Cognitive Engineering - Shifting Right with Gated.AI Testing - DevOps Next

2 | DevOps Next 2020 perforce.com
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Cognitive Engineering
Shifting Right (Digital Twin)
feat. GATED.AI Testing
J O N AT H O N W R I G H T

- COGNITIVE ENGINEERING

• Cognitive Engineering
• Digital Experiences (DX)
• Data visualization / stewardship
• Complex Event Processing
• Blockchain / Event Driven
• Context Brokering Platforms
• Natural language processing
• Situation Awareness
• People-Literate Technology
• Internet of Everything (IoE)
• Human Augmentation
• Ambient Experiences
• Gesture Control
• Brain Computer Interface (BCI)
• Emotion Detection
• Head-Mounted Displays
• Virtual Worlds
• Digital Offers
• City Data Exchange
• Atomic Level Mass Personalization
• Connected Vehicle
• Autonomous Vehicles (C2X)
• Enterprise Artificial Intelligence (AI)
• Ultra-Intelligence (Singularity)
• Artificial Intelligence Foundations
• Deep Reinforcement Learning
• Learning / Selfaware Software
• Neural Networks / Conversational
• Quantum / Fog / Edge Computing
• Cognitive Reckoning / Abstract Thinking
• Humanoid robots [Sophia]
• Vision / Sensors / Knowledge Replication
• Neuromorphic Hardware
• Shift Right / Digital Twins
• Cyber-physical systems
• Cryptocurrency Lawful hacking
• Cyber Threat / Zerotrust security
• Location-based authentication
• Growth / Performance hacking
• Flexible displays / Screenless interface
• Smart Dust / Workspace / Robots
• Nanotube Electronics
• Accumulated Reality
• Self-healing & aware systems
• GATED.AI
• Augmented Intelligence
• Chaos Engineering / Dark Canary
• Immersive / Human Augmentation
• Virtual / Augmented / Mixed Reality
• Intelligent / Enterprise of Things (EoT)
• Virtual Personal Assistants (VPA)
• Enterprise CrowdTesting
• Device Mesh / Sensorization
• Business / Digital transformation
• Tri-Modal / Shadow IT
• Interactive digital signage / Ambient UX
• Omni channel engagements
• Adaptive risk, trust & learning
• Microcontainerization [Unikernels]
• BI / Big Data [NoSQL]
• Wearables / Haptics / Gesture tech
• 4D / 3D printing
• Uberfication of services
• Object based storage
• Smart building technologies
• Gamification
COGNITIVE ENGINEERING + DIGITAL TWINS

WHAT IS COGNITIVE ENGINEERING?

COMPLEX EVENT PROCESSING (2015) – PREDICITVE CRIME

DIGITAL QUALITY - SMART CITY DATA EXCHANGE (2015)

CONTACT TRACING (2015) – LOCATION DATA (GPX)

CONTACT TRACING (2020) – GPS & GAENS

The recent announcement of
Apple and Google’s work will
enable Path Check to
accelerate our efforts by
providing a critical piece of
the technology that would
have been harder to do
without them. Path Check
technology is open source. –
We are the integration point
for public health agencies at
all levels to bring the best
open, free, privacy-by-design
technologies together.
GOOGLE APPLE EXPOSURE NOTIFICATIONS (GAENs)

HOW DOES GAENS WORK?

• Exploratory Testing
• Functional Testing
(Manual /
Automated)
• Performance Testing
• Security Testing
• Localizations Testing
• UX Design
• Multi-Language
CALL TO ACTION – GAENS NEEDS YOU!

CALL TO ACTION – TESTERS ASSEMBLE!

CALL TO ACTION – TESTERS AROUND THE WORLD
https://leanpub.com/AroundTheWorldWith80SoftwareTesters/

CALL TO ACTION – HELP SAVE LIVES!

- SHIFTING WRIGHT

SHIFTING RIGHT, OVER SHIFTING LEFT

COGNITIVE LEARNING
Build
Test
TrainEvolve
Design
Model
Evolve
Learn
Think
Ideas
Beliefs,
Values,
Culture
Heuristics
Semantics
Behaviors
Bias
Ethics
Solutions
Agile
Lean
Methods
and Skills

COGNITIVE THINKING
Insight
Observe
CognitiveThinking
Research
Interpreting
Modelling
ChallengingPredicting
Observing
Insight
Experiment
Hypothesis
Research
CognitiveThinking
Experiment
Interpreting
Modelling
Lean Thinking (Definition) - is a business methodology that aims to provide a new way to think about how to organize human activities to
deliver more benefits to society and value to individuals while eliminating waste.

DESIGN
SHIFT LEFT SHIFT RIGHT
DEVELOP DELIVER DISRUPT
COGNITIVE ADAPTIVE TESTING
SHIFTING RIGHT THEN LEFT

COGNITIVE INTELLIGENCE

COGNITIVE TESTING
BUILD TEST

COGNITIVE LEARNING
• Dark (Launching) Canary (Rollout)
• Smart anomaly detection (“Hypercube baselining”)
• System Learning
• Failure Prediction
• Predictive analysis, feedback loops
• Relationships predicted
• Root cause analysis of failures
Discover
InnovateAdapt
Disrupt
Focus
Experience
Evolve
Divide
Behaviours

- GATED.AI METHODOLOGY

GATED.AI - METHODOLOGY
• GOAL – CAPABILITY to identify and correctly categorise images of products.
• ACCURACY – REQUIREMENT be able to successfully categorise women’s
fashion. (1,000+ subcategories on the Amazon channel) with a
CLARIFICATION RATE of over 70%.
• TIME – TIMEFRAME per day be able to process over 10,000 product images.
• ENRICHMENT – SEMANTIC MODELS applying Data Engineering (Enhance,
Transform and Load) heuristics for mining ecosystem to enable A.I. Test
Data Lake.
• DATA – TRAINING DATA mining for clustering (Percentage Split) i.e.
Development Training Set (60%), Testing Training Set (30%) and Proving
Training Set (10%) of the training set sizes (5/10 million).

GATED.AI - GOALS
• A.I. CAPABILITY to identify and correctly categorise images

GATED.AI - ACCURACY
• A.I. REQUIREMENT be able to successfully categorise fashion.
• A.I. CLARIFICATION RATE of over 70%
“The ROC curve (receiver operating characteristic
curve) is a graph-based approach for measuring
the performance of a classification process.
Then the total successful predictions made divided
by the total predictions made (True Positive Rate
(TPR) vs. False Positive Rate) helps us establish
the training AUC (Area Under the ROC
Curve) performance against each of the training
data sets”

GATED.AI - TIME

DSSTNE Semantic Maps (ETL (Crawl / Harvest)+ RPA (Vision & Meta)) AI-Supervised ML (TensorFlow (CV) + Kaggle (SWARM) + mTurk)
GATED.AI - ENRICHMENT
• A.I. SEMANTIC MODEL applying Data Engineering (Enhance,
Transform & Load) heuristics.
CATEGORY
MATCHING
SIMILARITY
INDEX
ENRICH
TRAINING
DATA
SET

Mining Test Training Data (CRAWLER / SPIDER / SUBSET)
GATED.AI – TESTDATA.AI
• A.I. TRAINING DATA mining for clustering, number of
parameters and associated test training data set size.
Harvested Test Training Data (MindCube.io - Domain-Driven)
HAVESTED
TRAINING
DATA
SET
MINING
TRAINING
DATA
SET

GATED.AI – SIMPLE TEST EXAMPLE
• GOAL - A.I. CAPABILITY - Image category
mapping
• ACCURACY - A.I. CLASSIFICATION RATE = > 70%
• TIME - A.I TIMEFRAME = < 1 day for 10,000
• ENRICHMENT - A.I SEMANTIC - Harvest
manufactures website and internal ERP
platform.
• DATA - A.I TEST TRAINING DATA –
Training data set size
(5M/10M), semantic
model parameters (500)
& training nodes (4/8).
A B
RESULTS
MODEL 2 = PASS

Cognitive Engineering - Shifting Right with Gated.AI Testing - DevOps Next

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