This paper develops a new approach for estimating the internal model of an autonomous agent that can plan and act, by interrogating it. In this approach, the user may ask an autonomous agent a series of questions, which the agent answers truthfully. Our main contribution is an algorithm that generates an interrogation policy in the form of a sequence of questions to be posed to the agent. Answers to these questions are used to derive a minimal, functionally indistinguishable class of agent models. While asking questions exhaustively for every aspect of the model can be infeasible even for small models, our approach generates questions in a hierarchical fashion to eliminate large classes of models that are inconsistent with the agent. Empirical evaluation of our approach shows that for a class of agents that may use arbitrary black-box transition systems for planning, our approach correctly and efficiently computes STRIPS-like agent models through this interrogation process.

The Royal Academy of Engineering's Africa prize, now in its sixth year, is the continent's biggest award for engineering innovation. Sixteen African inventors from six countries – including, for the first time, Malawi – have been shortlisted to receive funding, training and mentoring for projects intended to revolutionise sectors ranging from agriculture and banking to women's health. The winner will be awarded £25,000 and the three runners-up will receive £10,000 each. This year's inventions include facial recognition software to prevent financial fraud, a low-cost digital microscope to speed up cervical cancer diagnosis, and two separate innovations made from water hyacinth plants. Four inventors spoke to the Guardian about their innovations and their plans to change Africa for the better.

Over the last year, my focus has been diverted from exploring analytics, new packages and blogging, to completing my dissertation. With the dissertation now complete and only final edits remaining, I had some spare time to spend on projects that I have been curating throughout the year. One such project that has been in the back of my mind for the last couple of months concern itself with with faster, scalable machine learning. This is where h2o comes in. We have been using h2o in production over the last year with great results.

As of late, a number of hot topics have arisen in data policy, notably: how to ensure data privacy for individuals; the role of government in the regulation of technology; and how best to effectively and ethically leverage big data. At the forefront of these discussions is regulation of artificial intelligence (AI). As governments race to regulate AI, they should proceed with caution and seek to balance the needs of society and the private sector. Despite its recent prevalence in public discussion, AI is not a new topic. Industry leaders, such as Jonathan Zittrain, have commented on the generative Internet and how such systems are facilitating new kinds of control. Similarly, Timnit Gebru, cofounder of Black in AI, discussed the diversity crisis facing AI systems.

This Artificial Intelligence Market research report is framed by using integrated advancements and latest technology to give the most excellent results. A method of standard market research analysis is put forth while elaborating the studies and estimations that are involved in this Artificial Intelligence Market report. Such plentiful information accompanied with deep market insights supports the decision of increasing or decreasing the production of goods depending on the general conditions of market and demand. The Artificial Intelligence Market report has a lot to offer to both established and new players in the industry with which they can completely understand the Artificial Intelligence Market. Global Artificial Intelligence Market is accounted for $15.70 billion in 2017 and is expected to reach $300.26 billion by 2026 growing at a CAGR of 38.8% during the forecast period. Artificial intelligence is an intelligence established by machines, in contrast to the natural intelligence displayed by humans and other animals.

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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