AI in healthcare and Automobile Industry using OpenPOWER/IBM POWER9 systems

Powering Up
AI in Healthcare and
Automotive
Clarisse Taaffe-Hedglin
Executive IT Architect
IBM Garage
IBM Systems
clarisse@us.ibm.com

Agenda
Use cases
The AI Ladder and Lifecycle
AI at Scale Themes

“AI is the
fastest-growing
workload”*
3
*Forrester Research Inc. “AI Deep Learning Workloads Demand a New Approach to Infrastructure”, by
Mike Gualtieri, Christopher Voce, Srividya Sridharan, Michele Goetz, Renee Taylor, May 4, 2018.
3 IBM IT Infrastructure / © 2021 IBM Corporation

Machine Learning Context
REINFORCEMENT
LEARNING
TRANSFER
LEARNING
“AI is the automation of automation” – Jensen Huang, GCG 2020

5
Analytics Modernization: From Data to Actions
010101010101010111100010011001010111
0000000000010101010100000000000 111101011
11000 000000000000 111111 010101 101010 10101010100
Prescriptive
What should
we do ?
Descriptive
What Has
Happened?
Cognitive
Learn
Dynamically
Predictive
What Will
Happen?
ACTION
DATA
HUMAN INPUTS
<
< >
< >
>
>
delivering faster insights with greater efficiency to impact more lives

Three broad categories of AI Use Cases
“Structured” Data Use Cases
Computer Vision Use Cases
- Big Data (Rows and Columns)
- Available AI Software More Accuracy !
This is sort of “Magic”
- a deep learning Model is trained to detect and classify objects
Natural Language Processing Use Cases
- A Model learns to read, hear and “understand” language

§ BIG, COMPLEX SYSTEMS
§ PERSONALIZATION
§ AUTOMATION
§ SIMULATING RELATIONSHIPS
§ VISUAL RECOGNITION
§ PATTERN DETECTION
§ CHATBOTS
§ DESIGN OF EXPERIMENTS
§ OPTIMIZATION
Thescenarios
AIcansolvefor
today

Addressable Markets And Fields For AI
RETAIL
Recommendation
engines, Precision
marketing
AGRICULTURE
Crop yield, Plant
disease, Remote
sensing
LIFE SCIENCES
Sequence
Analysis,
Radiology
UTILITIES
Smart Meter analysis,
Capacity planning
$
FINANCIAL SERVICES
Risk analysis
Fraud detection
CUSTOMER SERVICE
Chatbots, Helpdesk,
Automated
Expenses
LAW & DEFENSE
Threat analysis -
social media
monitoring
RESEARCH
Physics Modeling
Simulation
optimization
TRANSPORTATION
Optimal traffic
flows, Route
planning
CONSUMER GOODS
Sentiment
analysis
HEALTH CARE
Patient sensors,
monitoring, EHRs
MEDIA/ENTERTAINMENT
Advertising
effectiveness
OIL & GAS
Exploration,
Sensor analysis
AUTOMOTIVE
ADAS,
Maintenance
MANUFACTURING
Line inspection,
Defect analysis
AI and Autonomous Machine Learning will help
revolutionized every single industry making us
more productive and efficient to do things that
today are impossible to do.

A framework for designing, deploying, growing and optimizing infrastructure for HPC, AI and Cloud, created in
collaboration with world’s leading healthcare and life sciences institutions, and using Red Hat OpenShift, IBM
Power Systems, IBM Storage and open API endpoints.
From Data to Insight with an Optimal Reference Architecture
DATAHUB
High Performance Data Fabric & Catalog
Capable of Handling Exabytes of Data
and Trillions of Objects
ORCHESTRATION
High Performance Computing & AI
Platform Capable of Orchestrating
Thousands of Servers and GPUs
APPS & MODELS
Large-scale and high-throughput
workloads such as HPC, AI and Cloud
computing
MEDICAL TASKS
Genomics, molecular simulation,
structural analysis, diagnostics, data
fusion, manufacturing quality inspection.

10
Smart loves problems, and there has never been a bigger
problem facing our world.
Biomolecular Structure
Molecular Simulation
Genomics Medical Diagnostics AI
Data Fusion and AI
Bio-Informatics
Artificial intelligence and high-performance computing have already begun to attack the
virus, assisting in molecular drug discovery, genomics and medical image processing.

Data
Overload
Oceans of data
arise from rapid
digitization and
instrumentation
of healthcare.
App Chaos
Thousands of
applications,
workflows and
models are not
all following the
same rules.
Adoption
Vertically
integrated
toolsets with
heavy
customization
and vendor lock-
in create work
silos.
Performance
When scaling up
or out, most
institutions
cannot diagnose
or analyze the
performance
problems they
face.
Cost
Demanding
workloads
require well-
orchestrated
infrastructure to
manage, monitor
and control
costs.
Five key challenges to progress remain despite advances

© 2020 IBM Corporation 12
The Automotive Industry is undergoing a
major digital transformation driven by
Connected, Autonomous, Shared, Electrification imperatives
Car Business Model
Services focused
Vehicle focused
Digital Business Model
key focus
core competency
time to market
workbench
business model
users
user experience
weeks
networks
data driven
engineering
years
supplier tiers
unit sales
cars

AI is transforming how Automotive businesses operate
Group Name / DOC ID / Month XX, 2019 / © 2019 IBM Corporation 13
The AI Ladder Intelligent
Workflows
• Predictive
• Automated
• Agile
• Trusted
Connected
Vehicle / AD
CTO
Risk &
Compliance
CRO
Customer
Experience
CMO
Manufacturing
CMO
Enterprise /IT
Operations
CIO

However, AI is not magic
Trust in AI systems
and recommendations
Data complexity
and fluidity
Data privacy and
regulatory compliance
Intelligent workflows are complex to self-build
compounded by AI talent scarcity and cost
Capturing knowledge
in ML and DL models
Answer extraction with Natural
language understanding
Complex document
and expertise learning

Data
Insight
HPC Analysis &
Simulation
AI Inference &
Automation
Sensors
The Convergence of HPC and AI

Optimizing Medical Imaging
Enhance image identification with deep learning
to assist physicians and benefit patients
1300 MRI images trained by IBM Power
Systems and IBM Storage in just two hours,
compared to forty hours on traditional
architectures

97% Accuracy for Melanoma Detection for Dermoscopic Images
Melanoma vs. Atypical & Benign
Human*
Deep
Features
Ensembles CNN DRN
Doctor/
Expert
ImageNet + Sparse
Coding
+ Low-level + Auto-
Encoder
Deep
Learning
Deep
Residual
Learning
0.84 0.91 0.92 0.93 0.94 0.95 0.97
- 0.73 0.73 0.74 0.77 - -
* Estimated human expert performance
Use Case
Automatic skin lesion image analysis for
melanoma detection with Memorial Sloan
Kettering (MSK-CC)
Visual modeling techniques:
§ Deep Residual Networks
§ Conv. Neural Networks
§ Ensemble Models
Top Performance
= 97% Accuracy!!!
Melanoma vs. Atypical
Best
17
Think 2020 / DOC ID / Month XX, 2020 / © 2020 IBM Corporation

18
Advances in instrument
design, sample preprocessing
and mathematical methods
have enabled high volume
throughput imaging at atomic
scale.
Cryogenic electron
microscopes generate an
average of 5 TB of image data
per day
BIOMOLECULAR STRUCTURE
Massive Data Sets Require Massive Processing Capability

Accelerating Cryo-EM Imaging Analysis
Reduced time-to-completion for high resolution image
analysis jobs while increasing resource utilization
Using IBM AC922 cluster, more than 100 cryo-EM
high resolution image workload analysis jobs running
in parallel on Satori cluster
BIOMOLECULAR STRUCTURE

Simulation of millions of atoms requiring large computational
resources
Large scale simulation includes millions of
atoms
• Virus molecules
• Ribosomes
• Bioenergy system and complex
Solution
• High performance computing CPU and
GPUs accelerating performance
• Optimal memory and network bandwidths
scaling performance to hundreds of nodes
• Techniques to reduce number of simulations
Receptor
ligand
Virus molecule simulation Receptor-ligand fit
Cryptic binding site prediction Binding energy prediction
MOLECULAR SIMULATION

Molecular Dynamics Simulation Computational Intensity
A) Using NAMD to simulate influenza
B) virus (left)and Covid-19 (right)
B) Drug discovery:
protein receptor
C) In silico prediction of protein cryptic binding site D) Predicting protein receptor
ligand binding energy
Receptor
ligand
Large scale simulation
includes millions of atoms
• Virus molecules
• Ribosomes
• Bioenergy system and complex
Solution
• High performance computing
CPU and GPUs accelerating
performance
• Optimal memory and network
bandwidths scaling performance
to hundreds of nodes
• Techniques to reduce number of
simulations

Bayesian optimization
accelerated workflow
uses 1/3 of the
calculations to achieve 4
orders of magnitude
resolution increase
Optimizing Molecular Modeling
Achieves human level
performance in days
instead of months.
Accelerated Force Field Tuning Intelligent Phase Diagram Exploration

Faster
Better Cheaper
BOA accelerates
time to insight, time
to value, and time to
design by factors
Example:
IBM EDA ->100x faster
than brute force
BOA can find new and
unknown optima in a
design space because of
its lack of bias and
exploration algorithm
Example:
Infineon – 3x faster than
other methods and
4 orders of magnitude
better resolution
Nothing is cheaper than a
simulation which is never
run. BOA prevents
unnecessary work which
reduces all kinds of costs
Example:
GlaxoSmithKline –
reduced their screen
workload from 20k
experiments to 200
IBM
BOA
Bayesian Optimization Value
0 200 400 600 800
BOA
Greedy
Similarity
Diversity
count
Search Method Comparison
Drug Discovery Case - Single
Objective
All Data / Ties removed
Conclusion: >80% of the
time IBM BOA is the best
method with the least regret

Speed time to value, with pre-built AI apps and learnings from
thousands of AI engagements
24
Cognitive car manual
explaining increased
vehicle complexity
Generate new data
driven revenue stream
with contextual
connected services
Improved Customer
Safety and Recall
Engagement
Delivering superior quality using
AI and edge computing
Streamlining the recruitment
process and saving 40
percent of time needed in
application handling
Connected
Vehicle / AD
Risk &
Compliance
Customer
Experience
Manufacturing
IT
Operations

Designing a Formula 1 car is complex.
Validating component design is crucial,
but testing aerodynamics, either
physically or by simulation, is costly.

…leading to fewer
simulations and lower
development costs
Using IBM Bayesian Optimization
Accelerator to automatically
predict the next best set of
parameters to explore, we can
minimize the drag to lift ratio to
optimize the design of F1 car
components efficiently…
Formula 1 Design Exploration using AI and HPC

IBM Federal
IBM Federal / August 2020 / © 2020 IBM Corporation

A New Era of Autonomous Ships
IBM has partnered with ProMare, a U.K.-based
nonprofit research organization on building a fully
autonomous Mayflower, built for the 21st
Century.
The Mayflower will be one of the first self-navigating,
full-sized vessels to cross the Atlantic.
It will have no captain or crew; instead, it will use IBM
AI and Hybrid Cloud technologies to traverse the
Atlantic and gather data that will help safeguard the
health of the ocean and the industries it supports.
The ship will depart Plymouth, England and set sail for
Plymouth, MA in the spring of 2021 and will have the
ability to operate independently in very challenging
environments.
IBM Federal / August 2020 / © 2020 IBM Corporation
28

Scientific
Experiments
Navigation Systems SatCom / 4G / WiFi
Control Center
Hybrid Cloud Solution at the Edge
Edge Agent
Docker / RH
Intermittent
inbox /
outbox
Mission management
vessel monitoring,
planning, status,
Cloud
AI Captain
Ship
inbox /
outbox
Path Optimization:
weather, efficiency,
analytics
Safety Care
Kill switch
Scientific
Teams
Public portal
Edge Management
RH OCP
Weather
On Premises
Mayflower project / Oct 14th / © 2020 Submergence - MSubs - MarineAI - IBM
Ship / Edge
PowerCPU

Vision AI
Sensors
(every second)
Structure
Rules
Engine
Evaluate Decide
Optim Engine
Unstructured
To Structured
Data Fusion
COLREGS
1 to 1
problem
MISSION objectives
Weather
Multiship problem
Charts
Control
Hybrid Cloud and AI Architecture
Vessel Dynamic
Control / Robotics
Command center
Data Collection
Development
Action
Vision Dev
Rules Dev
Optim Dev
Edge Mgt
Cameras
Radar
Local
Weather
AIS
intermittent
Mayflower project / Oct 14th / © 2020 Submergence - MSubs - MarineAI - IBM
Weather
Vessel
Dynamic
Controlc
AI Captain

Manufacturing Hybrid Cloud and AI Architecture
Servers
GPU / FPGA
Storage
( ESS )
Quality Inspection
- Very low latency
- Device Inference?
Equipment Sensors
- low latency
Plant Optimization
- batch
Factory location …n
Quality Inspection
- Very low latency
Equipment Sensors
- low latency
Servers
GPU (IC922)
Storage
( ESS )
Optimization
- batch
Factory location 2
Cloud / IOT
Quality Inspection
- Very low latency
- Device Inference?
Equipment Sensors
- low latency
Servers
GPU / FPGA
Storage
( ESS )
Plant Optimization
- batch
Factory location 1
. . . .
AI Applications
and Data
Hybrid Cloud
- Containers
- Cloud Paks
Data and
meta-data
Servers
GPU
Storage
On-Prem
Enterprise
Systems
AI inferencing
In Transaction
Systems
Headquarters
Archive
AI
Model
Training

Data Science Exploration
to Production
Use Case Exploration
Data Science Model Build
Use Case Deployment in Production
Requires solution architecture
Deploy
Source: https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
Use Case Exploration
Data Science Model Build
Security, Privacy and Governance

COLLECT - Make data simple and accessible
ORGANIZE - Create a trusted analytics foundation
ANALYZE - Scale AI everywhere with trust & transparency
Data of every type, regardless
of where it lives
MODERNIZE
your data estate for an AI
and hybrid multicloud
world
INFUSE – Operationalize AI across business processes
The AI Ladder
A prescriptive approach to accelerating the journey to AI
AI
AI-optimized systems
infrastructure

Public data
Anything data system can pull
from the outside world for free
through web connections,
databases, IoT and sensors
Proprietary data
What private data from the
outside world could the system be
given permission to use?
Purchased data
What pre-trained data could the
system buy or subscribe to?
IBM Skills Academy / © Copyright 2018 IBM Corporation
Ground truth
Data used to define what the system
knows from day one
Domain knowledge
Data resources that can be used to
teach the system to understand and
be an expert in a particular field
Private data
Unique data the creator owns and
only shares internally
Personal public data
What unique data does the creator
share with the outside world?
Transaction and
application data
Machine,
sensor data
Enterprise
content
Image, geospatial,
video
Social data
Third-party data
Available Data Sources

12 July 2021/ © 2018 IBM Corporation
• Metadata is the structured data about the unstructured object
• Who, what, when, where, and why of account, container, object, stream, dir, file
• Perfect for indexing and searching
• Metadata may be separate from the data, stored with the data, or derived from the data
• Posix inode plus extended attributes
• Standard document headers (doc, ppt, mp3, dicom, pdf, jpeg, GeoTIFF)
• Custom metadata tags
• AI derived metadata
Age, Biomarkers, Developmental Stage, Cell
Surface, Markers, Cell Type/Cell Line,
Disease State, Extract Molecule, Genetic
Characteristics, Immunoprecipitation,
antibody, Organism
Biomedical
Natural Language
Processing
Image
Location
Size
Owner
Group
Permissions
Last-Modified
...
System
Metadata
Where is the data?

Metadata-Fueled Data Analysis
Large Scale Data Ingest
• Scan records at high speed
• Live event notifications
• Capture system-level tags
• Automatic indexing
Business-Oriented
Data Mapping
• Custom data tagging
• Content-inspection via APIs
• Policy-driven workflows
Data Activation
• Data movement via APIs
• Extensible architecture
• Solution Blueprints
Data Visualization
• Query billions of records
in seconds
• Multi-faceted search
• Drilldown dashboard
• Customizable reports

The Data: Biological Data Analytics
Biological
Data Analysis
Biomarker
Identification
Biodata
modeling and
Statistical
Analysis
Biodata
Visualization
Medical Images
Data analysis
Structural
Bioinformatics
Genomics
Sequence data
analysis
Biological Data Analytics
q Genomic Sequence Data: an explosive growth of biodata
q Sequence alignment
q Variant discovery and characterization
q Genomic profiling and pattern discovery
q Biomarker Identification: gene expression profile, RNA-seq, ChIP-
seq, microarray identification and validation, etc.
q Structural Bioinformatics: identify and predict 3D biomolecule
structures, such Cryo-EM data refinement, molecular dynamic
simulation, NMR, x-Ray crystallographic data, etc.
q Biodata Modeling & Statistical Analysis: biological pathways
analysis, Gene, clinical data cohorts study, data extraction, etc.
q Medical Image Processing: image segmentation, registration,
statistic modeling.
q Biodata Visualization: 3D molecule structures, genomics sequences
visualization, etc.
Ruzhu Chen @ 2019

Data Pipeline for AI
Insights Out
Trained Models,
simulations
Inference
Data In
Transient Storage
SDS/Cloud
Global Ingest
Throughput-oriented,
globally accessible
Cloud
ETL
High throughput, Random
I/O,
SSD/Hybrid
Archive
High scalability, large/sequential I/O
HDD Cloud Tape
Hadoop / Spark
Data Lakes
Throughput-oriented
Hybrid/HDD
ML / DL
Prep ⇨ Training ⇨ Inference
High throughput, low
latency,
Random I/O
SSD/NVMe
Classification &
Metadata Tagging
High volume, index &
auto-tagging zone
Fast Ingest /
Real-time Analytics
High throughput
SSD
Throughput-oriented,
software defined
temporary landing zone
capacity tier
performance tier performance &
capacity Tier
performance &
capacity Tier
performance tier
capacity tier
Fits Traditional and New Use Cases
EDGE COLLECT ORGANIZE ANALYZE INSIGHTS
INFUSE
IBM Spectrum Scale / Storage for AI / © 2020 IBM Corporation

Optimizing Precision Genomics
Reduced time-to-completion for long-running
jobs while increasing resource utilization
Using IBM, Sidra has completed hundreds of
thousands of computing tasks comprising
millions of files and directories, without
experiencing system downtime.

• Ease of use
• Optimize resources
• Scale workload
AI Frameworks /
Open-Source Libraries
AI Tools and
Applications
AI Software Landscape
AI
Infrastructure

OpenPOWER is a technical community
dedicated to expanding the the IBM Power architecture ecosystem
https://github.com/open-ce
Open-CE
Minimize time to value for
foundational ML/DL packages
Provide a flexible source-to-image
solution to provide a complete and
customizable AI environment.

Anaconda Environment for Applications
• Use anaconda enterprise network
(AEN) to manage cryo-EM software
repository on server.
• Easy to use and update software
Anaconda Architecture for Cryo-EM Analysis
Computation
Web Interface
Repo Install
Software
Control
Authentication
Anaconda Server
Compute Nodes
Database Users

Data Data Data
Microservices Containerized Workloads Multicloud Provisioning
Public Cloud
On-prem
ises
An architecture of loosely coupled
data services, easily refactored to
create containerized workloads
Stand-alone workloads composed of
microservices & data that are flexibly
deployed, orchestrated and managed
Agile provisioning of containerized
workloads in multicloud environments
and consumption of cloud services
Cloud Native Platforms
Agility o Efficiency o Cost Savings
IBM Cloud Pak for Data

Data Pipeline -The data that is feed into models has to be cleaned and structured to
produce accurate results
Real-Time (vs Batch) - Many AI applications have response times in milli-seconds and
in many cases have 100K+ IOT events per second (Latency, Latency, Latency)
Scalability - Ability to scale inference engine and manage infrastructure
Security - Applications running AI models in the field and back-offices
Multi-Tenancy - Multiple business applications leveraging shared infrastructure,
Multiple Models per Business Application
Tools Proliferation - Analytics, Data/Object Tagging, Model Training and Inferencing
Model Management - Continuous Training/Re-Training of Models, AI-DevOps, Ease of
Deployment
Transparency - Ability to explain decisions
A
C
C
U
R
A
C
Y
Typical AI Inferencing Considerations

Fairness Explainability Adversarial
Robustness
Transparency
Is it fair?
Is it easy to
understand?
Is it secure? Is it accountable?
Pillars of Trusted AI

IBM leadership in AI
in U.S. patents for 26th consecutive
year with over 1,600 new AI patents
Innovation
AI Research
Proven
AI Leader
AI Users
Trusted
In IDC market share, leadership in
the key 6 Forrester AI Waves and
winner of Gartner Customer Choice
Award.
in international usage with
hundreds of millions of users across
80 countries

Virtual assistance
for enhanced user
experience
Car manuals are usually consulted only as a last resort, and sometimes
not at all – which can compound what might start out as a relatively
minor issue.
Recognizing that many users are more willing to query an app, rather
than flip through a manual or phone a call center, Mercedes wanted to
create an an application that knows the car and its functionalities by
heart. Users would be able to pose questions specifically about their
vehicle, as they would to a knowledgeable Mercedes expert, and could
also ask general question about Mercedes features, such as the new EQ
design for Mercedes electric vehicles.
Results
Daimler and IBM have jointly developed “AskMercedes” which is a virtual
assistant based on IBM Watson conversational technology and the IBM
Cloud. It creates cognitive customer interactions and engages with the
digital cognitive manual, AskMercedes for more human and rich
interactions between Daimler and their customers.
The system can explain the displayed functions to the customer.
- IBM developed a Chatbot that helps drivers get immediate responses
and proactive suggestions about their cars
- AskMercedes can be accessed via an app or Facebook and other
messenger services – in future also in the vehicle itself
- AI powered by IBM Watson delivers a personalized and intuitive
experience added by Augmented Reality features
- Agile Development in a Cloud Garage approach
Source: https://www.ibm.com/blogs/think/
2017/11/end-of-the-car-manual/

Personalizing the
in-vehicle
experience
Volkswagen wanted to generate data driven revenue streams and
differentiate their digital Volkswagen We ecosystem with personalized
and contextual connected services.
The wanted to create a personal recommendation platform allowing
automotive & mobility companies to personalize internal and external
services increasing the acceptance and value of digital services,
resulting in a higher willingness to pay and/or brand attrition.
Results
“We Experience” follows a platform business model and addresses the
need of marketing personalization, connecting marketers with drivers,
who are receiving personalized contextual branded recommendations.
We Experience:
- Provides marketers a full-service solution to place targeted offers
Integrates smartphone and vehicle frontends
- Utilizes AI to analyze vehicle, user, context data to identify the right
offer at the right time and place to be distributed to a user.
- Marketers are paying for distributed, viewed, clicked offers
Combining an increasing merchant ecosystem with the variety of vehicle
data, analyzed with AI, We Experience generates valuable customers
interactions whilst unlocking new business models for Volkswagen.
Source: https://www.aperto.com/en/work/VW-We

Solving the recall
riddle
Honda was looking for a way to Improve handling of customer calls
regarding recalls, product updates, warranty extensions and field actions.
Working with IBM, Honda created Dave (Digital Assistant Virtual
Engineer) which is a virtual online agent to answer consumer questions
about Honda and Acura recalls 24/7.
Watson Engagement Advisor is trained to understand and respond to the
questions from customers on Recalls, Product Updates and Warranty
Extensions related to their vehicles
Customized chat UI that is integrated with Honda’s vehicle master and
CRM systems
“Ask Dave” chat can be launched from multiple Touchpoints
Results
- Improved Customer Experience with 24/7 support
- Improved Customer Experience with timely and accurate information
- Reduced call volume and costs
- Improved Customer Safety and Recall Engagement
Source:https://www.autonews.com/article/2017040
1/MOBILITY/304039963/honda-ibm-create-dave-
to-solve-recall-riddle

Delivering superior
quality using AI and
edge computing
The client is a leading global automotive supplier with a history of
delivering superior automotive components through innovative, quality-
centric manufacturing processes.
Now, the supplier is working with IBM to use edge computing and AI to
streamline and improve the quality of the welding process.
“Working with IBM, we're using edge computing and AI to take
advantage of a deeper level of data and analytics, such as visual
recognition and acoustics insights, to help our weld technicians diagnose,
predict and optimize outcomes.”
International Automotive Supplier
Solution Components:
IBM edge computing and AI solutions

Streamlining the
recruitment
process
Recruitment is a critical pillar to building the right human capital for an
organization. With hundreds of candidates going through its recruitment
process every month, car manufacturer ŠKODA AUTO was looking for
help to make the hiring of new employees more efficient and less time-
consuming not only for HR professionals but also for candidates.
ŠKODA AUTO implemented a virtual HR assistant to give job applicants
the ability to apply for a job through a virtual conversation rather than by
filling out extensive paper forms. Through the automated collection of
data, the solution delivers proactive and personalized services to Skoda's
HR team while providing data privacy.
Results
The solution is used today in two of the three ŠKODA factories in the
Czech Republic and currently collates, in Czech language, the
information from Czech job applicants, to streamline the collection and
storage of applicant's data.
Since its inception, the virtual HR assistant solution has saved
approximately 40 percent of the time monthly required to handle job
applications by HR employees.
Source: https://newsroom.ibm.com/2019-09-11-
SKODA-AUTO-Uses-IBM-Watson-Assistant-to-
Help-Improve-the-Efficiency-of-its-Recruitment-
Process

AI in healthcare and Automobile Industry using OpenPOWER/IBM POWER9 systems

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AI in healthcare and Automobile Industry using OpenPOWER/IBM POWER9 systems