Cognitive Computing Reasoning By Similarity With

Associative Memories

Paul Hofmann, PhD, CTO Saffron Technology October 2013

Cognitive Computing: Reasoning By Analogy

Cognitive Distance Is The Universal Measure for Reasoning By Similarity

Regularity <-> Randomness Signal <-> Noise

Distance between Strings

Cognitive Distance based on Kolmogorov Complexity CD ~ max {log(x),log(y)}-log(x,y) / ( logN-min{log(x),log(y)} ) à  the saddle is closer to the cowboy

Cilibrasi, Rudi L.; Vitanyi, Paul M.B. The Google Similarity Distance. IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No 3, March 2007, 370–383.

 

x=131M   “saddle”  y=87M  “movie”  

y=1,890M  

xy=73M   xy=8M  

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

Pairwise Similarity Is Not Enough

Cognition Is About Context -> Semantic Triples

Context Matters

The Power Of Dependencies

Triple Store Reduces Noise

XOR Problem – X, Y, Z 2 Random sources X and Y are made dependent by output Z Criminal? (Z) Employment Status (X) Buys a new car with cash (Y)

Pairwise correlations are very noisy

What is this an image of?

10/1/13   8   Saffron  Technology,  Inc.  All  Rights  Reserved.  

It’s close to this…

10/1/13   9   Saffron  Technology,  Inc.  All  Rights  Reserved.  

The Power of Dependenciesà where the value is

NoSQL - Associative Memories Are Truly Asynchronous Computing

Connec@ons  and  counts  =  synapses  and  strengths  

Hopfield Network  Emerging  pa=erns  

Ising  Model  for  order  à  disorder  phase  transi@on                          e.g.  Ferromagne@sm  

weights  are  determinis@c  à  parameter  free  

C  B  

A  

+1  

-­‐1  -­‐1  

Training  Vector  ABC  101  011    100    

C  B  

A  

+1  

-­‐1  -­‐1  

B  SAME  AS  ON  

B  DIFF  

FROM  OFF  

C  B  

A  

+1  

-­‐1  -­‐1  

{0,1}  

{0,1}  

{0,1}  

{0,1}  

{0,1}  

{0,1}  

{0,1}  

w0  

w3  

w1  

w10  w9  

w7  

w13  w12  

w5   w8  

w6  

w4  

w11  

w2  

Associative Memories - Asynchronous Computing

Large Scale Machine Learning on Sparse Matrices

Why is this so special? •  No static model à

parameter free, non-linear & instant incremental learning

•  Combines graph & statistics

•  Input vector of millions of attributes

•  Saffron stores & queries billions of triples

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  

John  Smith

 Prim

e  Minster  

14-­‐Ja

n-­‐09

flew  

meet  

rainy  

day  

aboard  

2  hours  

refid

 place  

person  

person  

time

verb  

verb  

keyw

ord  

keyw

ord  

ketword  

duratio

n

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

Lond

on&

John

&Smith

&

Unite

d&Airline

s&

14<Ja

n<09

flew&

meet&

rainy&

day&

aboard&

2&ho

urs&

refid

&

place&

person

&

organizatio

n&

time

verb&

verb&

keyw

ord&

keyw

ord&

ketw

ord&

duratio

n

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

Prim

e&&M

inster

Unite

d&&Airlines

14<Ja

n<09

flew&

meet&

rainy&

day&

aboard&

2&ho

urs&

refid

&

person

person

&

organizatio

n&

time

verb&

verb&

keyw

ord&

keyw

ord&

ketw

ord&

duratio

n

Place&&&&&&&&&&&&&&&&&London

•  ETL - unify structured & un-structured data, entity extraction, and build semantic graph with counts on edges

•  Reason by similarity using cognitive distance for all triples

The 4C’s Of Cognitive Computing

10/1/13   13   ©2013 Saffron Technology, Inc. All rights reserved.

Patterns: Connections, Counts, Context and Concepts in Hybrid Data Real-time learning about entities in data, their connections, their frequencies

Non-linear, parameter free, no rules, or modeling required

Who/what is related? How? Where? When?

Who/what is similar? How similar/different?

What could happen? Where? When?

What has been done before? Did it work?

SENSE-M

AK

ING

DEC

ISION

-MA

KIN

G

Google Twitter rss

FACEBOOK DATABASE SOCIAL NETWORKS

STOCKS Email

DATABASES EXCEL Word PDF

Reasoning  by  Similarity  

Predictive Maintenance

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 63% recall 18% false alarms Data  Sources:  Structured  and  unstructured,  Maintenance  records,  

purchase  orders,  work  orders,  everything  that  speaks  to  these  issues  

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Predict which part will break avoiding un-planned down time or premature maintenance caused by category (not asset) based maintenance. Use Saffron’s experience based learning and reasoning to analyze the unique characteristics of an aircraft’s life to determine it’s maintenance schedule: unify a pilot’s intuition & sensory recall, with the aircraft’s complete maintenance history, the aircraft’s flight routing and experience, and mechanics’ observations and experiences.

©2013 Saffron Technology, Inc. All rights reserved.

0%  

20%  

40%  

60%  

80%  

100%  

Saffron  CBM  Prior  CBM  

1 % false alarms

100% hits

Early Warning System

Strategic Early Warning System – Igor Ansoff Scan environment to detect weak signals & rare events to predict surprises •  Learn model •  Score threads in real time

using SMB triples of incoming emails.  

Real time decision if person contacting The Bill & Melinda Gates Foundation poses a threat. Real time threat scoring system of people and groups based on dynamic incremental machine learning approach on unified Web, enterprise data, email, streams, and other metadata.

Incidence  Repor,ng  Metadata  +  E-­‐mails  

Harvested  Web  Pages      (Terabytes  &  growing  )  

Structured and Unstructured Data

Personalized Pattern Recognition In Healthcare

The difficulty of this case –  Only 15 patients for each of 3 conditions –  90 metrics, 6 locations, 20 time frames –  We observes & score 10,000 attributes/beat*patient

-> 100 million triples / beat*patient

Sengupta, Partho P. Intelligent Platforms for Disease Assessment: Novel Approaches in Functional Echocardiography. Feigenbaum Lecture, American Society of Electrocardiography, July, 2013.

90%  Accuracy  76%  human  

54%  with  R,  C-­‐tree    

Even  becer:  More  data,  more  power  Higher-­‐level  hypermatrix  

Automate Echo Cardio Gram Diagnoses keeping human accuracy - Using traditional statistics has failed so far -

Use Saffron’s non-parametric incremental learning approach to predict the three different conditions from Echo Cardio Gram

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 

10/1/13   17   Saffron  Technology,  Inc.  All  Rights  Reserved.  

Twitter @paul_hofmann Email paul@hofmann.name Homepage www.paulhofmann.net Blog www.paulhofmann.net/blog Slide Share www.slideshare.com/paulhofmann LinkedIn www.linkedin.com/in/hofmannpaul

Technical Positioning

§  Our claim to true Cognitive Computing –  Many others assume AI logic over “facts” –  In contrast, see Hofstadter’s Surfaces and Essences:

•  Analogy as the Fuel and Fire of Thinking –  We reason by similarity using “Cognitive Distance”

§  Definition of truly “associative” –  Associations as connections (Qliktech, SAP, RDF, …) –  Associations as dependencies (a statistical meaning) –  The first is getting crowded, but we can do both

Idealized Graph Assumption

Assumed… But, Natural Graphs…

Small  degree  è    Easy  to  par@@on   Many  high  degree  ver@ces  

(power-­‐law  degree  distribu@on)    è    

Very  hard  to  par@@on  

Saffron’s 3rd Generation Cognitive Computing Platform

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Saffron: Advantage Market – Consumer Intelligence

OEM Partner and Customer Applications

Analytic Reasoning REST APIs ANALOGIES, CONNECTIONS, CLASSIFICATIONS, EPISODIC PATTERNS,

TEMPORAL TRENDS, and CUSTOMER DEFINED

INSIGHT

REASONING

SaffronMemoryBase ENTITY CONNECTIONS, COUNTS AND CONTEXT SPACES, MEMORIES,

MATRICES, ROWS, COLUMNS

SaffronAdmin DATA INGESTION TEMPLATES FOR

STRUCTURED DATA, Entity Extraction

3rd Party NLP Tools for Text Analysis

Structured Unstructured Streaming Other

KNOWLEDGE

INGESTION

DATA

Open Platform with Adapters for 3rd Party Tools

10/1/13   ©  Saffron  Technology,  Inc.  All  rights  reserved.    22  

Saffron Proprietary

Today

Open API Connectors

Drive Adoption

Collect and Harvest

•  Unstructured Content

•  Semi-Structured Data

•  Structured Data

Ingest and Unify Hybrid Data�

•  Data Dictionaries •  Ontologies •  Name Lists • Special parsers for disparate data types • Connector for SAP Text

Store and Analyze�

•  Connections & Counts

•  Semantic Analysis •  Statistical Analysis •  Clustering & Pattern •  Prediction

View and Report�

•  SaffronAdvantage™ •  Trends & Episodes •  Emerging Patterns •  Episodic Patterns •  Prediction

Saffron  API  Connectors  User Experience Statistics & Big

Data Stores Natural Language

& Sentiment Processing

Social & Open Source Content

Reasoning

10/1/13  ©  Saffron  Technology,  Inc.  All  rights  reserved.    

«Premium  Web  Content  Public Data �

 «Deep  Web  

 «Industry  News  

 «Blogs  –  Industry,  Financial,  more  

«Social  Media  

«Open  Source  News  

«Compe@tors  Sites  

Knowledge Store�

l  Matrices,    

Rows,  Columns  

En@ty    Memories  

l  

Enterprise  Stores  l  Connec@ons,  

Counts,  Context  

l  

l  Specialized  Stores  

Early Signals & Emerging

Trends �

n  Velocity  

 n  Threat  Scoring  (or  Opportunity)  

 n  New  &  Interes@ng  

n  Export  to  3rd  Party  Tools  

ªDatabase  

Private Data �

 ªTelephone/Email  

 ªPDF,  Word  Documents  

 ªMarke@ng  Data  

 ªSpreadsheets  

 ªProduc@on  Data  

ªCalendars  

ªERP  -­‐  SCM    ªCRM  

 ªData  Warehouse  

t  Temporal  &  Geo  Tagging  

Content Management �

En@@es,  Acribute    Extrac@on  

t  Custom      Extractors  

t  

t  Export  to  3rd    Party  Tools  

VOCt    

t  Sen@ments  

Event    Extractor  

t  

Saffron Admin™�

Saffron Memory Base®�

Saffron Advantage™�

Cloud  Hos@ng  u  

Culture �

 Saffron  Community  Blog  u  t  Change  Management  

 t  Strategic  Industry  Alliances    

t  Risk  Management  Process  Design  

Repor@ng  Customiza@ons  u   t  Analy@c  Product  Support  t  SenseMaking  Educa@on    Custom  Harves@ng  u  

User  Guides  u  

Public  Content  Management  u  

Insight�

Social  Listening­  

Brand  &  Reputa@on­  

­Spa@al  Associa@ons  

­Connec@on  (graph)  Analysis    

Customer  3600  View­    Distribu@on  Intelligence­  

 ­Temporal  Associa@on  Trends    Compe@@ve  Intelligence  ­    ­Similarity  Analysis  

 Sales  Intelligence­  ­Episodes,  Repea@ng  Pacerns    ­Contextual  Discovery  &  Diagnos@cs    

SenseMaking �Culture �

Saffron Advantage™�

Platform Approach to Intelligence

COMPETITIVE LANDSCAPE

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Competitive AND Complimentary Positioning

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Semantic Stores

Associative Memories

Statistical Packages

Data Visualization

COMPLEMENT UNIFY

UN

IFY

CO

MPLEM

ENT

•  Complements §  Efficient connection store for “higher” AI to

extract more formal relationships and control memory with business rules

§  Massive frequency store for any “flavor” of statistics, including use of Saffron to quickly discover and build more traditional models

§  Architectures §  Saffron methods of partitioning and

compression fit well with column-oriented infrastructure as one storage solution for both data and memories

§  Saffron is additive, not replacing data stores, but adding a memory base to a polymorphic architecture

§  Visualizations §  Saffron APIs add intelligence to existing

business applications and data-oriented visualizations providing smarter, faster access for business owners and operational users

Saffron vs. Selected Competitors

Advanced faceted search. No complete semantic graph and no counts for advanced statistics. Hybrid logic and statistics. No incremental ingestion/learning, real world is not a jeopardy question. Symbolics with add-on statistics. No unified representation, only a “bolt on” of traditional statistics. Lead in statistics, some text analytics. No semantic graph for hybrid analytics in combination. Associative ”experience” GUI but not an associative store. No complete graph, no count statistics. Manual link construction in GUI. No automated intelligence, no deep analytics. Biologically inspired AI toolkit. Nascent and unclear mix of traditional methods and new claims.

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Saffron’s Unique Capabilities v. MapReduce

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MapReduce Saffron MemoryBase

Distributed batch processing

New attributes -> code change

Low level assembler-like API

Generic framework

Distributed real-time transactions

Real-time update: no manual effort

High level, declarative API -> no programming

Optimized solution for advanced analytics

Saffron’s Unique Capabilities vs. RDBMS

10/1/13   ©2012  Saffron  Technology,  Inc.  All  rights  reserved.    28  

RDBMS Saffron (Matrices)

Table joins for semantics

Predefined schema

Limited keys & sorting joins

No natural partitioning

Structured data is fact-based

Nearest-neighbor is infeasible

Pre-joined matrices

Schema-less

Everything is a key, globally sorted

Shared-nothing parallelism

Knowledge is more exploitable

Nearest-neighbor is instant

WHAT DO ANALYSTS SAY ABOUT SAFFRON?

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HYBRID      

Converging Analytics of Hybrid of Data

10/1/13   ©2012  Saffron  Technology,  Inc.  All  rights  reserved.    30  

STRUCTURED    

CONTENT    

Source: Gartner

Analy=c  Processes:  Descrip=ve,  Diagnos=c,  Predic=ve,  Prescrip=ve  

Market-­‐Driven  Convergence  

Market-­‐Driven  Convergence  

It’s The Dawning Of The Age Of BI DBMS

Disparate  data  

Quickly changing

requirements

“Use associative when you can’t predict the future but need to prepare for anything”, 2011  

Associa=ve  index  

Component Hadoop  Apache  Projects   Commercial  distribu=on  or  Hadoop  integra=on  

App dev / scripting Pig, Cascading, WebHDFS

Integrate and transform Pig, Sqoop, Flume

DBMS SQL Hive, Derby

DBMS NoSQL Cassandra, Hbase

View on BI Ecosystem