Here are the slides from my York Festival of Ideas keynote yesterday, which introduced the festival focus day Artificial Intelligence: Promises and Perils. I start the keynote with Alan Turing's famous question: Can a Machine Think? and explain that thinking is not just the conscious reflection of Rodin's Thinker but also the largely unconscious thinking required to make a pot of tea. I note that at the dawn of AI 60 years ago we believed the former kind of thinking would be really difficult to emulate artificially and the latter easy. In fact it has turned out to be the other way round: we've had computers that can expertly play chess for 20 years, but we can't yet build a robot that could go into your kitchen and make you a cup of tea. In slides 5 and 6 I suggest that we all assume a cat is smarter than a crocodile, which is smarter than a cockroach, on a linear scale of intelligence from not very intelligent to human intelligence.
We live in the greatest time in human history. Only 200 years ago, for most Europeans, life was a struggle rather than a pleasure. Without antibiotics and hospitals, every infection was fatal. There was only a small elite of citizens who lived in the cities in relative prosperity. Freedom of opinion, human and civil rights were far away. Voting rights and decision-making were reserved for a class consisting of nobility, clergy, the military and rich citizens. The interests of the general population were virtually ignored.
In an earlier blog article I wrote about how human intelligence differs from artificial intelligence, namely human intelligence is general intelligence while artificial intelligence is specialized intelligence. The article provides "food for thought" for those who fear technology evolution, and specifically AI. In today's article I offer more reflections on the evolution of AI. Put in simple words, AI is about Thinking Machines. The English computer scientist Alan Turing was the first academic who proposed to consider the question "Can machines think?" in 1950.
For the most of my life, I have earned my living as a computer vision professional busy with image processing tasks and problems. In the computer vision community there is a widespread belief that artificial vision systems faithfully replicate human vision abilities or at least very closely mimic them. It was a great surprise to me when one day I have realized that computer and human vision have next to nothing in common. The former is occupied with extensive data processing, carrying out massive pixel-based calculations, while the latter is busy with meaningful information processing, concerned with smart objects-based manipulations. And the gap between the two is insurmountable. To resolve this confusion, I had had to return and revaluate first the vision phenomenon itself, define more carefully what visual information is and how to treat it properly. In this work I have not been, as it is usually accepted, biologically inspired . On the contrary, I have drawn my inspirations from a pure mathematical theory, the Kolmogorov s complexity theory. The results of my work have been already published elsewhere. So the objective of this paper is to try and apply the insights gained in course of this my enterprise to a more general case of information processing in human brain and the challenging issue of human intelligence.