Babies and Computers

1
Babies and Computers

• Are They Related? Abel Nyamapfene

2
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.
3
How do babies learn life skills?
Psychologist
How can you be as adaptive as a baby?
Computer Scientist
4
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

5
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

6
Emerging Computer Applications

• Social Interaction
• caregivers
• domestic
• helpmates
• Intelligent weaponry
• Games
• Medicine
• Education

7
Examples
Games
humanoids
Medical Diagnostics
Education
Weapons of War
8
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

9
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?
10
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
• 3Assess and Incrementally Improve the Models
• 4Apply knowledge to computer tasks

11
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

12
Empirical Investigation Neuroscientific

• Investigate
• Brain Maturation Processes
• Interaction of Brain Regions
• Interaction of Individual Neurons

13
Models of Development Based on Brain Neural
Processing
Actual Neurons Complex
14
Models of Development Based on Brain Neural
Processing
Artificial Neurons Very Very Simplified
15
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
16
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
17
2 Hebbian-linked Self Organising Architecture
Li, Farkas MacWhinney (2004)
Perceptual Input
Speech Input
Network was inspired by the belief that Brain
Modules are interlinked. It successfully
simulates Word-Object Mapping in children
18
3 An Approach that can associate Two Input
Types - Full counterpropagation network
(Hecht-Nielsen,1987)
19
4 Extending the Counterpropagation Approach to
Modelling Child Language (Nyamapfene Ahmad,
2007)
Competitive Neuron layer
Modal weights
Perceptual Input
Speech Input
Intentional Input
Model based on empirical evidence that children
have intentions and that brain has multimodal
neurons
20

• 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.

21

• 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

22
5 A Control-Theoretic Neural Multi-Net Model of
Child Language Acquisition (Nyamapfene, 2008)
23
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)

24
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)

25
Finally Yes, I Think Babies and Computers are
Related
Thank You!!??!!
26
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 264979-4984.
• 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