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.
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.
This paper deals with the relationship between intelligent behaviour, on the one hand, and the mental qualities needed to produce it, on the other. We consider two well-known opposing positions on this issue: one due to Alan Turing and one due to John Searle (via the Chinese Room). In particular, we argue against Searle, showing that his answer to the so-called System Reply does not work. The argument takes a novel form: we shift the debate to a different and more plausible room where the required conversational behaviour is much easier to characterize and to analyze. Despite being much simpler than the Chinese Room, we show that the behaviour there is still complex enough that it cannot be produced without appropriate mental qualities.
We never have been so closer to the future than we are now. There are news spreading across the media about the robots takeover of our jobs, driverless cars hitting the road with outstanding proficiency in driving standards, while at the same time, virtual assistants make us feel a bit less lonely telling us jokes and spending time with us. In fact, Siri, Alexa or Cortana have something machines didn't have before: a simulated human conscious capable of keep conversations with humans without being uncovered. AI is now at its most advanced development stage ever, but… do we need to worry about how smart are getting the robots? Will we ever need to?
According to the science magazine Nature, chemists are heralding a new artificial intelligence platform as a significant milestone. The platform has the potential to accelerate the process of drug discovery, and it should be able to make organic chemistry more efficient. The new platform is designed to help chemists to plan the syntheses of small organic molecules. Traditionally, chemists use the process of retrosynthesis, which is an established problem-solving technique whereby target molecules are recursively transformed into increasingly simpler precursors. The goal of retrosynthetic analysis is structural simplification.