Elemental cognition aims to articulate better communication between humans and machines, which involves intellectual engagement and continuous dialogue. One of the challenges in achieving human-like artificial intelligence is the amount of physical features that constitute input from our bodies.
Understanding human intelligence and emotions would be the fascinating result of building truly human-like intelligence. AGI may refer to AI that's smarter than humans, but distinct from human-like intelligence which requires emotions and consciousness for behavior generation.
The field of explainable AI aims to make AI systems capable of telling humans why they succeed or fail, rather than just providing optimal solutions. This could lead to AI systems becoming better storytellers in the future.
The future of Artificial Intelligence lies in creating systems that are deeply socially embedded, as opposed to treating AI as subservient to humans. Integrating AI systems with platforms like Twitter could allow for true creativity and enriched interactions.
The kindness of individuals could lead to better futures in societies where resources are strained. However, AI systems that pass Turing Tests may be exceptionally good liars, which poses a potential danger.
Judea Pearl discusses how demonstrating the generalization abilities of a system through the process of interaction and game theoretic can show the creation of machine intelligence. He also explains why being indistinguishable from a human is not the ultimate goal in AI development and how the Turing test can be counterproductive.