However, there are several practical considerations in the choice of meta-learning algorithm which can influence the computational efficiency and overall performance of MOCA. For the experiments in this paper, we leverage two meta-learning algorithms which offer a clean Bayesian learning interpretation, relatively low-dimensional posterior statistics, recursive updates for these statistics, and computationally efficient likelihood evaluation under the posterior predictive. For regression experiments, we use ALPaCA (Harrison et al., 2018); for classification experiments, we use a novel algorithm based on similar Bayesian updates which we refer to as PCOC, for probabilistic clustering for online classification. For completeness, we offer a high level overview of these algorithms and show how they fit into the MOCA framework in the following subsections.

Submodularity is one of the most well-studied properties of problem classes in combinatorial optimization and many applications of machine learning and data mining, with strong implications for guaranteed optimization. In this thesis, we investigate the role of submodularity in provable non-convex optimization and validation of algorithms. A profound understanding which classes of functions can be tractably optimized remains a central challenge for non-convex optimization. By advancing the notion of submodularity to continuous domains (termed "continuous submodularity"), we characterize a class of generally non-convex and non-concave functions -- continuous submodular functions, and derive algorithms for approximately maximizing them with strong approximation guarantees. Meanwhile, continuous submodularity captures a wide spectrum of applications, ranging from revenue maximization with general marketing strategies, MAP inference for DPPs to mean field inference for probabilistic log-submodular models, which renders it as a valuable domain knowledge in optimizing this class of objectives. Validation of algorithms is an information-theoretic framework to investigate the robustness of algorithms to fluctuations in the input/observations and their generalization ability. We investigate various algorithms for one of the paradigmatic unconstrained submodular maximization problem: MaxCut. Due to submodularity of the MaxCut objective, we are able to present efficient approaches to calculate the algorithmic information content of MaxCut algorithms. The results provide insights into the robustness of different algorithmic techniques for MaxCut.

Many inferential and learning tasks can be accomplished efficiently by means of distributed optimization algorithms where the network topology plays a critical role in driving the local interactions among neighboring agents. There is a large body of literature examining the effect of the graph structure on the performance of optimization strategies. In this article, we examine the inverse problem and consider the reverse question: How much information does observing the behavior at the nodes convey about the underlying network structure used for optimization? Over large-scale networks, the difficulty of addressing such inverse questions (or problems) is compounded by the fact that usually only a limited portion of nodes can be probed, giving rise to a second important question: Despite the presence of several unobserved nodes, are partial and local observations still sufficient to discover the graph linking the probed nodes? The article surveys recent advances on this inverse learning problem and related questions. Examples of applications are provided to illustrate how the interplay between graph learning and distributed optimization arises in practice, e.g., in cognitive engineered systems such as distributed detection, or in other real-world problems such as the mechanism of opinion formation over social networks and the mechanism of coordination in biological networks. A unifying framework for examining the reconstruction error will be described, which allows to devise and examine various estimation strategies enabling successful graph learning. The relevance of specific network attributes, such as sparsity versus density of connections, and node degree concentration, is discussed in relation to the topology inference goal. It is shown how universal (i.e., data-driven) clustering algorithms can be exploited to solve the graph learning problem.

We present an innovative methodology for studying and teaching the impacts of AI through a role - play game. The game serves two primary purposes: 1) training AI developers and AI policy professionals to reflect on and prepare for future social and ethical challenges related to AI and 2) exploring possible futures involving AI technology developm ent, deployment, social impacts, and governance. While the game currently focuses on the inter - relations between short -, mid - and long - term impacts of AI, it has potential to be adapted for a broad range of scenarios, exploring in greater depths issues of AI policy research and affording training within organizations. The game presented here has undergone two years of development and has been tested through over 30 events involving between 3 and 70 participants. The game is under active development, but pre liminary findings suggest that role - play is a promising methodology for both exploring AI futures and training individuals and organizations in thinking about, and reflecting on, the impacts of AI and strategic mistakes that can be avoided today.

Artificial intelligence is considered as a key technology. It has a huge impact on our society. Besides many positive effects, there are also some negative effects or threats. Some of these threats to society are well-known, e.g., weapons or killer robots. But there are also threats that are ignored. These unknown-knowns or blind spots affect privacy, and facilitate manipulation and mistaken identities. We cannot trust data, audio, video, and identities any more. Democracies are able to cope with known threats, the known-knowns. Transforming unknown-knowns to known-knowns is one important cornerstone of resilient societies. An AI-resilient society is able to transform threats caused by new AI tecchnologies such as generative adversarial networks. Resilience can be seen as a positive adaptation of these threats. We propose three strategies how this adaptation can be achieved: awareness, agreements, and red flags. This article accompanies the TEDx talk "Why we urgently need an AI-resilient society", see https://youtu.be/f6c2ngp7rqY.

In this work, we propose a novel OeSNN-UAD (Online evolving Spiking Neural Networks for Unsupervised Anomaly Detection) approach for online anomaly detection in univariate time series data. Our approach is based on evolving Spiking Neural Networks (eSNN). Its distinctive feature is that the proposed eSNN architecture learns in the process of classifying input values to be anomalous or not. In fact, we offer an unsupervised learning method for eSNN, in which classification is carried out without earlier pre-training of the network with data with labeled anomalies. Unlike in a typical eSNN architecture, neurons in the output repository of our architecture are not divided into known a priori decision classes. Each output neuron is assigned its own output value, which is modified in the course of learning and classifying the incoming input values of time series data. To better adapt to the changing characteristic of the input data and to make their classification efficient, the number of output neurons is limited: the older neurons are replaced with new neurons whose output values and synapses' weights are adjusted according to the current input values of the time series. The proposed OeSNN-UAD approach was experimentally compared to the state-of-the-art unsupervised methods and algorithms for anomaly detection in stream data. The experiments were carried out on Numenta Anomaly Benchmark and Yahoo Anomaly Datasets. According to the results of these experiments, our approach significantly outperforms other solutions provided in the literature in the case of Numenta Anomaly Benchmark. Also in the case of real data files category of Yahoo Anomaly Benchmark, OeSNN-UAD outperforms other selected algorithms, whereas in the case of Yahoo Anomaly Benchmark synthetic data files, it provides competitive results to the results recently reported in the literature.

Eivind Meyer is currently working on his Master's thesis, completing his five-year integrated Master's degree in Cybernetics and Robotics at the Norwegian University of Science and Technology (NTNU) in Trondheim. Having specialized in Real Time Systems, his research interests focus on adopting state-of-the-art Artificial Intelligence methods for Autonomous Vehicle Control. Haakon Robinson is a PhD candidate at the Norwegian University of Science and Technology (NTNU). He received a Bachelors degree in Physics in 2015 and completed a Masters degree in Cybernetics and Robotics in 2019, both at NTNU. His current work investigates the overlap between modern machine learning techniques and established methods within modelling and control, with a focus on improving the interpretability and be-E Meyer et al.: Preprint submitted to Elsevier Page 15 of 16 Taming an ASV for path following and collision avoidance using DRL havioural guarantees of hybrid models that combine first principle models and data-driven components.

Python is rapidly becoming the world's most popular programming language and its versatility has enabled it to achieve widespread adoption in finance, becoming the multipurpose tool of choice for quantitative analysts and other financial technologists. Join us for this hands-on two day training course led by Harsh Prasad that will provide you with a best practice framework for using Python for machine learning implementation. In-depth sessions will cover all the key elements of Python including classification, clustering, deep learning and natural language processing. Attendees are encouraged to share their specific challenges, discover solutions and network with each other in this open and discussion based learning environment.

SAN FRANCISCO, Dec. 17, 2019 (GLOBE NEWSWIRE) -- Holberton School, the two-year tuition-deferred college alternative educating the next generation of digital workers, announced the launch of their brand new Machine Learning curriculum which will be available at all eight world-wide Holberton campuses. The announcement was made at the flagship San Francisco campus featuring Grammy award-winner NE-YO, Black Girls Code founder and CEO Kimberly Bryant and representatives from Google (Tensorflow) and IBM. "Machine Learning, and by extension Artificial Intelligence, are increasingly dominating how we interact with technology at all levels, and the need for diversity has never been so urgent," said Gabriela de Queiroz, founder, AI Inclusive and R-Ladies. "Having programming skills isn't enough -- we need people who are aware of the ethical implications of AI, who can bring their diverse backgrounds, experiences, and perspectives to the workplace and incorporate them into the algorithms that will increasingly play a major role in healthcare, safety, and every other element of our lives." Machine Learning, which gives computers the capability to learn without being explicitly programmed, is already in use across the globe and is rapidly supplementing, and even replacing, traditional software development.

This article was written by Manav Subodh, co-founder of 1M1B and global senior fellow at the Innovation Acceleration Group, University of California, Berkeley. Artificial intelligence (AI) is no longer a technology of the future – it is well and truly here. In many ways, it is already shaping human interactions by getting out of research labs and entering the real world. And it is changing the world as we know it. It could not be more apparent that AI can change the world for the better – from creating new healthcare solutions to designing hospitals of the future, improving farming and food supply, helping refugees acclimatise to new environments, enhancing educational resources and access, and even cleaning our oceans, air and water supply.