18-24 November – A week of hand-on sessions and invited speakers in the industry framework!
The workshop started with Eric Bolo from Batvoice. Bolo presented some projects of the company, an example of the workflow, and FAQs for the recruitment process of a data scientist in a company that provides machine learning solutions.
Brad Hayes gave a talk about Explainable AI defining different types of systems and levels of explainability (opaque, comprehensible, interpretable). He also introduced the kind of information a collaborative system should provide and how that information should vary in relation to the human observer.
During the panel discussion with Brad Hayes, Kathleen Richardson, Joost Broekers and Marie Chamoux , we had the occasion to discuss about ethical issues, the importance of the definitions we use in research to identify our work and what are the intersections between industry and academia.
Furthermore, we had to train a model for image recognition, test the new Android API for Pepper, and present our Learning task to the speakers for collecting feedback from them about our work.