Well it's interesting to note that I, and fellow panelists Murray Shanahan and Lilian Edwards, were much more cautious last week in Cheltenham, than our illustrious predecessors. Both on the question can present day robots think: answer No. And will robots (or computers) be able to think any time soon: answer, again No.
The obvious conclusion is that 50 years of Artificial Intelligence research has failed. But I think that isn't true. AI has delivered some remarkable advances, like natural speech recognition and synthesis, chess programs, conversational AI (chatbots) and lots of 'behind the scenes' AI (of the sort that figures out your preferences and annoyingly presents personalised advertising on web pages). But what is undoubtedly true was Weisner, Selfridge and Shannon were being very optimistic (after all AI had only been conceived a decade earlier by Alan Turing). Whereas today, perhaps chastened and humbled, most researchers take a much more cautious approach to these kinds of claims.
But I think there are more complex reasons.
One is that we now take a much stricter view of what we mean by 'thinking'. As I explained last week in Cheltenham, it's relatively easy to make a robot that behaves as if it is thinking (and, I'm afraid, also relatively easy to figure out that the robot is not really thinking). So, it seems that a simulation of thinking is not good enough*. We're now looking for the real thing.
That leads to the second reason. It seems that we are not much closer to understanding how cognition in animals and humans works than we were 60 years ago. Actually, that's unfair. There have been tremendous advances in cognitive neuroscience but - as far as I can tell - those advances have brought us little closer to being able to engineer thinking in artificial systems. That's because it's a very very hard problem. And, to add further complication, it remains a philosophical as well as a scientific problem.
In Cheltenham Murray Shanahan brilliantly explained that there are three approaches to solving the problem. The first is what we might call a behaviourist approach: don't worry about what thinking is, just try and make a machine that behaves as if it's thinking. The second is the computational modelling approach: try and construct, from first principles, a theoretical model of how thinking should work, then implement that. And third, the emulate real brains approach: scan real brains in sufficiently fine detail and then build a high fidelity model with all the same connections, etc, in a very large computer. In principle, the second and third approaches should produce real thinking.
What I find particularly interesting is that the first of these 3 approaches is more or less the one adopted by the conversational AI programs entered for the Loebner prize competition. Running annually since 1992, the Loebner prize is based on the test for determining if machines can think, famously suggested by Alan Turing in 1950 and now known as the Turing test. To paraphrase: if a human cannot tell whether she is conversing with a machine or another human - and it's a machine - then that machine must be judged to be thinking. I strongly recommend reading Turing's beautifully argued 1950 paper.
No chatbot has yet claimed the $100,000 first prize, but I suspect that we will see a winner sooner or later (personally I think it's a shame Apple hasn't entered Siri). But the naysayers will still argue that the winner is not really thinking (despite passing the Turing test). And I think I would agree with them. My view is that a conversational AI program, however convincing, remains an example of 'narrow' AI. Like a chess program a chatbot is designed to do just one kind of thinking: textual conversation. I believe that true artificial thinking ('general' AI) requires a body.
And hence a new kind of Turing test: for an embodied AI, AKA robot.
And this brings me back to Murray's 3 approaches. My view is that the 3rd approach 'emulate real brains' is at best utterly impractical because it would mean emulating the whole organism (of course, in any event, your brain isn't just the 1300 or so grammes of meat in your head, it's the whole of your nervous system). And, ultimately, I think that the 1st (behaviourist - which is kind of approaching the problem from the outside in) and 2nd (computational modelling - which is an inside out approach) will converge.
So when, eventually, the first thinking robot passes the (as yet undefined) Turing test for robots I don't think it will matter very much whether the robot is behaving as if it's thinking - or actually is, for reasons of its internal architecture, thinking. Like Turing, I think it's the test that matters.
*Personally I think that a good enough behavioural simulation will be just fine. After all, an aeroplane is - in some sense - a simulation of avian flight but no one would doubt that it is also actually flying.