Enterprise Crowdsourcing for training Artificial Intelligence
Abstract
In this lecture you will learn through practical examples about fairness, bias and ethics in artificial intelligence. The following topics are discussed:
* Different types of Artificial Intelligence
* Basics of Machine Learning
* Issues with AI and machine learning
* Expert annotation and ground truth data
* Positioning of Watson knowledge studio
* Challenges with semantics in Natural Language Processing
* Challenges with visual recognition models
* Dense image captioning
* History of the wisdom of the crowd
* Examples of crowdsourcing initiatives
* Challenges in enterprise crowdsourcing
Speaker
As lead University Programs for IBM Netherlands, my work involves research, innovation and education together with our clients, IBM Research and Universities. My expertise is in Artificial Intelligence, AI Fairness and Ethics, Human Robotics and Crowdsourcing. I studied business informatics at the HZ University of Applied Sciences, followed by a masters degree in information sciences at the Vrije Universiteit Amsterdam. I continued working at the university as a scientific researcher on open-domain question answering in IBM Watson. In 2016 I won a national prize for our education at the Vrije Universiteit about the technology behind IBM Watson. After three years I joined the IBM Center for Advanced Studies and continued my work on Artificial Intelligence and developed Dutch speech models for IBM Watson. I am leading our university activities in the Netherlands since 2019, and am a member of the Dutch AI Coalition, and both IBM’s AI and AI ethics committee in the Benelux.
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