Bishop and AI news

Stephen Hawking thinks computers may surpass human intelligence and take over the world. This view is based on the ideology that all aspects of human mentality will eventually be realised by a program running on a suitable compu...


Connection Science

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Al-Rifaie on BBC

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Rose wins the Loebne...

After 2 hours of judging at Bletchley Park, 'Rose' by Bruce Wilcox was declared the winner of the Loebner Prize 2014, held in conjunction with the AISB.  The event was well attended, film live by Sky News and the special guest jud...


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...



AISB event Bulletin Item

CALL FOR PAPERS: Computational Trade-offs in Statistical Learning, SPAIN

Computational Trade-offs in Statistical Learning NIPS 2011 Workshop, Sierra Nevada, Spain

Since its early days, the field of Machine Learning has focused on developing computationally 
tractable algorithms with good learning guarantees. The vast literature on statistical learning 
theory has led to a good understanding of how the predictive performance of different algorithms 
improves as a function of the number of training samples.
By the same token, the well-developed theories of optimization and sampling methods have yielded 
efficient computational techniques at the core of most modern learning methods. The separate 
developments in these fields mean that given an algorithm we have a sound understanding of its 
statistical and computational behavior. However, there hasn't been much joint study of the 
computational and statistical complexities of learning, as a consequence of which, little is 
known about the interaction and trade-offs between statistical accuracy and computational 
complexity. Indeed a systematic joint treatment can answer some very interesting questions: 
what is the best attainable statistical error given a finite computational budget? What is the 
best learning method to use given different computational constraints and desired statistical 
yardsticks? Is it the case that simple methods outperform complex ones in computationally 
impoverished scenarios?

The goal of our workshop is to draw the attention of machine learning researchers to this rich 
and emerging area of problems and to establish a community of researchers that are interested 
in understanding computational and statistical trade-offs. We aim to define a number of common 
problems in this area and to encourage future research.

We would like to welcome high-quality submissions on topics including but not limited to:

* Fundamental statistical limits with bounded computation
* Trade-offs between statistical accuracy and computational costs
* Computation-preserving reductions between statistical problems
* Algorithms to learn under budget constraints
* Budget constraints on other resources (e.g. bounded memory)
* Computationally aware approaches such as coarse-to-fine learning

Interesting submissions in other relevant topics not listed above are welcome too. Due to the 
time constraints, most accepted submissions will be presented as poster spotlights.

* Shai Shalev-Shwartz
* Ben Taskar

Submissions should be written as extended abstracts, no longer than 4 pages in the NIPS latex 
style. NIPS style files and formatting instructions can be found at The submissions should include the authors' name and 
affiliation since the review process will not be double blind. The extended abstract may be 
accompanied by an unlimited appendix and other supplementary material, with the understanding 
that anything beyond 4 pages may be ignored by the program committee. The papers can be submitted 
at by Oct 17, 5PM PST.
Authors will be notified on or before Nov 4.

Alekh Agarwal
Alexander Rakhlin

Lon Bottou, Olivier Chapelle , John Duchi, Claudio Gentile, John Langford, Maxim Raginsky, 
Pradeep Ravikumar, Ohad Shamir, Karthik Sridharan, David Weiss, Nati Srebro