Jose A. Hernandez

Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different for the ‘machine kingdom’. While machines can be characterised by a set of cognitive abilities, and measuring them is already a big challenge, known as ‘universal psychometrics’, a more informative, and yet more challenging, goal would be to also determine the potential cognitive abilities of a machine. In this paper we investigate the notion of potential cognitive ability for machines, focussing especially on universality and intelligence. We consider several machine characterisations (non-interactive and interactive) and give definitions for each case, considering permanent and temporal potentials. From these definitions, we analyse the relation between some potential abilities, we bring out the dependency on the environment distribution and we suggest some ideas about how potential abilities can be measured. Finally, we also analyse the potential of environments at different levels and briefly discuss whether machines should be designed to be intelligent or potentially intelligent

Mundus vult decipi, ergo decipiatur—the world wants to be deceived, so let it be deceived.Artificial intelligence has been a deceiving discipline: AI addresses those tasks that, if performed by humans, would require intelligence, but have been solved without featuring any genuine intelligence. This delusion has come, in return, with algorithmic techniques that can reliably solve many of these tasks, from game playing to pattern recognition. AI applications are a success.However, AI has not solved “what makes [us] clever, the puzzle of intelligence”. Those that expect that this book brings the answer will have their hopes deceived. This book is about what does not make us clever, a restatement of Fodor’s “The mind doesn’t work that way” . Partridge swings between condescending scepticism and bitter negativism with most of what AI has become, and reaches fierce disparagement of the technological singularity visionaries.The main message of the book is that science is stil ..

This paper defines in a formal and computational way the notion of ‘consilience’, a term introduced by Whewell in 1847 for the evaluation of scientific theories. Informally, as has been used to date, a model or theory is ‘consilient’ if it is predictive, explanatory and unifies the evide-nce. Centred in a constructive framework, where new terms can be intro-duced, we essay a formalisation of the idea of unification based on the avoidance of ‘sepa-ration’. However, it is soon manifest that this classical approach is vulnerable to the introduction of fantastic concepts to unify disparate sub-theories. Our second approach is constructed by using a detailed evaluation of the relationship between the theory and the evid-ence by means of reinforcement propagation. With the use of reinfor-cement, fantastic concepts can be better detected and the role of con-silience for theory construction and revision can be specialised for dif-ferent inference mechanisms like explanatory induction, abduction, deduc-tion and analogy.