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Lunch Hour Lecture | Faces of the Future: AI's Journey Beyond the realm of strangeness

15 February 2024, 1:00 pm‚Äď2:00 pm

Image of a blue eye with a digital overlay

In this talk, Dr Krumhuber will give a brief historical perspective on how we have overcome the uncanny valley with AI faces that are now indistinguishable from human faces.

This event is free.

Event Information

Open to

All

Availability

Yes

Cost

Free

Organiser

¬“¬◊–„ Events

About the Lecture:

The extent to which a face appears alive or lifeless has long been a topic in psychology, with the idea that more humanlike-looking faces achieve greater familiarity until a point is reached at which subtle imperfections give a sensation of strangeness ‚Äď the uncanny valley effect. The uncanny valley effect term describes the sense of discomfort or unease we experience when we encounter a robot with certain human-like characteristics. With rapid advances in technology, AI-generated faces are now widely available and are being used for both helpful and criminal purposes, from finding missing children to transmitting political misinformation via fake social media accounts. In this talk, Dr Krumhuber will give a brief historical perspective on how we have overcome the uncanny valley with AI faces that are now indistinguishable from human faces. Also, Dr Krumhuber will present her recent work which found that White AI faces are judged as human more often than actual human faces‚ÄĒa phenomenon we term AI hyperrealism.

About the Speaker

Eva Krumhuber

Associate Professor Experimental Psychology at ¬“¬◊–„

Eva Krumhuber is associate professor in the Department of Experimental Psychology at University College London. Much of her work is concerned with the empirical investigation of the socio-cognitive and affective processes in human perception and behaviour. This includes research on facial expressions, especially morphological and dynamic features and their role in emotion interpretation. More recently, she started exploring commonalities and differences in human and machine classification of emotions, with a particular focus on how various elicitation methods (i.e., posed, spontaneous, naturalistic) influence recognition accuracy. She has published widely within the field of psychology and computer science.