As the No Free Lunch theorem formally states [1], algorithms for detecting communities in networks must make tradeoffs. In this work, we present a method for using metadata to inform tradeoff decisions. We extend the content map equation, which adds metadata entropy to the traditional map equation, by introducing a tuning parameter allowing for explicit specification of the metadata's relative importance in assigning community labels. On synthetic networks, we show how tuning for node metadata relates to the detectability limit, and on empirical networks, we show how increased tuning for node metadata yields increased mutual information with the metadata at a cost in the traditional map equation. Our tuning parameter, like the focusing knob of a microscope, allows users to "zoom in" and "zoom out" on communities with varying levels of focus on the metadata.

Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We design a task-specific evaluation for a question answering task and evaluate how well a model interpretation improves human performance in a human-machine cooperative setting. We evaluate interpretation methods in a grounded, realistic setting: playing a trivia game as a team. We also provide design guidance for natural language processing human-in-the-loop settings.

SAP has expanded its SAP Leonardo Machine Learning platform to enable developers and data scientists to create more sophisticated artificial intelligence (AI) tools and drive business value. The updates come alongside a new investment in intelligent robotic process automation (RPA) intended to help eliminate time-intensive manual tasks across the firm's portfolio, as noted in a Tuesday press release.

What's the last snack you had a work? Chances are it wasn't particularly healthy. The food people eat during the workday tends to contain high amounts of sodium and refined grain, according to a recent survey by the Center for Disease Control. Unsurprisingly, it's loaded with empty calories -- the average worker consumes roughly 1,300 calories each week in snacks alone. That's why Stanford graduate and former Microsoft product manager Michael Heinrich founded Oh My Green, a San Francisco-based provider of food and wellness services that leverages artificial intelligence and wireless sensors to help corporate employees make healthier choices.

The way we look at learning and education has been changing in recent times. Newer subjects like artificial intelligence are gaining more popularity as technology evolves at a rapid pace. However, getting the first-hand experience and understanding these newly developing subjects becomes extremely difficult for professionals. Machine learning and artificial intelligence are enablers that increase our productivity, but what is the best way to learn these new and evolving topics? UpGrad's PG program in Machine Learning and Artificial Intelligence in collaboration with IIIT-Bangalore has been ranked No 1 by Analytics India Magazine.

Third-grade students at the Douglass G Grafflin School in Chappaqua, New York, participate in an interactive learning session with the Nintendo Labo: Variety Kit for the Nintendo Switch system, led by Rebecca Rufo-Tepper, Co-Executive Director of the Institute of Play. Nintendo Labo is going to school. The video game maker is teaming up with the non-profit Institute of Play to bring its Do-It-Yourself (DIY) Labo cardboard gaming kits to 100 elementary schools around the country. "Our goal is to help teachers and students have fun with the basic principles of science, technology, engineering, art and mathematics, collectively known as STEAM," says Nintendo of America President and COO Reggie Fils-Aime. "We believe Nintendo Labo can be a powerful learning tool to foster 21st Century skills such as communication, collaboration, creativity, critical thinking and problem solving."

This article was originally published on OpenDataScience.com. Let's see if we can start down that direction by laying the groundwork for the very fundamentals of our understanding of machine learning – namely, what actually constitutes a machine learning problem? It's kind of strange question that you might think you know the answer to, but it actually has a very formal definition that we'll outline here. The most important step you can take is to start by asking yourself: do I think there's a pattern? The fundamental assumption that underlies all machine learning problems is that there is a pattern.

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This paper investigates the adversarial Bandits with Knapsack (BwK) online learning problem, where a player repeatedly chooses to perform an action, pays the corresponding cost, and receives a reward associated with the action. The player is constrained by the maximum budget $B$ that can be spent to perform actions, and the rewards and the costs of the actions are assigned by an adversary. This problem has only been studied in the restricted setting where the reward of an action is greater than the cost of the action, while we provide a solution in the general setting. Namely, we propose EXP3.BwK, a novel algorithm that achieves order optimal regret. We also propose EXP3++.BwK, which is order optimal in the adversarial BwK setup, and incurs an almost optimal expected regret with an additional factor of $\log(B)$ in the stochastic BwK setup. Finally, we investigate the case of having large costs for the actions (i.e., they are comparable to the budget size $B$), and show that for the adversarial setting, achievable regret bounds can be significantly worse, compared to the case of having costs bounded by a constant, which is a common assumption within the BwK literature.

We study online learning when partial feedback information is provided following every action of the learning process, and the learner incurs switching costs for changing his actions. In this setting, the feedback information system can be represented by a graph, and previous work provided the expected regret of the learner in the case of a clique (Expert setup), or disconnected single loops (Multi-Armed Bandits). We provide a lower bound on the expected regret in the partial information (PI) setting, namely for general feedback graphs ---excluding the clique. We show that all algorithms that are optimal without switching costs are necessarily sub-optimal in the presence of switching costs, which motivates the need to design new algorithms in this setup. We propose two novel algorithms: Threshold Based EXP3 and EXP3.SC. For the two special cases of symmetric PI setting and Multi-Armed-Bandits, we show that the expected regret of both algorithms is order optimal in the duration of the learning process with a pre-constant dependent on the feedback system. Additionally, we show that Threshold Based EXP3 is order optimal in the switching cost, whereas EXP3.SC is not. Finally, empirical evaluations show that Threshold Based EXP3 outperforms previous algorithm EXP3 SET in the presence of switching costs, and Batch EXP3 in the special setting of Multi-Armed Bandits with switching costs, where both algorithms are order optimal.