent neural networks and the encoder-decoder schema. These architectures are able to tackle challenges as having low-resources or morphology variations. This manuscript focuses on describing how these neural networks have been integrated to enhance different aspects and models from statistical MT, including language modeling, word alignment, translation, reordering, and rescoring. Then, we report the new neural MT approach together with a description of the foundational related works and recent approaches on using subword, characters and training with multilingual languages, among others. Finally, we include an analysis of the corresponding challenges and future work in using deep learning in MT.

Mobile robot navigation in complex and dynamic environments is a challenging but important problem. Reinforcement learning approaches fail to solve these tasks efficiently due to reward sparsities, temporal complexities and high-dimensionality of sensorimotor spaces which are inherent in such problems. We present a novel approach to train action policies to acquire navigation skills for wheel-legged robots using deep reinforcement learning. The policy maps height-map image observations to motor commands to navigate to a target position while avoiding obstacles. We propose to acquire the multifaceted navigation skill by learning and exploiting a number of manageable navigation behaviors. We also introduce a domain randomization technique to improve the versatility of the training samples. We demonstrate experimentally a significant improvement in terms of data-efficiency, success rate, robustness against irrelevant sensory data, and also the quality of the maneuver skills.