Here’s an IAI debate with David Chalmers, Kate Devlin, and Hilary Lawson.

In ultra-brief summary, Lawson points out that there are still things that computers perform poorly at; recognising everyday real-world objects, notably. (Sounds like a bad prognosis for self-driving cars.) Thought is a way of holding different things as the same. Devlin thinks computers can’t do what humans do yet, but in the long run, surely they will.

Chalmers points out that machines can do whatever brains can do because the brain is a machine (in a sense not adequately explored here, though Chalmers himself indicates the main objections).

There’s some brief discussion of the Singularity.

In my view, thoughts are mental or brain states that are about something. As yet, we have no clear idea of what this aboutness is and how it works, or whether it is computational (probably not, I think) or subserved by computation in a way that means it could benefit from the exponential growth in computing power (which may have stopped being exponential). At the moment, computers do a great imitation of what human translators do, but to date they haven’t even got started on real meaning, let alone set off on an exponential growth curve. Will modern machine learning techniques change that?