Connectionists: Fwd: Call for submissions: NIPS 2017 Workshop on Cognitively Informed Artificial Intelligence

[Michael Mozer]

NIPS 2017 Workshop on Cognitively Informed Artificial Intelligence
December 9, 2017 in Long Beach, CA
Workshop website: https://sites.google.com/view/ciai2017/home
Conference website: https://nips.cc/

*October 20, 2017: Deadline for contributed paper submissions*

   - Submit your contribution (in PDF format) to cognitivelyinformedAI@
   gmail.com


November 3, 2017: Notification of contributed paper acceptances

November 10, 2017: Final program announced

December 9, 2017: Workshop (Long Beach, CA)
Overview

The goal of this workshop is to bring together cognitive scientists,
neuroscientists, and AI researchers to discuss opportunities for improving
machine learning, by leveraging our scientific understanding of human
perception and cognition. There is a history of making these connections:
artificial neural networks were originally motivated by the massively
parallel, deep architecture of the brain; considerations of biological
plausibility have driven the development of learning procedures; and
architectures for computer vision draw parallels to the connectivity and
physiology of mammalian visual cortex. However, beyond these celebrated
examples, cognitive science and neuroscience has fallen short of its
potential to influence the next generation of AI systems. Areas such as
memory, attention, and development have rich theoretical and experimental
histories, yet these concepts, as applied to AI systems so far, only bear a
superficial resemblance to their biological counterparts.

The premise of this workshop is that there are valuable data and models
from cognitive science that can inform the development of intelligent
adaptive machines, and can endow learning architectures with the strength
and flexibility of the human cognitive architecture. The structures and
mechanisms of the mind and brain can provide the sort of strong inductive
bias needed for machine-learning systems to attain human-like performance.
We conjecture that this inductive bias will become more important as
researchers move from domain-specific tasks such as object and speech
recognition toward tackling general intelligence and the human-like ability
to dynamically reconfigure cognition in service of changing goals. For ML
researchers, the workshop will provide access to a wealth of data and
concepts situated in the context of contemporary ML. For cognitive
scientists, the workshop will suggest research questions that are of
critical interest to ML researchers.

The workshop will focus on three interconnected topics of particular
relevance to ML:

(1) *Learning and development*. Cognitive capabilities expressed early in a
child’s development are likely to be crucial for bootstrapping adult
learning and intelligence. Intuitive physics and intuitive psychology allow
the developing organism to build an understanding of the world and of other
agents. Additionally, children and adults often demonstrate
“learning-to-learn,” where previous concepts and skills form a
compositional basis for learning new concepts and skills.

(2) *Memory*. Human memory operates on multiple time scales, from memories
that literally persist for the blink of an eye to those that persist for a
lifetime. These different forms of memory serve different computational
purposes. Although forgetting is typically thought of as a disadvantage,
the ability to selectively forget/override irrelevant knowledge in
nonstationary environments is highly desirable.

(3) *Attention and Decision Making*. These refer to relatively high-level
cognitive functions that allow task demands to purposefully control an
agent’s external environment and sensory data stream, dynamically
reconfigure internal representation and architecture, and devise action
plans that strategically trade off multiple, oft-conflicting behavioral
objectives.

Our long-term goals are:

   - to incorporate insights from human cognition to suggest novel and
   improved AI architectures;
   - to facilitate the development of ML methods that can better predict
   human behavior; and
   - to support the development of a field of ‘cognitive computing’ that is
   more than a marketing slogan一a field that improves on both natural and
   artificial cognition by synergistically advancing each and integrating
   their strengths in complementary manners.

Organizers
Angela Yu, UCSD
Brenden Lake, NYU
Mike Mozer, U. Colorado Boulder
Contributing to the workshop

The goal of this workshop is to bring together cognitive scientists,
neuroscientists, and AI researchers to discuss opportunities for improving
machine learning, by leveraging our scientific understanding of human
perception and cognition. We have reserved time for contributed papers and
posters. We welcome submissions that present at least preliminary results.
We are specifically aiming to identify work showing that
cognitively-informed models and learning systems outperform standard AI/ML
approaches.

We will select based on (1) the depth to which cognitive principles,
theories, and models inform the system, and (2) the performance advantage
of the cognitively informed system. We encourage submissions making contact
with any area of cognition―attention, perception, development, memory,
learning from experience, judgment and decision making―which elucidate the
computational principles or mechanisms that allow people to outperform
machines, and which suggest novel approaches to solving AI challenges such
as: flexible and generalizable learning, task-dependent information
acquisition and processing, avoidance of catastrophic forgetting, and
operating subject to energy (computational efficiency) constraints.

We prefer brief submissions of up to four pages, excluding references,
formatted in NIPS style. No need to anonymize submissions. If you have a
longer manuscript already submitted and under review, you may submit the
manuscript instead. Accepted submissions will be posted on the workshop
page if the authors wish, but otherwise the submissions will be used only
for reviewing contributions.

Submit your contribution (in PDF format) to cognitivelyinformedAI at gmail.com.
Feel free to contact the organizers if you have questions about the
relevance of your research for the workshop.

*NOTE: The main NIPS 2017 conference is currently sold out (waitlist
available). A limited number of workshop registrations are still available,
so please register ASAP if you intend to contribute to or attend the
workshop. Registrations can be cancelled before Nov. 15th, 2017 for a full
refund. *
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