The Hive Think Tank: AI in The Enterprise by Venkat Srinivasan

© Rage Frameworks Inc, 2016. All rights reserved.

Enabling the Intelligent Enterprise

AI in the EnterpriseThe Hive Think TankJan 26, 2017

© Rage Frameworks Inc, 2016. All rights reserved. | 2

The Resurgence of AI …it’s possible

Source: The Intelligent Enterprise in the Era of Big Data, Srinivasan, Wiley, 2016

Google’s DeepMind wins historic Go content 4-1 The recent accident on a Tesla vehicle in autopilot mode


AI in the EnterpriseKey Dimensions of Machine Intelligence

…it’s possible

Computer Visioning Solutions

Non-Visioning Solutions

Computational Statistics

Knowledge Acquisition / Representation

Computational Linguistics



AI in the EnterpriseA Taxonomy of Machine Intelligence Problem Types

…it’s possible

Ad Hoc SearchClustering

Prediction [Quantitative data]

Extraction

Classification [Qualititative, Hybrid data]

Interpretation[Natural language,Other data]

Prediction, Classification

ArtificialIntelligence

(Machine Intelligence)

Intelligence ThruExplicitly

Assumed Models of Data

Learnfrom Data Algorithmically

Learn to Interpret/UnderstandMeaning



AI in the EnterpriseMachine Intelligence Acquisition Methods

…it’s possible



AI in the EnterpriseMachine Intelligence Acquisition Methods

…it’s possible

Pragmatics

AutomatedKnowledgeDiscoverer

DomainDiscourseModel

Public Content

PrivateContent

RAGEKnowledgeNet™

WordNetConceptNetFrameNet…

Cognitive Semantic NetworksDeep Parsed Linguistic MapsTopic ClustersSyntactic ResultsSemantic RolesSeed Concept (Optional)

Knowledge Type Constraints


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AI in the EnterpriseMachine Intelligence - Functional Architecture

…it’s possible



AI in the EnterpriseMachine Intelligence VS Intelligent Machines

…it’s possible

Machine Intelligence

Computational Statistics

Knowledge Acquisition / Representation

Computational Linguistics


Intelligent Machine

Ingest

Process

Decide

Document

Communicate

Intelligence

Analytics

Integrating into a Mission Critical Production

Business Process


Examples of Intelligent Machines in the Enterprise …it’s possible

Wealth Management Active Advising

Commercial Loan Origination

Financial Statement Spreading

Client Onboarding

Data Quality Monitoring

Real Time Intelligence for Cap Markets

Knowledge Management

Customer and Market Intelligence

RAGE KYC Framework

RTITM : Credit & Supplier Risk

Sales Lead Generation

Automated Contract Review

Customer Service IntelligenceAutomated Billing Reconciliation

Supply Chain Cost Audit

Business Rules Engine Model Engine

NLP Engine

Quality Assurance Framework

Web Services Engine

Decision Tree Engine

Computational Linguistics Engine

Model Network Engine

Data Access Engine

Desktop Integration Engine

Connector Factory Engine

Questionnaire Engine

Real Time Content Integration Engine

Assignment Engine

Message Engine

External Object Engine

Extraction EngineRepository

Intelligent Doc Builder Engine

User Interface Engine

Process Assembly Engine



Extraction from Semi, Unstructured DocumentsFinancial Information


RAGE LiveSpread™ Process Flow

…it’s possible

Data extraction from any format including: pdf, excel, images, paper, web scraping etc.

Extraction Normalization User defined Normalization

Exceptions and Quality

Presentation and Analytics

Integration

Normalized using -Industry templates;; Pull from footnotes;;Footnote interpretation linked to line items;; 30 plus languageRules buy Country;;

User defined normalization ruleset via self service screens

Exception handling of data accuracy, in-built quality assurance and business rule compliance

Presentation of spread data and financial ratio calculations

Integration into client’s core systems

Analytics (add on)

Credit score cards and risk monitoringEquity modelsCustom Analytics (M&A deal sourcing, Audit etc.)

Feature snapshot• Industry specific normalization of data• Analysis of revolving credit lines• Auditors’ opinion on the financial statements captured• Key break-ups from notes to financials• Industry ID data: NAICS, SIC or GICS codes• Adjustments for extraordinary/one-time/non-cash items• Details on operating leases and contractual obligations• Financial covenant tracking and alerts• Automated QA checks• Multiple MRA load/delivery options

LiveSpread

AutomatedReceiverFax

Email

HumanExperts

Automated Extractor

AutomatedNormalizer

Golden Corpus

AutoDiscovered Extraction Rules

SourceDocs

Data Feeds [Acctgpkgs]

• PDF Processor• OCR Enhancer• Computational Geometry Engine

• Tabular Extractor• NLP• Quality Assurance Rules• Traceable links to source

LiveSpreadUpload™

SpreadData

• NLP• Flexible Normalization Templates

• Quality Assurance Rules

Auto Discovered Mapping R

HumanExperts

Exceptions

• Traceable Linguistics based Deep Learning

LiveSpread™An Intelligent Machine for Financial Statement Processing


Input Form and Format Variability …it’s possible


Normalization ExampleNon English Document

…it’s possible

Normalized Output

Italian document

Normalization rules


Example of Extraction from Footnotes …it’s possible

Notes to the financial statements (note 4)

Final Output - Spreadsheet

Balance Sheet

After pulling out breakups from notes to the financials

Before capturing the breakups

Breakups for fixed assets identified and extracted from notes


Example of Extraction from Footnotes …it’s possible

Key breakups for Operating expenses were pulled from Operating Leases note as they were unavailable in the Income Statement.

Notes to the financial statements

Final Output - SpreadsheetIncome Statement


Normalizing GAAP Rules Across Countries …it’s possible

Final Output - SpreadsheetOriginal Document

Normalized Metadata – Rule FileCanadian GAAP

US GAAP

Bank charges map differently to Interest expenses [As per Canadian GAAP] and to Other expenses [As per US GAAP]



Classification with Natural Language UnderstandingCustomer Service Intelligence



U.S.

Background on the customer data analytics project

| 19

• The objective: To aggregate all the unstructured data, within Seibel, from various communication types with the customers, extract, interpret, analyze, and deliver insights to make decisions rooted in data and insights.

• Key questions for the analysis:

• What are the primary reasons reasons customers are contacted or customers contact us? How do these reasons rank by volume?

• What are the underlying reason customers are contacted or customers contact us?

• Do these reasons shed light on the process elements or processes that may be resulting in repeated customer outreach to us or customer dissatisfaction?

• Are there any inefficiencies in customer service processes, based on the service request fulfillment attributes e.g. number of times back and forth communication with the customers, which can shed some light on the process inefficiencies?

© Rage Frameworks Inc., 2016. All rights reserved

Emails

Semantic Topic –Order Rescheduling

Semantic Topic -‐Order Cancellation

Subject:Weston Pallet CountFrom: kgxxxxxTo: Exxxx Dxxxxx; Jxxxxx fxxxxx; CxxxCC: Daxx Wxxxx; Dxxx RxxxxxDate: 2014-‐11-‐06 12:25:57

Hi Eliza,

The count for today is 1299 @ 11:30 am

The pallet count is high with production requirements. Please cancel Thursday 3 pm load 4703423658.

Take Care,Kexxxx

From: Cxxxx-‐CxxxSent: Thursday, November 06, 2014 12:42 PMTo: Kxxxx Gxxxxxx; Exxxx Dxxxxx; Jxxxxx fxxxxx; CxxxCc: Daxx Wxxxx; Dxxx RxxxxxSubject: RE: Weston Pallet Count

Good day,

Please be advised that PO#4703423658 has been changed to tomorrow delivery at 3pm as requested in yesterdays email.

Please see the remaining orders for today/tomorrow;

Thank you/Merci, Allxxxx Mcxxxx

Original Topic – Pallet Count


Natural Language Understanding + ExtractionLogistics Cost Audit & Contract Review


© Rage Frameworks Inc, 2016. All rights reserved.| 22

Intelligent Machine for Cost AuditHow machine learning is applied to deliver insights and speed-to-value?

…it’s possible

Extract Integrate Interpret and Categorize

Reconcile and Analytics

VisualizationClassify

Extract content from wide-variety of document types

Decompose documents, discover taxonomy, normalize

taxonomy

Train to interpret in specific business context and extract targeted data for

analytics

Apply data mapping, business rules,

calculations, models, and user driven learning

• Yes ML

• Format detection

• Pixel correction

• Character recognition

• Linguistics correction

• Numeric correction

• Yes ML

• Machine learns from exception management performed by humans

• Yes ML

• Train the machine to interpret based on business context not rules

• Connect the information for the same provision across documents

• No ML

• RAGE configurable connector factory is used to rapidly, non-intrusively integrate with hundreds of data source (SAP, CRM, TMS, Legacy etc.).

• Yes ML

• Auto-discover document structure, key provisions, tables

• Auto-discover key concepts, and relationships

• Assisted ML to finalize taxonomy and target output

Connect with a variety enterprise/legacy systems just via configuration

Customizable user interface developed just via configuration

• No ML

• Rapidly configurecustom UI to display right charts, visuals.

• Can be customized by users

• Can be changed very rapidly as the needs change

Information flow

Machine learning

Output • Very little to no IT

time needed • Extract clean content from heterogeneous quality and variety (PDF types, images) of documents

• Entire document is read

• Provisions are classified based on language/concept relationship not key words and positions

• High accuracy of content categorization as the search is business context (e.g. Kroger) driven

• Human based exception management declines dramatically.

• Custom user interface to deliver specific insights that can be changed rapidly without coding


RAGE AI Classification and Categorization Process Assisted deep learning is deployed for taxonomy creation

…it’s possible

Load Document

Auto Discovery

Filter the Auto Discovered Output

Build Ontology (SI

App)

Upload Document not seen by the system

Execute Contract Review Process

Output Not ExtractedOutput

Extracted

False PositivePartial Match

[Low confidence score]

Accurate Extraction

Validate the Output

Document Decompositions


Classification Process – Document DecompositionMachine learning automatically identifies document hierarchy and relationships

…it’s possible

PDF Contract Agreement

Domain Discourse Model

Document decomposition helps identify sections, sub-sections and their relationship with each other


Classification Process – Auto DiscoveryExample to discover and related content from tables (e.g. Schedule A and Invoices)

…it’s possible

The engine parses the entire table content even though there are multiple variations within a single table and treat each one of them separately. The variations are as follows:

Route InformationMileage InformationDrop InformationFees InformationTotal

1 23

4

5

Document Type: Invoice

12

345


Interpretation with Natural Language UnderstandingReal Time Intelligence

Fund Managers/Competitive & Market Intelligence/Customer/Supplier Risk


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RTI systematically interprets and analyzes all publicly and privately available content in the context of a company, an industry and macro environment, to generate RTI Signal

Heatmaps draw attention to securities with the most change in their cumulative signal strength highlighting the overall impact on a company from the market developments around it

For each company, the RTI Signal can be further broken down by specific business drivers that may be impacting a company

RTI Signal leads the stock price for 30 – 40% of the companies in RAGE portfolio (Coverage over 8000 companies)

For each company, the cumulative RTI Signal can be tracked over time with key triggers by date

4. alpha – RTI vs Stock price 3. Company view over time

1. Portfolio View 2. Company view by business drivers

Stock Price (Log)

RTI Cumulative Score

1.66

1.68

1.7

1.72

1.74

1.76

1.78

1.8

1.82

1.84

-‐1.5

-‐1

-‐0.5

0

0.5

1

1.5

2

04/01 04/22 05/13 06/03 06/24 07/15 08/05 08/26 09/16 10/07 10/28 11/18

RTI Stock Price (Log)

RTI is not a black box: Drill down into the business drivers to see specific content pertaining to that driver deemed relevant by the RAGE Semantics Engine

| 28

5Expand the Factors to drill down into content pertaining to that factor

29

Impact Network – Wal-Mart Stores, Inc. (WMT) Plans To Unseat Amazon.com, Inc. (AMZN) Prime (Topic: Expansion and Closure;; Score: 0.3)

1stOrder Effect

http://learnbonds.com/118763/wal-mart-stores-inc-wmt-plans-to-unseat-amazon-com-inc-amzn-prime/118763/

Topic: Expansion and ClosureDriver: Product LaunchSector: RetailPrimary Impact: Low Medium Positive

RAGE SI

Engine

S1

S2

S3

S4

S5

Impact Network[Deep Semantic Interpretational Map]

S6

S7

S1: Wal-Mart Stores, Inc. (NYSE:WMT) plans to rival Amazon.com, Inc. (NASDAQ:AMZN) with the launch of a new delivery system that costs less.

30

Real Time IntelligenceRTI Signal Leads Stock Price - Wal-Mart Stores, Inc. [WMT.N]

©Rage Frameworks Inc, 2016. All rights reserved.

Business Driver - Same Store SalesJan 7th, 2015 - RetailNext -Foot traffic dropped 8.3 percent during November and December versus a year ago at the specialty stores and large retailers .

0

5

10

15

20

25

30

35

55

60

65

70

75

80

85

90

95

Alpha Signal Rating

Stock Price

Business Driver – Consumer Confidence Oct 15th, 2015 – Bloomberg.com - Improving views of personal finances signal the turmoil in financial markets and slowdown in hiring is not affectingconsumer psyches, which bodes well for sustained gains in consumer spending.

Business Driver - ExpansionJuly 22, 2015 –Supermarketnews.comThe new 1.2-million-square-foot center is part of a "next-generation" network to support Walmart's rapidly growing e-commerce business. It features state-of-the-art automation and warehousing systems.

Business Driver – Retail SalesJan 20th, 2016 – economywatch.com Americans spent $626.1 billion in the holiday season, representing a 3.7 percent increase on a year-over-year basis when including online sales.

Signal

StockPrice


AI in the EnterpriseSummary



AI in the EnterpriseMachine Intelligence Acquisition: Method Fit

…it’s possible

Source: The Intelligent Enterprise in the BigData Era, Srinivasan, Wiley, 2016

n How important is it to start with a high level of accuracy [precision and recall]? How expensive is a mistake? Both false positive and false negative.

n How much variability is there in the underlying phenomenon and therefore data? The larger the variability like unstructured text, the training sample needs to be extremely large to get reasonable results

n Can you live with a black box? Do you need transparency in the engine’s reasoning? Do you need to trace its reasoning so you can understand ‘causality’?

n Random Forests [Breiman] and Natural Language Understanding [RAGE AI™] are traceable methods. High levels of variability and/or high cost of mistakes strongly imply traceable and transparent methods.


Summary …it’s possible

n AI seems to be back in full force and this time getting integrated into the mainstream

n Big Data. The ability to analyze entire populations vs samples has allowed assumption-free algorithmic approaches to flourish vs the traditional ‘data model’. We are letting the data tell us the story vs assuming prior behavior of data;; but key challenges wrt text are context, language and traceability

n Deep learning with deep linguistic parsing in context will allow us to create ‘natural language understanding’ in machines vs just ‘natural language processing’

n AI vs Machine Intelligence. AI = Automation including knowledge-based tasks. Machine Intelligence = embedding intelligence and learning from data and experts continuously to enable AI.

n With all these advances, enterprise business architecture will change dramatically. Execution will be largely thru Intelligent Machines. Design will be machine informed. The rate of change in the role of humans will accelerate.




AI in the EnterpriseThe Hive Think TankJan 26, 2017

Technology

The Hive Think Tank: AI in The Enterprise by Venkat Srinivasan