Many traditional philosophical questions take new twists in the context of intelligent machines. For example: What is a mind? What is consciousness? Where do we draw the line on responsibility for actions when dealing with robots, computers, programming? Do human beings occupy a privileged place in the universe?
Like a film critic asked if the Oscars got it right this year, one has to feel a sense of standing too close to the frame, the field of vision too narrow to provide the context necessary for proper judgment. After spending an afternoon among the various installations that comprise "Thinking Machines: Art and Design in the Computer Age: 1959–1989," I wonder if this anxiety applied to the team tasked with creating this exhibit. In this case, I think not. Here, closeness to the frame is a virtue, not a vice.
Our lives are already enhanced by AI – or at least an AI in its infancy – with technologies using algorithms that help them to learn from our behaviour. As AI grows up and starts to think, not just to learn, we ask how human-like do we want their intelligence to be and what impact will machines have on our jobs?
Materialism isn't merely inconsistent with all the empirical evidence. The ideology of materialist metaphysics also contributes to a stunted evidential base. Psychonauts from the scientific counterculture use entactogens to enrich their introspective consciousness. More radically, adopting the experimental method discloses uncharted state-spaces of consciousness that have never been recruited by natural selection for any information-signaling purpose. Drug-naïve scientific materialists are prone to dismiss such exotic states as psychotic "noise".
The tech industry's collection and digitization of huge troves of data, combined with new sets of algorithms and more powerful computing, has helped inject new energy into a machine-learning field that's been around for more than half a century. But computers are still "far off" from truly understanding what they're reading, said Michael Littman, a Brown University computer science professor who has tasked computers to solve crossword puzzles.