Besides millions and millions of tweets on new innovations, AI was at the centre of constant brainstorming among experts, researchers and influencers. With the year 2019 coming to an end, here are our top 10 tweets that we found very both thoughtful and intriguing in the context of artificial intelligence (AI). In January 2019, Demis Hassabis, Founder & CEO DeepMindAI introduced with his tweet AlphaStar, the first AI to defeat a top professional player in StarCraft which is one of the most challenging Real-Time Strategy games. In a series of matches, AlphaStar beat Team Liquid's Grzegorz Komincz, one of the world's best professional StarCraft players. Demis highlighted that the technology behind Alpha Star could also be used for other applications such as weather prediction.

Leonel Garcia-Marques CICPSI Faculdade de Psicologia Universidade de Lisboa Portugal garcia_marques@sapo.pt Abstract --An agent, artificial or human, must be continuously adjusting its behaviour in order to thrive in a more or less demanding environment. An artificial agent with the ability to predict human emotional valence in a geospatial and temporal context requires proper adaptation to its mobile device environment with resource consumption strict restrictions (e.g., power from battery). The developed distributed system includes a mobile device embodied agent ( SensAI) plus Cloud-expanded ( Expanse) cognition and memory resources. The system is designed with several adaptive mechanisms in a best effort for the agent to cope with its interacting humans and to be resilient on collecting data for machine learning towards prediction. These mechanisms encompass homeostatic-like adjustments such as auto recovering from an unexpected failure in the mobile device, forgetting repeated data to save local memory, adjusting actions to a proper moment (e.g., notify only when human is interacting), and the Expanse complementary learning algorithms' parameters with auto adjustments. Regarding emotional valence prediction performance, results from a comparison study between state-of-the-art algorithms revealed Extreme Gradient Boosting on average the best model for prediction with efficient energy use, and explainable using feature importance inspection. Therefore, this work contributes with a smartphone sensing-based system, distributed in the Cloud, robust to unexpected behaviours from humans and the environment, able to predict emotional valence states with very good performance. I NTRODUCTION The scientific evidence of epigenetics reveal on/off mechanisms inside chromosomes of human agents and reinforces the importance of any entity continuous adaptation to its environment.

Fifteen years after a magnitude 9.1 earthquake and tsunami struck off the coast of Indonesia, killing more than 200,000 people in over a dozen countries, geologists are still working to understand the complex fault systems that run through Earth's crust. While major faults are easy for geologists to spot, these large features are connected to other, smaller faults and fractures in the rock. Identifying these smaller faults is painstaking, requiring weeks to study individual slices from a 3D image. Researchers at the University of Texas at Austin are shaking up the process with deep learning models that identify geologic fault systems from 3D seismic images, saving scientists time and resources. The developers used NVIDIA GPUs and synthetic data to train neural networks that spot small, subtle faults typically missed by human interpreters.

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.

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A simple device, just a heat and movement sensor attached to digital camera, has revolutionised the way that conservationists learn about animals in the wild. Camera traps are a very simple solution to the task of working out when, where and how wildlife interacts with its environment. Monitoring populations without damaging habitats, these relatively simple devices have provided some astonishing finds including revealing species previously hidden in the untouched depths of the forest. Elusive new creatures aren't their only speciality, however, as in 2015, similar devices helped reveal that the critically endangered Javan rhinoceros was breeding and significantly adding to its tiny population. After identifying a likely area for a sighting, usually with the help of local guides, traps are placed at animal height on trees and posts and left to wait until wildlife walks by.

In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.

As the transformative potential of AI has become increasingly salient as a matter of public and political interest, there has been growing discussion about the need to ensure that AI broadly benefits humanity. This in turn has spurred debate on the social responsibilities of large technology companies to serve the interests of society at large. In response, ethical principles and codes of conduct have been proposed to meet the escalating demand for this responsibility to be taken seriously. As yet, however, few institutional innovations have been suggested to translate this responsibility into legal commitments which apply to companies positioned to reap large financial gains from the development and use of AI. This paper offers one potentially attractive tool for addressing such issues: the Windfall Clause, which is an ex ante commitment by AI firms to donate a significant amount of any eventual extremely large profits. By this we mean an early commitment that profits that a firm could not earn without achieving fundamental, economically transformative breakthroughs in AI capabilities will be donated to benefit humanity broadly, with particular attention towards mitigating any downsides from deployment of windfall-generating AI.

A stunning panorama of Mars shows the final resting place of NASA's Opportunity. The image is a series of 354 individual pictures snapped by the rover over a 29-day period before it shutdown completely and declared'dead' by the American space agency earlier this year. The desolate Martian landscape known as Perseverance Valley was the last thing the rover saw and now serves as its graveyard. The panorama is composed of 354 individual images provided by the rover's Panoramic Camera (Pancam) from May 13 through June 10, or sols (Martian days) 5,084 through 5,111. The panorama combines images from three different Pancam filters, which admit light centered on wavelengths of 753 nanometers (near-infrared), 535 nanometers (green) and 432 nanometers (violet). A stunning panorama of Mars shows the final resting place of NASA's Opportunity.

KUALA LUMPUR – Disney has cut a lesbian kiss from the latest "Star Wars" movie, Singapore's media regulator said on Tuesday, so that more children can watch it. The two minor female characters embrace but do not kiss in the version of "The Rise of Skywalker" shown in Singapore, local media said, as the ninth film in the celebrated science-fiction series rakes in millions from loyal fans. "The applicant has omitted a brief scene which under the Film Classification Guidelines would require a higher rating," a spokeswoman from Singapore's Infocomm Media Development Authority said. Disney, which owns the "Star Wars" production company Lucasfilm, did not respond to a request for comment on its decision to cut the scene from the last installment of the second highest-grossing movie franchise of all time. It concludes a story that began in 1977, when filmmaker George Lucas introduced a young hero named Luke Skywalker and delighted audiences with a galaxy of robots, furry warriors known as Wookiees and a host of other eclectic characters.