4 research outputs found
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Whether current or near-term AI systems could be conscious is a topic of
scientific interest and increasing public concern. This report argues for, and
exemplifies, a rigorous and empirically grounded approach to AI consciousness:
assessing existing AI systems in detail, in light of our best-supported
neuroscientific theories of consciousness. We survey several prominent
scientific theories of consciousness, including recurrent processing theory,
global workspace theory, higher-order theories, predictive processing, and
attention schema theory. From these theories we derive "indicator properties"
of consciousness, elucidated in computational terms that allow us to assess AI
systems for these properties. We use these indicator properties to assess
several recent AI systems, and we discuss how future systems might implement
them. Our analysis suggests that no current AI systems are conscious, but also
suggests that there are no obvious technical barriers to building AI systems
which satisfy these indicators
Kinematical analysis of the nutation speed reducer
This paper discusses the development of a Nutating Speed Reducer (NSR) which is characterized by high reduction ratio, high tooth contact ratio, very high torque to weight/volume ratio, quiet and smooth operation under load and very high efficiency. All of these advantages are due to the presence of conjugate face-gear pairs, which incorporate each other, which called nutating/rotating gear mechanism. Details of the NSR, its kinematics, gear tooth load capacity, and mesh efficiency are explained. The NSR component speeds and speed reduction ratios of the NSR are calculated. Effect of the varying nutation angles on the geometry of the NSR is discussed and compared