[Comp-neuro] 2010 Conference on Artificial General Intelligence (AGI) Call for Demonstrations

Tsvi Achler achler at uiuc.edu
Thu Nov 26 02:32:53 CET 2009

2010 Conference on Artificial General Intelligence (AGI)
Call for Demonstrations

The ability to look beyond what is learned and apply the learned
information to new scenarios distinguishes humans and animals from AI
artifacts.  The goal of the Artificial General Intelligence (AGI)
Community is to better understand these gaps.
AGI 2010 is pleased to offer an integrative demonstration track with an
opportunity to evaluate the best and most flexible AI applications.
We are also pleased to extend the paper deadline to allow researchers
an opportunity to combine papers with the demonstrations to represent
and explain their approaches in the best light.
Demonstrations should be either live computer simulations or physical
demonstrations.   Methods will be evaluated based on: (1) extent and
coverage of learning compared to (2) the number of scenarios the
methods are applicable.  Discussions will follow to form a consensus
on what constitutes the most promising strategies.
Demonstration application forms are attached and due on Jan 15, 2010.
The new paper deadline is December 1, 2009.
Please join the AGI community in our quest for general intelligence.
Any questions can be addressed to Tsvi Achler at achler at gmail.com

2010 Conference on Artificial General Intelligence (AGI)
March 5-8, Lugano, Switzerland
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AGI 2010 Demonstration Form
Complete and Return to achler at gmail.com by January 15

First Name

Last Name



Institution or Company



Briefly describe the demonstration. What is the system?  What will the audience see?

Describe the system's relevance to the AGI community.

How much computational resources are required?

Notes, Comments or Suggestions

Evaluating General Intelligence

The goal of the following experimental section is to quantify general intelligence applicability based on: the extent and coverage of learning-setup compared to the number of untrained or novel scenarios the method is applicable.  Some systems may encompass machine learning methods while others employ ontology rules.  Others may follow completely different paradigms.  Please answer the questions as best applicable.

Required resources for the system (estimate of degrees of freedom).  How many training examples, variable parameters, or ontology rules were required to implement this system?  

What (and how many) scenarios can the demonstration capture without retraining or rewriting new rules, adjusting parameters and so on?

Email achler at gmail.com
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