By the end of April 2018, blockchain-oriented projects have already collected a total of $6.1 billion year-to-date via initial coin offerings (ICOs), according to CoinSchedule data. This is a new record, as we saw $5.6 billion raised during the last year. Almost 60% of all the projects cover three markets -- communications, finance, and trading & investing -- with machine learning & AI getting only 2.7%. So we have an unexplored market here, with the artificial intelligence (AI) being so much ignored by the general public despite it being one of the revolutionary technologies along with blockchain. AI Crypto, an artificial intelligence (AI) and blockchain-oriented company headquartered in Singapore, wants to combine both of the transformative technologies and bring a game-changing product to the market.

Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al (2013) used stochastic distances between complex multivariate Wishart models which, differently from other measures, are computationally tractable. In this work we assess the robustness of such approach with respect to errors in the training stage, and propose an extension that alleviates such problems. We introduce robustness in the process by incorporating a combination of radial basis kernel functions and stochastic distances with Support Vector Machines (SVM). We consider several stochastic distances between Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square, R\'{e}nyi, and Hellinger. We perform two case studies with PolSAR images, both simulated and from actual sensors, and different classification scenarios to compare the performance of Minimum Distance and SVM classification frameworks. With this, we model the situation of imperfect training samples. We show that SVM with the proposed kernel functions achieves better performance with respect to Minimum Distance, at the expense of more computational resources and the need of parameter tuning. Code and data are provided for reproducibility.

We report a procedure that, in one step from continuous data with minimal preparation, recovers the graph found by Sachs et al. \cite{sachs2005causal}, with only a few edges different. The algorithm, Fast Adjacency Skewness (FASK), relies on a mixture of linear reasoning and reasoning from the skewness of variables; the Sachs data is a good candidate for this procedure since the skewness of the variables is quite pronounced. We review the ground truth model from Sachs et al. as well as some of the fluctuations seen in the protein abundances in the system, give the Sachs model and the FASK model, and perform a detailed comparison. Some variation in hyper-parameters is explored, though the main result uses values at or near the defaults learned from work modeling fMRI data.

A few weeks ago, I attempted to sit through Samsung's live-streamed Galaxy S9 smartphone launch event. I nearly fell asleep at my desk. Or the relocated fingerprint sensor, which is exactly what it sounds like. I realize this is a first-world problem, but in the 11 years since the release of the iPhone, advances in personal technology have gone from breakthrough to, well, pretty broke. What--and where--is the next revolutionary product, the thing that rewrites the rules and alters our lives forever?

Wall Street, venture capitalists, technology executives – all have important reasons to understand the growth and opportunity in the artificial intelligence market, but the inherent vagueness of the term makes any single valuation extremely difficult. Indeed, the term "artificial intelligence" is notorious for having a relatively amorphous definition. In order to put together an executive brief for market size and projected growth of AI, I've molded this article around (a) AI-related industry market research forecasts, and (b) a limited number of reputable research sources for further insight into AI valuation and forecasting, in addition to select and relevant quotes. Bear in mind that different market research firms define "artificial intelligence" according to varying criteria. To make this summary article more useful, we've quickly broken down all reports by source, definition / meaning of "artificial intelligence", valuation, and timeline.

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.