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Saffron at IBM Almaden Cognitive Computing
Associative Memories

Cognitive Distance
Early Warning System
Protect The Foundation from physical and reputation threats

Detect weak signals to predict threat
Early warning system to score threats from people & groups based
on dynamic incremental machine learning
Structured and Unstructured Data

Incidence Reporting
Metadata + E-mails

Harvested Web Pages
(Terabytes & growing )

Strategic Early Warning
System – Igor Ansoff
Scan environment to
detect weak signals &
rare events to predict
surprises
Pattern Recognition In Healthcare
Automate Echocardiogram Diagnoses
90 metrics, 6 locations, 20 time frames
10,000 attributes/beat*patient
-> 100 million triples / beat*patient
Heat maps show separation of disease
states. Associations between variables in
restrictive cardiomyopathy (red) separate
from dominant associations in constrictive
pericarditis (green)
Intelligent Platforms for Disease Assessment
Novel Approaches in Functional Echocardiograph,
Partho P. Sengupta, in JACC: Cardiovascular Imaging, 11/2013

Saffron 90%
Best doctor 76%
State of the art
C-tree 54% using 7
attributes
Watch The Video With Dr. Sengupta
Part 1
http://www.youtube.com/watch?v=rGkyDkDmZts
10:30 - nice Big data setup
12:30 - 14:00 Intelligent Computing
Part 2
http://www.youtube.com/watch?v=SAby6-tMvng
4:40 - Look inside the dataset as a matrix
5:30 - Saffron <<< here it is
6:16 - Associate Memory Reasoning
7:17 - heat map where I can see a pattern
7:56 - 8:26 compare patterns and accuracy of 89.6%
8:51 - 9:07 need to do pattern recognition for intelligent assessment

11/22/20
13

5

Saffron Technology, Inc. All Rights Reserved.
Match Made in Heaven
Cognitive Distance Associative Memories
Universality
• Cognitive Distance is universal
•

C. Bennett, IBM, 1997; M Hutter, IDSIA, 2000 AIXI

• Nonparametric, incremental, deterministic weights

Context
• Cognitive Distance depends on context
• AM fabric stores context – complete graph

Compression
• K Complexity measures compressibility
• Associative Memories are perfect compressor
Kolmogorov Complexity – Signal vs. Noise

Snake eyes are regular sequence -> regular cause, meaning
probability > 0
for snake eyes!

100X

Place a huge bet on
simple outcomes – fair
dice have no pattern
How Do Extract Similarity Automatically?

xy=73M

What is closer to cowboy?
1. saddle or
2. movie

“movie”
y=1,890M

xy=8M

x=131M

Cognitive Distance based on Kolmogorov Complexity
Approximating Kolmogorov Complexity K(x) ~ log x/N we get
CD ~ max {log(fx),log(y)}-log(x,y) / ( logN-min{log(x),log(y)}
 the saddle is closer to the cowboy

“saddle”
y=87M
Not Always So Easy - Context Resolves Ambiguity

Cognition Is About Context
Cognitive Distance Allows for Condition
CD|c ~ max {log(xc|c),log(yc|c)}-log(xc,yc|c) /
( logN-min{log(xc|c),log(yc|c)} )
The Bride: Scaling Associative Memory
NoSQL - Associative Memories Are Truly
Asynchronous Computing
Ising Model for order  disorder phase transition
e.g. Ferromagnetism
H = -J / 2å SiSj - hå Si
i, j

i

Hopfield Network
weights are
deterministic 

parameter free
Connections and counts
synapses and strengths
Saffron’s Solution - Large Scale Machine Learning on
Sparse Matrices
Build the Brain
1. Unify structured & un-structured data
2. Extract entities
3. Build semantic graph with counts on edges  stored as triples
John Smith flew to London on 14 Jan 2009 aboard United Airlines to meet with Prime Minister for 2 hours on a rainy day.
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Why is this so special?
• Non-parametric, nonlinear & instant
incremental learning
• Graph & statistics
• Millions of features
• Saffron stores &
queries billions of triple
counts

refid

1

1

2 hours

1

duration

1234
London 1
John Smith 1

person&

refid
place
person

person

Make the Brain Think
• Reason by similarity with
cognitive distance
Happy Ending – Offspring of KC & AM
 Discovery – Search
– Entity ranking and semantic context
– Convergence – the distance over time

 Classification
– Predicting risk (bad, good)
– Customer life time value
– Echocardiogram diagnosis

 Clustering
– Evolutionary trees, languages, music
– Novelty detection: spare parts, planes, etc.
Take Away
Advanced cognitive computing to
perform like super brains
Google

rss

Twitter

FACEBOOK

Email

STOCKS EXCEL
Word

DATABASE
SOCIAL NETWORKS

PDF

DATABASES

By matching Cognitive Distance with
Associative Memories we are able to
• reason by similarity
• learn instantly &
incrementally w/o parameters
• Discern Context
Enterprise proven

11/22/20
13

14

©2013 Saffron Technology, Inc. All rights reserved.
Twitter
Email
Homepage
Blog
Slide Share
LinkedIn

@paul_hofmann
phofmann@saffrontech.com
www.paulhofmann.net
www.paulhofmann.net/blog
www.slideshare.com/paulhofmann
www.linkedin.com/in/hofmannpaul

Watch Dr. Sengupta Partho’s video on YouTube
http://www.youtube.com/watch?v=rGkyDkDmZts
15

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Saffron at IBM Almaden Cognitive Computing

  • 3. Early Warning System Protect The Foundation from physical and reputation threats Detect weak signals to predict threat Early warning system to score threats from people & groups based on dynamic incremental machine learning Structured and Unstructured Data Incidence Reporting Metadata + E-mails Harvested Web Pages (Terabytes & growing ) Strategic Early Warning System – Igor Ansoff Scan environment to detect weak signals & rare events to predict surprises
  • 4. Pattern Recognition In Healthcare Automate Echocardiogram Diagnoses 90 metrics, 6 locations, 20 time frames 10,000 attributes/beat*patient -> 100 million triples / beat*patient Heat maps show separation of disease states. Associations between variables in restrictive cardiomyopathy (red) separate from dominant associations in constrictive pericarditis (green) Intelligent Platforms for Disease Assessment Novel Approaches in Functional Echocardiograph, Partho P. Sengupta, in JACC: Cardiovascular Imaging, 11/2013 Saffron 90% Best doctor 76% State of the art C-tree 54% using 7 attributes
  • 5. Watch The Video With Dr. Sengupta Part 1 http://www.youtube.com/watch?v=rGkyDkDmZts 10:30 - nice Big data setup 12:30 - 14:00 Intelligent Computing Part 2 http://www.youtube.com/watch?v=SAby6-tMvng 4:40 - Look inside the dataset as a matrix 5:30 - Saffron <<< here it is 6:16 - Associate Memory Reasoning 7:17 - heat map where I can see a pattern 7:56 - 8:26 compare patterns and accuracy of 89.6% 8:51 - 9:07 need to do pattern recognition for intelligent assessment 11/22/20 13 5 Saffron Technology, Inc. All Rights Reserved.
  • 6. Match Made in Heaven Cognitive Distance Associative Memories Universality • Cognitive Distance is universal • C. Bennett, IBM, 1997; M Hutter, IDSIA, 2000 AIXI • Nonparametric, incremental, deterministic weights Context • Cognitive Distance depends on context • AM fabric stores context – complete graph Compression • K Complexity measures compressibility • Associative Memories are perfect compressor
  • 7. Kolmogorov Complexity – Signal vs. Noise Snake eyes are regular sequence -> regular cause, meaning probability > 0 for snake eyes! 100X Place a huge bet on simple outcomes – fair dice have no pattern
  • 8. How Do Extract Similarity Automatically? xy=73M What is closer to cowboy? 1. saddle or 2. movie “movie” y=1,890M xy=8M x=131M Cognitive Distance based on Kolmogorov Complexity Approximating Kolmogorov Complexity K(x) ~ log x/N we get CD ~ max {log(fx),log(y)}-log(x,y) / ( logN-min{log(x),log(y)}  the saddle is closer to the cowboy “saddle” y=87M
  • 9. Not Always So Easy - Context Resolves Ambiguity Cognition Is About Context Cognitive Distance Allows for Condition CD|c ~ max {log(xc|c),log(yc|c)}-log(xc,yc|c) / ( logN-min{log(xc|c),log(yc|c)} )
  • 10. The Bride: Scaling Associative Memory
  • 11. NoSQL - Associative Memories Are Truly Asynchronous Computing Ising Model for order  disorder phase transition e.g. Ferromagnetism H = -J / 2å SiSj - hå Si i, j i Hopfield Network weights are deterministic  parameter free Connections and counts synapses and strengths
  • 12. Saffron’s Solution - Large Scale Machine Learning on Sparse Matrices Build the Brain 1. Unify structured & un-structured data 2. Extract entities 3. Build semantic graph with counts on edges  stored as triples John Smith flew to London on 14 Jan 2009 aboard United Airlines to meet with Prime Minister for 2 hours on a rainy day. 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 verb flew 1 1 1 1 1 1 1 1 1 verb meet 1 1 1 1 1 1 1 1 1 keyword rainy 1 1 1 1 1 1 1 1 1 keyword day 1 1 1 1 1 1 1 1 keyword aboard 1 1 1 1 1 1 1 1 1 duration 2 hours 1 1 1 1 1 1 1 1 1 1 1 rainy 1 1 1 1 1 1 1 keyword day 1 1 1 1 1 1 1 1 keyword aboard 1 1 1 1 1 1 1 1 1 duration 2 hours 1 1 1 1 1 1 1 1 1 1 keyword keyword verb verb time person place person 1 day 1 1 1 & 1 & 1 1 1 1 1 1 1 United& Airlines& 1 1 1 & 1 & 1 1 1 1 1 1 14<Jan< 09 1 1 1 1 & 1 & 1 1 1 1 1 verb& flew& 1 1 1 1 1 & 1 & 1 1 1 1 verb& meet& 1 1 1 1 1 1 & 1 & 1 1 1 keyword& rainy& 1 1 1 1 1 1 1 & 1 & 1 1 keyword& day& 1 1 1 1 1 1 1 1 & 1 & 1 keyword& aboard& 1 1 1 1 1 1 1 1 1 & 1 & duration 2& hours& 1 1 1 1 1 1 1 1 1 1 day& aboard& 2& hours& duration 1234 refid& Place&&&&&& &&&&&& &&&&& London & ketword& organization& keyword& Prime& Minster& 1 time person& flew& 1 1 rainy& 1 1 meet& 1 1 verb& 1 1 keyword& 1 1 14<Jan<09 1 1 verb& 1 1 United& & Airlines 1 John& Smith 1 organization& 1 & 1 & person& John& Smith 1 Person Prime Minister Prime& & Minster 1234 aboard 1 ketword 1 flew meet 1 rainy verb meet 1 14-Jan-09 1 Prime Minster 1 London 1 John Smith flew 1 1 1 day 1 verb 2 hours 1 duration 1 rainy 1 1 aboard 1 14-Jan-09 1 ketword United Airlines 1 time keyword organization 1 keyword 1 1 flew 1 1 meet 1 1 verb 1 1 14-Jan-09 1 1 1 verb 1 1 John Smith 1 1 1 United Airlines 1 time John Smith 1 person London 1 person organization place 1 1234 1 1 1 1 1 London 1 1 1 1 1 1 refid 1 1 1 1 1 1 place 1 1 1 1 1 1 1 1234 1 1 1 1 1 14-Jan-09 1 refid 1 1 1 1 1 Prime Minister 1 time 1 1 1 1 time refid& 1 1 1 person Organization United Airlines 1234 1 1 keyword Why is this so special? • Non-parametric, nonlinear & instant incremental learning • Graph & statistics • Millions of features • Saffron stores & queries billions of triple counts refid 1 1 2 hours 1 duration 1234 London 1 John Smith 1 person& refid place person person Make the Brain Think • Reason by similarity with cognitive distance
  • 13. Happy Ending – Offspring of KC & AM  Discovery – Search – Entity ranking and semantic context – Convergence – the distance over time  Classification – Predicting risk (bad, good) – Customer life time value – Echocardiogram diagnosis  Clustering – Evolutionary trees, languages, music – Novelty detection: spare parts, planes, etc.
  • 14. Take Away Advanced cognitive computing to perform like super brains Google rss Twitter FACEBOOK Email STOCKS EXCEL Word DATABASE SOCIAL NETWORKS PDF DATABASES By matching Cognitive Distance with Associative Memories we are able to • reason by similarity • learn instantly & incrementally w/o parameters • Discern Context Enterprise proven 11/22/20 13 14 ©2013 Saffron Technology, Inc. All rights reserved.

Notas del editor

  1. another example of predicting at the personal (consumer) level
  2. 3 univcomp machines: von Neumann architecture – CPU and RAM; cellular automata (von Neum &amp; StaniUlam); associative memories –synapses as compute and storage unit -&gt; content addressable associative memory -&gt; asynchronous, reaching fixed point - Hopfield nets (homomorph to Ising model -&gt; node&apos;s behavior is deterministic moves to a state to minimize energy of itself &amp; its neighbors -&gt; Lapunov, emerging patterns