jeff jonas - ibm - presentation at the chief data officer forum, government

© 2016 IBM Corporation

Context ComputingAnd the Rise of Sensemaking Systems

CDO GovernmentJune 8-9, 2016

Jeff Jonas, IBM FellowChief Scientist, Context Computinghttp://www.twitter.com/jeffjonaswww.jeffjonas.typepad.com

http://www.twitter.com/jeffjonas

http://www.jeffjonas.typepad.com/

© 2016 IBM Corporation2

Jeff JonasIBM FellowChief Scientist, Context Computing Founded Systems Research & Development (SRD) in 1985

Architected, designed, developed roughly 100 systems over the last three decades

– Defense, intelligence– Financial services– Gaming– Law enforcement

Acquired by IBM in 2005

Currently focused on Context Computing, Sensemaking and Privacy by Design (PbD)


”The data must find the data and the

relevance must find you.”


Trend: Organizations Are Getting Dumber

4

Time

Incr

easi

ng C

ompu

te P

ower

Sensemaking Algorithms

Available Observation

Space ContextEnterpriseAmnesia

Every two days now we create as much information as we did from the dawn of civilization up until 2003.”

~ Eric Schmidt, CEO Google


Trend: Organizations Are Getting Dumber

5

Time

Incr

easi

ng C

ompu

te P

ower

Sensemaking Algorithms

Available Observation

Space ContextWHY?


Algorithms at Dead End.

You Can’t Squeeze Knowledge

Out of a Pixel.

6


No Context

[email protected]


Context

“Better understanding something by taking into account the things around it.”


I ducked as the bat flew my way.

Another exciting baseball game.


In Context

VendorHigh ValueAsset

Job Applicant

FormerEmployee Bad Guy

[email protected]


Context Accumulating

ContextAccumulation

ContextualizedObservations

Observation(Any kind of data from

any kind of sensor)


Context Informs Decisioning

ContextAccumulation


ObservationIn Context

Decisioning

Act

Data Finds Data Relevance Finds YouThe data is the question!

Observation(Any kind of data from

any kind of sensor)


The Puzzle Metaphor

Imagine an ever-growing pile of puzzle pieces of varying sizes, shapes, colors

What it represents is unknown – there is no picture on hand

Is it one puzzle, 15 puzzles, or 1,500 different puzzles?

Some pieces are duplicates, missing, incomplete or have errors

Some pieces may even be professionally fabricated lies

Until you take the pieces to the table, it is nearly impossible to assess the scene


Puzzling Images: Courtesy Ravensburger © 2011

270 pieces90%

200 pieces66%

150 pieces50%

6 pieces2%

30 pieces10% (duplicates)


First Discovery


More Data Finds Data


Duplicates in Front Of Your Eyes


First Duplicate Found Here


Incremental Context – Incremental Discovery

6:40pm START

22min “Hey, this one is a duplicate!”

35min “I think some pieces are missing.”

37min “Looks like a bunch of hillbillies on a porch.”

44min “Hillbillies, playing guitars, sitting on a porch, near a barber sign and a banjo!”


150 pieces50%


Incremental Context – Incremental Discovery

47min “We should take the sky and grass off the table.”

2hr “Let’s switch sides, and see if we can make sense of this from

different perspectives.”

2hr10m “Wait, there are three … no, four puzzles.”

2hr18m “I think you threw in a few random pieces.”


How Context Accumulates

With each new observation one asserts: 1) Un-associated; 2) near neighbors; or 3) connected

Must favor the false negative

New observations sometimes reverse earlier assertions

Some observations produce novel discovery

The emerging picture helps focus collection interests

As the working space expands, computational effort increases

Then given sufficient observations there comes a tipping point whereby decision certainty increases while compute effort decreases!


Big Data [in context]. New Physics.

More data: better the predictions– Lower false positives– Lower false negatives

More data: bad data good– Suddenly glad your data is not perfect

More data: less compute


Big Data

Pile of ______ Information In Context


One Essential Form of Context: “Entity Resolution”

Is it 5 people each with 1 account or is it 1 person with 5 accounts?

Is it 20 cases of SARS in 20 cities or one case reported 20 times?

If one cannot count, one cannot estimate vector or velocity (direction, speed).

Without vector and velocity prediction is nearly impossible.


Who is Fang Wong?

Fang WongTop 100 Customer

F A WongSeattle, DOB: 6/12/82

Former Customer

@FangWong2.5M Followers

[email protected] Subscriber

Fang [email protected] Department’s

Prospect List


Resolving the Fang Wong

Fang WongTop 100 Customer

F A WongSeattle, DOB: 6/12/82

Former Customer

@FangWong2.5M Followers

[email protected] Subscriber

Fang [email protected] Department’s

Prospect List


Resolving the Fang Wong

Fang WongTop 100 Customer2.5M Followers

Newsletter Subscriber


Graphing the (resolved) Fang Wong

Bill SmithMember of the Board

Employee

Customer

Customer

FraudsterFang Wong

Top 100 Customer2.5M Followers



Contextualizing Sandy Maden


Sandy MadenJob Applicant

Employee

Lives With

Co-signer

FormerEmployee

(term no rehire)

Customer Customer

Customer

FraudsterFang Wong




“Entities”


Sandy MadenJob Applicant

Employee

Lives With

Co-signer

FormerEmployee

(term no rehire)

Customer Customer

Customer

FraudsterFang Wong



Company

Boat

Plane

RouterCar

Asteroid


ENTITY RESOLUTION: NEW THINK


Entity Resolution Different Degrees of Difficulty

Exactly Same

Fuzzy

IncompatibleFeatures

Deceit

Bob Jones123455

Bob Jones123455

Bob Jones123455

Robert T Jonnes000123455

Bob Jones123455

Bob@TheCo

Bob Jones123455

Ken Wells550119


Key Features Enable Entity Resolution

Name License Plate No. Serial Number

Address VIN MAC AddressDate of Birth Make IP AddressPhone Model MakePassport Year ModelNationality Color Firmware

VersionBiometric Etc. Etc.Etc.

People Cars Router


Consider Lying Identical Twins

#123Sue3/3/84UberstanExp 2011

PASSPORT#123Sue3/3/84UberstanExp 2011

PASSPORT

Fingerprint

DNA Most TrustedAuthority

“Same person – trust me.”

Most TrustedAuthority


The same thing cannot be in two places … at the same time.

Two different things cannot occupy the same space … at the same time.


Space & Time Enables Absolute Disambiguation

Name License Plate No. Serial Number

Address VIN MAC AddressDate of Birth Make IP AddressPhone Model MakePassport Year ModelNationality Color Firmware

VersionBiometric Etc. Etc.Etc.

People Cars Router When When When

Where Where Where


“Life Arcs” Are Also Telling

42

Bill Smith4/13/67

Salem, Oregon

Bill Smith4/13/67

Seattle, Washington

Address HistoryTampa, FL 2008-2016Biloxi, MS 2005-2008NY, NY 1996-2005Tampa, FL 1984-1996

Address HistorySan Diego, CA 2005-2016San Fran, CA 2005-2005Phoenix, AZ 1990-2005San Jose, CA 1982-1990


OMG


Space-Time-Travel

Cell phones are generating a staggering amount of geo-locational data – 600B transactions per day being created in the US alone

This data is being “de-identified” and shared with third parties – in volume and in real-time

Your movement quickly reveals where you spend your time

Re-identification (figuring out who is who) is somewhat trivial

And, oh so powerful predictions …

44


The 10 People I Spend the Most Time With(Not at Home and Not at Work) Michelle Pfeiffer Greg Bob Amanda Ivan Shelby Lindsey Adam Brooke

45

He must be

following me!


Unfair Advantage?

The Uberstan intelligence service preempts the next mass protest in real-time

A political opponent is crushed and resigns two days after announcing their candidacy

46


Consequences

Space-time-travel data is the ultimate biometric

Adoption is now accelerating at a blistering pace

It will enable enormous opportunity

It will unravel one’s secrets

It will challenge existing notions of privacy

47


Toying with Publically Available Cell Phone Data

35,831 Call Data Records (CDRs)– 6 months: From 08-31-2009 through 02-27-2010

18,391 Total Number of Usable CDR’s– Excluded CDRs with missing latitude, longitude, time, flow, or accuracy>250 meters

2,444 Hangouts– Minimum of 2 events, spanning at least 15 minutes, in a 610m STB

The Pattern of Life– 130 Hangouts total– 64 Hangouts 3 or more times48

Ummm … seems we are

living in habitrails!


Hangouts

50


Getting to Know Malte Spitz

51

Six months of my life in 35,000 recordshttp://www.malte-spitz.de/blog/4103927.html

http://www.malte-spitz.de/blog/4103927.html


ARCHITECTURAL CONSIDERATIONS


Action

Red Analytics

Green Analytics

Blue Analytics

ObservationSpace

Old School: Isolated Analytics


ObservationSpace

ActionInformationIn Context

Next: General Purpose Sensemaking

Data Finds Data Relevance Finds You

Sensemaking


ObservationSpace

ActionInformationIn Context


Helping Focusing Human Attention

Sensemaking

General Purpose • Insider Threat• Marketing• Next Best Action• Anti-Money Laundering• Asteroid Hunting

Simultaneously!


Sensemaking Architecture

Deep Reflection

DiscoveredPatterns

ContextAccumulation



Decisioning

ActObservation(Any kind of data from

any kind of sensor)


Data MiningDeep Learning

Feature Extraction Transformation

Scoring & Predictive ModelsEvent Processing

Context Computing


The most competitive organizations

are going to make sense of what they are observing

fast enough to do something about it

while they are observing it.


Related Blog Postswww.jeffjonas.typepad.com

Data Finds Data

Puzzling: How Observations Are Accumulated Into Context

Big Data. New Physics.

G2 is 4

Fantasy Analytics


http://jeffjonas.typepad.com/jeff_jonas/2009/07/data-finds-data.html

http://jeffjonas.typepad.com/jeff_jonas/2015/01/g2-is-4.html

http://jeffjonas.typepad.com/jeff_jonas/2012/11/fantasy-analytics.html


“No one writes bomb on manifest!”


Googe: [IBM CDO Lookbook]

.including Context Computing which will be avail first through Watson Analytics


Email: [email protected]: www.jeffjonas.typepad.com

Twitter: http://www.twitter.com/jeffjonas

Questions?

mailto:[email protected]




WIDENING OBSERVATION SPACESA SNEAK PEEK INTO MY CURRENT WORK


Dealing with Probabilities

Deep Reflection

DiscoveredPatterns

ContextAccumulation



Decisioning


any kind of sensor)

Certainty6.25%


Additional DataOriginal Certainty

Dealing with Probabilities

Mark Smith123 Main StreetSanta Rosa, CADOB: 5/12/1974

Mark SmithSanta Rosa, CA702.433.8871

Confirmed across 3 credit bureaus:Mark Smith123 Main StreetSanta Rosa, CADOB: 5/12/1974702.433.8871

Confirmed across two data aggregators:Mark Smith, Santa Rosa, 05/12/74- Only one observed123 Main Street, Santa Rosa, CA- No other Marks- No other Smiths702.433.8871- Exclusive to Mark Smith

(*) 16 Mark Smiths live in Santa Rosa, CA [ref: http://www.intelius.com/results.php?trackit=63&ReportType=1&qf=Mark&qmi=&qn=Smith&qs=CA&qc=Santa+Rosa]

Certainty6.25%*

Decision Certainty


Using Curiosity to Increase Decision Certainty

Deep Reflection

DiscoveredPatterns

ContextAccumulation



Decisioning


any kind of sensor)

SelectiveCuriosity

Figure Out Who to Ask Yes

Make Request(s)

Assembly of Responses into

ObservationsCertainty

6.25%

Is it worth being curious

about?


Before

Deep Reflection

DiscoveredPatterns

ContextAccumulation



Decisioning


any kind of sensor)

Certainty 6.25%


After

Deep Reflection

DiscoveredPatterns

ContextAccumulation



Decisioning


any kind of sensor)

Decision Certainty


SELECTIVE CURIOSITY IN ACTIONA TRUE STORY


Why Selective Curiosity MattersPatent US8620927

There are many domains where 99% accuracy is just not good enough e.g.,– Elections– Healthcare– National security– Police investigations– Self-driving cars

In the coming era of Internet of Things, robots, and cognitive computing “decision certainty” is going to make or break these coming technologies.

Selective Curiosity will make this possible …

jeff jonas - ibm - presentation at the chief data officer forum, government

Data & Analytics