AISB convention 2017

  In the run up to AISB2017 convention, I've asked Joanna Bryson, from the organising team, to answer few questions about the convention and what comes with it. Mohammad Majid al-Rifaie (https://twitter.com/mohmaj) Tu...


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Harold Cohen

Harold Cohen, tireless computer art pioneer dies at 87   Harold Cohen at the Tate (1983) Aaron image in background   Harold Cohen died at 87 in his studio on 27th April 2016 in Encintias California, USA.The first time I hear...


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Dancing with Pixies?...

At TEDx Tottenham, London Mark Bishop (the former chair of the Society) demonstrates that if the ongoing EU flagship science project - the 1.6 billion dollar "Human Brain Project” - ultimately succeeds in understanding all as...


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Computerised Minds. ...

A video sponsored by the society discusses Searle's Chinese Room Argument (CRA) and the heated debates surrounding it. In this video, which is accessible to the general public and those with interest in AI, Olly's Philosophy Tube ...


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Connection Science

All individual members of The Society for the Study of Artificial Intelligence and Simulation of Behaviour have a personal subscription to the Taylor Francis journal Connection Science as part of their membership. How to Acce...


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Notice

AISB opportunities Bulletin Item

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

http://www.ru.nl/newstaff/working_at_radboud/details/details-vacature?pad=%2fnewstaff&recid=527875

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: 
http://www.ru.nl/newstaff/working_at_radboud/details/details-vacature?pad=%2fnewstaff&recid=527875