Researchers from the Netherlands-based Water, Peace and Security partnership (WPS) have announced the creation of a global forecasting tool that can predict where conflicts arising from water insecurity are most likely to break out. The system uses artificial intelligence to create patterns from a wide rage of geographical and socio-economic data and can identify potential conflict'hotspots' up to a year in advance. Keep an eye on the world Get Geographical's latest news delivered straight to your inbox every Friday, plus a collection of free eBooks on the subjects that matter to you! Susanne Schmeier, a senior lecturer in water law and diplomacy at the HE Delft Institute for Water Education, which leads the WPS, explains that predicting these types of conflicts isn't as simple as it might first appear. 'There is increasingly a discourse that links water scarcity and security with conflict instability,' she says, 'but at the same time our understanding is quite limited as to what actually links them.' It isn't simply a case of conflicts increasing every time a water security incident occurs.

New Delhi: E-commerce companies are focusing on artificial intelligence (AI) and virtual reality with a view to cut logistics costs and identify fraudulent orders, said a report by global auditing and consulting firm PwC. With an emerging middle-class population of more than 500 million and approximately 65% of the population aged 35 or below, India represents a highly aspirational consumer market for retailers across the globe, said the PwC TechWorld report. "E-commerce players are revamping their technology strategies to maintain their competitive edge. Most e-commerce platforms are upping their investments in areas such as conversational commerce, artificial intelligence (AI), virtual reality (VR)/augmented reality (AR) and analytics technologies," it said. It observed that to identify fraudulent orders, reduce return rate and also cut down on logistics cost, e-commerce companies are investing in robotics and AI heavily.

Here's our daily update in tweets, live from IJCAI (International Joint Conference on Artificial Intelligence) in Macau. Like yesterday, we'll be covering tutorials and workshops. Now attending the #tutorial "Argumentation and Machine Learning: When the Whole is Greater than the Sum of its Parts" by @CeruttiFederico, & learning about #ML mechanisms that create, annotate, analyze & evaluate arguments expressed in natural language.#AI Now: "Dialogues with Socially Aware Robot Agents – Knowledge & Reasoning using Natural Language," an invited #IJCAI2019 talk by Prof. Kristiina Jokinen Her start: "The quality of #intelligence possessed by humans and #AI is fundamentally different."#Bridging2019 On his second slide: #AGI "needs fresh methods with cognitive architectures and philosophy of mind."#AI

Driving a Tesla might be the closest thing we have to driving a car from the future… But turns out, thieves are not really into them, or electric cars in general. A Tesla vehicle reportedly burst into flames on the side of a Russian highway over the weekend after colliding with a tow truck, once again raising safety concerns over the automaker's semi-autonomous driving system known as Autopilot. This comes on the heels of other accidents involving Tesla vehicles on Autopilot crashing into stationary cars on the road. Reuters reported on Sunday that the Tesla Model 3 driver told local media that Autopilot was active during the crash. The driver, identified as Alexei Tretyakov, also said he was still holding the steering wheel when the incident occurred.

This paper presents a physics-based data-driven method to learn predictive reduced-order models (ROMs) from high-fidelity simulations, and illustrates it in the challenging context of a single-injector combustion process. The method combines the perspectives of model reduction and machine learning. Model reduction brings in the physics of the problem, constraining the ROM predictions to lie on a subspace defined by the governing equations. This is achieved by defining the ROM in proper orthogonal decomposition (POD) coordinates, which embed the rich physics information contained in solution snapshots of a high-fidelity computational fluid dynamics (CFD) model. The machine learning perspective brings the flexibility to use transformed physical variables to define the POD basis. This is in contrast to traditional model reduction approaches that are constrained to use the physical variables of the high-fidelity code. Combining the two perspectives, the approach identifies a set of transformed physical variables that expose quadratic structure in the combustion governing equations and learns a quadratic ROM from transformed snapshot data. This learning does not require access to the high-fidelity model implementation. Numerical experiments show that the ROM accurately predicts temperature, pressure, velocity, species concentrations, and the limit-cycle amplitude, with speedups of more than five orders of magnitude over high-fidelity models. Moreover, ROM-predicted pressure traces accurately match the phase of the pressure signal and yield good approximations of the limit-cycle amplitude.

Linear Logic and Defeasible Logic have been adopted to formalise different features of knowledge representation: consumption of resources, and non monotonic reasoning in particular to represent exceptions. Recently, a framework to combine sub-structural features, corresponding to the consumption of resources, with defeasibility aspects to handle potentially conflicting information, has been discussed in literature, by some of the authors. Two applications emerged that are very relevant: energy management and business process management. We illustrate a set of guide lines to determine how to apply linear defeasible logic to those contexts.

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations.

Manipulation and locomotion are closely related problems that are often studied in isolation. In this work, we study the problem of coordinating multiple mobile agents to exhibit manipulation behaviors using a reinforcement learning (RL) approach. Our method hinges on the use of hierarchical sim2real -- a simulated environment is used to learn low-level goal-reaching skills, which are then used as the action space for a high-level RL controller, also trained in simulation. The full hierarchical policy is then transferred to the real world in a zero-shot fashion. The application of domain randomization during training enables the learned behaviors to generalize to real-world settings, while the use of hierarchy provides a modular paradigm for learning and transferring increasingly complex behaviors. We evaluate our method on a number of real-world tasks, including coordinated object manipulation in a multi-agent setting. See videos at https://sites.google.com/view/manipulation-via-locomotion

Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems. In the final part, we present the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field.