for the BICA Community

siennaredwoodAI and Robotics

Feb 23, 2014 (3 years and 5 months ago)

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Technical Goals

for the BICA Community

Mark R. Waser

mailto:MWaser@BooksIntl.com

http://BecomingGaia.wordpress.com

Goal
-

2008

creating 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
-

2009

creating 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
-

2010

creating 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 challenge

2008
-

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