General AI (Artificial Intelligence) is coming closer thanks to combining neural networks, narrow AI and symbolic AI. Yves Mulkers, Data strategist and founder of 7wData talked to Wouter Denayer, Chief Technology Officer at IBM Belgium, to share his enlightening insights on where we are and where we are going with Artificial Intelligence. Join us in our chat with Wouter. Yves Mulkers Hi and welcome, today we're together with Wouter Denayer, Chief Technology Officer at IBM. Wouter, you're kind of authority in Belgium and I think outside the borders of Belgium as well on artificial intelligence. Can you tell me a bit more about what you're doing at IBM and What keeps you busy? Wouter Denayer Yeah, Yves, thank you, and thanks for having me. Of course, if you call me an authority already, I think if you call yourself an authority, then something is wrong. It's almost impossible to follow everything that's going on in AI, the progress is actually amazing. I do love to follow everything that's going on as much as possible, especially focussing on what IBM Research is doing, we can come back to that later. In my role as CTO for IBM Belgium, I communicate a lot with C-level people in our strategic clients. Sometimes global clients that really want to know what's coming, what is this AI thing. People understand more or less.

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Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

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This thesis rigorously studies fundamental reinforcement learning (RL) methods in modern practical considerations, including robust RL, distributional RL, and offline RL with neural function approximation. The thesis first prepares the readers with an overall overview of RL and key technical background in statistics and optimization. In each of the settings, the thesis motivates the problems to be studied, reviews the current literature, provides computationally efficient algorithms with provable efficiency guarantees, and concludes with future research directions. The thesis makes fundamental contributions to the three settings above, both algorithmically, theoretically, and empirically, while staying relevant to practical considerations.

Learn how to deal with infrequent, rare, and unseen categories. Learn how to work with skewed variables. Learn techniques used in organizations worldwide and in data competitions. Increase your repertoire of techniques to preprocess data and build more powerful machine learning models. Learn how to deal with infrequent, rare, and unseen categories.

Selection for third-cycle studies is based on the student's potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Consideration will be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme. Technology and society is a third-cycle subject that encompasses multidisciplinary and interdisciplinary studies of technology's role, interplay and importance in different sectors of society. The position is linked to a highly interdisciplinary research project that explores how AI transparency relates to consumer trust.

Regression-based machine learning models are used to predict the value of a continuous attribute. As with all supervised machine learning problems the model is trained using a set of features (X) to learn the mapping to a target variable (y). In the case of regression, the target is a continuous variable such as the price of a house. Probably the simplest regression algorithm is linear regression. Simple linear regression, where there is only one feature and one target, is represented by the equation shown below.

In this paper we look at popular fairness methods that use causal counterfactuals. These methods capture the intuitive notion that a prediction is fair if it coincides with the prediction that would have been made if someone's race, gender or religion were counterfactually different. In order to achieve this, we must have causal models that are able to capture what someone would be like if we were to counterfactually change these traits. However, we argue that any model that can do this must lie outside the particularly well behaved class that is commonly considered in the fairness literature. This is because in fairness settings, models in this class entail a particularly strong causal assumption, normally only seen in a randomised controlled trial. We argue that in general this is unlikely to hold. Furthermore, we show in many cases it can be explicitly rejected due to the fact that samples are selected from a wider population. We show this creates difficulties for counterfactual fairness as well as for the application of more general causal fairness methods.

Bayesian optimization is a technique for optimizing black-box target functions. At the core of Bayesian optimization is a surrogate model that predicts the output of the target function at previously unseen inputs to facilitate the selection of promising input values. Gaussian processes (GPs) are commonly used as surrogate models but are known to scale poorly with the number of observations. We adapt the Vecchia approximation, a popular GP approximation from spatial statistics, to enable scalable high-dimensional Bayesian optimization. We develop several improvements and extensions, including training warped GPs using mini-batch gradient descent, approximate neighbor search, and selecting multiple input values in parallel. We focus on the use of our warped Vecchia GP in trust-region Bayesian optimization via Thompson sampling. On several test functions and on two reinforcement-learning problems, our methods compared favorably to the state of the art.