Big Data LDN 2017: Pervasive Intelligence: the Future of Big Data, Machine Learning & Iot
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Pervasive Intelligence:
The Future of Big Data, Machine Learning and IoT
Matt Aslett, Research Director,
Data Platforms & Analytics
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451 Research is a leading IT research & advisory company
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Research & Data
Advisory
Events
Go 2 Market
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A combination of research & data is delivered across thirteen
channels aligned to the prevailing topics and technologies of
Digital Infrastructure… from the datacenter core to the
mobile edge.
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Foundational elements
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IoT
Big
Data
AI
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Artificial intelligence
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ARTIFICIAL INTELLIGENCE
The quest to build software running on machines
that can ‘think’ and act like humans
MACHINE LEARNING
A subset of artificial intelligence focused on using
algorithms that learn and improve without being
explicitly programmed to do so
DEEP LEARNING
A branch of machine learning based on a specific set
of algorithms that attempt to mimic the human
brain in the form of multi-layered neural networks
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The AI Automation Spectrum
Intelligent systems, including the automation of control
systems via robotics, warehouse operations and smart
factories, as well as intelligent operations that optimize
decision support for business systems, such as supply
chain, logistics and datacenter automation.
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The AI Automation Spectrum
Taking advantage of deep-learning algorithms to
automate the replication of human perception,
cognition and communication. Examples include
speech recognition, natural-language processing and
generation, image and facial recognition, and
behavioral analysis. 25
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The AI Automation Spectrum
The application of artificial intelligence specifically to drive
commercial initiatives through improved customer engagement
and improved customer service (via virtual assistants, chatbots,
customer segmentation, recommendations, churn prediction,
likelihood to purchase and fraud detection).
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The AI Automation Spectrum
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Speech
Recognition
Language
Translation
Facial
Recognition
Image
Recognition
Recommendation
Engines
Virtual
Assistants
Fraud
Analysis
Customer
Segmentation
Churn
Analysis
Chatbots
Likelihood
to Purchase
Smart
Factories
Smart
Sensors
Robotics
Warehouse
Automation
Logistics
Supply
Chain
Industrial
Controls
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The AI Automation Spectrum
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Speech
Recognition
Language
Translation
Facial
Recognition
Image
Recognition
Recommendation
Engines
Virtual
Assistants
Fraud
Analysis
Customer
Segmentation
Churn
Analysis
Chatbots
Likelihood
to Purchase
Smart
Factories
Smart
Sensors
Robotics
Warehouse
Automation
Logistics
Supply
Chain
Industrial
Controls
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Foundational elements
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IoT
Big
Data
AI
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IoT
Big
Data
AI
Regulations
Data
Science
Predictive
Analytics
Blockchain
Invisible
Infrastructure
Contextual
Experience
Deep
Learning
Serverless
Ethics
Machine
learning
Universal
Risk
Data
Lake
Sensors
Edge
Robots
UX
5G
Digital
Twins
AR/VR
Pervasive
Intelligence
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Trends and implications: Converged Siloes
The industry-changing potential of Pervasive Intelligence is being driven by the
progressive convergence of foundational elements: big data analytics, the Internet of
Things, artificial intelligence and machine learning
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Short Term Medium Term Long Term
New supplier categories will be
“born” with tenets of Pervasive
Intelligence as the key value
proposition. These suppliers will
bring disparate data sources
together in real-time to enhance
service experiences.
Functional convergence blurs the
line between the three pillars of
big data, IoT and artificial
intelligence. Cutting edge
innovation in voice interfaces,
virtual reality, augmented reality
and mixed reality.
IoT data no longer something to be
stored and processed separately
but just another data type for
analytics alongside traditional
enterprise data sources.
Example trends – many more in the complete report.
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Trends and implications: Data and Computation
The transformation of enterprises, industries and government by Pervasive Intelligence
is accelerated due to the rise of enormous data volumes, along with access to new levels
of compute performance, driven at both the Contextual Experience and Invisible
Infrastructure layers.
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Short Term Medium Term Long Term
Enablers like human language
interfaces, haptics, tactile Internet,
VR simulation and light-field
displays for MR/AR show promise
as revolutionary UX enhancements
on the application side.
Public models leveraging
distributed ledger infrastructure
like blockchain will enable highly
complex eco-systems to exchange
data and value, creating seamless
exchanges between micro-
suppliers, consumers, brokers, and
utilities.
Advances in emerging areas such
as quantum computing,
implantable sensors, swarm
intelligence, energy harvesting and
smart materials herald the start of
the next technology revolution.
Example trends – many more in the complete report.
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Trends and implications: Data as the Ultimate Asset
Pervasive Intelligence has the potential to rapidly change the technology product and
services landscape. Vendors that are able to translate data into value will survive and
thrive. Those that do not will be left behind
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Short Term Medium Term Long Term
Incumbent vendors emphasize
their ability to deliver machine
learning, big data analytics and
serve IoT use-cases using existing
technologies, while also making
numerous tuck-in acquisitions to
fill portfolio gaps.
Incumbent vendors explore
strategic acquisitions and
development to re-architect
portfolios. Pervasive Intelligence is
a consistent top three M&A
category.
The flagship mission critical
application portfolios of the past
have been transformed. Incumbent
vendors that previously invested in
R&D and strategic acquisitions
remain, but their product
portfolios are in large part
unrecognizable.
Example trends – many more in the complete report.
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Trends and implications: Sweeping Social Implications
As a critical component of the next wave of industrial revolution, Pervasive Intelligence
will bring about fundamental and wide-ranging positive societal implications - changing
how we live, work, play, transact and travel
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Short Term Medium Term Long Term
Early successful projects begin to
deliver differentiating,
personalized and automated
services in industries such as
telecommunications, energy,
financial services, and healthcare.
Societal improvements include
lowered transportation and
communication costs, improved
vendor responsiveness to
consumer behavior, and improved
access to information, products
and services.
Improved government-public
engagement and socialized
policymaking processes, advances
in healthcare (robotic and
remotely-controlled medical
services, individual treatments,
genomics testing).
Example trends – many more in the complete report.
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Trends and implications: The Risk Factor
Concerns over security, privacy, data integrity and automation will rise to the surface in
response to the potential uses of Pervasive Intelligence and will be increasing
influencers on adoption trends
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Short Term Medium Term Long Term
Security and privacy remain chief
concerns related to deployment.
The number of different systems
and permutations of vendor
ensures systemic security will be
an unsolved problem for the
foreseeable future.
Negative implications of Pervasive
Intelligence is felt, including:
inequality between the data rich
and data poor, increased reliance
on a few platform-oriented
companies controlling interaction
between buyers, sellers and third
parties.
Potential for segregation of
workforce into low skill/low pay
and high skill/high pay, an
increasing wealth gap between
providers of capital vs providers of
labor, as well as privacy challenges.
Example trends – many more in the complete report.
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Data Haves and Have Nots
The potential negative societal implications of
Pervasive Intelligence include the increased
reliance on a small number of platform-oriented
companies that control the forces of production
for generating value from data, including
• the subjects of labor (the data)
• the instruments of labor (algorithms)
• the labor (skilled data scientists)
• and the means of distribution (cloud services)
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