The philosophy of science has oscillated between believing that everything is knowable and that somethings will always be unknowable. In 1872, the German physiologist, Emil du Bois-Reymond declared ‘we do not know and will not know’ implying that there would always be limits to our scientific knowledge. Thirty years later, David Hilbert, a German mathematician stated that nothing is unknowable in the natural sciences. He believed that by considering some things to be unknowable we limited our ability to know. However, Kurt Godel, a Viennese mathematician who moved to Princeton in 1940, demonstrated in his incompleteness theorems that for any finite mathematical system there will always be statements which are true but unprovable and that a finite mathematical system cannot demonstrate its own consistency. I think that this implies some things will remain unknowable or at least uncertain. Godel believed that his theorems implied that the power of the human mind is infinitely more powerful than any finite machine and Roger Penrose has deployed these incompleteness theorems to argue that consciousness transcends the formal logic of computers, which perhaps implies that artificial intelligence will never replace human intelligence [see ‘Four requirements for consciousness‘ on January 22nd, 2020]. At a more mundane level, Godel’s theorems imply that engineers will always have to deal with the unknowable when using mathematical models to predict the behaviour of complex systems and, of course, to avoid meta-ignorance, we have to assume that there are always unknown unknowns [see ‘Deep uncertainty and meta-ignorance‘ on July 21st, 2021].
I suspect that artificial intelligence is somewhere near the top of the ‘Hype Curve’ [see ‘Hype cycle’ on September 23rd, 2015]. At the beginning of the year, I read Max Tegmark’s book, ‘Life 3.0 – being a human in the age of artificial intelligence’ in which he discusses the prospects for artificial general intelligence and its likely impact on life for humans. Artificial intelligence means non-biological intelligence and artificial general intelligence is the ability to accomplish any cognitive task at least as well as humans. Predictions vary about when we might develop artificial general intelligence but developments in machine learning and robotics have energised people in both science and the arts. Machine learning consists of algorithms that use training data to build a mathematical model and make predictions or decisions without being explicitly programmed for the task. Three of the books that I read while on vacation last month featured or discussed artificial intelligence which stimulated my opening remark about its position on the hype curve. Jeanette Winterson in her novel, ‘Frankissstein‘ foresees a world in which humanoid robots can be bought by mail order; while Ian McEwan in his novel, ‘Machines Like Me‘, goes back to the early 1980s and describes a world in which robots with a level of consciousness close to or equal to humans are just being introduced to the market the place. However, John Kay and Mervyn King in their recently published book, ‘Radical Uncertainty – decision-making beyond numbers‘, suggest that artificial intelligence will only ever enhance rather replace human intelligence because it will not be able to handle non-stationary ill-defined problems, i.e. problems for which there no objectively correct solution and that change with time. I think I am with Kay & King and that we will shortly slide down into the trough of the hype curve before we start to see the true potential of artificial general intelligence implemented in robots.