saffron at ibm almaden cognitive computing
DESCRIPTION
Story line: By now you have heard a lot about the promise and the limits of CG. This presentation is meant to show you how we can realize more of the promise by overcoming some important limitations. The way I’ll demonstrate this is by showing how two core ideas that have been around for a while when implemented well can increase the power of machines to act like super brains. The two ideas are AM and CD based on KC. • Love story between Kolmogorov Complexity (KC) & Associative Memories (AM). Associative memory means the ability to associate huge amounts of data and find pattern in real time much faster than human beings can do. At Saffron Technologies we have scaled AM to Big Data. We use CD a way to find meaning in the data and reason likes humans do but much more powerfully and faster. When CD and AM are combined it is like a match made in heaven that realizes the promise of cognitive computing. This perfect match overcomes the inability of most machine learning approaches to be able to add new knowledge on the fly in a consistent way. • Use cases. I’m going to share with you 2 applications where we have applied cognitive computing. Where we are helping human beings to make decisions based on data that had already existed but had no meaning until we applied AM. o Gates o Mount Sinai • Why are KC & AM a match made in heaven? What draws our lovers AM & CD together are 3 qualities. o Universality o Context matters o Compression – sparse coding • What is Kolmogorov Complexity? o Discern the signal from the noise to make better decisions o Alice and the judge • How do use KC ⊕ as absolute measure for information distance between objects o Cowboy, saddle and movie • We need context to resolve ambiguity o Meaning comes from context o Cognitive distance allows for context o Associative Memories allow for context since they implement graph • What are associative memories? One message, the weights are deterministic. The weights are the strength of the connection between the neurons. These weights are deterministic. We do not need optimization to calculate them but they are baked in. • How has Saffron Technologies implemented them? • What are the applications of Cognitive Distance on top of Associative Memories? • Summary – What is Saffron’s contribution to cognitive computing?TRANSCRIPT
Associative Memories Cognitive Distance
Early Warning System
Structured and Unstructured DataStrategic Early Warning
System – Igor Ansoff
Scan environment to
detect weak signals &
rare events to predict
surprises
Protect The Foundation from physical and reputation threats
Early warning system to score threats from people & groups based
on dynamic incremental machine learning
Incidence ReportingMetadata + E-mails
Harvested Web Pages (Terabytes & growing )
Detect weak signals to predict threat
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
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
11/22/2013
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?
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
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
Connections and countssynapses and strengths
Hopfield Network
Ising Model for order disorder phase transition
e.g. Ferromagnetism
weights are
deterministic
parameter free
H = -J / 2 SiSj - h Sii
åi, j
å
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
Lond
on
John
Sm
ith
Prim
e M
inst
er
14-J
an-0
9
flew
mee
t
rain
y
day
aboa
rd
2 ho
urs
refi
d
plac
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pers
on
pers
on
tim
e
verb
verb
keyw
ord
keyw
ord
ketw
ord
dura
tion
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 JohnSmith 1 1 1 1 1 1 1 1 1 1
organization UnitedAirlines 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 2hours 1 1 1 1 1 1 1 1 1 1
1234
Lond
on
John
Smith
UnitedAirlines
14-Jan-09
flew
meet
rainy
day
aboard
2ho
urs
refid
place
person
organization
time
verb
verb
keyw
ord
keyw
ord
ketw
ord
duration
Person
PrimeMinister
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&&M
inster
United&&A
irlines
14<Jan<09
flew
&
meet&
rainy&
day&
aboard&
2&hours&
refid&
person
person&
organization&
time
verb&
verb&
keyw
ord&
keyw
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ketw
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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
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
11/22/2013
14 ©2013 Saffron Technology, Inc. All rights reserved.
DATABASE
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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
15
Twitter @paul_hofmann
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Watch Dr. Sengupta Partho’s video on YouTube
http://www.youtube.com/watch?v=rGkyDkDmZts