IBM Research - Almaden Colloquium: The Cognitive Enterprise November 19, 2013 at IBM Research - Almaden in San Jose, CA. IBM Research has convened a stellar list of speakers including the founder of Palm, Jeff Hawkins; Paul Hofmann, CTO of Saffron Technology; John Hollar, President of the Computer History Museum; Dick Karp, Turing Award and Kyoto Prize recipient; Olivier Lictharge, Baylor School of Medicine; and distinguished panelists from the Silicon Valley VC community.

1. Reasoning by Similarity on Top of
an Associative Memory Fabric
Paul Hofmann, PhD, CTO Saffron Technology
Talk given at IBM Research - Almaden Colloquium: The Cognitive Enterprise
November 19th, 2013
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2. Associative Memories  Cognitive Distance

3. Predictive Maintenance
12/3/202
0
3 ©2013 Saffron Technology, Inc. All rights reserved.
I remember
this feeling,
it means…Temperature
was 100F+
with high
winds
I remember
Tail #123
reported a
similar issue.
The last time the
pilot reported
this problem…
RESULT: 100% recall 1% false alarms
Up from 66% recall 16% false alarmsData Sources: Structured and unstructured, Maintenance records,
purchase orders, work orders, everything that speaks to these issues
Reduce down time of aircraft
Use multiple sources of data to learn from collective experience
0%
20%
40%
60%
80%
100%
Saffron
CBM
1 % false alarms
100% hits
Predict before part breaks

4. Early Warning System
Structured and Unstructured Data
Strategic Early Warning
System – Igor Ansoff
Scan environment to
detect weak signals &
rare events to predict
surprises
Find the unknown enemy to protect The Foundation
Early warning system to score threats from people & groups based
on dynamic incremental machine learning
Incidence Reporting
Metadata + E-mails
Harvested Web Pages
(Terabytes & growing )
Detect weak signals to predict threat

5. Pattern Recognition In Healthcare
Intelligent Platforms for Disease Assessment
Novel Approaches in Functional Echocardiograph,
Partho P. Sengupta, in JACC: Cardiovascular Imaging, 11/2013
Automate Echocardiogram Diagnoses
Heat maps show separation of disease
states. Associations between variables in
restrictive cardiomyopathy (red) separate
from dominant associations in constrictive
pericarditis (green)
State of the art
C-tree 54% using 7
attributes
Best doctor 76%
Saffron 90%
90 metrics, 6 locations, 20 time frames
10,000 attributes/beat*patient
-> 100 million triples / beat*patient

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?
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
x=131M
“saddle”
y=87M
“movie”
y=1,890M
xy=73M xy=8M
What is closer to cowboy?
1. saddle or
2. movie

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
Connections and counts
synapses and strengths
Hopfield Network
Ising Model for order  disorder phase transition
e.g. Ferromagnetism
weights are
deterministic 
parameter free

12. Saffron’s Solution - Large Scale Machine Learning on
Sparse Matrices
Why is this so special?
• Non-parametric, non-
linear & instant
incremental learning
• Graph & statistics
• Millions of features
• Saffron stores &
queries billions of triple
counts
refid 1234 1 1 1 1 1 1 1 1 1 1
place London 1 1 1 1 1 1 1 1 1 1
person John Smith 1 1 1 1 1 1 1 1 1 1
person Prime Minister 1 1 1 1 1 1 1 1 1 1
time 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
1234
London
JohnSmith
PrimeMinster
14-Jan-09
flew
meet
rainy
day
aboard
2hours
refid
place
person
person
time
verb
verb
keyword
keyword
ketword
duration
Organization
United Airlines
refid 1234 1 1 1 1 1 1 1 1 1 1
place London 1 1 1 1 1 1 1 1 1 1
person John Smith 1 1 1 1 1 1 1 1 1 1
organization United Airlines 1 1 1 1 1 1 1 1 1 1
time 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
1234
London
JohnSmith
UnitedAirlines
14-Jan-09
flew
meet
rainy
day
aboard
2hours
refid
place
person
organization
time
verb
verb
keyword
keyword
ketword
duration
Person
Prime Minister
John Smith flew to London on 14 Jan 2009 aboard United Airlines to meet with Prime Minister for 2 hours on a rainy day.
refid& 1234 1 1 1 1 1 1 1 1 1 1
person& John&Smith 1 && 1 1 1 1 1 1 1 1 1
person& Prime&Minster& 1 1 && 1 1 1 1 1 1 1 1
organization& United&Airlines& 1 1 1 && 1 1 1 1 1 1 1
time 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 &
1234
John&Smith
Prime&&Minster
United&&Airlines
14<Jan<09
flew&
meet&
rainy&
day&
aboard&
2&hours&
refid&
person
person&
organization&
time
verb&
verb&
keyword&
keyword&
ketword&
duration
Place&&&&&&&&&&&&&&&&&
London
Build the Brain
1. Unify structured & un-structured data
2. Extract entities
3. Build semantic graph with counts on edges  stored as triples
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. Convergence: Cognitive Distance over Time

15. Take Away
12/3/202
0
15 ©2013 Saffron Technology, Inc. All rights reserved.
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Advanced cognitive computing to
perform like super brains
By matching Cognitive Distance with
Associative Memories we are able to
• reason by similarity
• learn instantly &
incrementally w/o parameters
• Discern Context
Enterprise proven

16. 16
Twitter @paul_hofmann
Email phofmann@saffrontech.com
Homepage www.paulhofmann.net
Blog www.paulhofmann.net/blog
Slide Share www.slideshare.com/paulhofmann
LinkedIn www.linkedin.com/in/hofmannpaul
Watch Dr. Sengupta Partho’s video on YouTube
http://www.youtube.com/watch?v=rGkyDkDmZts