babies and computers - are they related?
DESCRIPTION
Talk presented to undergraduate Computer Science students at the University of Exeter (Feb 2009)TRANSCRIPT
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Babies and Computers
Are They Related? – Abel Nyamapfene
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Abstract:
Current opinion suggests that language is a cognitive process in
which different modalities such as perceptual entities,
communicative intentions and speech are inextricably linked. In
this talk I discuss my belief that the problems psychologists are
grappling with in child development are also the same problems
computer scientists working in artificial intelligence and robotics
are facing. I show how computational modelling, in conjunction
with the availability of empirical data, has contributed to our
understanding of child language acquisition, and how this
knowledge has advanced progress in robotics.
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Psychologist How do babies learn life skills?
How can you be as adaptive as a
baby?Computer Scientist
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Basic Computer Organisation Von Neumann Architecture
• stored program: data and programs are stored together
• sequential control: programs that are executed sequentially.
• Algorithmic: Everything to be done defined beforehand
• Program implements algorithm in computer friendly language
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Von Neumann Architecture Pros & Cons
Good for procedures that can be pre-defined before execution: e.g:• numerical
computation• Word processing • Car assembly• Precision surgery
Poor for procedures that
have to bee adapted on a
situation by situation
basis e.g:• Language processing• Pattern processing• Artificial human
assistant
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Emerging Computer Applications
• Social Interaction – caregivers – domestic – helpmates
• Intelligent weaponry
• Games
• Medicine
• Education
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Examples
humanoidsGames
Medical DiagnosticsWeapons of War
Education
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Features Common To Intelligent Computer Applications
• Computer applications still fall far short of expectations
• Applications only work well within well specified environments
• Application scalability is limited
• Processing capability has little or no incremental capability
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In Comparison:
Children come into the world with little or no cognitive
skills but exhibit developmental progression of increasing
processing power and complexity. An example is
language where children progress from no language, to
babbling, to one-word utterances, two-word utterances
and finally full adult speech – almost all the children .
What can Computing learn from Children?
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Learning from Child Development
1: Carry out Empirical Investigations of Developmental Activities
- Behavioural Investigation
- Neuroscientific Investigation
2: Use Empirical Data to develop Models of Development process
3:Assess and Incrementally Improve the Models
4:Apply knowledge to computer tasks
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Empirical Investigation:
Behavioural
• Observe developmental activity – e.g. language acquisition– Track single child from conception to stage of
full acquisition – “Keep a Diary”– Study sizeable number of children at same
stage of development– Carry out ethically approved psychological
investigations on children etc
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Empirical Investigation: Neuroscientific
Investigate:• Brain Maturation
Processes• Interaction of Brain
Regions• Interaction of
Individual Neurons
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Models of Development Based on Brain Neural Processing
Actual Neurons: Complex
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Models of Development Based on Brain Neural Processing
Artificial Neurons: Very Very Simplified
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Some Models of One-Word Child Language
“Dada” instead of “Here comes Daddy.”
“Uh oh” instead of “I am happy.”
“More” instead of “Give me some more”
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1: A multilayer perceptron network for mapping images to text (Plunkett et al, 1992).
Network by Plunkett et al simulates word – image association and exhibits same developmental learning as a child, but learning mechanism not biologically feasible
Image (input)
Image (output)
Label representation
Label (output)
Label (input)
Image representation
joint internal representation
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2: Hebbian-linked Self –Organising Architecture Li, Farkas & MacWhinney (2004)
activated neuron
Unidirectional links from Perception to Speech Neuron
LayersSecond SOM
First SOM
Unidirectional links from Speech and Perception Neuron Layers
Perceptual Input
Speech Input
Network was inspired by the belief that Brain Modules are interlinked. It successfully simulates Word-Object Mapping in children
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3: An Approach that can associate Two Input Types: - Full counterpropagation network
(Hecht-Nielsen,1987)
x input layer
x output layer
cluster layer
y input layer
y output layer
Z1
Z2
ZN
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4: Extending the Counterpropagation Approach to Modelling Child Language
(Nyamapfene &Ahmad, 2007)
Perceptual Input Speech Input
Modal
weights
Competitive Neuron layer
Intentional Input
Model based on empirical evidence that children have intentions and that brain has multimodal neurons
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I have described some investigations of child
language acquisition through:
• Physically observing infants acquiring language
• Studying relevant brain structures
• Building, testing and modifying brain inspired computer models of child language acquisition.
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Current Conclusions on Child Language
Acquisition Suggest That:
• Child language has multiple inputs that need to be processed simultaneously
• Language acquisition takes place through social interaction with caregivers
• Children have desires, have emotions, set and modify goals, monitor ongoing speech acts and generate communicative intentions which lead to speech utterances
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5: A Control-Theoretic Neural Multi-Net Model of Child Language Acquisition
(Nyamapfene, 2008)
EnvironmentDesires
Emotions
Drive
Communicative
intentionsSingle-Word
Utterance
Caregiver
response
Goals
Block diagram of a control systems approach to modelling child language
at the one-word early child language acquisition stage
Child
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From Child Development To Computing
Cynthia Breazeal has
developed Kismet, a
robot that employs drives
and emotions to interact
with a human – based
on social interaction of
an infant and a caregiver (Breazeal and Brooks, 2004)
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Current & Future Projects
• Developing a multimodal neural network model that learns from Child - directed Speech using cross-situational techniques
• Implementing the control-theoretic model of child language acquisition presented in this talk using neural multi-nets
• Migrating child work onto a robotic platform – (circa 2009 – 2010)
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Finally: Yes, I Think Babies and Computers are Related
Thank You!!??!!
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References
• C. Breazeal and R. Brooks (2004). "Robot Emotion: A Functional Perspective," In J.-M. Fellous and M. Arbib (eds.) Who Needs Emotions: The Brain Meets the Robot, MIT Press (forthcoming 2004).
• R. Hecht-Nielsen (1987). “Counterpropagation Networks,” Applied Optics 26:4979-4984.
• P. Li, I. Farkas, B. MacWhinney (2004). “Early lexical development in a self-organizing neural network,” Neural Networks 17: 1345 - 1362
• A. Nyamapfene (2008). “Computational Investigation of Early Child Language Acquisition Using Multimodal Neural Networks: A Review of Three Models,” Artificial Intelligence Review (submitted).
• A. Nyamapfene and K. Ahmad (2007). “A Multimodal Model of Child Language Acquisition at the One-Word Stage,” 20th IJCNN: International Joint Conference on Neural Networks, 12th-17th August, 2007, Orlando, Florida, USA
• K. Plunkett , C. Sinha, MF. Muller, O. Strandsby (1992). “Symbol grounding or the emergence of s symbols? Vocabulary growth in children and a connectionist net,” Connection Science 4: 293-312