ibm & cognitive computing · pdf fileibm & cognitive computing ... john opel, former...

An Approach to Understanding

Frank Greco [email protected] [email protected] 1/28/2016

IBM & Cognitive Computing

mailto:[email protected]

© 2015 IBM Corporation

In Memory of Marvin Minsky

http://www.nytimes.com/2016/01/26/business/marvin-minsky-pioneer-in-artificial-intelligence-dies-at-88.html?_r=0





My Background

▪ BA, Mathematics, U Chicago

▪ MS, Computer Science and Mathematics, U Minnesota, with help from U Chicago, Todd Dupont & IBM

▪ IBM; work with multiple industries in multiple roles – Developer, – Data Center Technical Coordinator, – Consultant - I/T Strategy & Application Architecture, – SW Application Architect -Technical Advisor & Facilitator

▪ Current Activities with the University – IBM Client teams

• Center for Research Informatics • UC Medical Center • Research Computation Center • …

– U Chicago Cognitive Computing Challenge : https://goo.gl/yrg1gX – Academic Initiative – Watson & Bluemix

https://goo.gl/yrg1gX


Agenda

▪Perspective On IBM

▪Cognitive Computing –Motivation and Definition –Watson as an Example

▪Vision, Challenges, and Considerations

▪Ways to Engage


2 Notable IBM - U Chicago Connections

▪ John Opel, Former IBM CEO – U Chicago MBA

▪ Irving Wladawsky-Berger – “I retired from IBM on May 31, 2007 after 37 years with the company, where I was responsible for identifying emerging

technologies and marketplace developments that are critical to the future of the IT industry. I was also responsible for our university relations office and for the IBM Academy of Technology where I served as Chairman of the Board of Governors. I led a number of IBM’s company wide initiatives including the Internet and e-business, supercomputing and Linux…. former member of University of Chicago Board of Governors for Argonne National Laboratories, the Board of Overseers for Fermilab, and BP's Technology Advisory Council. I am a Fellow of the American Academy of Arts and Sciences as well as a Fellow of London’s Royal Society of Arts. Having been born in Cuba and come to the US at the age of 15, I was named 2001 Hispanic Engineer of the Year. I have an M.S. and Ph. D. in physics from the University of Chicago.”


Result from running the comment on the previous page through the Personality Insights API

▪ You are inner-directed, restrained and strict.

▪ You are philosophical: you are open to and intrigued by new ideas and love to explore them. You are authority-challenging: you prefer to challenge authority and traditional values to help bring about positive changes. And you are driven: you have high goals for yourself and work hard to achieve them.

▪ Your choices are driven by a desire for self-expression.

▪ You consider achieving success to guide a large part of what you do: you seek out opportunities to improve yourself and demonstrate that you are a capable person. You are relatively unconcerned with taking pleasure in life: you prefer activities with a purpose greater than just personal enjoyment.

https://developer.ibm.com/watson/blog/2015/03/23/ibm-watson-personality-insights-science-behind-service/











IBM Vital Stats (Wikipedia)

Type Public

Traded asNYSE: IBMDow Jones Industrial Average ComponentS&P 500 Component

IndustryIT consulting IT services Computer software Computer hardware

Founded June 16, 1911; 104 years agoEndicott, New York, U.S.[1]

Founder Charles Ranlett FlintHeadquarters Armonk, New York, U.S.Area served 170 countries

Key people Ginni Rometty (Chairman, President and CEO)

Products See IBM productsRevenue US$ 92.793 billion (2014)Operating income US$ 19.986 billion (2014)Net income US$ 12.023 billion (2014)Total assets US$ 117.53 billion (2014)Total equity US$ 11.868 billion (2014)Number of employees 379,592 (2014)Divisions Hardware, Services, SoftwareWebsite IBM.com

http://dbpedia.org/page/Category:IBM





Perspective on IBMHistory: Transformative Research

© 2015 IBM Corporation© 2015 International Business Machines Corporation

The Present Does Not Occur Without the Past…Evolution to Cognitive Computing

10

Cognitive EraProgrammatic EraTabulation Era


▪ Thomas Watson Sr. – Fired from NCR ; “THINK” ▪ 1911 C-T-R (Computing - Tabulating – Recording) ▪ 1928

– Ben Wood, professor Columbia University – Pioneered standardized tests; led to the problem of scoring 10’s of 1,000’s of tests – Inspired Tom Watson Sr how IBM machines could be used to measure intellect and psychology – Professors pushed IBM into scientific computing … – Thomas J Watson Astronomical Computing Bureau at Columbia

▪ 1940’s – WWII ; Los Alamos Project; Enrico Fermi

▪ 1945 – Watson Sr. Estabkished the first corporate pure science research laboratory connected to a university ▪ 1950’s Tom Watson Jr.

– Sets up a true research division --> Place where one could go deep and invent something – Information age – Van Neumann holds court – Arthur Samuel – Artificial intelligence – checkers (1952) – Marvin Minsky, in 1959, co-founded the M.I.T. Artificial Intelligence with his colleague John McCarthy, who is credited with coining the term “artificial intelligence.” – Perceptron invented at Cornel by Rosenblatt

▪ 1960’s – IBM 360 Development Fred Brooks – Mythical Man Month – US Space Program... – J.C.R. Licklider writes Man-Computer Symbiosis – Anti-trust

▪ 1970’s – Ralph Gomory, head of IBM Research tied research with (product) development - joint programs – Expanded collaboration with Universities (today 6,000 U’s and 30,000 faculty members) – Personal Computer

▪ 1990’s – Innovation by acquistion – Deep Blue plays chess

▪ 2000’s – Open Innovation – Collaboration with clients, amonsgt internal organizations, coopetition

▪ 2011 – Watson Wins Jeopardy! ▪ Today On

– Collaboration across global systems (Systems of systems) – Cognitive ecosystem bult atop a hybrid Cloud platform

A Selective Thread Through IBM’s History

Cognitive Era

Programmatic Era

Tabulation Era


Progress Does Not Occur Without Collaboration and Innovation

12

Primary & Applied Research

Product Development Application

Business & GovernmentAcademia IBM

“The only way you survive is you continuously transform into something else. It's this idea of continuous transformation that makes you an innovation company.” Ginni Rometty, IBM CEO


Impact on Traditional Research-Developer-User Roles

End Use

Application Development

System Development

Research

End Use (Training Cycles)

R&D (More tightly

coupled)

Continuous Delivery

Programmatic Cognitive Exploratory

I/T disciplines

LOB & Cognitive specialist collaboration

Involvement

Skill Mix


12 labs. 6 continents. ~3,000 Researchers $6B R&D budget

Brazil (2010)

Almaden - 1956

Austin - 1995

Watson1945/1961

IBM Research-Africa (2012)

Dublin (2011)

Tokyo (1982)

China (1995)

Melbourne (2010),

India (1998)

Haifa (1972)

Zurich (1956)

IBM's global research labs are focused on four key areas: Industries and Solutions: This organization will focus on the transformation of business through data. Computing as a Service: This organization will advance all areas surrounding the transformation of IT through Cloud. Cognitive Computing: This organization will be comprised of the teams focused on next generation IBM Watson capabilities, data and information management, human-computer interface and computer science. Science & Technology: IBM will continue to invest in fundamental science to advance the core technologies that will create the future of computing and enable this new model.

IBM Research - The world’s largest private research institution

http://www-03.ibm.com/press/us/en/pressrelease/38568.wss







China

WatsonAlmaden

Austin

TokyoHaifaZurich

India

Dublin

Melbourne

Brazil

IBM Research labs Labs added since 2010

Kenya

Accolades

Nobel Laureates

▪ Georg Bednorz and Alex Mueller, also of Zurich, in 1987, for research in superconductivity;

▪ Gerd Bining and Heinrich Rohrer, of the Zurich Research Center, in 1986, for the scanning tunneling microscope;

▪ Leo Esaki, of the Thomas J. Watson Research Center in Yorktown Heights, N.Y., in 1973, for work in semiconductors.

Turing Award Recipients

▪ Francis Elizabeth Allen (2006) - Optimizing compiler techniques and automatic parallel execution…

▪ Fred Brooks (1999) - Contributions to computer architecture, operating systems, and software engineering.

▪ James Nicholas Gray (1998) - Transactions / ACID properties… ▪ John Cocke (1987) - RISC, CYK algorithm…

▪ Edgar Codd (1981) - Fundamental and continuing contributions to the theory and practice of database management systems

▪ John Backus (1977) - FORTRAN, BNF, Functional programming…

Five Nobel Laureates

10 U.S. National Medals of Technology

5 U.S. National Medals of Science

6 Turing Awards

19 inductees in the National Academy of Sciences

14 inductees into the U.S. National Inventors Hall of Fame – the most of any company

23 inductees in the U.S. National Academy of Engineering.

Skills in mathematics, computer science, chemistry, physics, operations research, economics, anthropology and many more

http://www.nobelprize.org/nobel_prizes/physics/laureates/1987/





http://www.nobelprize.org/nobel_prizes/physics/laureates/1973/esaki-facts.html

http://amturing.acm.org/award_winners/allen_1012327.cfm

http://amturing.acm.org/award_winners/brooks_1002187.cfm

http://amturing.acm.org/award_winners/cocke_2083115.cfm

http://amturing.acm.org/award_winners/cocke_2083115.cfm

http://amturing.acm.org/award_winners/codd_1000892.cfm

http://amturing.acm.org/award_winners/backus_0703524.cfm


Accomplishments

2015 23th Consecutive Year of Patent Leadership 2011 Watson System 2009 Nanoscale Magnetic Resonance Imaging (MRI) 2008 World’s First Petaflop Supercomputer 2007 Web-scale Mining 2006 Core Extensible Markup Language (XML) Standards 2006 Services Science, Management, Engineering (SSME) 2005 Cell Processor 2004 Blue Gene/L 2003 Carbon Nanotubes 2000 Java Performance 1997 Copper Interconnect Wiring 1997 Secure Internet Communication (HMAC, IPsec) 1997 Deep Blue 1994 Design Patterns 1994 Silicon Germanium (SiGe) 1990 Statistical Machine Translation 1987 High-Temperature Superconductivity* 1986 Scanning Tunneling Microscope* 1980 Reduced Instruction Set Computing (RISC) 1971 Speech Recognition 1970 Relational Database 1967 Fractals 1966 One-Device Memory Cell 1957 FORTRAN 1956 Random Access Memory Accounting Machine (RAMAC)

Floppy disk Hard disk drive Electronic keypunch Automated Teller Machine (ATM) Magnetic strip card Virtual machine Universal Product Code (UPC) Financial swap SABRE airline reservation system Dynamic random access memory (DRAM) US Space Program Twitter Partnership Apple Partnership The Weather Company Human Genome project National Geographic Genographic project Green Horizons project


ExamplesU.S. PATENT #8,547,214: System for preventing handheld device use while operating a vehicle

THINK OF IT AS…A mobile phone forcefield for textaholics. This innovation can analyze your fingerprints, voice patterns, retina images, heartbeat and more—to prevent you from using a handheld device while operating a vehicle. So less attention on your phone and more on that moose that just stepped out in front of you… U.S. PATENT #8,509,526: Object finder within digital images

THINK OF IT AS… ‘Ctrl+F’ for finding images instead of words. This patented tool ‘learns’ what an object looks like, then recognizes it, working more like the brain than a simple search tool.


Fun Facts

Won an Emmy!

https://www.research.ibm.com/index.shtml

http://www.research.ibm.com/articles/madewithatoms.shtml#fbid=RE-IdwgKQZj












Cognitive ComputingAn Introduction


Jeopardy Recap


Cognitive Computing - Definition

▪ Cognitive systems form a category of technologies that use natural language processing, [speech and image recognition, and computer vision] and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition. These systems will learn and interact to provide expert assistance to scientists, engineers, lawyers, and other professionals in a fraction of the time it now takes [at scale].

▪ In addition to human like interaction and machine learning, cognitive systems generally are based around a domain specific semantic model.

▪ Cognitive systems are trained; and probabilistic in their behavior.

The allure of understanding the brain, what it means to know, to create, to reason is not a new topic. AI has been around for 50+ years…So, why now?

http://www.research.ibm.com/cognitive-computing/index.shtml#fbid=RE-IdwgKQZj

http://www.research.ibm.com/cognitive-computing/index.shtml#fbid=RE-IdwgKQZj


Data Grows Exponentially – Demands New Technology and Strategy

You are here

44 zettabytes

unstructured data

2010 2020

structured data

Source: IBM GTO 2015


The cost of not knowing is of critical concern


Data at the Edge – Changing How We Look at Data

90%

By 2017

Of data created over the last 10 years was never captured or analyzed

The collective computing and storage capacity of smartphones will surpass all worldwide servers

60%

2X

Of valuable sensory data loses value in milliseconds

Rate of data creation compared to the expansion of bandwidth over the past decade

Source: IBM GTO 2015


Convergence of Need and Feasibility (and interest)

▪ Exponential Growth of Predominately Unstructured (dark) Data

▪ Human Constraints: – It’s not humanly possible to keep up with all the data – The reasoning experts bring to bear is not always conducive to deterministic programmatic

approaches – Even if it were, domain experts are typically not programmers

▪ Economic Considerations: – The cost of not knowing is of critical concern

▪ Increased Feasibility: Key enabling technologies are more readily available – Biologically inspired systems research – Parallel processing / grid systems* – On going research in NLP, machine learning, scientific/statistical computing e.g.,

• named entity recognition and relation extraction – Advances in processor, storage, and network technologies ** – Semantic web / linked data – Open systems

* Computing, Cognition and the Future of Knowing; Dr. John E. Kelly III, Sr. VP, IBM Research and Solutions Portfolio ** More will be needed IBM GTO 20150


TD Gammon, 1992 (Gerald Tesauro)

▪ a self-teaching neural network that learned to play backgammon at human world championship level

Chess, Deep Blue, 1997 ▪ A finite, mathematically well-defined search space

▪ Limited number of moves and states

▪ Grounded in explicit, unambiguous mathematical rules

▪ We learned something important: H+C > H or C alone

Human Language, Watson, 2011

▪ Ambiguous, contextual and implicit

▪ Grounded only in human cognition

▪ Seemingly infinite number of ways to express the same meaning

Challenges: Past & Present

Watson ExampleA Humanities Approach


▪ Given – Rich Natural Language Questions – Over a Broad Domain of Knowledge

▪ Deliver – Precise Answers: Determine what is being asked & give precise response – Accurate Confidences: Determine likelihood answer is correct – Consumable Justifications: Explain why the answer is right – Fast Response Time: Precision & Confidence in <3 seconds

Objective

http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/gallery.html?lnk=ushpv18ce3

Watson Application Gallery:















Keyword Approach is Insufficient


Cognitive Reasoning


Architectural Decisions & Principles

▪ Separation of concerns

▪ Implemented on UIMA (Unstructured Information Management Architecture) Pipeline

▪ Designed to be Trained (Machine Learning )

▪ Build domain knowledge from both structured and unstructured data – No single monolithic data structure – Each component has and reasons over their own internal representation of structured

data – Did not curate large databases or common-sense axioms for answering questions, rather

exploit what was available on the web. – DBpedia

▪ Defined notion of error, such as precision and recall

▪ Natural Language Processing & Information Retrieval (corpus linguistics)

▪ “Generate then Type”

▪ Headroom trade-offs / iterative development & experimentation cycle


Watson: Departures from Rigidity

▪ Does not rely completely on formal structured knowledge or a translation of natural language into a formal structured query into a precompiled knowledge base.

▪ NOT a type-then-generate approach.

▪ HTF Closed domain QA systems are constrained by question templates map to underlying structured queries.

▪ HTF Open domains QA systems adopt a pipeline of passage search against a reference corpus and generate candidates of the expected answer type (limited).

▪ HTF systems did not exploit inherit characteristics of extensions to the newswire corpus; Watson added Title search and TIC passage search.

▪ HTF QA system adopt a semantic-type based approach to produce answers to match the expected type on the basis of a static ontology.

▪ Watson does not: it uses document metadata to create a substantially large pool of candidates that are later typed coerced then scored.

HTF: Here-to-fore Q&A attempts


Watson Deep QA Architecture - Run Time


Watson Architecture - Training Time


Watson: Iterative Development Cycle Juxtaposed with Cognitive Training (Conceptual)

Domain Identification

Corpus Identification

Training Instance Identification

Data Ingestion

Training cycle

Testing cycle

Production cycle


Metrics

▪ Precision – percentage of questions that the systems gets right out of those it chooses to answer.

▪ Percent Answered – percentage of questions answered (correctly or incorrectly).

▪ Accuracy – Precision if all questions are answered.

▪ Recall – percentage of correctly answered questions where relevant information exists.

▪ Precision@70 - precision when system attempts to answer only 70% of questions

McNemar’s test was used to measure statistical significant for accuracy comparisons

Fischer’s Randomization test was used to measure significant of Precision@70 metric


Example performance graph


McNemar’s Test

TyCor %correct TyCor %incorrect Row total

NoTyCor %correct a b a + bNoTyCor %incorrect c d c + dColumn total a + c b + d n

The null hypothesis of marginal homogeneity states that the two marginal probabilities for each outcome are the same, i.e. pa + pb = pa + pc and pc + pd = pb + pd.

H0 : pb = pc

H1 : pb -= pcTest statistic: X2 = (b-c)2/(b+c)

X2c = (|b-c|-1)2/(b+c) , correction for continuity.

P < 0.05 (Pr Type I error i.e., false positive)


“You don’t understand an answer until you understand the question to which it is the answer.”

Dr. John Goldsmith, Computational Linguistics (class notes), University of Chicago, January 2, 2013.


Step 1: Question Analysis


Step 1: Question Analysis - StrategyWatson both represents a question as a graph of either syntactic relations in a parse (ESG) or

(PAS) or as deep semantic relations rule-matched to an ontology.

QA is implemented as Prolog Rules over PAS and various external databases such as WordNet on top of a UIMA pipeline.

Key Aspects:

▪ Slot grammar parsers (XSG)

▪ Predicate–argument Structure (PAS)

▪ Named entity recognizers/Detectors (NER/NED)

▪ Co-reference/Anaphora (pronoun) ID

▪ Type identification (places, people, dates, and so on)

▪ Relationship detection

▪ Keyword identification

▪ Term/Lexical Answer Type (LAT) identification

Machine learning to determine most likely LATs to consider further

Multiple queries formed, based on full question, LAT, and terms, or inferences

▪ Precision = #Correctly Detected LATs/#Detected LATs ▪ Recall = #Correctly Detected LATs/#LATs in Manually

annotated Set ▪ F1 = 2(Precision) (Recall)/Precision+ Recall ▪ Per Question Recall =

#Question with at least one correctly detected LAT #Questions with at least one manually annotated LAT


English Slot Grammar


Predicate Argument Structure (for the previous ESG Case)

chandelier (1) look (2, subj:1, comp:3) great (3) but (4, lconj:2, rconj:9) [top predicate] nowadays (5) not (7) usually (8) use (9, subj:1, obj:11, vadv:5, vadv:7, vadv:8) item (11, nrel:17) [determiner: these] from (12, objprep:13) which (13) their (14) name (15, ndet:14) [determiner: their] derive (17, obj:15, comp:12)


The General Idea

• I heard that Edison invented the phonograph in 1877. • I heard that Edison invented a phonograph in 1877. • I heard Edison invented the phonograph in 1877. • I heard that Edison was inventing the phonograph in 1877. • I heard that the phonograph was invented by Edison in 1877.

In the PAS, all of the above have identical structure. Any relation rule written over PAS that matches any one of these sentences will match the others. Any question that aligns well in the PAS (e.g., “Who invented the phonograph” ) with one of the passages will align well with the others. Hence, a wide net is cast.


Departures from HTF

Prior Works▪ Developed under the influence of TREC/TAC

(Test Retrieval Conference/Text Analytics Conference) – factoid based

▪ Define a focus as “the set of all maximal noun phrases in the question they co-refer with the answer…no separate concept of LAT (Bunescu & Huang)

▪ Use question analysis to identify semantic answer type from a fixed ontology

▪ Question classification not clearly distinct from answer type detection

Watson▪ Focus / LAT distinction ▪ Separation of concerns where question

analysis identifies the LAT, but the type coercion components determine semantics

▪ Watson separates Q classification and Q Section from LAT detection;


Where We are in the Pipeline (Hypothesis Generation)


Step 2: Primary Search


Step 3: Hypothesis Generation


Step 3 Hypothesis Generation (and the use of Structured Data and Inference)

Architectural Decisions & Departures:

▪ Structured data e.g., DBPedia, Freebase,YAGO (Yet Another Great Ontology) is selectively used to retrieve and score candidate answers.

▪ “Utilize document meta-data to associated salient concepts [represented in frames] with respect to each document and thus create a pool of plausible candidate answers “

▪ Avoid a pipeline of passage search against a reference corpus and generate candidates of the expected answer type

▪ Avoid the traditional approach of trying to precisely and completely translate language into a formal representation

▪ Avoid encoding and populating an underlying schema in a form suitable for answering questions.

▪ Design choice to not curate large databases or common-sense axioms. Wanted to build reusable and adaptable technology.

Futures:

▪ Ontology based coercion –> leverage Linked Open Data for increased coverage and finer grained type information

▪ AI Planning-based solution to interpreting and populating frames.

▪ Automatically induce typical concepts and relations in a frame from a large corpus using data mining.


▪ Type Coercion, a departure from prior approaches that limit answers searches with type constraints.

▪ Late binding of answer to LAT.

▪ Logical Framework

▪ MultipleTyCor components

▪ Used throughout the pipeline – Form of evidence scoring – Uses both structured and unstructured sources e.g., YAGO, WordNet, PRISMATIC

Answer Scoring: Type Coercion (TYCOR)

EDM Type Retrieval

PDM Type Alignment

Candidate

LAT TyCor

EDM: Entity Disambiguation and Matching PDM: Predicate-Argument Disambiguation and Matching

YAGOGender Closed LATLexicalNED WordNet

Wiki-CategoryWiki-ListWiki-Intro Identify Passage PRISMATIC


TyCor Example: Consider a question asking for an “emporer” and the candidate answer is “Napoleon”

▪ Step 1: EDM – TyCor uses Wikipedia or derived source

e.g., YAGO, Wiki-List) to find dbbedia:Napolean

– Also finds dbpedia:Napoleon%28card_game_%29, scores this low

– WordNet TyCor finds 3 difference word senses

– Passage TyCor finds “Napoleon” in supporting passages

– NED & Identify TyCor declare “Napoleon” and entity

▪ Step 2:PDM – Basically a similar process looks at the

LAT focusing on “emporer” viz., • In this case NED TyCor marks

“emporer” as a NOMINAL reference to a Political leader

• As with TR, some PDM strategies return a string and defer to TA


TyCor Example Continued…

▪ Step 3: TR (Text Retrieval) – TyCor strategies that identify a formal

entity during EDM e.g. DBPedia URL or WordNet synset are generally able to look up 1 or more formal types for that entity in a structured source e.g.

• WordNet TyCor labels ”Napoleon” and instance of “emporer”

• NED TyCor labels “Napoleon” as A NAME reference to a Political leader

– The TyCor strategies that product text string defer to Type Alignment

▪ Step 4: TA (Text Alignment) – For the TyCors , such as WordNet,

YAGO, NED that produce formal types in a Type hierarchy from TR and PDM,the alignment process matches the graphs ie., looking for subsumption, disjointness, etc. e.g, is the entity type (from TR) a hyponym (more specific instance) of the question’s type (from the PDM)?

– Where text strings are produced from TR and PDM viz, Wiki-List TyCor , the strategy is to conclude that a “French monarch” is an “emperor” by parsing both (e.g., finding that “monarch” is the headword of “French monarch”) and matching terms using resources such as WordNet and Wikipedia redirects.


4A: Answer Scoring


4B: Passage Scoring


Step 6: Final Confidence Merging and Ranking


Final Confidence Merging and Ranking

Regularized logistic regression is applied as follows:

QAi: Question-Answer Pair is an instance. Each instance QAi has a feature vector Xi consisting of ranking scores Each instance is labeled Yi as +1 or -1. i=1,…,M

Regularized* Objective function:

Where g is the logistic function.

http://cs229.stanford.edu/notes/cs229-notes1.pdfGradient descent optimization:

*regularized to avoid overfitting

http://www.holehouse.org/mlclass/07_Regularization.html

http://cs229.stanford.edu/notes/cs229-notes1.pdf







Phase Based Machine Learning Framework

Hitlist Normalization Rank and retain top 100

Base Partition into question classes

Transfer Learning For Uncommon question

Answer Learning Merge evidence between equivalent answers and select canonical forms

Elite Successive refinement – top 5

Multi-Answers Join answers candidates for Question requiring multiple answers

Evidence Merging Post-processing

Evidence Diffusion Diffuse Evidence between related answers

Each phase has these 3 steps:

Classification

Evidence Merging Post-processing Classification







Successive classification cycles of training and application

Training using Manual Answer key

Application to Testing data

Training using Manual Answer key

Application to Testing data

Phase n

Phase n+1

Evidence Merging

Post- processing Removes/derivces fetures

Classifier: Training Mode Produce model

Classifier: Application Mode Rank/estimate confidence

Evidence Merging

Post- processing Removes/derivces fetures

Contribution to feature set

Contribution to feature set

Model

Model


“Minimal” Deep QA Pipeline (Complete)

Vision, Challenges and Considerations


Watson Oncology

https://www.mskcc.org/about/innovative-collaborations/watson-oncology







Vision: Building a Society of Cognitive Agents

Watson

Cognitive Agent to AgentOutage

Model

Consequence Table

Smart Swaps

Lighting

Critical Sites

Objective Identification

Sensitivity Analysis

Sentiment Analysis

Systems of cognitive agents that collaborate effectively with one another

Cognitive agents that collaborate effectively with people through natural user interfaces

A nucleus from which an internet-scale cognitive computing cloud can be built

Personal Avatar

Deep Thunder

Crew Scheduler

News

Human to Human

Cognitive Agent to Human


The Challenges

▪ Curation and the challenges of handling ever increasing volume of data

▪ Integration of new forms of input e.g., visual, audio, which will require

▪ Specialized and scalable infrastructure

▪ Closed domain adaptation

▪ Cross domain understanding

▪ Unsupervised learning

▪ Relation detection in text

▪ Integration of cognitive capabilities into existing processes and applications

All requiring collaboration across a variety of scientific, technological, and academic disciplines.


Factorization of Cognitive components

https://console.ng.bluemix.net/catalog/?org=6cd214ad-7568-44ef-bfad-cb9b6b8fa44d&space=539c8391-27b5-4c6f-8534-bbe2d5ddd32a&context=services





Ethical Consideration

“Technology creates possibilities and potential, but ultimately, the future we get will depend on the choices we make. Technology is not destiny. We shape our destiny.” – Erik Brynjolfsson”, MIT


Ways to Engage…

▪ UChicago Cognitive Computing Challenge (http://cognitivecomputingchallenge-sites.uchicago.e) – Sign up for the "IBM Watson Cognitive Computing Practicum" being offered this Spring! You'll get to

work with me and Cornelia! – Got an idea for a cognitive project of your own? Submit it here: https://goo.gl/yrg1gX and we'll

evaluate it.

▪ UChicago Departments of – Computer Science – Statistics – Linguistics – Psychology – Neurology – …

▪ IBM Cloud – http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/gallery.html?lnk=ushpv18ce3 – http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html – https://console.ng.bluemix.net/

▪ IBM & IBM Research – http://www-03.ibm.com/employment/ – http://www.research.ibm.com/careers/

▪ References – https://www.research.ibm.com/labs/watson/ – https://www.research.ibm.com/cognitive-computing/index.shtml

▪ And there is the world at large!

http://cognitivecomputingchallenge-sites.uchicago.edu/

http://cognitivecomputingchallenge-sites.uchicago.edu/
















http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html












https://console.ng.bluemix.net/

https://console.ng.bluemix.net/

http://www-03.ibm.com/employment/

http://www.research.ibm.com/careers/

http://www.research.ibm.com/careers/

https://www.research.ibm.com/labs/watson/

https://www.research.ibm.com/labs/watson/

https://www.research.ibm.com/cognitive-computing/index.shtml

https://www.research.ibm.com/cognitive-computing/index.shtml

Appendix


Systems of Insight

Architecture: Portfolio Integration

Existing Systems of Record

Cognitive Component

Corpus

Data

Eng

agem

ent

Laye

r

End Users

References


References

1. Dr. John E. Kelly III,Senior VP, IBM Research and Solutions Portfolio; “Computing, Cognition and the Future of Knowing”, Whitepaper.

2. D. A. Ferrucci; “Introduction to This is Watson”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

3. A. Lally, J. M. Prager, M. C. McCord, B. K. Boguraev, S. Patwardhan, J. Fan, P. Fodor,and J. Chu-Carroll; “Question analysis: How Watson reads a clue”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

4. M. C. McCord, J. W. Murdock, and B. K. Boguraev; “Deep parsing in Watson”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

5. J. Chu-Carroll, J. Fan, N. Schlaefer, and W. Zadrozny; “Textual resource acquisition and engineering”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

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References Continued…

8. J. W. Murdock, A. Kalyanpur, C. Welty, J. Fan, D. A. Ferrucci, D. C. Gondek, L. Zhang, and H. Kanayama; “Typing candidate answers using type coercion”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

9. J. W. Murdock, J. Fan, A. Lally, H. Shima, and B. K. Boguraev; “Textual evidence gathering and analysis”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

10. C. Wang, A. Kalyanpur, J. Fan, B. K. Boguraev, and D. C. Gondek; “Relation extraction and scoring in DeepQA”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

11. A. Kalyanpur, B. K. Boguraev, S. Patwardhan, J. W. Murdock, A. Lally, C. Welty, J. M. Prager,B. Coppola, A. Fokoue-Nkoutche, L. Zhang, Y. Pan, and Z. M. Qiu; “Structured data and inference in DeepQA”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

12. J. M. Prager, E. W. Brown, and J. Chu-Carroll; “Special Questions and techniques”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

13. J. Chu-Carroll, E. W. Brown, A. Lally, and J. W. Murdock; “Identifying implicit relationships”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

14. A. Kalyanpur, S. Patwardhan, B. K. Boguraev, A. Lally, and J. Chu-Carroll; “Fact-based question decomposition in DeepQA”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.



15. D. C. Gondek, A. Lally, A. Kalyanpur, J. W. Murdock, P. A. Duboue, L. Zhang, Y. Pan, Z. M. Qiu,and C. Welty; “A framework for merging and ranking of answers in DeepQA” ; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

16. E. A. Epstein, M. I. Schor, B. S. Iyer, A. Lally, E. W. Brown, and J. Cwiklik; “Making Watson fast”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

17. G. Tesauro, D. C. Gondek, J. Lenchner, J. Fan, and J. M. Prager; “Simulation, learning, and optimization techniques in Watson’s game strategies”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

18. B. L. Lewis; “In the game: The interface between Watson and Jeopardy!”; IBM J. Res. & Dev., vol 56, no. ¾, May/Jul 2012.

19. IBM Global Technology Outlook 2015.

20. K. Maney, S. Hamm, J.M. O’Brien; Making the World Work Better The Ideas That Shaped A Century and a Company; NY, IBM Press, 2011.

21. Dr. John Goldsmith, Computational Linguistics (class notes), University of Chicago,January 2, 2013.



22. J. Hurwitz, M. Kaufman, A. Bowles; Cognitive Computing and Big Data Analysis; Indianapolis, Wiley & Sons, 2015.

23. D. Ferrucci, E. Brown, J. Chu-Carroll, J. Fan, D. Gondek, A.A. Kalyanpur, A. Lally, J. William Murdock, E. Nyberg, J.Prager, N. Schlaeger, C. Welty; “The AI Behind Watson – The Technical Article”; AI Magazine Fall, 2010.

24. Alfio Massimiliano Gliozzo; “Cognitive computing – Deep QA in IBM’s Watson Jeopardy!¨ system and beyond” (Classroom slides) ; 2014.

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