Coming of Age of Artificial Intelligence - Home -...

Coming of Age of Artificial Intelligence

Disruption, Transformation Ahead

Cyrille Bataller

Copyright © 2015 Accenture All rights reserved. 2

Unified Desktops/Mashups

Robotics in Business Operations: Understanding the Landscape

Robotic Process Automation

Artificial Intelligence

Cognitive Robotics/ Virtual Agents

Multiple screens are consolidated into

a single view for the operator thus

saving the time to toggle between

screens. Suitable for contact center

type applications.

Automation of transactions and work

flow activities by handling input,

processing, and output of data across

systems.

Systems that gain knowledge from

data as “experience” and generalize

what is learned in upcoming situations.

Sense, comprehend, act – and learn.

Natural language dialogue.

Robotics

Spectrum

Basic Automation – Minibots

Applying technology to manipulate

existing application software to

complete a process. Primarily based

on XL, AutoHotKey, or Visual Basic.

http://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAQQjRw&url=http://www.blueprism.com/&ei=er47U_qmDKSL0AWHpIGoBw&usg=AFQjCNHyZtxaUTUeVOc1gvzapNo7PVvJ8A&bvm=bv.63934634,d.d2k

http://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAQQjRw&url=http://www.blueprism.com/&ei=er47U_qmDKSL0AWHpIGoBw&usg=AFQjCNHyZtxaUTUeVOc1gvzapNo7PVvJ8A&bvm=bv.63934634,d.d2k

http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&docid=o5NqsyOABMPvYM&tbnid=XLiYeNamCOwbiM:&ved=0CAUQjRw&url=http://www.gleanster.com/category/voice-of-the-customer&ei=pb47U76pBs3M0AXd6YG4BQ&bvm=bv.63934634,d.d2k&psig=AFQjCNGtw_nObvq3vjdljFviCnJItsKNZQ&ust=1396510743893890

http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&docid=o5NqsyOABMPvYM&tbnid=XLiYeNamCOwbiM:&ved=0CAUQjRw&url=http://www.gleanster.com/category/voice-of-the-customer&ei=pb47U76pBs3M0AXd6YG4BQ&bvm=bv.63934634,d.d2k&psig=AFQjCNGtw_nObvq3vjdljFviCnJItsKNZQ&ust=1396510743893890


AI: IT Systems that can Sense, Comprehend, Act – and LearnMaybe the Most Disruptive Emerging Technology on the Horizon

Artificial Intelligence enables machines to interact naturally with their

environment, people and data. These systems create more intuitive interactions

and extend the capabilities of what either human or machine can do on their own.

From coding to training

AI enables intelligent systems that learn from a body of knowledge

without analyzing and coding all business rules manually.

Advances in machine learning, coupled with big

data and cheap, ubiquitous cloud computing

will unleash remarkable new potential for

organizations across industries.


AI: IT Systems that can Sense, Comprehend, Act – and LearnA Collection of Technologies Underpinned by Machine Learning

Source: Gartner's Hype Cycle for Human-Computer Interactions

Artificial Intelligence encompasses

multiple technologies that enable

computers to

• perceive the world (e.g.,

computer vision, audio

processing or sensor

processing)

• analyze and understand the

information collected (e.g.,

natural language processing or

knowledge representation)

• make informed decisions

(e.g., inference engines,

machine learning/deep learning

or expert systems)


Initial research focused on Artificial Intelligence (AI), "the science aiming to create intelligent machines that are as capable as humans.”

Distinction between “strong” and “weak” AI. Also, recently, the focus has shifted from AI to Intelligence Augmentation (IA), intelligent systems that can support humans in their activities.

Artificial Intelligence HistoryVarious Attempts and “Winters” since Computing was Invented

Source: Accenture

Secondary Research

Audi’s driverless

race car

Border control

“robots”

Google driverless car

1.7M miles accident-free

Ask Sgt Star

Ask Jenn

http://en.wikipedia.org/wiki/Turing_test

http://en.wikipedia.org/wiki/Dartmouth_Conferences

http://en.wikipedia.org/wiki/Perceptron

http://en.wikipedia.org/wiki/Arthur_Samuel

http://www-03.ibm.com/ibm/history/exhibits/specialprod1/specialprod1_7.html

http://en.wikipedia.org/wiki/Shakey_the_robot

http://en.wikipedia.org/wiki/ELIZA

http://en.wikipedia.org/wiki/Xcon

http://en.wikipedia.org/wiki/Driverless_car#1990s

http://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)

http://www.ai.mit.edu/projects/humanoid-robotics-group/kismet/kismet.html

http://asimo.honda.com/

http://en.wikipedia.org/wiki/Watson_(computer)

http://www.apple.com/ios/siri/

http://www.google.com/landing/now/#whatisit

http://www.ipsoft.com/

http://www.wired.com/autopia/2010/03/audi-autonomous-tts-pikes-peak/

http://online.wsj.com/news/articles/SB10001424052702304244904579276851803909952?mod=WSJEurope_hpp_LEFTTopStories

http://uk.businessinsider.com/driverless-car-technology-just-got-a-huge-boost-2015-5?r=US

http://www.nextit.com/clients/us-army

http://www.nextit.com/clients/us-army

http://www.goarmy.com/ask-sgt-star.html

http://www.alaskaair.com/


Artificial Intelligence Toolbox – Various Application Domains

Text AnalyticsResearch Assistants

Image AnalyticsMultimedia

SearchCognitive Robotics

Virtual Agents Expert Systems

Video Analytics Identity Analytics Speech Analytics Data VisualizationDomain-specific

CalculationsRecommendation

SystemsSelf-Adjusting IT

Systems

- Text Analytics: process large volumes of unstructured text and classify/group etc

- Research Assistant: natural language search engine to retrieve subset of relevant documents from large unstructured dataset

- Deep Learning: apply deep learning to detect certain characteristics – e.g. cancer tumor in MRI scan, or find tall trees from a drone

- Multimedia Search: retrieve specific images, videos or sounds based on content rather than user metadata

- Cognitive Robotics: automate manual processes using UIs to access multiple systems e.g. testing, claims processing

- Virtual Agent: automate helpdesks to solve user issues

- Expert System: learn a product manual and answer questions or assist a user with step by step instructions

- Video Analytics: apply computer vision to CCTV cameras

- Identity Analytics: recognize people based on what they have/are/know/do incl. biometrics

- Speech Analytics: speech to text/speech recognition, text to speech

- Data Visualization: ask questions in natural language on large structured dataset

- Domain-specific Calculations: perform calculations for a specific domain in natural language

- Recommendation Systems: provide “people like you” type recommendations

- Self-Adjusting IT Systems: ability for IT system to adjust based on historical usage patterns

Sense

Comprehend

Act

Computer Vision

Audio Processing

Sensor ProcessingNatural Language Processing

Knowledge RepresentationInference Engines

Machine Learning

Expert Systems

Technologies

Capabilities

Solutions

Affective Computing

AutomaticTrend

Detection

Anomaly Detection

Semantic Web

Ontology Learning

Augmented Reality

Text Analytics


Case Study: PharmacoVigilanceMonitoring Adverse Drug Reactions

Creation of Adverse Drug Events (ADE) is required for assessing safety of a drug against usage, severity

etc. NLP can help generate Adverse Event signals from various therapeutic reports, establish

associations and report suspected new cases

Individual Case Safety

Reports and

Narratives database

Regulatory

warning and self

reporting

Social and

Public Data

Text Screening

and extractionEntity

Recognition

Semantic

modeling and

signal

generation

Case

Suggestion

Relevant text

portions are

extracted

1Entities like

Patient,

Symptom,

Drug, Usage

etc. are

recognized

2Relationships

between

Patients,

Disease,

Symptom,

Drug, Dosage

etc are

established

3Based on

Statistical

association

measures on

aggregate data

a case is

suggested to

the writer

4

Assisted PV Processing


Accenture Text Analyzer for PharmacoVigilance


Accenture Text Analyzer for PharmacoVigilance

Inference on Expectedness and Causality

Cognitive Robotics


Benefits of Robotic Automation

Reduces transactional errors

Drives higher accuracy

Mistake proof processes

Enhances compliance & controls

Drives improvements in business outcome through improvement in time, quality &

cost of transaction

Accelerating business outcomes without increasing program complexity or headcount

Increases customer satisfaction

Reduces cost, as we automate transaction processing

Helps to provide higher productivity benefits

Higher efficiency in process & reduction of non value added activities


Case StudiesValue Delivered by Robotic Automation

Client Productivity ImpactTools Deployed Business Outcomes

Large Distribution

(Order Entry process)21 FTEsOCR

Large retail client

(Contract Management

@Client)

25% FTE reductionMashups

Large manufacturing

(Invoice Processing for

Single Line invoices PO

& Non-PO Automated)

30% FTE reductionMashups

Large retail client

(PTP, RTR and Store

Accounting)

28 FTEsRPA

Large Consumer

Goods

(Exit process

automation)

50% FTE reductionRPA

Large Retail client

(Order Entry Indexing)

• 52% reduction in transaction handling time (10.5mins to 5mins)

• Enables adherence to peak demand TAT SLA

• Elimination of defects due to manual entry

• 25% process automated

• Real Time TAT achieved

• BOI - Enabling sales team to focus on top line

• Potential new opportunities for @client

• 30% productivity benefit

• TAT Reduced to 1 Day (earlier 3 days)

• 100% Accuracy

• 30% Productivity Benefit

• Accuracy improved to 100%

• Transactional handling time reduced from 90 mins to 45 mins

• Improved DPA (Data Privacy Act) compliance

• Real Time TAT achieved

• with 100% accuracy20% FTE reductionMashups

Image Analytics


Case Study: Auto Insurance Claims ProcessingKey Technology: Deep Learning

Computer science: The learning machines Nature News & Comment, 2014

Deep Learning: different layers of abstraction to replicate human cognition


Case Study: Auto Insurance Claims ProcessingAutomated Classification of Car Damage Level

Classify if a car is

undamaged (new),

damaged or totaled

Problem Statement

• An Insurance Company wanted to automate claims processing using advanced machine learning technology, namely Deep Learning.

• When customers sent a picture of their damaged car, the Company would like to have the ability of automatically detect the level of

damage and use it to, for example, order spare parts and possibly detect fraud, if any.

• Accenture developed a Convolutional Neural Network algorithm (which belongs to the family of Deep Learning techniques) using a data

set of toy images.

•90% accuracy achieved.

• By automatically detecting level of damage, an Insurance Company saves on sending a human to assess the damage

• Apply the same technique for more use cases and other lines of businesses like Home Insurance with enhanced complexity and accuracy

For Auto Insurance, spare parts could be ordered automatically

For Home Insurance, evaluate building resistance, check if customers are telling the truth about additions to houses, identify multiple damages

Similarities/differences in damage patterns could be used to detect fraud

Value Delivered

Video Analytics


Video AnalyticsSafety, Security, Operation and Marketing Insights

Application of Computer Vision to automate observation of video surveillance cameras, generating real time alerts

and detailed structured meta data for trend and anomaly detection

Singapore Safe City Oil Major Fuel Terminals French Police EventsFrench Telco

counting/conversion rate


Example in Traffic Management

94.5%count accuracy

98.7%count accuracy

91.8%count accuracy


Examples in an Airport


Examples in Retail

Identity Analytics


Case Study: UK BA, BAA – London AirportsSelf Clearance for EU ePassport Holders


Case Study: Amsterdam SchipholSelf Clearance for EU ePassport Holders

Virtual Agents


Virtual AgentsGoal: a Virtual Helpdesk Assistant that Interacts, Solves and Learns like a Human Agent

Communicates with the

customer using natural

language speech, online

chat and email…

Forwards a customer

query to another

department when

required…

Provides fact or

knowledge-based

answers to customer

queries when

appropriate...

Provides a resolution to a

customer query in a

variety of ways where

necessary…

Escalates to a supervisor

or another agent when

appropriate...

Understands the

customer's query or issue

and associated context...

Follows a specific process

or script to resolve a

customer query or issue

and can deviate from it

when appropriate…

Documents the customer

query and outcomes in the

ticket tracker system...

Disambiguates a

customer query by asking

clarifying questions when

required...

Accesses corporate back-

office systems to obtain

specific information or

triggers transactions when

required...

Performs offline tasks and

follow ups on a customer

query when required

Identifies and utilizes

pertinent data and

information specified by

the customer...

Asks for and utilizes

additional customer

documentation when

necessary

Learns new skills,

information an capabilities

through documentation,

training observation and

active discovery...


• Phase I established the end-to-end flow where most customer enquiries

would be escalated to human agents and “observed”.

• Phase II increases the Customer Experience scope, to enable a virtual

agent to answer 25-35% of customer inquiries, including:

• Phase II also introduces Cognitive Robotics to access WQM (BMC

Remedy) to trigger events.

Currently Being Piloted at an Oil Services Company

Urgent Payment Requests (18%)

Invoice Statements (12%)

Missing Invoices –Not Due (8%)

OfflineProcesses and follow ups

Redirects (6%)

MySupplierPortal Access and Updates (3%)

Remittance Query (7%)

Where To?


… 30 Years Ago

Ripped from the Headlines


Accenture Technology Vision 2015Digital Business Era – Stretch Your Boundaries

www.accenture.com/technologyvision

http://www.accenture.com/technologyvision


Workforce Re-ImaginedMore Effective, More Interesting


Workforce Re-ImaginedBuilding Trust in Intelligent Systems


• Cognitive technologies promise to automate or augment a wide range of work activities that today are largely done by humans, including manual workers and knowledge workers.

• Delivering business value from artificial intelligence requires understanding the nature of the work being done along two dimensions:

o Data Complexity: degree to which complex unstructured changing data needs to be taken into account

o Work Complexity: degree to which individuals need to apply their judgment and interpret a variety of information

Turning Artificial Intelligence into Business Value


Turning Artificial Intelligence into Business Value – Healthcare


Turning Artificial Intelligence into Business Value – Banking


Technology-Rich

Business-Outcome Focused

People First Mindset

Creating an Artificial Intelligence Capability

Expert

Systems

Computer

Vision

Inference

EnginesMachine

Learning

Robotic

Process

Automation

Deep

LearningSensor

Processing

Knowledge

Repre-

sentationMini

Bots

Emotion

Recognition

Gesture

Recognition

Ontologies

Neural

Networks

Biometrics

Natural

Language

Processing

Video

Analytics


Doing Things Differently

Doing Different Things

Artificial IntelligenceExponential Business Potential


For More Information

Cyrille Bataller

Managing Director

Artificial Intelligence

@CyrilleBataller

Mobile: +33 6 85 54 24 14

[email protected]

www.accenture.com/aitechnology

#AI

mailto:[email protected]

http://www.accenture.com/aitechnology

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