Scientists have found eight more mysterious repeating radio bursts emanating from deep space, which more than quadruples the known number of signals from earlier this year. The new signals were found by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) radio telescope, and give scientists a much broader data set that they hope may help finally unlock their origin. With the discovery, described in a paper submitted to The Astrophysical Journal Letters, the number of repeating radio bursts signals has climbed to 11. The new signals will aid scientists in their efforts to trace the origin and cause of mysterious radio bursts from deep space. According to Nature, the results of a separate observation from researchers in Australia have yet to be published, but bring the number of findings this month alone to nine total.

Not all voice assistants can handle the same requests. We put Siri, Alexa and Google to the test. Gone are the days when you'd write a note on a piece of paper to remind someone to do something. Instead, you now leave reminders on your smart speaker. Google Assistant devices in the U.S., U.K. and Australia will get an upgrade over the next few weeks to allow you to set reminders for other people.

Convolutional Neural Networks (CNNs) are often described as black boxes due to the difficulty in explaining how they reach their final output for a given task. Consequently a number of techniques have been developed to aid in the process of explainability. These techniques range from the scoring of individual pixels to reflect their impact on the networks decision making, to the scoring of larger regions of the image. Scoring larger regions allows for the results to be more easily interpreted. A popular technique for explaining images is LIME [10]. This uses superpixels, contiguous regions for visualisation, allowing a level of interpretability that may not be present in individual pixel scoring. However, this increased interpretability comes at a cost. The LIME technique relies on perturbing the input image and repeatedly passing it to the network to build an understanding of how important each superpixel region is to the final classification. This requires multiple perturbed images to be passed through the network, by default 1000 in the released code.

Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps people to gain more experience from mistakes and thus to perform better in similar future tasks. This paper investigates the counterfactual thinking for agents to find optimal decision-making strategies in multi-agent reinforcement learning environments. In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for agents. To this end, our model generates several possible actions (intent actions) with a parallel policy structure and estimates the rewards and regrets for these intent actions based on its current understanding of the environment. Our model incorporates a scenario-based framework to link the estimated regrets with its inner policies. During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously. To verify the effectiveness of our proposed model, we conduct extensive experiments on two different environments with real-world applications. Experimental results show that counterfactual thinking can actually benefit the agents to obtain more accumulative rewards from the environments with fair information by comparing to their opponents while keeping high performing efficiency.

Jaguar Land Rover is trailing an in-car system that changes temperature, music and lighting in response to a driver's mood. The system gauges a driver's mood with a driver-facing camera and biometric sensing, and adjusts the heating, ventilation and air conditioning, media and ambient lighting to help tackle stress and tiredness. "Personalisation settings could include changing the ambient lighting to calming colours if the system detects the driver is under stress, selecting a favourite playlist if signs of weariness are identified, and lowering the temperature in response to yawning or other signs of tiring," the company said. The systems uses AI to get to know the owners moods better over time, Jaguar Land Rover added. "In time the system will learn a driver's preference and make increasingly tailored adjustments," the company said.

Members of the public have said there is no justification for the use of facial recognition technology in CCTV systems operated by a private developer at a 67-acre site in central London. It emerged on Monday that the property developer Argent was using the cameras "in the interests of public safety" in King's Cross, mostly north of the railway station across an area including the Google headquarters and the Central Saint Martins art school, but the precise uses of the technology remained unclear. "For law enforcement purposes, there is some justification, but personally I don't think a private developer has the right to have that in a public place," said Grant Otto, who lives in London. He questioned possible legal issues around the collection of facial data by a private entity and said he was unaware of any protections that would allow people to request their information be removed from a database, with similar rights as those enshrined in GDPR. Jack Ramsey, a tourist from New Zealand, echoed his concerns. He said: "It makes you think: 'What sort of information they are trying to get from us?' Are they trialling a new system for security reasons, are they tracking every person who comes in the area – maybe for information that could be bought by the shops, like'Our customer comes here three times a week, is there a way we can target him more?'"

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.

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.

This is important, as more and more enterprises tend to make business decisions based on the user sentiment behind their products as expressed through these videos. Multimodal fusion is considered a key step in multimodal sentiment analysis. Most recent work on multimodal fusion (Poria et al., 2017; Zadeh et al., 2018c) has focused on the strategy of obtaining a multimodal representation from the independent unimodal representations. Our approach takes this strategy one step further, by also requiring that the original unimodal representations be reconstructed from the unified multimodal representation. The motivation behind this is the intuition that different modalities are an expression of the state of the mind. Hence, if we assume that the fused representation is the mind-state/sentiment/emotion, then in our approach we are ensuring that the fused representation can be mapped back to the unimodal representations, which should improve the quality of the multi-modal representation. In this paper, we empirically argue that this is the case by showing that this approach outperforms the state-of-the-art in mul-timodal fusion. We employ a variational autoencoder (V AE) (Kingma and Welling, 2014), where the encoder network generates a latent representation from the unimodal representations.

The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. In this article, we propose an algorithm that addresses these limitations. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation. Using various synthetic and real datasets, we demonstrate the wide applicability and usefulness of our algorithm, which we call the stray algorithm. We also demonstrate how this algorithm can assist in detecting anomalies present in other data structures using feature engineering. We show the situations where the stray algorithm outperforms the HDoutliers algorithm both in accuracy and computational time. This framework is implemented in the open source R package stray.