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Technical Goals for the BICA Community
Mark R. Wasermailto:MWaser@BooksIntl.com
http://BecomingGaia.wordpress.com
Goal - 2008creating a computational equivalent of the
natural mind in its higher cognitive abilities
Specific topics include • cognitive architectures inspired by the brain, • constraints borrowed from biology, • human-like learning and self-sustained cognitive
growth, • self-regulated learning assistance, • natural language acquisition, • emotional and social intelligence, • metrics and • a roadmap to solving the challenge.
Goal - 2009creating a real-life computational equivalent
of the human mind
Specific topics include • Bridging the gap between AI and biology: robustness, flexibility, integrity• BICA models of learning: bootstrapped, self-regulated (SRL), meta-
learning• Scalability, limitations and ‘critical mass’ of human-like learning• Biological constraints vital for learning• Physical support of conscious experience• Formal theory of cognitive architectures • Emotional feelings and values in artifacts • Measuring minds of machines and humans
Subgoals – 2008 & 2009 Part I.
cognitive architectures inspired by the brain
Formal theory of cognitive architectures
constraints borrowed from biology
Biological constraints vital for learning
human-like learning and self-sustained cognitive growth
self-regulated learning assistance
BICA models of learning: bootstrapped, self-regulated (SRL),
meta-learning
natural language acquisition
(NONE)
Subgoals – 2008 & 2009 Part II.
emotional and social intelligence
Physical support of conscious experience
Emotional feelings and values in artifacts
metrics
Measuring minds of machines and humans
a roadmap to solving the challenge
Bridging the gap between AI & biology: robustness, flexibility, integrity
Scalability, limitations and ‘critical mass’ of human-like learning
Goal - 2010creating a real-life computational equivalent
of the human mind
four schools of thought:
(1) computational neuroscience, that tries to understand how the brain works in terms of connectionist models;
(2) cognitive modeling, pursuing higher-level computational description of human cognition;
(3) human-level artificial intelligence, aiming at generally intelligent artifacts that can replace humans at work; and
(4) human-like learners: artificial minds that can be understood by humans intuitively, that can learn like humans, from humans and for human needs.
Subgoals – 2008-2010 Part I.
computational neuroscience (connectionist modeling)
cognitive architectures (low-level)
biological constraints (low-level) ???
cognitive modeling
cognitive architectures (high-level)
biological constraints (high-level)
human-level artificial intelligence (that can replace humans at work)
human-like learners/human-like artificial minds
human-like learning
natural language acquisition
emotional and social intelligence
Subgoals – 2008-2010 Part II.
metrics
a roadmap to solving the challenge2008 - creating a computational equivalent of the
natural mind in its higher cognitive abilities
2009-2010 - creating a real-life computational
equivalent of the human mind
(human-level AGI)
(human-like AGI+)
safety!
Toward a Comparative Repository of Cognitive Architectures, Models, Tasks and Data
•Introduction (discussion panel agenda - by Christian Lebiere)
First Step: Comparative Table of Cognitive Architectures
•Current comparative table: HTML | XLS | PDF
• Old comparative table - from Pew & Mavor, 1998
Complementary Frameworks for Comparison (4)
Related Sites (3)
What Is Our Goal?
A united working community dedicated to a specific common goal (2008 or 2010?)
– OR –What Do We Want To Be?
A social networking community dedicated to sharing/collecting information and recruiting
Thursday, November 5, 4:00 pm – 5:45 pm, Westin Arlington Gateway
Hotel 1
AAAI 2009 Fall Symposium Series
Arlington, Virginia – November 5 7, 2009‐
Panel Discussion:Comparative Repository of
Architectures, Models, Tasks and Data
Chair: Christian Lebiere
Objective
To identify the necessary means to achieve greater rates of convergence and incremental progress in cognitive modeling through the use of a shared repository of computational cognitive architectures, models, tasks and data.
Why do we need a repository?
2. To provide a centralized resource, that modelers, students, and teachers can access when they want to start a modeling research project.3. To have an immediate and organized way to access an overview of relevant information.4. To enable the reuse of models.5. To encourage the development of modeling tools and standards.
How are we going to spread it?
1. To facilitate direct comparison of different architectures.
How are we going to make it work?Uploading tasks and code as currently existing is not enough. The following issues should be considered.
1. A standard API between cognitive architectures and task simulation environments is needed to assure portability across tasks and models.
2. Models need to be updated and kept current.
3. Infrastructure funding should be provided by some source,
4. Before proceeding with the implementation, some informal polls or surveys should be taken to study the modelers’ habits and needs