Miscellaneous

Dr. Wojciech Samek discusses methods, applications and recent developments of Explainable AI, in particular, demonstrates the effectivity of explanation techniques such as Layer-wise Relevance Propagation (LRP) when applied to various datatypes (images, text, audio, video, EEG/fMRI signals) and neural architectures (ConvNets, LSTMs). LRP provides information about individual predictions, e.g., heatmaps visualizing which pixels have been most relevant for the model to arrive at its decision. The talk will finish with a discussion of challenges and open questions in the field of explainable AI.

Dr. Wojciech Samek has founded and is heading the Machine Learning Group at Fraunhofer Heinrich Hertz Institute since 2014. He studied computer science at Humboldt University of Berlin, Heriot-Watt University and University of Edinburgh from 2004 to 2010 and received the Dr. rer. nat. degree with distinction (summa cum laude) from the Technical University of Berlin in 2014.

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Machine learning and AI models can be found in nearly every aspect of society today, but their inner workings are often as much a mystery to their creators as to those who use them. UC Berkeley’s Trevor Darrell, Krishna Gade of Fiddler Labs, and Karen Myers from SRI International will discuss what we’re doing about it and what still needs to be done.

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