DAY 2 Recordings

24 MARCH 2022

PLEASE NOTE THAT OWING TO COPYRIGHT OR INTELLECTUAL PROPERTY PERMISSIONS WE ARE UNABLE TO SHARE RECORDINGS OF SOME SESSIONS

VIDEO: DAY 1 SUMMARY: Arsen Abdulali (University of Cambridge)

VIDEO: DAY 2 MORNING SESSION SUMMARY: Josie Hughes (EPFL)

VIDEO: DAY 2 CLOSING PANEL DISCUSSION

PLENARY TALKS

KOHEI NAKAJIMA (University of Tokyo)

VIDEO: PHYSICAL RESERVOIR COMPUTING AND ITS RELEVANCE TO EMBODIED INTELLIGENCE

Abstract: Input-driven dynamical systems can be viewed as information processing devices, and reservoir computing (RC) is one of the recent approaches that can explore this perspective in practice. Because of its generic nature, RC is not limited to digital simulations of neural networks, and any high-dimensional dynamical system can serve as a reservoir if it has the appropriate properties. The approach using a physical entity rather than abstract computational units as a reservoir is called physical reservoir computing (PRC). In this presentation, through a number of examples, we will explore how the RC/PRC framework can provide a novel view of embodied intelligence and soft robotics.

JOANNA BRYSON (Hertie School of Governance)

VIDEO: SOCIAL COMMUNICATION OF MEANING TO AI

Abstract: Is semantic meaning derived only from direct empirical experience of the world, or can it be transmitted between agents and co-constructed as a community? I will suggest neither of these is entirely true. Rather semantic meaning must be socially constructed. Language is by its nature a collaborative process of reifying the most useful concepts for a particular society for their collaborative opportunities. This talk can be seen as starting from our 2017 paper on the presence of human implicit biases in AI, which also demonstrates those bias’s origins in our lived experience. From this I consider the impacts of embodied experience on human and artificial intelligence. I also discuss briefly implications for ethics.

ALLISON OKAMURA (Stanford university)

VIDEO: UNDERSTANDING AND DESIGNING FOR HUMAN HAPTIC INTELLIGENCE

Abstract: Haptic devices allow touch-based information transfer between humans and intelligent systems, enabling communication in a salient but private manner that frees other sensory channels. For such devices to become ubiquitous, their physical and computational aspects must be intuitive and unobtrusive. The amount of information that can be transmitted through touch is limited in large part by the location, distribution, and sensitivity of human mechanoreceptors. Not surprisingly, many haptic devices are designed to be held or worn at the highly sensitive fingertips, yet stimulation using a device attached to the fingertips precludes natural use of the hands. Thus, we explore the design of a wide array of haptic feedback mechanisms, ranging from devices that can be actively touched by the fingertips to multi-modal haptic actuation mounted on the arm. We demonstrate how these devices are effective in virtual reality, human-machine communication, and human-human communication.