Affective Robots: Emotionally Intelligent Machines

Automatic emotion recognition is an emerging area which leverages and combines knowledge from multiple fields such as machine learning, computer vision and signal processing. It has potential applications in many areas including healthcare, robotic assistance, education, market survey and advertising. Another usage of this information is to improve Human Computer Interaction with what can be described as Affective Computing, an interdisciplinary field that expands into otherwise unrelated fields like psychology and cognitive science. The concept of „affective robots“ refers to leveraging these emotional capabilities in humanoid robots to respond in the most appropriate way based on the user’s current mood and personality traits. In this article, we explore the emotion recognition capabilities of Pepper the robot and how they perform in contrast to other cutting-edge approaches. Weiterlesen

Causal Inference and Propensity Score Methods

In the field of machine learning and particularly in supervised learning, correlation is crucial to predict the target variable with the help of the feature variables. Rarely do we think about causation and the actual effect of a single feature variable or covariate on the target or response. Some even go so far as to say that „correlation trumps causation“ like in the book „Big Data: A Revolution That Will Transform How We Live, Work, and Think“ by Viktor Mayer-Schönberger and Kenneth Cukier. Following their reasoning, with Big Data there is no need to think about causation anymore, since nonparametric models will do just fine using correlation alone. For many practical use cases, this point of view may seem acceptable — but surely not for all.

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inovex retrospex [June 2015]

Well, turns out that collecting and curating interesting news about technology does not work out on a weekly basis when it is not your main focus. So here we go: inovex retrospex will from now on be published monthly, highlighting the most interesting news of the past 4 weeks. Weiterlesen