The raging Australian and Amazon wildfires have raised a burning question for all of us - why the very technology, that has been a major facilitator to human evolution and growth could not predict, manage or control its destruction? To those of us who are in the business of technology, it is time to ask a few tough questions in our boardroom meetings and take ownership of solving the problem. After all, what is growth worth if the planet itself is in peril? As someone who has witnessed the digital revolution unfold, I may not have a full-proof plan to address the climate emergency, in fact, we don't even have the visibility of all evolving technologies that may be required to solve the climate emergency. But, I am clear and convinced that we have to start now and start with the available technologies which in their own right are very powerful and transformational.

We propose a new method for clustering multivariate time-series data based on Dynamic Linear Models. Whereas usual time-series clustering methods obtain static membership parameters, our proposal allows each time-series to dynamically change their cluster memberships over time. In this context, a mixture model is assumed for the time series and a flexible Dirichlet evolution for mixture weights allows for smooth membership changes over time. Posterior estimates and predictions can be obtained through Gibbs sampling, but a more efficient method for obtaining point estimates is presented, based on Stochastic Expectation-Maximization and Gradient Descent. Finally, two applications illustrate the usefulness of our proposed model to model both univariate and multivariate time-series: World Bank indicators for the renewable energy consumption of EU nations and the famous Gapminder dataset containing life-expectancy and GDP per capita for various countries.

Causal explanations present an intuitive way to understand the course of events through causal chains, and are widely accepted in cognitive science as the prominent model humans use for explanation. Importantly, causal models can generate opportunity chains, which take the form of `A enables B and B causes C'. We ground the notion of opportunity chains in human-agent experimental data, where we present participants with explanations from different models and ask them to provide their own explanations for agent behaviour. Results indicate that humans do in-fact use the concept of opportunity chains frequently for describing artificial agent behaviour. Recently, action influence models have been proposed to provide causal explanations for model-free reinforcement learning (RL). While these models can generate counterfactuals---things that did not happen but could have under different conditions---they lack the ability to generate explanations of opportunity chains. We introduce a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models. We employ a recurrent neural network to learn opportunity chains and make use of decision trees to improve the accuracy of task prediction and the generated counterfactuals. We computationally evaluate the model in 6 RL benchmarks using different RL algorithms, and show that our model performs better in task prediction. We report on a study with 90 participants who receive explanations of RL agents behaviour in solving three scenarios: 1) Adversarial; 2) Search and rescue; and 3) Human-Agent collaborative scenarios. We investigate the participants' understanding of the agent through task prediction and their subjective satisfaction of the explanations and show that our distal explanation model results in improved outcomes over the three scenarios compared with two baseline explanation models.

The Ethiopian Council of Ministers has decided to establish an artificial intelligence (AI) research and development center. The move was taken "to safeguard Ethiopia's national interests through the development of artificial intelligence services, products and solutions based on research, development and implementation," the Prime Minister's Office said in a statement issued on late Friday. The decision calls for "a conducive environment for beginner developers and startups working in the artificial intelligence sector." This was the latest of a series of measures taken by Ethiopia, Africa's second populous nation with a a population of about 107 million, to step up AI research and development in particular and advance information and Communications technology (ICT) in general. In November, Ethiopia signed a memo with Chinese e-commerce giant Alibaba Group on the creation of an Electronic World Trade Platform (eWTP).

World AI Show - Kuala Lumpur will be Malaysia's biggest confluence of Tech, Partnerships and go-to-market strategies. The potential of Artificial Intelligence is far from being fully exploited thus making now the best time for Malaysia to sprint to the front of the race. WAIS is an unprecedented opportunity that pushes an agenda of #AIForAll Malaysians as we bring AI which is affordable for businesses across. This event is Co-Located with the World Blockchain Summit (23-24 March 2020). World AI Show is a thought-leadership-driven, business-focused, global series of events taking place in strategic locations across the world.

There has recently been increasing interest, both theoretical and practical, in utilizing tensor networks for the analysis and design of machine learning systems. In particular, a framework has been proposed that can handle both dense data (e.g., standard regression or classification tasks) and sparse data (e.g., recommender systems), unlike support vector machines and traditional deep learning techniques. Namely, it can be interpreted as applying local feature mappings to the data and, through the outer product operator, modelling all interactions of functions of the features; the corresponding weights are represented as a tensor network for computational tractability. In this paper, we derive efficient prediction and learning algorithms for supervised learning with the Canonical Polyadic (CP) decomposition, including suitable regularization and initialization schemes. We empirically demonstrate that the CP-based model performs at least on par with the existing models based on the Tensor Train (TT) decomposition on standard non-sequential tasks, and better on MovieLens 100K. Furthermore, in contrast to previous works which applied two-dimensional local feature maps to the data, we generalize the framework to handle arbitrarily high-dimensional maps, in order to gain a powerful lever on the expressiveness of the model. In order to enhance its stability and generalization capabilities, we propose a normalized version of the feature maps. Our experiments show that this version leads to dramatic improvements over the unnormalized and/or two-dimensional maps, as well as to performance on non-sequential supervised learning tasks that compares favourably with popular models, including neural networks.

Artificial intelligence (AI) has been a topic of major research for many years. Especially, with the emergence of deep neural network (DNN), these studies have been tremendously successful. Today machines are capable of making faster, more accurate decision than human. Thanks to the great development of machine learning (ML) techniques, ML have been used many different fields such as education, medicine, malware detection, autonomous car etc. In spite of having this degree of interest and much successful research, ML models are still vulnerable to adversarial attacks. Attackers can manipulate clean data in order to fool the ML classifiers to achieve their desire target. For instance; a benign sample can be modified as a malicious sample or a malicious one can be altered as benign while this modification can not be recognized by human observer. This can lead to many financial losses, or serious injuries, even deaths. The motivation behind this paper is that we emphasize this issue and want to raise awareness. Therefore, the security gap of mammographic image classifier against adversarial attack is demonstrated. We use mamographic images to train our model then evaluate our model performance in terms of accuracy. Later on, we poison original dataset and generate adversarial samples that missclassified by the model. We then using structural similarity index (SSIM) analyze similarity between clean images and adversarial images. Finally, we show how successful we are to misuse by using different poisoning factors.

Proof assistants offer tactics to facilitate inductive proofs. However, it still requires human ingenuity to decide what arguments to pass to those induction tactics. To automate this process, we present smart_induct for Isabelle/HOL. Given an inductive problem in any problem domain, smart_induct lists promising arguments for the induct tactic without relying on a search. Our evaluation demonstrated smart_induct produces valuable recommendations across problem domains.

The "Hey, Update My Voice" movement, in partnership with UNESCO, was born out of this context with the goal of teaching respect towards virtual assistants and, in addition, asking tech companies to update their assistants' responses. Because if that happens to them, imagine what happens in real life to real women. Every day around the world, virtual assistants suffer abuse and harassment of all kinds. In Brazil, for example, Lu, the virtual assistant of Magazine Luiza stores, has been victimized by this sort of violence. Worldwide, cases have been reported involving Siri and Alexa, among others.

Davos, Switzerland – "Has China Won?" Kishore Mahbubeni, the Singaporean author and intellectual, greets me warmly in a conference lounge here and hands me a card promoting the March release of his new book, bearing that provocative question as its title. The cover blurb announces that he will explain "how, while America became arrogant and distracted, a three-thousand-year-old civilization is well on the way to becoming the number one power in the world." The year ahead is likely to provide the most profound trial yet for that thesis and for the durability of China's rise. Several new shocks and challenges, ranging from a potential pandemic to slowing growth, will test the resilience of China's authoritarian leadership and the state-run capitalist system that has provided the country four decades of record growth. It thus also could mark a significant year for the emerging, generational clash, not of civilizations as Samuel Huntington had argued, but rather of economic and political systems, between democratic and authoritarian capitalism.