AISB Convention 2015

The AISB Convention is an annual conference covering the range of AI and Cognitive Science, organised by the Society for the Study of Artificial Intelligence and Simulation of Behaviour. The 2015 Convention will be held at the Uni...


Yasemin Erden on BBC

AISB Committee member, and Philosophy Programme Director and Lecturer, Dr Yasemin J. Erden interviewed for the BBC on 29 October 2013. Speaking on the Today programme for BBC Radio 4, as well as the Business Report for BBC world N...


Mark Bishop on BBC ...

Mark Bishop, Chair of the Study of Artificial Intelligence and the Simulation of Behaviour, appeared on Newsnight to discuss the ethics of ‘killer robots’. He was approached to give his view on a report raising questions on the et...


AISB YouTube Channel

The AISB has launched a YouTube channel: ( The channel currently holds a number of videos from the AISB 2010 Convention. Videos include the AISB round t...


Lighthill Debates

The Lighthill debates from 1973 are now available on YouTube. You need to a flashplayer enabled browser to view this YouTube video  



AISB opportunities Bulletin Item

PhD student position in "Computationally realistic architectures for a Bayesian brain", Nijmegen, THE NETHERLANDS

PhD position 'Computationally realistic architectures for a Bayesian brain'

Faculty of Social Sciences
Vacancy number: 24.21.13
Closing date: 14 July 2013

This PhD project aims to advance our understanding of the computational 
foundations of probabilistic inference and learning in the brain. 
According to current theory, even only approximately computing 
probabilistic inferences is computationally intractable for situations 
of real-world complexity. This is in marked contrast to the efficiency 
of inference and learning as done by the brain in practice. The 
objective of the project is to resolve this paradox by developing a new 
theory that explains the efficiency of inference and learning as done by 
the brain in practice. Using an innovative approach that combines formal 
modeling, parameterized complexity analysis and computer simulation, we 
aim to identify parameters of a computational architecture that can make 
a probabilistic brain computationally efficient. The project will 
furthermore involve conceptual (philosophical) analysis to derive the 
implications of this new theory for current debates in the philosophy of 
cognitive science.

For more details about the position and its conditions, see: