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.

Artificial intelligence (AI) can detect signs of existing or emerging A-fib in ECGs that exhibit normal sinus rhythm, Mayo Clinic researchers have found. Their retrospective analysis, published online yesterday in the Lancet, reports a high degree of accuracy with only one ECG, and this accuracy increases when AI is applied to multiple ECGs from the same patient. "A very common clinical scenario is that someone comes to the hospital with an ischemic stroke, and we want to know whether they have atrial fibrillation," senior author Paul A. Friedman, MD (Mayo Clinic, Rochester, MN), noted to TCTMD. "We have done previous work using neural networks, machine learning, that found that it was extremely powerful in detecting subtle patterns [in ECG tracings], and we wondered: If someone had atrial fibrillation yesterday, is there any way that it might leave a trace of a finding on an ECG today that's too subtle for a human to read but a computer could pick up?" To find out, the investigators, led by Zachi I. Attia, MSc, and Peter A. Noseworthy, MD, drew upon records in the Mayo Clinic Digital Data Vault.

-- Model free reinforcement learning suffers from the high sampling complexity inherent to robotic manipulation or locomotion tasks. Most successful approaches typically use random sampling strategies which leads to slow policy convergence. In this paper we present a novel approach for efficient exploration that leverages previously learned tasks. We exploit the fact that the same system is used across many tasks and build a generative model for exploration based on data from previously solved tasks to improve learning new tasks. The approach also enables continuous learning of improved exploration strategies as novel tasks are learned. Extensive simulations on a robot manipulator performing a variety of motion and contact interaction tasks demonstrate the capabilities of the approach. In particular, our experiments suggest that the exploration strategy can more than double learning speed, especially when rewards are sparse. Moreover, the algorithm is robust to task variations and parameter tuning, making it beneficial for complex robotic problems. I. INTRODUCTION Deep reinforcement learning has attracted a lot of attention for robotic applications where full robot models can be difficult to identify, especially for contact dynamics, and lead to computationally challenging planning and control problems.

Sudden shrieks of radio waves from deep space keep slamming into radio telescopes on Earth, spattering those instruments' detectors with confusing data. And now, astronomers are using artificial intelligence to pinpoint the source of the shrieks, in the hope of explaining what's sending them to Earth from -- researchers suspect -- billions of light-years across space. Usually, these weird, unexplained signals are detected only after the fact, when astronomers notice out-of-place spikes in their data -- sometimes years after the incident. The signals have complex, mysterious structures, patterns of peaks and valleys in radio waves that play out in just milliseconds. That's not the sort of signal astronomers expect to come from a simple explosion, or any other one of the standard events known to scatter spikes of electromagnetic energy across space. Ever since the first one was uncovered in 2007, using data recorded in 2001, there's been an ongoing effort to pin down their source.

What is it exactly that makes humans so smart? In his seminal 1950 paper, "Computer Machinery and Intelligence," Alan Turing argued human intelligence was the result of complex symbolic reasoning. Philosopher Marvin Minsky, cofounder of the artificial intelligence lab at the Massachusetts Institute of Technology, also maintained that reasoning--the ability to think in a multiplicity of ways that are hierarchical--was what made humans human. Patrick Henry Winston begged to differ. "I think Turing and Minsky were wrong," he told me in 2017. "We forgive them because they were smart and mathematicians, but like most mathematicians, they thought reasoning is the key, not the byproduct." Winston, a professor of computer science at MIT, and a former director of its AI lab, was convinced the key to human intelligence was storytelling. "My belief is the distinguishing characteristic of humanity is this keystone ability to have descriptions with which we construct stories. I think stories are what make us different from chimpanzees and Neanderthals. And if story-understanding is really where it's at, we can't understand our intelligence until we understand that aspect of it."

We live in a connected world and generate a vast amount of connected data. Social networks, financial transaction systems, biological networks, transportation systems, and a telecommunication nexus are all examples. The paper citation network displayed in Figure 1 is another example of connected data. The nodes represent research papers, while the edges illustrate citations between papers, with the various colour indicative of a report's subject, with seven colours coding seven topics. Representing connected data is possible using a graph data structure regularly used in Computer Science.