For example, it is of clinical interest to understand associations between: (a) alcoholism and the electrical activity of different brain regions over time collected from electroencephalography (EEG) (Li et al., 2010); (b) cognitive function and three-dimensional white-matter structure data collected from diffusion tensor imaging (DTI) (Goldsmith et al., 2014) for patients with multiple sclerosis (MS); and (c) cognitive impairment and brain's metabolic activity data collected from three-dimensional positron emission tomography (PET) imaging (Wang et al., 2014). Our work focuses on the problem of identifying brain network connections that are associated with neurocognitive measures for HIVinfected individuals. The outcome (response) is a continuous variable and the predictors are matrix representations of functional connectivity between the brain's cortical regions. Biophysical considerations motivate our interest in estimating a matrix of regression coefficients that has the following two properties: (i) it should be relatively sparse, since we aim to identify connections that most strongly predict the outcome; and more importantly, (ii) the response-related connections form clusters, since brain activity networks are known to consist of densely connected regions. These two properties translate to the coefficient matrix having relatively small clusters, or blocks of nonzero entries, which implies that it is low-rank. Hence, we aim to solve the matrix regression problem by estimating a coefficient matrix that is both sparse and low-rank. To further illustrate our approach, consider the three matrices in Figure 1. The one in the left panel is sparse, but full-rank, the one on the right panel is low-rank, but not sparse, while the one in the middle panel is both low-rank and sparse, which is the structure we are interested in. To find such a solution, we propose a regularization method called SParsity Inducing Nuclear Norm EstimatoR (SpINNEr).

The problem of specifying high-level knowledge bases for planning becomes a hard task in realistic environments. This knowledge is usually handcrafted and is hard to keep updated, even for system experts. Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. These approaches can synthesize action schemas in Planning Domain Definition Language (PDDL) from a set of execution traces each consisting, at least, of an initial and final state. In this paper, we propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown. In addition, we contribute with a compilation to classical planning that mitigates the problem of learning static predicates in the action model preconditions, exploits the capabilities of SAT planners with parallel encodings to compute action schemas and validate all instances. Our system is flexible in that it supports the inclusion of partial input information that may speed up the search. We show through several experiments how learned action models generalize over unseen planning instances.

The health of people and communities around the world has been improving over time. For example, the steep decline in child and maternal mortality is a key indicator of positive momentum. However, progress has not been equal across the globe, and there is a great need to focus on societal issues such as reducing health inequity and improving access to care for underserved populations. While researchers work to unlock life-saving discoveries and develop new approaches to pressing health issues, advancements in technology can help accelerate and scale new solutions. That is why we are launching AI for Health, a new $40 million, five-year program to empower researchers and organizations with AI to improve the health of people and communities around the world.

When a mysterious illness first pops up, it can be difficult for governments and public health officials to gather information quickly and coordinate a response. But new artificial intelligence technology can automatically mine through news reports and online content from around the world, helping experts recognize anomalies that could lead to a potential epidemic or, worse, a pandemic. In other words, our new AI overlords might actually help us survive the next plague. These new AI capabilities are on full display with the recent coronavirus outbreak, which was identified early by a Canadian firm called BlueDot, which is one of a number of companies that use data to evaluate public health risks. The company, which says it conducts "automated infectious disease surveillance," notified its customers about the new form of coronavirus at the end of December, days before both the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) sent out official notices, as reported by Wired.

Navigating a new indoor space without any prior knowledge or even a map is a challenging task for a human, let alone a robot. To help develop intelligent machines that interact more effectively with complex 3D environments, Facebook researchers developed a GPU-accelerated deep reinforcement learning model that achieves near 100 percent success in navigating a variety of virtual environments without a pre-provided map. To achieve this breakthrough, the team focused their work on developing an efficient approach to scaling RL models, which require a significant number of training samples, using multi-node distribution. "A single parameter server and thousands of (typically CPU) workers may be fundamentally incompatible with the needs of modern computer vision and robotics communities," the researchers explained in their post, Near-perfect point-goal navigation from 2.5 billion frames of experience. "Unlike Gym or Atari, 3D simulators require GPU acceleration…. The desired agents operate from high-dimensional inputs (pixels) and use deep networks, such as ResNet50, which strain the parameter server. Thus, existing distributed RL architectures do not scale and there is a need to develop a new distributed architecture."

It was almost ten years ago when Sherry Turkle warned that the world was headed for a place where humans would be interacting socially with machines, like robots. Turkle is a MIT professor and social scientist who has been working on human-technology interaction and what it will mean for the human race. She is the author of several books including Alone Together and Reclaiming Conversation which explore the impact of technology on some of the aspects that actually make humans humans. Over the years, through her books and numerous talks, Sherry Turkle has explained the dangers of people trying to replace each other with machines including the smartphone and robots, but the world seems to have taken little heed as today we see companies inventing robots for all sorts of tasks and even for human relationships. Remember the Chinese inventor of a female robot whom he married in 2017?

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).