the hive think tank: ai in the enterprise by venkat srinivasan
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© 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
© Rage Frameworks Inc, 2016. All rights reserved. | 3
AI in the EnterpriseKey Dimensions of Machine Intelligence
…it’s possible
Computer Visioning Solutions
Non-Visioning Solutions
Computational Statistics
Knowledge Acquisition / Representation
Computational Linguistics
Source: The Intelligent Enterprise in the Era of Big Data, Srinivasan, Wiley, 2016
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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
Source: The Intelligent Enterprise in the Era of Big Data, Srinivasan, Wiley, 2016
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AI in the EnterpriseMachine Intelligence Acquisition Methods
…it’s possible
Source: The Intelligent Enterprise in the Era of Big Data, Srinivasan, Wiley, 2016
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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
Source: The Intelligent Enterprise in the Era of Big Data, Srinivasan, Wiley, 2016
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AI in the EnterpriseMachine Intelligence - Functional Architecture
…it’s possible
Source: The Intelligent Enterprise in the Era of Big Data, Srinivasan, Wiley, 2016
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AI in the EnterpriseMachine Intelligence VS Intelligent Machines
…it’s possible
Machine Intelligence
Computational Statistics
Knowledge Acquisition / Representation
Computational Linguistics
Source: The Intelligent Enterprise in the Era of Big Data, Srinivasan, Wiley, 2016
Intelligent Machine
Ingest
Process
Decide
Document
Communicate
Intelligence
Analytics
Integrating into a Mission Critical Production
Business Process
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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
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Enabling the Intelligent Enterprise
Extraction from Semi, Unstructured DocumentsFinancial Information
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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
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
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Input Form and Format Variability …it’s possible
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Normalization ExampleNon English Document
…it’s possible
Normalized Output
Italian document
Normalization rules
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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
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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
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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]
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Enabling the Intelligent Enterprise
Classification with Natural Language UnderstandingCustomer Service Intelligence
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U.S.
Background on the customer data analytics project
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• 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
Enabling the Intelligent Enterprise
Natural Language Understanding + ExtractionLogistics Cost Audit & Contract Review
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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 Frameworks Inc, 2016. All rights reserved.| 23
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
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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
© Rage Frameworks Inc, 2016. All rights reserved.| 25
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
Enabling the Intelligent Enterprise
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
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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]
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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
Enabling the Intelligent Enterprise
AI in the EnterpriseSummary
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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.
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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.
Source: The Intelligent Enterprise in the Era of Big Data, Srinivasan, Wiley, 2016
© Rage Frameworks Inc, 2016. All rights reserved.
Enabling the Intelligent Enterprise
AI in the EnterpriseThe Hive Think TankJan 26, 2017
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