Coming of Age of Artificial Intelligence

Transcription

Coming of Age of Artificial Intelligence
Disruption, Transformation Ahead
Cyrille Bataller
Robotics in Business Operations:
Understanding the Landscape
Robotics
Spectrum
Robotic Process Automation
Unified Desktops/Mashups
Basic Automation – Minibots
Automation of transactions and work
flow activities by handling input,
processing, and output of data across
systems.
Artificial Intelligence
Cognitive Robotics/ Virtual Agents
Systems that gain knowledge from
data as “experience” and generalize
what is learned in upcoming situations.
Sense, comprehend, act – and learn.
Natural language dialogue.
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.
Applying technology to manipulate
existing application software to
complete a process. Primarily based
on XL, AutoHotKey, or Visual Basic.
Copyright © 2015 Accenture All rights reserved.
2
AI: IT Systems that can Sense, Comprehend, Act – and Learn
Maybe 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.
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AI: IT Systems that can Sense, Comprehend, Act – and Learn
A Collection of Technologies Underpinned by Machine Learning
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)
Source: Gartner's Hype Cycle for Human-Computer Interactions
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Artificial Intelligence History
Various Attempts and “Winters” since Computing was Invented
Initial research focused on Artificial Intelligence (AI), "the science aiming to create intelligent
machines that are as capable as humans.”
Google driverless car
1.7M miles accident-free
Audi’s driverless
race car
Ask Jenn
Ask Sgt Star
Border control
“robots”
Source: Accenture
Secondary Research
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.
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Artificial Intelligence Toolbox – Various Application Domains
Solutions
Text Analytics
Research
Assistants
Image Analytics
Multimedia
Search
Cognitive
Robotics
Video Analytics
Identity Analytics
Speech Analytics
Data Visualization
Domain-specific
Calculations
Capabilities
Semantic Web
Automatic
Trend
Detection
Anomaly
Detection
Ontology
Learning
Computer Vision
Sense
Technologies
Audio Processing
Sensor Processing
Comprehend
Act
Inference Engines
Machine Learning
Expert 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,
Virtual Agents
Expert Systems
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
Recommendation
Self-Adjusting IT - Expert System: learn a product manual and
Systems
Systems
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
Augmented
Affective
recognition, text to speech
Reality
Computing - 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
Natural Language Processing
like you” type recommendations
- Self-Adjusting IT Systems: ability for IT
system to adjust based on historical usage
Knowledge Representation
patterns
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Text Analytics
Case Study: PharmacoVigilance
Monitoring 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
Assisted PV Processing
Individual Case Safety
Reports and
Narratives database
Regulatory
warning and self
reporting
Social and
Public Data
Text Screening
and extraction
1
Relevant text
portions are
extracted
Semantic
modeling and
signal
generation
Entity
Recognition
2
Entities like
Patient,
Symptom,
Drug, Usage
etc. are
recognized
3
Relationships
between
Patients,
Disease,
Symptom,
Drug, Dosage
etc are
established
Case
Suggestion
4
Based on
Statistical
association
measures on
aggregate data
a case is
suggested to
the writer
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Accenture Text Analyzer for PharmacoVigilance
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Accenture Text Analyzer for PharmacoVigilance
Inference on Expectedness and Causality
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Cognitive Robotics
Benefits of Robotic Automation
Reduces cost, as we automate transaction processing
Helps to provide higher productivity benefits
Higher efficiency in process & reduction of non value added activities
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
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Case Studies
Value Delivered by Robotic Automation
Client
Tools Deployed
Productivity Impact
Business Outcomes
Large Retail client
(Order Entry Indexing)
Mashups
20% FTE reduction
• Real Time TAT achieved
• with 100% accuracy
25% FTE reduction
•
•
•
•
25% process automated
Real Time TAT achieved
BOI - Enabling sales team to focus on top line
Potential new opportunities for @client
Large retail client
(Contract Management
@Client)
Mashups
Large manufacturing
(Invoice Processing for
Single Line invoices PO
& Non-PO Automated)
Mashups
Large Distribution
(Order Entry process)
OCR
21 FTEs
• 52% reduction in transaction handling time (10.5mins to 5mins)
• Enables adherence to peak demand TAT SLA
• Elimination of defects due to manual entry
Large Consumer
Goods
(Exit process
automation)
RPA
50% FTE reduction
• Transactional handling time reduced from 90 mins to 45 mins
• Improved DPA (Data Privacy Act) compliance
Large retail client
(PTP, RTR and Store
Accounting)
RPA
28 FTEs
• 30% Productivity Benefit
• Accuracy improved to 100%
30% FTE reduction
• 30% productivity benefit
• TAT Reduced to 1 Day (earlier 3 days)
• 100% Accuracy
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Image Analytics
Case Study: Auto Insurance Claims Processing
Key Technology: Deep Learning
Deep Learning: different layers of abstraction to replicate human cognition
Computer science: The learning machines Nature News & Comment, 2014
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Case Study: Auto Insurance Claims Processing
Automated 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.
Value Delivered
• 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
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Video Analytics
Video Analytics
Safety, 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
French Telco
counting/conversion rate
Oil Major Fuel Terminals
French Police Events
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Example in Traffic Management
98.7%
94.5%
count accuracy
count accuracy
91.8%
count accuracy
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Examples in an Airport
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Examples in Retail
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Identity Analytics
Case Study: UK BA, BAA – London Airports
Self Clearance for EU ePassport Holders
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Case Study: Amsterdam Schiphol
Self Clearance for EU ePassport Holders
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Virtual Agents
Virtual Agents
Goal: 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…
Understands the
customer's query or issue
and associated context...
Provides fact or
knowledge-based
answers to customer
queries when
appropriate...
Identifies and utilizes
pertinent data and
information specified by
the customer...
Accesses corporate backoffice systems to obtain
specific information or
triggers transactions when
required...
Asks for and utilizes
additional customer
documentation when
necessary
Performs offline tasks and
follow ups on a customer
query when required
Learns new skills,
information an capabilities
through documentation,
training observation and
active discovery...
Follows a specific process
or script to resolve a
customer query or issue
and can deviate from it
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...
Disambiguates a
customer query by asking
clarifying questions when
required...
Documents the customer
query and outcomes in the
ticket tracker system...
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Currently Being Piloted at an Oil Services Company
•
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:
Urgent
Payment
Requests
(18%)
•
Invoice
Statements
(12%)
Missing
Invoices –
Not Due (8%)
Offline
Processes
and follow
ups
Redirects
(6%)
MySupplier
Portal Access
and Updates
(3%)
Remittance
Query (7%)
Phase II also introduces Cognitive Robotics to access WQM (BMC
Remedy) to trigger events.
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Where To?
Ripped from the Headlines
… 30 Years Ago
29
Accenture Technology Vision 2015
Digital Business Era – Stretch Your Boundaries
www.accenture.com/technologyvision
30
Workforce Re-Imagined
More Effective, More Interesting
31
Workforce Re-Imagined
Building Trust in Intelligent Systems
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Turning Artificial Intelligence into Business Value
•
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:
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
o
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Turning Artificial Intelligence into Business Value – Healthcare
34
Turning Artificial Intelligence into Business Value – Banking
35
Creating an Artificial Intelligence Capability
Video
Analytics
Natural
Language
Processing
Technology-Rich
Business-Outcome Focused
Biometrics
Sensor
Processing
Mini
Bots
Deep
Learning
Expert
Systems
Knowledge
Representation
Computer
Vision
People First Mindset
Robotic
Process
Automation
Emotion
Recognition
Inference
Engines
Machine
Learning
Neural
Networks
Gesture
Recognition
Ontologies
36
Exponential Business Potential
Doing Things Differently
Doing Different Things
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For More Information
Cyrille Bataller
Managing Director
www.accenture.com/aitechnology
#AI
@CyrilleBataller
Mobile: +33 6 85 54 24 14
cyrille.bataller@accenture.com
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Coming of Age of Artificial Intelligence

Transcription

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