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The Recognition Factor
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1. Practical Applications
2. Distributed Knowledge
3. Patterns and Clustering4. Pattern Recognition
5. Reliable Networks
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You already know a
lot of this stuffWhat I am trying to
do is to get you to
see it differently,more clearly
Then you will see
things in everydayknowledge differently
than you did before
1
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Spot thePlanets
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Spot the Planets
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The Theory
To teach is
to model and to demonstrate
To learn is
to practice and to reflect
Pretty simple, eh?
No cheats, no shortcuts
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To model what?
To practice what?
That is the topic of this talk
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For example
Evaluate [6 - (5 - 7(7 - 3) + 5)] + 4
2. -33
3. 28
4. 215. 13
To teach the concept of brackets, would you use this
same example over and over? Of course not.
Why not?
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Because you are trying to teach aconcept, not a fact
And the concept is something
deeperthan what you see in anygiven example
Fair enough
http://classes.aces.uiuc.edu/ACES100/Mind/c-m2.html
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But is the concept best
thought of as:
A rule?
A pattern?
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2Representation
treestands for
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stands for?
Or is caused by?
Distributed Representation
= a pattern of connectivity
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The theoryConcepts are not words
They arepatterns in a network
(like the mind, like society)There is no specificplace the concept is located it
is distributed as a set of connections across the
network
Other concepts are embedded in the same network
they form parts of each other,
they effect each other
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3Self-organizing systems acquire new structure without specific
interference from the outside. They exhibit qualitative macroscopic
changes such as bifurcations or phase transitions.
http://www.christianhubert.com/hypertext/self_organization.html
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The way things connect is reflective
of the properties of those things
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They obey
the laws of
physics
(Force patterns inconstruction
http://paginas.ufm.
)
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They are influenced by
external stimuli
http://www.williamcalvin.com/1990s/1995Handbook.htm
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Scale-free
networks andpower laws
are
just one type
of network
where earlylinks are
attractors
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Different kinds of networks detect different kinds of patterns
http://neural.cs.nthu.edu.tw/jang/courses/cs5652/lippman.gif
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4
We are natural pattern recognizers
thats what our brains do
hierarchical neural network for visual pattern recognition
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Some things (like edge detection) we
do because of the way were wired
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For mostthings, though,
there is more
at work
http://www.mcs.drexel.edu/~gcmastra/strange2.html
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What is it?
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Duck Rabbit
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Attractors = the tendency of the network tointerpret a phenomenon one way as
opposed to another
(energy states of variousneural net configurations)
Associative memory =pattrerns of connectivity =
the creation of attractors =
recognition
http://7ka.mipt.ru/~yevin/vismath/
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Knowledge is like recognitionLearningis likeperception
the acquisition of new patterns of
connectivity
through experience
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Like I said, you already know this phenomenon,
youve already seen it
Emergent Learninghttp://growchangelearn.blogspot.com/2007/02/emergent-learning.html
Tom Haskins
"Now I get it"
A-ha!
"Out of the blue"
"My mind leaped""Did an about-face"
"Shut up and did it"
Sudden breakthrough
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Knowledge is
recognition
Its a belief you
cant not have
Like after youvefound Waldo
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5Pattern Recognition
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http://www.sund.de/netze/applets/BPN/bpn2/ochre.html
Pattern recognition is based on similarity
between the current phenomenon and
previously recognized phenomena
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What we wantis for students to
recognize patterns in existing networks in communities of experts,
communities of practice
Thats why we model and demonstrate
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But what kindof
network do we
want to model for
our students?
For that matter,what kind of
network do we
want for
ourselves?
To maximize
knowledge?
To little connection and information never propagates
Too much connection and
information propagates too
quickly
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The internet itself illustrates a sound set of
principles, grounded by two majorcharacteristics: simple services with realistic
scope. "Simple service or simple devices with
realistic scope are usually able to offer a
superior user experience compared to acomplex, multi-purpose service or device".
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Effective networks are
Decentralized
Distributed
Disintermediated
Disaggregated
Dis-Integrated
Democratic
Dynamic
Desegregated
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Democratic =
The Semantic Condition
Reliable networks support
Autonomy
Diversity
Openness Connectivity
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How is this practical?
Ask yourself
To teach the concept of brackets, would you use thissame example over and over? Of course not.
Why not?
Because of the need fordiversity.
Diverse experiences create better networks
than monotonous experiences
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Thank you
http://www.downes.ca