Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. Novelty detection is one of the classic challenges in Machine Learning and a core component of several research areas such as fraud detection, intrusion detection, medical diagnosis, data cleaning, and fault prevention. While numerous algorithms were designed to address this problem, most methods are only suitable to model continuous numerical data. Tackling datasets composed of mixed-type features, such as numerical and categorical data, or temporal datasets describing discrete event sequences is a challenging task. In addition to the supported data types, the key criteria for efficient novelty detection methods are the ability to accurately dissociate novelties from nominal samples, the interpretability, the scalability and the robustness to anomalies located in the training data. In this thesis, we investigate novel ways to tackle these issues. In particular, we propose (i) an experimental comparison of novelty detection methods for mixed-type data (ii) an experimental comparison of novelty detection methods for sequence data, (iii) a probabilistic nonparametric novelty detection method for mixed-type data based on Dirichlet process mixtures and exponential-family distributions and (iv) an autoencoder-based novelty detection model with encoder/decoder modelled as deep Gaussian processes.

In this paper, we present some theoretical work to explain why simple gradient descent methods are so successful in solving non-convex optimization problems in learning large-scale neural networks (NN). After introducing a mathematical tool called canonical space, we have proved that the objective functions in learning NNs are convex in the canonical model space. We further elucidate that the gradients between the original NN model space and the canonical space are related by a pointwise linear transformation, which is represented by the so-called disparity matrix. Furthermore, we have proved that gradient descent methods surely converge to a global minimum of zero loss provided that the disparity matrices maintain full rank. If this full-rank condition holds, the learning of NNs behaves in the same way as normal convex optimization. At last, we have shown that the chance to have singular disparity matrices is extremely slim in large NNs. In particular, when over-parameterized NNs are randomly initialized, the gradient decent algorithms converge to a global minimum of zero loss in probability.

Experience-based planning domains (EBPDs) have been recently proposed to improve problem solving by learning from experience. EBPDs provide important concepts for long-term learning and planning in robotics. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating concrete solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we extend previous work to generate a set of conditions as the scope of applicability for an activity schema. The inferred scope is a bounded representation of a set of problems of potentially unbounded size, in the form of a 3-valued logical structure, which allows an EBPD system to automatically find an applicable activity schema for solving task problems. We demonstrate the utility of our approach in a set of classes of problems in a simulated domain and a class of real world tasks in a fully physically simulated PR2 robot in Gazebo.

Bipolar Argumentation Frameworks (BAFs) admit several interpretations of the support relation and diverging definitions of semantics. Recently, several classes of BAFs have been captured as instances of bipolar Assumption-Based Argumentation, a class of Assumption-Based Argumentation (ABA). In this paper, we establish the complexity of bipolar ABA, and consequently of several classes of BAFs. In addition to the standard five complexity problems, we analyse the rarely-addressed extension enumeration problem too. We also advance backtracking-driven algorithms for enumerating extensions of bipolar ABA frameworks, and consequently of BAFs under several interpretations. We prove soundness and completeness of our algorithms, describe their implementation and provide a scalability evaluation. We thus contribute to the study of the as yet uninvestigated complexity problems of (variously interpreted) BAFs as well as of bipolar ABA, and provide the lacking implementations thereof.

Despite what Donald Trump would have you believe, the biggest factor when it comes to American employment is automation, not job theft by Mexico or China or other foreign countries that the president says "you've never even heard of." Although as John Oliver points out, Trump is the same person who reportedly pronounced Nepal and Bhutan as nipple and button, so the list of countries he's never heard of might be higher than average. Elsewhere in the segment, Oliver stopped listing fake countries long enough to explain in detail how machines are replacing jobs in some fields and how that can actually a good thing (unless you want to kill a lumberjack). He also broke the news to some kids who will probably grow up to do jobs that don't already exist, like "crypto-baker" or "snail rehydrater." Good thing that unlike "mermaid doctor," the job of "culture blogger" will never be replaced by BEEP BOOP ERROR 404.

BEN GOERTZEL: We can have no guarantee that a super intelligent AI is going to do what we want. Once we're creating something ten, a hundred, a thousand, a million times more intelligent than we are it would be insane to think that we could really like rigorously control what it does. It may discover aspects of the universe that we don't even imagine at this point. However, my best intuition and educated guess is that much like raising a human child, if we raise the young AGI in a way that's imbued with compassion, love and understanding and if we raise the young AGI to fully understand human values and human culture then we're maximizing the odds that as this AGI gets beyond our rigorous control at least it's own self-modification and evolution is imbued with human values and culture and with compassion and connection. So I would rather have an AGI that understood human values and culture become super intelligent than one that doesn't understand even what we're about.

An autonomous'robot shark' has been deployed at a Devon harbour to devour plastic waste before the tide takes it out to sea. The'Wasteshark' was designed to tackle the scourge in ocean pollution and protect the marine area's local wildlife and ecosystem. The high-tech aquadrone was released in lfracombe Harbour, the first in the UK following successful launches in five countries, including South Africa and UAE. An autonomous robot'shark' has been deployed at a Devon harbour to eat up plastic waste before the tide takes it out to sea. The'Wasteshark' was designed to tackle the scourge in ocean pollution to protect the marine area's local wildlife and ecosystems Wasteshark can'swallow' up to 60kg of debris in one trip and if running five days a week could clear 15 tons of waste from waterways every year, according to experts.

Artificial intelligence research group OpenAI last month made the unusual announcement: It had built an AI-powered content creation engine so sophisticated that it wouldn't release the full model to developers. Anyone who works in cybersecurity immediately knew why. Phishing emails, which try to trick recipients into clicking malicious links, originated 91 percent of all cyberattacks in 2016, according to a study by Cofense Inc. Combining software bots to scrape personal information from social networks and public databases with such a powerful content generation engine could produce much more persuasive phishing emails that might even mimic a certain person's writing style, said Nicolas Kseib, lead data scientist at TruSTAR Technology LLC. The potential result: Cybercriminals could launch phishing attacks much faster and on an unprecedented scale. That danger neatly sums up the never-ending war that is the state of cybersecurity today, one in which no one can yet answer a central question: Will artificial intelligence provide more help to criminals or to the people trying to stop them?

Expert human drivers perform actions relying on traffic laws and their previous experience. While traffic laws are easily embedded into an artificial brain, modeling human complex behaviors which come from past experience is a more challenging task. One of these behaviors is the capability of communicating intentions and negotiating the right of way through driving actions, as when a driver is entering a crowded roundabout and observes other cars movements to guess the best time to merge in. In addition, each driver has its own unique driving style, which is conditioned by both its personal characteristics, such as age and quality of sight, and external factors, such as being late or in a bad mood. For these reasons, the interaction between different drivers is not trivial to simulate in a realistic manner. In this paper, this problem is addressed by developing a microscopic simulator using a Deep Reinforcement Learning Algorithm based on a combination of visual frames, representing the perception around the vehicle, and a vector of numerical parameters. In particular, the algorithm called Asynchronous Advantage Actor-Critic has been extended to a multi-agent scenario in which every agent needs to learn to interact with other similar agents. Moreover, the model includes a novel architecture such that the driving style of each vehicle is adjustable by tuning some of its input parameters, permitting to simulate drivers with different levels of aggressiveness and desired cruising speeds.

Lee uses photographs as part of a large ongoing study to understand births, deaths, and the movement of more than 3,000 giraffes in East Africa. He and his team take digital photos of each animal's unique and unchanging spot patterns to identify them throughout their lives. But before pattern recognition software can process the images to identify individuals, the research team has to manually crop each photo or delineate an area of interest.