Anna Ridler (b. 1985, UK) is an artist and researcher. She has exhibited at institutions such as the V&A Museum, Ars Electronica, HeK Basel, Impakt and the Barbican Centre and has degrees from the Royal College of Art, Oxford University and University of Arts London. She was a 2018 EMAP fellow and was listed by Artnet as one of nine “pioneering artists” exploring AI’s creative potential. She is interested in working with collections of information, particularly self-generated data sets, to create new and unusual narratives in a variety of mediums, and what happens when things cannot fit into discrete categories. She is currently interested in the intersection of machine learning and nature and what we can learn from history.
Dr. Chris Welty is a Sr. Research Scientist at Google in New York, and an Endowed Professor of Cognitive Systems at the VU University, Amsterdam. His main area of interest is the interaction between structured knowledge (e.g. freebase), unstructured knowledge (e.g. natural language text), and human knowledge (e.g. crowdsourcing). His latest work focuses on understanding the continuous nature of truth in the presence of a diversity of perspectives, and he has been working with the google maps team to better understand user contributions that often disagree.
University of Amsterdam
In the field of cultural heritage, artificial intelligence technologies are increasingly adopted to extract and interpret the ‘Big Data of the Past’ – the historical information that forms the basis of a society’s collective memory and identity. At the same time, these technologies still struggle with the complex nature of historical data, in particular when it comes to interpreting its meaning and relevance. Hence, the implementation of such computational techniques is often combined with facilities for engaging human intelligence, in the form of crowdsourcing or citizen science projects. This lecture reflects on the opportunities of AI and Citizen Science workflows for accessing, interpreting and reusing cultural heritage data, assessing the extent to which these solutions are responsive to the complexity of human culture and meaningful to its users. A range of examples from the practice of (audiovisual) archives will be discussed, including human-machine workflows for transcribing and understanding written and spoken text, analyzing images and generating new objects at the Amsterdam City Archives, EYE Film Museum, BBC, and the Netherlands Institute for Sound and Vision. Areas of use include the cultural heritage sector itself (European Time Machine project), the creative industries (AI generated compilation films at EYE and BBC) and digital humanities research (human-computer workflows in the CLARIAH Media Suite). Building on the history of ideas about how computational technologies can be assistive to human intelligence, I will argue we need to design frameworks for an ‘amplified intelligence’, a collaboration between humans and machines that is responsive to the cultural values our archives, museums and libraries uphold.
Julia Noordegraaf is professor of Digital Heritage in the department of Media Studies at the University of Amsterdam. Within the Faculty of Humanities she acts as director of the digital humanities research program and lab Creative Amsterdam (CREATE) that studies the history of urban creativity using digital data and methods. Noordegraaf’s research focuses on the preservation, exhibition and reuse of audiovisual and digital cultural heritage. She has published, amongst others, the monograph Strategies of Display (2004/2012) and, as principal editor, Preserving and Exhibiting Media Art (2013) and acts as principal editor of the Cinema Context database on Dutch film culture. She currently leads research projects on the conservation of digital art (in the Horizon 2020 Marie Curie ITN project NACCA) and on the reuse of digital heritage in data-driven historical research (besides CREATE in the project Virtual Interiors as Interfaces for Big Historical Data Research). She is a former fellow of the Netherlands Institute for Advanced Study in the Humanities and Social Sciences and acts as board member for Media Studies in CLARIAH, the national infrastructure for digital humanities research, funded by the Netherlands Organization for Scientific Research, NWO. Noordegraaf currently coordinates the realization of the Amsterdam Time Machine and acts as Vice President of the European Time Machine Organization that aims to build a simulator for 5.000 years of European history.
Mounia Lalmas is a Director of Research at Spotify, and the Head of Tech Research in User Engagement, where she leads an interdisciplinary team of research scientists in Boston, London, New York and San Francisco, working on personalization and discovery. Mounia also holds an honorary professorship at University College London. Her work focuses on studying user engagement in areas such as native advertising, digital media, social media, search, and now music.
Visipedia is a network of people and machines designed to harvest and organize visual information and make it accessible to anyone anywhere. I will explore technical challenges arising from Visipedia and discuss their implications for computer vision, machine learning, human-machine systems and visual psychology. I will discuss the case study, an automated field guide to the birds of North America. Key contributions include a method for characterizing the multidimensional wisdom of crowdworkers and a classification pipeline combining input from humans and machines. I will conclude by discussing several open issues encompassing algorithm development to community engagement.
Dr. Pietro Perona is the Allen E. Puckett Professor of Electrical Engineering at Caltech. He directs Computation and Neural Systems (www.cns.caltech.edu), a PhD program centered on the study of biological brains and intelligent machines. Professor Perona’s research centers on vision. He has contributed to the theory of partial differential equations for image processing and boundary formation, and to modeling the early visual system’s function. He is currently interested in visual categories and visual recognition.
We welcome everyone who is interested in crowdsourcing and human computation to: