The 10 most innovative cognitive solution providers 2018

JUNE 2018
www.insightssuccess.com
Derek Meyer
CEO
Redeﬁning AI
Capabilities with
Dataflow Technology
Cognitive Computing:
Computing Revolution Enhancing
the Human Way of Living
Computing Revolution
RPA and Cognitive
Technologies:
Merger of Innovative Approach
towards Customer Engagement
Tech Capture
Most
Solution Providers
2018

Cognitive Tech:
Transcendence of
the Digital Universe.
mpeccably innovative technologies have been reforming our digital presence. This reformation has its
Iroots in the balance between the human conscience and machines. The industrial ecosystem has been
at the receiving end of the technological benefits of this balance. It has relished the consumption of
each relevant tech advent and will continue the same till eternity.
One prominent innovation that has leapt the timeline of prolific usability is Artificial Intelligence. The
blend of tangible and intangible intellect, that of a machine and a human conscience is no more restricted
to Sci-Fi novels and cinemas. This unison has conceived cognitive computing and many similar innovative
solutions that ceaselessly disrupt the digital world.
The most prominent usage of cognitive tech can be observed in the Virtual assistant of a smartphone.
Intelligently using preset databases for language translation, interactive decision making, voice detection,
augmented reality and a myriad of other activities are what encompass the uses of a virtual assistant.
Another use of cognitive tech's potential nowadays, is the Facial Recognition or more commonly known
as Face Unlock tool, offered by every other smartphone manufacturer. This feature examines the users'
facial attributes with the use of a phone's camera and stores the recorded data to unlock the phone
dynamically.
This innovation has reformed the way a user interacts with technology. Be it a website or a smarthome
appliance, cognitive technology's outspread has been contagious, exhibiting seamless possibilities. These
opportunities have been exploited by a plethora of innovative companies to revolutionize the digital
universe with reformative products and solutions.
Thus to emphasize upon such contemporary organizations, Insights Success has curated a list of “The 10
Most Innovative Cognitive Solution Providers, 2018,” who are excelling their provision of best-in-class

cognitive solutions that incessantly advocate ingenuity in technological innovation and global
services.
Our cover story features Wave Computing, which is amongst the few of the world's leading AI
solution providers that offer assured deep learning computing systems. The Silicon Valley based
company is renowned for its innovative system solutions that leverage dataflow technology to
provide high-performance training and high-efficiency inference at scale, enabling enterprises to
drive better business value from their data. Wave Computing is revolutionizing AI and deep learning
with its dataflow-based systems. It has already initiated early testing and installation of its first-
generation AI systems product, and is now focused on ensuring its solutions work seamlessly with its
customers' environments.
In this issue, we have also enlisted companies which are providing insightful and innovative
solutions to enhance the applications of Cognitive solutions. Find Solution Artificial Intelligence
Ltd: which develops AI-driven motivation software which uses a Deep Learning model to read users'
emotions and generate real-time interaction and motivation; Intelligent Voice: boosts the intelligence
of one's calls, and puts them to work; Pixoneye: which is a data analytics SaaS company, which
analyses and provides consumer insights via platform dashboard by using machine learning on
mobile users' photo-galleries to create advanced user segmentations; neurIOT: which builds
solutions which possess human-like intelligence that employ cognitive science, AI and IoT to deliver
predictive solutions; Presenso: which develops solutions for Predictive Maintenance in the Industrial
Internet of Things and makes them accessible to maintenance and reliability professionals; BurstIQ:
which leverages blockchain and machine intelligence to enable data from disparate sources to be
brought together to create a single, unified data repository, and to be shared quickly and easily while
still maintaining strict security standards and HIPAA compliance; and AEye: which develops
advanced vision hardware, software, and algorithms that act as the eyes and visual cortex of
autonomous vehicles; and CloudMedx Inc.: a software development company, provides cloud-based
predictive health analytics and care coordination platform.
Also, make sure to scroll through the articles written by our in-house editorial team and CXO
standpoints of some of the leading industry experts to have a brief taste of the sector.
Let's start reading!
Abhishaj Sajeev

Enhancing the
Human Way
of Living
Redening AI
Computing with
Dataow Technology
Cover Story
Tech Capture
RPA and Cognitive Technologies:
Merger of Innovative
Approach towards
Customer Engagement
26
42
10
Articles

CONTENTS
Thought Leader
AI Saved the
Audio Star
Cognitive Insights
The Role & Challenges of
Data Needed for Cognitive
Computing and AI
Think AI
AI: From Artificial
To Authentic
BurstIQ:
Technology to
Revolutionize Healthcare
Aeye:
Safe & Reliable
Vehicle Autonomy
18
36
50
22
24

Find Solution
Artificial Intelligence:
Tailored Interactive
Curriculum through AI
CloudMedx:
Comprehensive Unified
Healthcaren
neurIOT:
Molding Cognition to
Deliver Precise Prediction
Intelligent Voice:
Speech Recognition for
the AI Age
Presenso:
Aiding Industrial Development
with Artificial Intelligence
Pixoneye:
Building Technology that
Learns,Trains and
Predicts Entirely on Device
30
32
38
40
46
48

sales@insightssuccess.com
Corporate Ofces:
June, 2018
Database Management Stella Andrew
Technology Consultant David Stokes
Circulation Manager Robert, Tanaji
Research Analyst Chidiebere Moses
Steve, Joe, Alan, Vishal
Editor-in-Chief Pooja M. Bansal
Anish MillerManaging Editor
Jenny Fernandes
Art & Design Director Amol Kamble
Associate Designer Shubham Mahadik
Visualiser David King
Senior Sales Manager Passi D.
Business Development Executives
Marketing Manager John Matthew
Executive Editors
Assistant Editors
Bhushan kadam
Art & Picture Editor BelinPaul
Co-designer Rahul kavanekar
KhannaJayant
Peter CollinsBusiness Development Manager
Sales Executives
David, Kevin, Mark, Prasad
SME-SMO Executives
Prashant Chevale, Uma Dhenge, Gemson, Irfan
Online Marketing Strategist
Alina Sege, Shubham, Vaibhav K
Digital Marketing Manager Marry D’Souza
Technical Specialist Amar, Pratiksha
Technical Head Jacob Smile
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Abhishaj Sajeev
Contributing Editors
Kaustav roy
Kedar kulkarni

Redening AI Capabilities with
DataFlow Technology
We harness the power of dataﬂow
technology for deep learning.
“
“
Cover Story

The10Most
Solution Providers 2018
INN VAT VE
C GNIT VE
Derek Meyer
CEO
Wave Computing

AI powered solutions to empower systems and
business workflows.
Wave Computing is amongst the few of the world’s
leading AI solution providers that offer assured deep
learning computing systems. The Silicon Valley based
company is renowned for its innovative system solutions
that leverage dataflow technology to provide high-
performance training and high-efficiency inferencing at
scale, enabling enterprises to drive better business value
from their data. Wave Computing is revolutionizing AI
and deep learning with its dataflow-based systems.
Delivering Prolific AI Powered Computing Systems
with Superior Efficiency
Unlike other start-ups in the AI hardware space, which
are still in early stages of developing or defining their
product, Wave Computing is already starting early
testing and installation of its first-generation AI systems,
and is now focused on ensuring its solutions work
seamlessly with its customers’ environments. The
company’s deep learning systems leverage its unique
dataflow technology to eliminate the need for a co-
processor (e.g., a CPU or GPU), offering high-
performance, high-efficiency training and inferencing
computing solutions that scale for any implementation.
Wave Computing is bringing deep learning to the data,
wherever the data is—from the datacenter to the edge of
the cloud. Its first product, a ‘plug and play’ dataflow
appliance, is ideal for data scientists that want to
experience faster machine learning without the need for
IT involvement - either from a budgetary or technical
support perspective.
The Wave dataflow appliance is purpose-built for in-
office environment constraints such as space, power and
cost, while outperforming existing datacenter servers for
machine learning workloads. Ideal for both Recurrent
Neural Networks (RNNs) and Convolutional Neural
Networks (CNNs), it is a complete system that enables
data scientists to get rolling on their machine learning
workloads right out of the box.
The world has experienced three great industrial
revolutions over the past 100 years, driven by
steam, electricity and then transistors. Now,
Artificial Intelligence (AI) is poised to drive the next
great wave of technological evolution.
People – and businesses – are generating torrents of data
every day, which in turn is changing the way we work,
play, communicate and even shop. AI is made possible
by high-performance “super computers” that are able to
use this data to perform tasks normally requiring human
intelligence, such as visual perception, speech
recognition, decision-making, and translation between
languages. And with AI expenditure expected to reach
$46 billion by 2020, according to an IDC report, there’s
no sign of the technology slowing down.
The general benefit of AI is that it replicates the
decisions and actions of humans without being impacted
by human shortcomings, such as fatigue, illness or
distractions. It is also easier for companies to achieve
more consistent performance across multiple AI
machines than it is across multiple human workers. AI
simply helps reduce errors and enables a greater degree
of accuracy and precision. Although AI offers numerous
benefits and can drive businesses significantly, it has
some risks as well.
An increasing number of companies are trying
to jump into the AI space and offering
Wave Computing provides
faster results and improved
accuracy for data-driven
business applications with its
revolutionary new
compute appliance.
“ “

Turning Vision into Reality
The success of any organization depends on the ability of
its leaders to convert their vision into reality. If the
leaders are capable of encouraging every single
employee of their organization, and incepting an astute
team of professionals that share the same vision, they
can achieve any desired target in an expected time
period. Derek Meyer is a perfect example of such
visionary leadership.
Derek Meyer is the CEO of Wave Computing. He brings
more than 20 years of executive management, corporate
strategy, product development and go-to-market
experience to Wave. He has been instrumental in leading
Our dataow-based systems
are designed to exploit both
data parallelism and
operational parallelism
at the same time, accelerating
the time-to-market for AI
applications while
being cost effective.
“
“

the company’s initiatives to deliver the world’s first
dataflow-based solutions for the rapidly expanding deep
learning market, spanning the datacenter to the edge.
Providing World’s Fastest Dataflow Computer for
Machine Learning
Machine learning is redefining the way that enterprises
do business, enabling organizations to solve complex
business problems with AI and deep learning. Wave
Computing’s revolutionary new AI appliance delivers
orders of magnitude improvement in neural network
performance over existing legacy GPU based systems,
providing blazingly fast results and improved accuracy
that enables faster data driven insight.
Converting Challenges into Opportunity
Datacenter-centric AI applications today need many
weeks to train using coprocessors such as GPUs, only to
require a different architecture for inferencing at the
edge. The lack of a common AI platform, spanning from
the datacenter to the edge of the cloud, slows market
growth and reduces productivity of data scientists.
Converting this challenge into
opportunity, Wave Computing
has acquired MIPS Tech,
Inc. (formerly MIPS
Technologies), a global
leader in RISC processor
Intellectual Property (IP)
and licensable CPU cores.
The acquisition will
accelerate Wave’s strategy
of offering AI acceleration
from the datacenter to the
edge of cloud by
extending the company’s
products beyond AI
systems to now also
include AI-enabled
embedded solutions.
While explaining about the new acquisition, Derek
Meyer, CEO of Wave Computing asserts, “This
acquisition of MIPS allows us to combine technologies
to create products that will deliver a single ‘datacenter-
to-edge’platform, ideal for AI and deep learning. We’ve
already received very strong and enthusiastic support
from leading suppliers and strategic partners, as they
affirm the value of data scientists being able to
experiment, develop, test and deploy their neural
networks on a common platform.”
A Vision to Bestow AI Industry with Fastest and Most
Scalable Solutions
Wave Computing’s vision is to deliver AI systems that
benefit all. Since its inception in 2011, the experts at
Wave have endeavored to bestow the AI industry with
the fastest and most scalable dataflow-based deep
learning solutions. The company has begun initial testing
and installation of its ground-breaking products and is
expanding its roadmap of AI system solutions to bring AI
to anywhere the data is, from the datacenter to the edge
of cloud.
Wave Computing is
revolutionizing the AI
industry with the industry’s
fastest, most scalable
dataow-based deep
learning solutions.
“ “

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Management BriefCompany Name
Aeye
aeye.ai
Luis Dussan
Co-founder & CEO
Aeye develops advanced vision hardware, software, and
algorithms that act as the eyes and visual cortex of
autonomous vehicles.
BurstIQ
burstiq.com
Frank Ricotta
CEO & Founder
The BurstIQ platform leverages blockchain and machine
intelligence to enable data from disparate sources to be brought
together to create a single, unified data repository, and to be
shared quickly and easily while still maintaining strict security
standards and HIPAA compliance.
Deep Force
deepforce.com
May-chen Martin-Kuo
CTO & Co-founder
Deep Force offers businesses a Deep Learning platform that
simplifies AI adoption on-device.
Find Solution Artificial
Intelligence Ltd.
findsolutionai.com
Ms. Viola Lam
CEO
Find Solution AI develops AI-driven motivation software which
uses a Deep Learning model to read users’ emotions and
generate real-time interaction and motivation.
neurIOT builds solutions which possess human-like intelligence
that employ cognitive science, AI and IoT to deliver
predictive solutions.
Tpixoneye is a data analytics SaaS company, which analyses and
provides consumer insights via platform dashboard by using
machine learning on mobile users’ photo-galleries to create
advanced user segmentations.
Presenso develops solutions for Predictive Maintenance in the
Industrial Internet of Things and makes them accessible to
maintenance and reliability professionals.
Wave Computing is a Silicon Valley company that is
revolutionizing artificial intelligence (AI) and deep learning
from the data center to the edge with its dataflow-based systems
and embedded solutions.
Intelligent Voice
intelligentvoice.com
Nigel Cannings
Technical Director
&
CTO
Intelligent Voice boosts the intelligence of your calls, and puts
them to work for you.
neurIOT
neuriot.com
Sanjeev Thukral
Managing Partner,
Co-founder & CEO
Pixoneye
pixoneye.com
Nadav Israel
CTO,
Ofri Ben-Porat
CEO
Presenso
presenso.com
Eitan Vesely
Co-founder & CEO
Wave Computing
wavecomp.com
Derek Meyer
CEO
CloudMedx Inc.
cloudmedxhealth.com
Tashfeen Suleman
CEO
CloudMedx Inc., a software development company, provides
cloud-based predictive health analytics and care
coordination platform.

AI Saved the
StarAudi
Assaf Gad
VP Marke ng &
Strategic Partnerships
Audioburst
MM 201818
Thought Leader

Useful Artificial Intelligence (AI) is no longer
science fiction. There’s an estimated 33 million
voice-first, AI enabled devices in homes today
and the mass adoption is indisputable evidence of AI’s
growing influence in today’s world.
AI has been particularly compelling within the audio
industry. While a somewhat unexpected combination, the
marriage of AI and audio comes on the back of the
popularity of smart assistants. This has put AI in the
limelight and has brought back audio from the shadows
of video-related content online.
From a 10,000 foot view, AI systems deliver a pre-
programmed set of responses and can only respond to a
pre-set number of questions. AI essentially decides what
actions are the most appropriate to take based on a
combination of data sets fed over a period of time.
AI takes Center Stage in Audio Search
In the past, audio content had its set of challenges due to
the lack of an organized repository or archive making it
almost impossible to sift through. Although from a
search perspective, we currently have established and
robust search engines that are capable of pulling up
millions of text-based articles or videos, almost no
platforms that are able to provide results that are purely
in an audio format – including specific audio clips from
local radio stations or podcasts.
At Audioburst, we aim to change that. AI is making
voice, once the golden child with the advent of the radio,
a next generation technology. That technology
essentially delivers the building blocks allowing audio
content to be indexed and easily searched online, in the
same manner as text or video. Our adoption of AI and
machine learning tools help us analyze millions of
minutes of live and pre-recorded audio content each day.
Through this process, we create live transcriptions of
speech, which are then turned into digestible audio clips
that are indexed and tagged so that they are searchable
by keyword, context or topic.
This then establishes an ecosystem in which audio content
consumption and discovery is in line with the consumer
demand and appetite for voice-based search and voice-
first experience. And, as new consumer devices, geared
towards delivering and interacting with content through
voice, continue to spread and become more popular,
audio delivery as a search result will become
mainstream.
Additionally, the audio content itself – and its creators –
are the biggest benefactors from this new wave powered
by AI. The ability to make audio more searchable has
granted the genre a new lease on life, prolonging the
shelf life of the content and allowing it to reach a wider
audience. The indexing of audio via AI makes this type
of content much more shareable, which inevitably
delivers greater value to content creators as well as
generates new opportunities for new voice-related
content.
More conversational search will ensure that AI grows
smarter and better understands user intent—it’s very
much a mutually beneficial relationship where the more
it’s put into practice, the better and more refined the
process becomes.
Above all, AI’s functionality rests on the data that feeds
into it. As our lives become increasingly interconnected,
AI technologies that are fueling the transition to voice
search will experience a huge wave enabling the
technology to become more accurate and deliver a more
personalized audio experience and voice search.
Ultimately, humans are most used to voice interactions.
We use our voice on a daily basis and it’s the unique
characteristic that brings everyone together. The value of
AI is its ability to better understand and predict what
users actually want, based on data from users’ patterns,
behavior, language and preferences. AI offers a concrete
business application for voice-activated and audio-
related industries, ensuring that audio content can
compete in an increasingly screen first world.
MM 2018 19

Omnichannel Agent
and Customer
Engagement Solutions
Simplify and personalize the customer experience,
empower agents and achieve business success
with one workspace for all channel interactions,
application integrations, and CX reporting.

AEye is a pioneer of artificial
perception and the creator of
iDAR™ (Intelligent
Detection and Ranging), a perception
system that that acts as the eyes and
visual cortex of autonomous
vehicles.
First-generation LiDAR technologies
use siloed sensors, rigid
asymmetrical data collection
methods, and post-processing, which
lead to latency as well as over- and
under-sampling of information. By
contrast, iDAR optimizes data
collection - decreasing data volume
but increasing its quality and
relevance - for accelerated perception
and path-planning.
About AEye
AEye is based in the San Francisco
Bay Area and backed by world-
renowned investors, including
Kleiner Perkins Caufield & Byers,
Airbus Ventures and Intel Capital.
Since the first demonstration of its
solid state LiDAR scanner in 2013,
AEye has pioneered breakthroughs in
intelligent sensing. Its iDAR
technology combines the world’s
first agile MOEMS LiDAR, pre-
AEye measures its performance
based on the quality, reliability, and
speed of information their system
delivers to autonomous vehicle path-
planning software. The difference is
clear, but maintaining this clear
distinction over time is challenging.
The second challenge relates to
AEye’s success in addressing the
first. The market has been coming to
them. At CES, AEye was flooded
with interest from every major
Automobile OEM and every Tier
One Automotive parts supplier.
As a small company with limited
resources, their biggest challenge is
prioritizing which partners are the
best fit for AEye and investing the
appropriate amount of time in each to
ensure mutual success. Investing in
the wrong partner - either because
technology is not a good fit, product
development timelines do not align,
or commercialization expectations do
not sync - drains AEye’s limited
resources while compromising their
ability to hit the market window.
The Visionary
Luis Dussan, Co-Founder and CEO
of AEye, is a two-decade veteran of
fused with a low-light camera and
embedded AI to create software-
definable and extensible hardware
that can dynamically adapt to real-
time demands.
By enabling intelligent prioritization
and interrogation, AEye’s iDAR can
target and identify objects within a
scene 10 to 20 times more effectively
than LiDAR-only products. iDAR
delivers higher accuracy, longer
range, and more intelligent
information to optimize path
planning software. This radically
improves autonomous vehicle safety
and performance at a reduced cost.
Overcoming Challenges
AEye’s primary challenges are two-
fold. The first of these is that the
company is in a dynamic, noisy
market.
There are over 60 companies
building 3D sensing technologies for
autonomous vehicles. All but AEye
are sensor-only, point solutions.
AEye has created an integrated
perception system that incorporates
data from sensors. While point
solution providers want to be judged
by sensor-level technical features,
AEye:
Safe & Reliable Vehicle Autonomy
MM 201822

electro-optics. He has served as chief
technologist of EO Sensors/LADAR
at Northrop Grumman, chief
engineer at Lockheed Martin, and
systems engineer at NASA’s Jet
Propulsion Laboratory.
At NASA, he worked on its deep
space network. At Lockheed, Luis
ensured visual accuracy of the Sniper
Advanced Targeting Pod (ATP), a
multimillion dollar system used by
fighter jets to detect, identify and
engage tactical-size targets outside
the range of most enemy air
defenses.
He holds a B.Sc. in Electrical
Engineering and Computer Science,
an M.Sc. in Quantum Optics, and an
M.Sc. in Optics & Photonics. Luis
put his Ph.D. in Computational
Physics on hold to start AEye.
Products by AEye
AEye’s AE100 is a leading edge
artificial perception system for
autonomous vehicles, ADAS and
mobility markets. It incorporates
breakthrough advancements in
perception and path-planning.
their iDAR™ artificial perception
system. The company also conducted
182 car demonstrations at the show
with every major automotive OEM
and Tier 1 organization in the
industry.
The Future
AEye is now optimizing its
proprietary software to make sure it
delivers the best data to the path-
planning software.
Most companies spend the majority
of their time in the fusion and
decimation of data, and then they do
a little bit of perception. AEye wants
to optimize that process: acquiring
the most information with the fewest
amounts of ones and zeros.
Perception can develop on top of that
model, which allows the company to
move industries like automotive,
construction, and ITS infrastructure
into the next realm of autonomous
capability.
The AE100 is based on AEye’s
iDAR perception system. iDAR
mimics how a person’s visual cortex
focuses on and evaluates potential
driving hazards. Using embedded AI
within a distributed architecture,
iDAR critically and dynamically
assesses general surroundings, while
applying differentiated focus to track
targets and objects of interest.
As a scalable, integrated system,
iDAR delivers more accurate, longer
range, and more intelligent
information faster.
Achievements
AEye has been awarded foundational
patents for its solid state MEMs-
based agile LiDAR and embedded AI
technology. In 2017, the company
successfully conducted the first live
metropolitan demo ever of a 360-
degree solid state LiDAR system,
showcasing its ability to collect real-
time, high-density point clouds at up
to 300 meters.
At CES 2018, AEye introduced its
first product for the automotive
market, the AEye AE100, based on
Luis Dussan
Co-Founder & CEO
AEye’s disruptive approach to
autonomous vision puts intelligence
at the sensor layer,promising the
kind of real-time perception that is
critical to the rollout of safe
autonomous vehicle systems.
“ “
MM 2018 23

Most people consider their
health and that of their
loved ones to be their
number one priority. However, we
have relied far too long on generic
healthcare delivery.
As we hurtle forward in this digital
era, technological advances are not
only uncovering better treatment
methods and more effective
medicines but also revolutionizing
how clinicians personalize them to
each patient. Today, we can receive
medical attention specific to our
genetics, lifestyle, and environment.
The key to this revolution is
information, and at the core of the
revolution is BurstIQ.
Your Medical Data, Organized
It would be a gross understatement to
say that BurstIQ specializes in data.
The company has developed a unique
blockchain-based platform to collate,
organize, and retrieve a wide range of
medical data, and present it in the
most effectual manner to each touch
point in the healthcare network.
It gives the patient complete access
to, and ownership of their data while
enabling companies to connect,
develop, and commercialize their
healthcare applications, platforms,
and services.
Patients can choose to share as little
or as much of their personal data as
they desire with these institutions and
companies. As a reward, they can
enjoy complimentary or discounted
medication and treatment, participate
in clinical trials, obtain all-round
support, and benefit from reduced
insurance premiums.
However, the system’s biggest
advantage is that healthcare can be
personalized to the individual patient.
Biotech and pharmaceutical
companies will be able to identify
participants with the ideal profiles for
their clinical trials using genomic and
proteomic information. The
limitations of geography and
economic status will become
irrelevant.
The potential for cooperation extends
beyond just the patient-provider
nexus; BurstIQ’s platform also
facilitates interactions between
companies and organizations at other
points along the healthcare chain. It is
the ideal environment to encourage
more collaboration between
researchers and is poised to usher in
an era of unsurpassed medical
progress.
Rethinking Basics
The concept of using technology to
create a medical ecosystem is not new
Not only does this empower
individuals with their personal
medical information, it also allows
them to voluntarily share it with
specific care providers and research
entities if it suits their own needs.
This has created an unparalleled
channel for direct communication
between researchers and individual
patients that has the potential to make
medicine and treatment more
personal, precise, and effective than
ever before.
To deliver this range of features,
BurstIQ’s proprietary platform
exploits blockchain technology and
machine intelligence to gather data
from disparate sources into a single,
unified repository of data. The
company has invested considerable
resources to ensure that the
information can be retrieved and
shared quickly and easily while
maintaining strict security standards.
Limitless Potential
At the heart of the BurstIQ platform is
a desire to foster and promote
interaction and collaboration between
all entities involved in every aspect of
healthcare. This starts with the
individual patient and branches out to
clinics, hospitals, pharmacies,
pharmaceuticals, health equipment
manufacturers, insurers, and more.
BurstIQ:
Technology to Revolutionize Healthcare
MM 201824

in itself; electronic health records and
similar software have been part of the
medical landscape for many years.
However, BurstIQ is the world’s first
and only true combination of
blockchain, Big Data and machine
intelligence in the industry.
The company’s competitors still store
data in traditional, off-chain data
warehouses whereas the BurstIQ
platform allows large data sets to be
stored and analyzed on-chain.
Because blockchain is inherently open
and transparent, BurstIQ has
supplemented the system with best-
in-class security features that far
exceed HIPAA requirements.
Necessary Progress
The adage ‘Necessity is the mother of
invention’ holds very true in the case
of BurstIQ.
Before starting the company, its
Founder and CEO, Frank Ricotta
was stunned to receive three separate
notices that his personal data had been
compromised. A violation of that
nature has the ability to inspire a
rethink, and Frank was just the man to
do it.
“I’ve had a lifelong passion for
combining security and cooperative
intelligence to solve very hard
problems,” he says, “This passion has
allowing large enterprises to leverage
its blockchain data as a way of
boosting their internal data
capabilities. Now, the company has
opened a Series A private equity and
token offering to accelerate growth
with B2C and SMEs.
At the end of the Series A, the
platform SDK will be opened to a
broad developer community which
will facilitate the launch of the
marketplace side of the platform. This
stage will significantly increase
individuals’ engagement with their
own data and stimulate the creation of
health-related products and data
exchanges.
In this way, BurstIQ is laying the
groundwork for more medical
progress in a shorter timeframe than
was ever possible before. It is a
scenario where everybody wins.
carried forward through my entire
career.”
That career had been spent in
cybersecurity, machine intelligence,
and high-capacity networks.
Beginning with a rudimentary AI
application in the mid-1980s, Frank
has gone on to work on advanced
military-grade high-capacity networks
and cryptographic solutions for the
U.S. Air Force.
He also led the development of
security and network solutions for the
healthcare sector at Recondo
Technologies, and he was working
there when the breach of his personal
data spurred him to action. He left
Recondo in late 2014 and started
working on concepts for a next-
generation privacy solution for the
healthcare industry.
BurstIQ was officially founded in
April 2015 with an initial seed round
of $250,000 from PV Ventures. Frank,
together with co-founder, Brian
Jackson, and Chief Data Scientist,
Tyson Henry, have built the BurstIQ
platform into the end-to-end
enterprise blockchain solution that
forms the foundation of the business.
Success through Diversification
The BurstIQ platform has already
found tremendous success by
Frank Ricotta
Founder & CEO
We believe data can unleash
the power of innovation. We
also strongly believe that
individuals and their care
providers should have more
control of their data. That’s why
we created our manifesto.
“
“
MM 2018 25

nce, Dean Kamen, an American engineer,
Oinventor, and businessman, stated that, “Every
once in a while, a new technology, an old
problem, and a big idea, turn into an innovation.” It is
not always about the technology, thinking out of the box
also result in an innovation. However, new innovations
in technologies are evolving and streamlining the
complex processes.
As technology keeps on evolving, it never fails to amaze
humans with its advancements. Today, the technological
revolution is delivering smarter solutions that are
simplifying the ways in which humans are living, across
the globe. The Artiﬁcial Intelligence is making a
remarkable growth in the technological industry.
However, in case of computing systems, it is certain that
computers are getting faster and smarter day by day. But
now, the idea is to make computers artiﬁcially
intelligent. It comes as surprise that computers have
developed an ability to think and analyze without human
involvement. This enhanced ability has been largely
aided by Cognitive Computing.
Cognitive Computing has evolved by combining
computer and cognitive sciences. It refers to the
computational model that involves imitation of human
thought processes. It enables computers to understand
data, generate insights and use them as a learning
experience in future. Through cognitive computing, most
complicated problems can be solved by penetrating the
complexity of big data and exploiting the power of
natural language processing and machine learning.
Technologies Assisting the Development of Cognitive
Computing
In implementing cognitive computing, three
technologies play a vital role. These include Big Data,
Machine Learning and Cloud Computing.
With a view of building a new class of systems learned
from experiences, cognitive computing provides a broad
assistance and derives insights to unlock the value of big
data. The use of big data helps cognitive computing to
ease the approach of combining analytics, problem
solving, and communication with human decision
makers.
An enormous amount of data is stored in the cloud
which can act as a source for the machine learning
algorithms. Machine learning is all about using
algorithms to enable computers to analyze data and
predict the information fed to them. By leveraging
machine learning, cognitive computing can deliver a
simulated conversation to mimic human interaction
when delivering a service.
In the current scenario, the usage of cloud involves
computing, storage, and networking. An extensive
computing power is required to analyze huge amount of
data in real time. Whereas, the cognitive computing
system bears a pressure that varies on the basis of data
fed into the system. Thus it becomes viable for cognitive
Enhancing the
Human Way of Living
MM 201826

computing systems to opt for cloud computing solutions
as it provides scalable computing for analyzing the data.
This ultimately becomes ideal solutions for cognitive
computing models
Scope of Cognitive Computing
There are three capabilities that serve the
importance of cognitive computing which involves
engagement, decision, and discovery. Opening
new doors for innovations, these capability areas
directly relate to the ways people think and work
and demonstrate increasing levels of cognitive
capability.
Ÿ Engagement:
Cognitive Systems fundamentally
change the way of interaction between
humans and machines. The human
capabilities are extended by leveraging
their ability to provide expert assistance
through these machines. These systems
provide expert assistance in developing
deep domain insights. For instance, chatbot
technology is the best example that
enables engagement, as it is pre-trained
with domain knowledge for quick
adoption in different business-
speciﬁc applications.
Ÿ Decision:
Cognitive computing systems
possess decision-making
capabilities. Decisions made by
cognitive systems are evidence-
based, bias-free, and continuously
evolve based on new information,
outcomes and actions. These systems
perform more as an advisor by suggesting a
set of alternatives to human users, as these
are the ones who make the ﬁnal decisions.
Discovery:
One of the epitomical capabilities cognitive
computing possesses is discovery. These
systems can discover insights which perhaps
cannot be discovered even by the most brilliant
human beings. Cognitive systems not only understand
the vast amount of information, but also involve the
skills to develop them. There is a dire need of these
MM 2018 27

systems in various domains such as medical research,
etc., as it supports new discoveries and insights.
With Scope comes Challenges
There often arises a barrier in methods of implementing
a new technology. Similarly, cognitive computing system
holds the potential to bring innovation, but it also faces
some challenges that need to be figured out.
From a technical viewpoint, the cognitive computing
system has limited capacity to analyze the risk in case of
unstructured data due to socio-economic, culture,
political environmental, and people oriented factors.
Thus, cognitive technology requires a human
involvement for complete risk analysis and final decision
making, as it cannot work without the support of human
intelligence.
Another challenge that comes in a way of implementing
cognitive technology involves meticulous training-data
processes. Initially, cognitive systems require a training
data to completely understand the process and improvise,
which likely becomes the reason for its slow adoption.
There are some cases where enterprises not only need
sufficient training data set, but also skilled resources who
can invest time in tuning the cognitive engine before
valuable outputs can be gained.
The other challenges also include its cost of
implementation, to be precise, intelligence augmentation
instead of artificial intelligence, privacy and legal
implication, and managing the change.
The Future of Intelligence
Cognitive computing systems differ from current
computing applications as it represents a set of new-age
services, built using state-of-the-art Natural Language
Processing algorithms, Artificial Intelligence, Machine
Learning, Analytics backed by massive computing
power. It is assisting businesses in the services of
sentiment and tone analysis, speech-to-text conversion
and vice versa, language translation, automated chats,
personality insights and many more.
The systems of cognitive computing can be applicable in
various industrial verticals that are dealing with huge
amount of unstructured data that needs to be analyzed
and processed to address various operational concerns.
Further, it also can venture into other areas of businesses
including consumer behavior analysis, customer service
bots, etc.
The outcomes of Cognitive computing are proving to be
a boon for businesses, healthcare industry, personal lives,
and many more segments. It is the next big thing in the
world of intelligence.
MM 201828

Delivering medical care is
very rewarding as a
profession but it can also be
very stressful. All too often,
clinicians have to contend with
incomplete or inaccurate medical
records. This is compounded by the
timeframes between asking for tests
and getting the results. Together,
these shortcomings complicate
patient care and have the potential to
result in harm.
Some progress has already been
made, and Electronic Medical
Records (EMRs) are commonplace.
However, such systems collate data
but fail to fully utilize its potential.
An intelligent system is required, one
which can covert bland data into
actionable information.
It is in this arena that CloudMedx
shines.
Powerful Insights
CloudMedx is a world-class clinical
AI platform that combines machine
learning with Neuro-linguistic
Programming (NLP) developed
specifically for the healthcare
industry. It was created to give both
healthcare providers and patients
unprecedented insight into the
medical journey.
currently visiting, it becomes
incredibly difficult to filter out the
relevant facts.
Such scenarios contribute to clinician
burnout, which further erodes the
level of care that they are able to
provide. With CloudMedx, AI-driven
analytics sifts through all available
data and separates the extraneous
from the relevant. This gives
healthcare providers specific insights
while highlighting potential conflicts
and red flags.
Not only does this streamline the
process of delivering care, but it also
places doctors in an ideal position to
diagnose diseases early and to more
accurately determine prognoses.
Large Footprint
The CloudMedx platform is not
geared only to patients and the
healthcare providers with whom they
are in direct contact. It broadens the
scope to include researchers.
This is a critical difference because it
creates an entirely new avenue for
the development and advance of
medicine. Researchers do not have to
invest financial resources and time
into seeking out individual patients
for clinical trials or to obtain relevant
The platform does not simply collate
and store data, as is the case with
legacy systems, but proactively
searches for trends and markers
hidden within vast volumes of
information. This feat is achieved
through the use of evidence-based
algorithms and big data architecture.
The resulting transparency allows
clinicians to obtain unsurpassed
insight into every patient’s unique
medical history, tendencies, and risk
factors, then formulate an actionable
plan that uses that information to
deliver the best diagnoses, in-person
care, and medication.
By creating an environment where
clinical partners at all levels are privy
to the right information, without the
limitations of time or geography,
CloudMedx is able to ensure that
every patient receives the best
attention and results at every juncture
of medical care.
Bridging Gaps
One of the most common limitations
of current medical systems that
doctors cite is the vast volume of
data presented to them for each
patient. Because not all the
information will be related to the
issue for which the patient is
CloudMedx:
Comprehensive Unified Healthcare
MM 201830

data; the profiles of individuals with
the exact requirements can be made
available to them.
This compresses the timeline
required by pharmaceuticals to
complete research into new
treatments. Concurrently, patients
who would otherwise not have access
to the latest medicines and treatments
can receive the best care and enjoy a
better quality of life.
Altruistic Intent
CloudMedx is the breakthrough
platform developed by Tashfeen
Suleman, the company’s CEO.
Tashfeen is responsible for dictating
the company’s vision and strategy.
He believes that the power of data
can save lives when we combine
innovation with technology.
Tashfeen is a Computer Science
major with industry experience in big
data and AI, as well as product
design, development, and
commercialization. For the past 13
years, he has been a serial
entrepreneur, technology enthusiast,
and executive manager. In that time,
he has worked with some of the
world’s largest companies, including
prohibitive factor. Medical
institutions and research centers
invest massive amounts in a single
platform and are reluctant to move
on to newer, improved ones until
they believe they have recouped their
investment costs.
In this time, the information
collected and stored begins to display
the silo effect; there are massive
amounts of discrete data available,
but they cannot be co-related to
deliver better care.
CloudMedx completely changes this
landscape by offering a versatile,
interconnected platform that is able
to streamline all this information
across healthcare providers with the
patient’s permission. Clinical
analytics tools then assist patients,
doctors, and researchers to achieve
the end results that they seek.
The CloudMedx team is very
confident that their platform is
flexible and adaptable enough to be
applied outside of healthcare. They
are already considering how the
underlying technology can serve to a
wider clientele.
Microsoft, where he worked on the
Windows phone.
The CloudMedx project was inspired
by personal experience; an incident
where Tashfeen’s father was
misdiagnosed prompted him to
consider how technology could
benefit healthcare delivery.
Today, Tashfeen is a frequent guest
speaker at various health- and AI-
related talks, forums, and
symposiums. He has also appeared
on TV to talk about the path that
CloudMedx is blazing for the
healthcare industry.
Why It Works
The latest developments in AI and
machine learning have happened so
rapidly that no one can predict
accurately where the technology will
head even in the near future. What is
not contested is the fact that it has the
potential to inspire, drive, and
accelerate progress in the medical
arena like nothing that has come
before.
The healthcare management industry
is fertile ground for innovation
largely because cost has been a
Tashfeen Suleman
CEO
We leverage the latest clinical
algorithms,machine learning
technology, advanced
natural language processing,
and a proprietary clinical
contextual ontology to
improve patient journeys.
“
“
MM 2018 31

Find Solution AI has developed
an AI-driven Motivation Model
Software incorporated with a
Deep Learning model to read users’
emotions and generate real-time
interaction and motivation.
FSAI’s innovative product is
distributed as Software as a Service
(Saas) solutions for educators, schools,
healthcare providers and corporates. It
is capable of real-time understanding
of each user’s behavior and cognitive
awareness. Find Solution uses this
capability to create a tailor-made,
interactive curriculum where all users
have the chance to participate and
engage in learning and compliance
training.
FSAI’s AI-driven motivation model
currently has 16 patents pending
worldwide. The company’s USP lies in
being able to instantly measure the
emotions of learners while they are
working on a given math exercise.
This allows an assessment of their
individual knowledge, understanding,
learning traits, and behavior.
The real-time objective is to motivate
the learner, maximize learning
efficiency, minimize pressure from
exams, and to develop self-confidence.
Based on the AI report, parents and
teachers can gain a deep understanding
of students’ academic performance and
personal development.
to 12% with customized learning.
Maximizing the Potential of AI
The term ‘Artificial Intelligence’ too
often conjures up images of advanced
technology used for entertainment or
to make life more pleasurable. FSAI
decided to innovate and explore the
true potential of artificial intelligence
by implementing it in education and
training.
FSAI’s Founder, Ms. Viola Lam is a
multi-award-winning educator with 12
years of education experience. She has
served thousands of students through
her three FS Education Centers.
Viola’s self-developed unique
motivation and learning methodology
helps students improve performance
by as much as 10% within 2 months.
Her husband, Mr. Matthijs Dolsma is
a Chief Software Architect. He has
10 years of experience in the field of
artificial intelligence, specifically in
Machine learning and Machine
imaging in semi-conducting industries
in NXP (former Philips). It was there
that he developed a deep learning
model to process the production
images of NFC payment chips with 70
billion data per machine annually with
99.9999% efficiency and accuracy.
The company is integrating the ideas
of advanced technology and
interactive education, resulting in a
unique self-motivation model on
teaching and learning. “We believe the
uniqueness of the model will be
Astounding Products
Launched in Q3 2017, 4LittleTrees is
FSAI’s first AI-driven iPad Motivation
Application. It comes with almost
100,000 preloaded mathematical
questions and other educational
material for students aged 5 to 18 to
learn mathematics in class or after
class.
In the past four months, 15 Hong
Kong government schools have
subscribed to the application; these
contracts are valued at USD 2.1
million.
One of FSAI’s best-selling products,
4LittleTrees (4LTs) is not suitable for
just teachers and students, but also for
organizations like professional bodies,
corporate firms, and education
institutions. FSAI always upholds its
motto of “Smart Learning, Positive
Mind”.
The company understands the demand
for AI solutions. Its mission is to solve
real-world challenges by providing an
adaptive and personalized learning
experience. Using four unique
algorithms, 4LTs can figure out a
user’s needs based on the dynamics of
their emotions and their performance
on any topic or subject.
This understanding is then used as a
tool to motivate them.
4LTs also provides prediction and
increases learning efficiency from 3%
Find Solution
Artificial Intelligence:
Tailored Interactive Curriculum through AI
MM 201832

popular and used widely in both
primary schools and secondary
schools”, asserts Ms. Lam.
4LTs has been specially developed to
create a better learning atmosphere for
children with special needs and their
teachers, and to provide
comprehensive data on conditions
such as ADHD and Autism.
Distinguished Leadership
FSAI was founded in Hong Kong in
2016 by Ms. Viola Lam who is also
the CEO of the company. She won the
Entrepreneur of the Year 2015 Award
from Youth Business International.
This is one of the world’s most highly-
regarded awards because it celebrates
young entrepreneurs who make
positive contributions to the society.
Ms. Lam was selected by the judges
over 1,000 notable competitors from
68 countries for her far-sighted vision
for education, clear and sustainable
business model, benefits to the
community, and her ambitious plans
for growth.
2017 was a significant and fruitful year
for FSAI – it won 10 different awards
including being named the Xin Hua
Net–AI Learning Application Winner,
Ing Dan–iFuture 2017 winner,
Cyberport Incubator Pitch day winner,
st
1 runner-up at the Chinese
government’s Innovation &
Entrepreneur Competition 2017,
finalist at the Harvard Business School
behaviors. Related time-consuming
procedures can be eliminated. Users’
psychological reactions regarding
study progress and mental
development could be unveiled with
effective statistical intelligence.”
Future Outlook
FSAI’s current focus is on the Hong
Kong market with its 1,100 schools
and a B2B2G model worth eight
billion HKD annually. The company is
expanding into compliance training
and providing user cognitive
awareness insights in healthcare in
Hong Kong and three big cities in
China.
It expects to close Series A in Q2 2018
for further development in software
and expanding markets. FSAI also
expects to build a third-party SDK and
to use its motivation models in
different industries in Q3 2018.
The company is working with
corporates to provide white label or
data insight for user behavior. It plans
to implement a B2B model in China
and India in 2018.
Pitching Top 25 Innovators, Top 25 AI
Companies, the Best Performance of
Cyberport 2017, finalist, and the Most
Innovative Award of JUMPSTARTER
2017. Most recently, FSAI became the
Gold Winner in Smart Living, ICT
Award 2018
Grateful to Clients and Investors
FSAI faced numerous challenges
pertaining to resources and financial
support. However, it has managed to
overcome them and is grateful for the
support from its clients and investors.
“Their trust means a lot to us. We have
successfully raised USD 1 million from
Japanese and Korean investors in just
six months. Their comments on our
products have stimulated us and
contributed a lot to our development,
making our product more attractive
and user-friendly. As a startup
company, it is very challenging for us
to extend our business network. Yet, we
were extremely lucky that we had a
strong support from Cyberport. We
successfully started the pilot learning
program with two MNCs and thirteen
government schools in 4 months”, says
Ms. Lam.
Industry Scenario
Ms. Lam asserts, “There is positive
growth potential in terms of using
cognitive computing. We believe AI is
going to be popular worldwide and
expect that deep learning in a
motivation model can provide instant
data insights for different user
Ms. Viola Lam
CEO
Our mission is to
solve real-world
challenges by providing
an adaptive and
personalized learning
experience.
“ “
MM 2018 33

In the last few years, AI and cognitive computing have
made breathtaking strides driven by developments in
machine learning, such as deep learning. Deep learning
is part of the broader ﬁeld of machine learning that is
concerned with giving computers the ability to learn
without being programmed. Deep learning has had some
incredible successes. But, one of the biggest challenges of
deep learning is the need for training data.
Large volumes of data are needed to train networks to do
the most rudimentary things. This data must also be
relatively clean to create networks that have any meaningful
predictive value. For many organizations, this makes
machine learning impractical. It’s not just the mechanics of
creating neural networks that’s challenging (although this is
itself a hard task), but also the way to organize and structure
enough data to do something useful with it.
There is an abundance of data available in the world—more
than 180 zettabytes (1 zettabyte is equal to 1 followed by 21
zeros) predicted by 2025. Ninety-nine percent of the data in
the world is not yet analyzed, and more than 80 percent of it
is unstructured, meaning that there is plenty of opportunity
and hidden gems in the data we are collecting. Sadly,
however, much of this data is not in any state to be
analyzed.
So, what can enterprises do?
You need to think about data differently from how you do
today. Data must be thought of as a building block for
information and analytics. It must be collected to answer a
question or set of questions. This means that it must have
the following characteristics:
Ÿ Accuracy: While obvious, the data must be accurate.
Ÿ Completeness: The data must be relevant, and data that
is necessary to answer the question asked must be
present. An obvious example of incomplete data would
be a classroom where there are 30 students, but the
teacher calculates the average for only 15.
Ÿ Consistency: If there is one database indicating that
there are 30 students in a class and a second database
showing that there are 31 in the same class then this is
an issue.
Ÿ Uniqueness: If a student has different identiﬁers in two
separate databases, this is an issue as it opens the risk
that information won’t be complete or consistent.
Ÿ Timeliness: Data can change, and the AI model may
need to be updated.
Role & Challenges
of Data Needed
Cognitivefor
Computing & AI
The
MM 201836
Cognitive Insights

Beyond the data itself, there are severe constraints that can impede analytics and deep learning, including security and access,
privacy, compliance, IP protection, and physical and virtual barriers. These constraints need to be thought about. It doesn’t
help the enterprise if it has all the data but the data is inaccessible for various reasons. Often, steps need to be taken such as
scrubbing the data so that no private content remains. Sometimes, agreements need to be made between parties that are
sharing data, and sometimes technical work needs to happen to move the data to locations where it can be analyzed. Finally,
the format and structure of the data needs to be considered. Legacy data might be plentiful, but may be incompatible with the
problem at hand.
The moral of the story is that we are deluged with data, but often the conditions do not allow the data to be used. Sometimes,
enterprises are lucky, and with some effort, they can put the data into good shape. Very often, enterprises will need to rethink
how to collect or transform data to a form that is consumable. Agreements can be made to share data or merge data sets, but
completeness issues often remain.
As noted earlier, the key to success is to start with a question and then structure the training data or collect the right data to
answer the question. While immense barriers remain in collecting training data, there is clearly a push by enterprises toward
higher quality data evinced by the growing inﬂuence of data scientists. I am very optimistic that the corpus of high-quality
training data will improve, thus enabling a wider adoption of AI across enterprises of all sizes.
Rajeev Dutt is the CEO and
Co-founder of
DimensionalMechanics, a
Seattle based start-up in
artiﬁcial intelligence. A
veteran of Intel, Microsoft,
HP, and BBC, Rajeev has
spent 17 years in high tech
including positions as CEO in
two media/AI-oriented
startups.
Rajeev Dutt
CEO & Co-founder
About The Author
MM 2018 37

Intelligent Voice is a London based
leading innovative technology firm
which develops enhanced speech
to text solutions and secure voice
recognition process. Its innovations
hold enormous potential for enterprises
that can “see” the verbal conversations
by converting voice calls, video and
other audio into Smart Data.
Intelligent voice has made various
breakthroughs in speech to text
analysis systems. One such
breakthrough is its GPU technology
backed by an NVIDIA processor
enabling the world’s fastest Automatic
Speech Recognition (ASR). This
innovation has set a new bar for
efficiency by dramatically reducing the
cost of Speech processing versus its
CPU counterparts.
Additionally, Intelligent Voice has
recently patented a method of
‘Preserving Privacy of Data in the
Cloud’. Intelligent Voice is bringing a
new layer of security to the world of
business which is increasingly
concerned about securing data.
Intelligent Voice services are widely
used by Government, legal and
financial sectors. It helps clients from
banking sectors as well as Government
Agencies globally. Apart from its
headquarter in London, Intelligent
biometric voice profile, businesses can
ensure that they have taken every
measure to find and delete customer
data instantly.
Additionally, the company also
provides a Credibility Analysis service
with its unique software service.
Using this, businesses can assess
individual behaviour and their
credibility in relation to business
objectives and interests. This
technology can potentially highlight
fraudulent Insurance claims, vulnerable
investors and even people who simply
do not understand what they are being
told.
One of the biggest concerns in the
corporate world is the increasing risk
of possible lawsuits, frauds due to the
increasing erosion of loyalty on the
part of employees and consumers in a
competitive and dynamic world. The
technology introduced by Intelligent
Voice empowers businesses to
safeguard their long-term interests
against potential fraudulent practices
from various different sources.
The Torchbearer
Founder and CTO of the Company,
Nigel Cannings is a Lawyer by
profession. Nigel carries over 25 years
of experience in both Law and
Voice also has offices in New York and
San Francisco.
Delivering Best Solutions to
Optimize Business Processes
Intelligent Voice offers businesses an
opportunity to explore their verbal
conversation in the form of accurate
and scannable text. It allows businesses
to source their data which was
previously inaccessible.
Its transcribing technology enables
clients to be engaged in their
operations in real-time. Whether a
business has received a complaint from
a customer, or its interests are being
comprised through a disclosure or
specific problems related to its newly
released software are being discussed;
businesses now have access to a
goldmine of smart data. Moreover, the
technology can be used aggressively to
collect data, compile it and gather
invaluable insights for a smoother
business operation.
Similarly, Businesses can also use this
technology to deal with complex and
potentially high cost-incurring legal
compliance. For example, the recently
introduced GDPR legislation requires
businesses to delete the data of
consumers whenever they ask for it.
Using the ability to search on a
Intelligent Voice:
Speech Recognition for the AI Age
MM 201838

Technology. He is also a regular
presenter at various prestigious
industry events including
NVIDIA’s GTC summit and Jefferies’
AI conference.
Nigel is passionate about the
advancement of voice technology and
along with the Intelligent Voice’s
research team has secured various
government grants and funding. Most
recently, he received an award through
the European Commission to develop
interactive conversational artificial
intelligence known as the ’Empathic
project’. The interactive “health bot”
will be built with the aim to help
elderly people live independently
within their home.
A Journey of Struggle and
Challenges
The Intelligent Voice company was
born out of a personal experience,
during which Nigel, during his time as
a lawyer, experienced the pain of
reviewing audio files manually for
hours. This was the beginning of the
innovation that is Intelligent Voice
today. Along with his team and Ben
Shellie, the CEO at its helm,
Intelligent voice continues to stay
ahead of the curve with ground-
breaking innovation.
Future Endeavours
Intelligent Voice is mainly focusing on
privacy processing. The company is
already offering high-level certainty at
speech to text process on local devices
like an Android phone. It also offers
encrypted data placed in the cloud and
calls it “Privacy Preserved Processing”.
The company’s next step is to build
advantages of cloud-based services like
Dropbox, without giving up on privacy
safety. Today, users can either opt for
an encryption or they can roam the
virtual world among cyber criminals as
they please. Thanks to the Intelligent
Voice technology, homomorphic
encryption is to the rescue along with
other technologies like Intel SGX;
genuine cloud privacy is just around
the corner.
In the near future,, Intelligent Voice
also aims to engage its customer
approach along the path of innovation,
by understanding customer fulfilment.
By this, the company believes, it will
eventually create a fulfilling vision for
the mutual benefit of the company as
well as for the customer.
Personal challenges of Nigel, today
have been transformed into a glowing
solution for businesses all over the
world. With various features like
smart data, security and voice-
activated personal assistants, managing
audio has become more crucial and an
important aspect. Through Intelligent
Voice, users can securely keep their
audio data with accuracy and cost
efficiency.
Disruptive Developing Trends of the
Cognitive Computing Industry
There are many brands which are
aggressively concentrating on the
power of developing cloud services
such as Alexa and Google Home,
which is ruling the market. Users are
not aware of what exactly the system is
analysing for their intended purposes.
It carries a big query, a concern with
data privacy. Users are not aware if
their profound personal data is safe or
not when it is being passed through
cloud providers.
Intelligent Voice predicts there will be
a real move towards processing “on the
edge”, with intent analysis being done
on device. Users will be able to
associate directly with the companies
in a secure shield without taking cloud-
platform as an intermediate.
Nigel Cannings
Technical Director & CTO
We provide speech
processing for the
privacy conscious.
“ “
MM 2018 39

The IoT industry has gained
momentum in its application
within numerous arenas and
appliances. This universal
applicability has made IoT a
quintessential part of the incessant
technological development. One
innovative idea of combining IoT
with Artificial Intelligence and
Cognitive Science to develop
predictive information has been
conceived by neurIOT.
Based out of Los Angeles, CA, the
company builds solutions which
possess human-like intelligence that
employ AI and IoT. The organization
strives to deliver the power of
prediction to the common man driven
by the tremendous hunger to know
the future. The complexities of Data
Science and AI, and how they can aid
businesses is simplified by neurIOT
by visualizing itself as the provider of
prediction solutions to these
businesses and other entities.
neurIOT’s solutions may be applied
across various sectors and industry
disciplines such as retailers,
manufacturers, life science, semi-
conductor companies, police
departments, recruiters, and anyone
solutions and can bring about very
high level of business process
optimization.
The second set of offerings is targeted
towards police departments across the
US. These solutions are being offered
through the company’s other venture
called Predictive Police Solutions
Inc. The first solution herein, is for
academy recruitments. This solution
uses ML approach to predict hiring
outcomes for police recruitment
process. Four sets of predictions here
include Selection for Academy,
Success in Academy, Success in Field
Training and Good Hire/High Risk
Hiring. Second solution is a machine
learning model built using past crime
data which helps identify a criminal
for next reported crime.
Exemplary Leadership
neurIOT is comprised of a team of
three founders, Sanjeev Thukral the
Managing Partner, Founder and
CEO; Lynn Jervik, the Head of
Business Development; and Anil
Kalra, the Head of Semiconductor
BU.
Sanjeev has been an avid technocrat,
who has built and run different
wanting to know the future, has
historical data and perspective to help
build such a solution.
Redefining Innovation
neurIOT’s first set of offerings are
targeted towards
manufacturer/distributor/retailer
segment. The organization has three
prediction solutions employing
Machine Learning (ML) based
algorithms for the entire supply
chain. Although such solutions are
already existent, the way in which
neurIOT makes a difference is in the
use of Machine Learning and
incorporating a host of external
parameters such as weather patterns,
events, and social media among
others.
The second set of solutions predicts
demand to the manufacturer from
distributors, again leveraging ML
based techniques. Whereas, the third
solution predicts input costs for the
manufacturer. Comprehending that
cost prediction is far more complex
and involves bringing in a host of
different other parameters, the
organization considers this triad of
ML based offerings as significantly
better and different from traditional
neurIOT:
Molding Cognition to Deliver
Precise Prediction
MM 201840

businesses over a 25 year career span.
In the last seven years, he has been
deeply involved in development and
selling of AI and IOT solutions for
customers across US, Europe and
Asia. His ability to combine business
strategy, technical expertise, industry
knowledge and sales experience helps
him drive this business from the
front.
Whereas, Lynn is a seasoned IT
professional with vast knowledge of
implementing transaction based
computer systems in finance and
retail industry. Anil is a semi-
conductor expert, who brings a very
different flavor to the mix. He leads
the company’s efforts towards
employing IOT and semi-conductor
knowledge while solving business
problems.
Rendering Excellence
neurIOT’s strength lies in Cognitive
science and the diversity of its team
including a very diverse senior
management. The company not only
has Data Scientists but also
experienced retailers, lifetime ERP
consultants, an ex-Police Official, a
life-time semi-conductor expert
among many others in its team. “It’s
to the EDGE. We’ll soon find ML
computer devices, Sensors and
Actuators bundled together
accomplishing specific tasks at
EDGE level.”
Envisaging New Horizons
The company looks at neurIOT as a
mothership of AI, solving common
business problems across diverse
business verticals. From this
mothership, it plans on spawning
vertically focused businesses. One
such example is its venture,
Predictive Police Solutions Inc,
which is focused on solving key
issues confronting Police
Departments across the US, using AI
and ML. In the future, the
organization foresees Retail &
Distribution, Life-Sciences and Semi-
conductor as other similar business
verticals. These are the areas where it
is already solving some very
compelling problems.
neurIOT is currently focused on the
US and Indian markets, where it has
business entities. The company’s
roadmap also includes building SaaS
variants of its AI solutions, which are
enabled to handle other geographies
including Europe, Australasia and the
Middle-East.
important to bring business context to
a Cognitive Science problem, to be
able to connect with common
business issues, and there lies our
strength,” exerts Sanjeev.
By combining the teams’ expertise
and applying it to the weather-based
prediction model, neurIOT strives to
deliver value to the customer.
Another instance relates to solving
the problem of Recruitment for US
Police Department. Its project team
has an ex-Police Official, a seasoned
IT professional and a Data Scientist.
This has helped the company predict
hiring outcomes for the police
department.
The Leader’s Perspective
While describing his take on the
diversification of IoT and AI, Sanjeev
states, “I already see very fast pace of
adoption of Cognitive Science in the
industry today. This has been possible
due to much higher level of
accessibility and maturity of this
technology now. In very near future, I
can see Cognitive Science as one of
the key components of any
organization’s business operations.
Distributed AI is the next big thing.
What’s happening now is AI coming
Sanjeev Thukral
Managing Partner,
Co-founder & CEO
Our solutions
are Human
Like Intelligent.
“ “
MM 2018 41

RPA and Cognitive
Technologies:
Merger of Innovative Approach
towards Customer Engagement
MM 201842
Tech Capture

At present, Artificial Intelligence and Robotic
Process Automation (RPA) plays an important
role towards innovative approaches in customer
engagement to amplify employee capabilities and
explore new business models. At the same time,
machines are filled with deep learning capabilities which
promise technical process and human-machine
partnership. The next step of AI is the digital economy
and to the next level is cognitive computing. It offers
society an incomparable opportunity to make smarter and
more informed decisions.
RPA is the use of software with artificial intelligence
(AI) and machine learning capabilities. The complete
process is derived from the tasks which include
queries, calculations, and maintenance of records
and transactions. And the cognitive
technology enables functionality of the
human brain through various means,
including language processing. The
merger of this both technologies
has a unified approach towards
innovation.
As every business
implement its digital
transformation process,
technological
complexity arises
from increased data
consumption. It has
still remained one of
the biggest challenges
for the businesses to
tackle. It is also difficult
to manage and drive up IT
expenditure. It inhibits
organizations’ to scale its ability
and it also takes many forms.
Organizations with the forward-thinking
approach are exploring its innovative ways to
control its advancements in RPA and cognitive
technologies to gain competitive advantages in the
growing digital economy.
Insights on Merging of RPA and Cognitive
Technology
The objectives for cognitive RPA is divided into two
major parts, mirror human intelligence and simulate the
human thought process. Cognitive technologies’
perception is appearing with the simplistic way in the
organization with these technologies to increase its
complex process.
Companies are using software robots for the
implementation of the job of automating routine and
repetitive processes. And now the whole process is
getting smarter at replicating human behavior with
improved accuracy. The process undertakes tasks which
require cognitive intelligence and predictive ability.
Through the merger of the technologies, RPA has
become an important key aspect for business lifecycle
including strategy, marketing, and customer experience.
Businesses implement the component which enhances
the functionality of the process such as Natural Language
Processing (NLP). It includes machine learning
techniques and also enables robots to actively learn from
humans.
The process also expands perceptual and judgment-based
activities which were previously undertaken by humans
only. But with the emergence of both the technologies,
the process is accomplished in shared manner
exclusively by robots. The process has the ability to
dynamically analyze data that human beings will never
be able to deliver. So, cognitive RPA is effectively
minimizing human involvement by providing unified
products.
Identifying products and objects
The relevant information is extracted from various
standardize documents such as emails synthesizing
gigabytes of data into structured groups of software
robots.
The products are no longer relying on constant human
inputs in which it out-match existing employees’
MM 2018 43

own ability that implement effectively by shrinking
down to weeks.
The organizations are driven by unified cognitive
solutions which are striving to invent another great
cognitive revolution. One that is driven by delivering
delightful customer experiences across borders and
devices.
Examples of such applications run across industries and
verticals; they can be found in:
Ÿ HR and recruitment – It is helpful by providing
effective screening of candidates based on
predetermined specifications.
Ÿ Insurance – The process eliminates repetitive and
manual data-entry tasks to reduce processing
delays.
Ÿ Financial services – The process is
used to improve back-office
banking processes to remove its
manual processes dependency.
Ÿ Processing services – This
application is been used as constructing
improved invoice verification processes to
optimize resources.
Ÿ CRM/ERP systems – It also exhibits
automating record maintenance for critical
processes and input collection.
Ÿ Billing operations – The application is helpful for
maintaining synchronized records across global retail
chains.
The whole process and functionality deliver instances of
service robots that can help prepare meals, assist
shoppers, support workers and even engage customers. It
is predictable in the next few years that there will be a
complete transformation where it is established how
organization and humans will connect. Ultimately, it is
concluded that humans and robots both work better when
they work together. Humanity’s next great revolution
depends on the co-existence of both. While AI has
indicated the first few steps on this journey,
cognitive RPA lights up the path ahead.
productivity and rarely make mistakes. The
transformative effect of this digital workforce on
the economy is already being seen around the
world. Many organizations increasingly
collaborate with its human workforce with digital
counterparts to enhance overall productivity and
reduce cost.
Through insights of the
organization, the impact of RPA
can be noticeable in the
months. Cognitive
technology has its
MM 201844

Headquartered in London,
England, Pixoneye
emerged out of the need to
protect end users’ data and went on
to establish its name as a leading AI
building company. The vision of
Pixoneye revolves around the idea of
allowing people to share as low data
as possible with the brands while
getting as much from their brands as
possible.
The one of a kind company focuses
on building technology that learns,
trains, and predicts entirely on device
in order to minimize privacy
concerns and increase security and
privacy of data. Its cutting-edge and
state-of-the-art technology allows it
to analyze the users’ data on the
device itself. This way, it never
requires the users to share their data
on the cloud and by doing so,
minimizes the use of their data as
currency between brands.
Recognized amongst “The 10 Most
Innovative Cognitive Solution
Providers, 2018” by Insights
Success, herein we look at some of
the key highlighted points of
The Ability to Analyze Personal
Images
Incepting in 2014, Pixoneye chose to
start with the most sensitive of data
sets – personal images. Personal
Images require the most attentions to
privacy and most complex
computing capabilities to analyze
privately and securely on the device.
Pixoneye discovered that the
personal photo gallery is by far the
most amazing data set that end users
possess. Its IP lays in its ability to
analyze a photo gallery as a key to
user understanding; Ofri Ben-Porat,
CEO of Pixoneye, sums it up
perfectly by saying “…we don’t care
if there are pictures of dogs in your
gallery rather do we care if you are a
dog owner.”
Pixoneye doesn’t care what’s in the
photo; it simply analyzes those
photos to identify what it speaks
about the person.
A Long yet Rewarding Journey
With Pixoneye being a deep tech
company, it had to initially spend the
first couple of years researching and
Pixoneye and its road towards
success.
Solutions that Shouts out
Efficiency
Pixoneye enable brands to access
first party data at extremely high
accuracy whilst protecting the end
users by keeping all their data ring-
fenced and secured on their own
device.
“In this ever-lasting battle between
data privacy and data appetite, we
want to make sure that we utilize the
capabilities of our connected devices
to avoid having data flowing freely
on the cloud” mentions the CTO of
Pixoneye, Nadav Israel. The
products of the company allow
brands to engage with their end users
on a very personal level without any
privacy risks. Pixoneye’s main
product lies in its understanding of
each user so as to enable brands to
increase the lifetime values of their
customers.
Nadav adds “We know exactly what
the users need without knowing who
those users are.”
Pixoneye:
Building Technology that Learns,
Trains and Predicts Entirely on Device
MM 201846

developing, without having the
ability to truly sell the product (or
even have a product at all). There
were far too many challenges
revolving across the company,
including the expensiveness of
researchers and the constant struggle
of keeping the investors hanging on
to a vision without a proper product
or a big client base.
Although, Pixoneye goes above and
beyond to analyze personal photo
galleries in the most secured and
private manner, it is still challenging
for the company to educate brands
and end-users to warm them up to
this new data set.
A Combination of Experience
Resulting in the Overall Success
The two Co-founders of Pixoneye,
Nadav Israel (CTO) and Ofri Ben-
Porat (CEO), come from two
completely contrasting backgrounds
and bring-in different set of skills to
the table, thereby eradicating any
chances of overlapping.
Nadav boasts over 15 years of
experience in the computer vision
and ML industry. He has been
reached 90% accuracy, thereby
increasing the proﬁtability and
engagement for the brands by a huge
margin. It has allowed users to ask
for less data and give a lot more to
their end users.
“We see our future leading the On-
device developments. With the ability
of mobile devices to run more and
more complex computer processes we
believe that we can achieve a world
where every users’intention is met
with an intelligent interaction
completely on device and without
ever needing to give up their data,”
Ofri concludes.
responsible for a lot of common tech
advancements that we use today,
including facial recognition for
Samsung and gesture recognition for
smart Tvs.
To the contrary, Ofri comes from a
completely opposite side of the
industry. He holds a rich background
in marketing and running his own
ventures, like Bars, Restaurants,
Mobile tourist agency, and a delivery
app. Ofri has even served as the
senior marketing advisor to the
minister of tourism in Israel, where
his passion for the mobile and digital
world pushed him to join Nadav.
According to the company, “At
Pixoneye, Nadav doesn’t speak to
people and Ofri doesn’t speak to
computers and they live a beautiful
frictionless life together. The
combination of the two allows us to
take a real stake at the very difﬁcult
process of commercializing AI on a
B2B level.”
Paving a Future that brings
Effective Results
Today, Pixoneye’s data has already
Nadav Israel
CTO
Knowing what
you want
without knowing
who you are.
“ “
MM 2018 47
Ofri Ben-Porat
CEO

C
oncealed within exabytes of
sensor data generated by
industrial machines are micro-
patterns that can tell us when a
machine is likely to fail. Until now,
these patterns could not be recognized,
even by the most advanced statistical
packages.
Presenso develops solutions for
Predictive Maintenance within the
Industrial Internet of Things (IIoT). It
presents this information directly to
maintenance and reliability
professionals so its clients no longer
need to hire Big Data experts to deploy
and maintain AI based solutions.
Presenso continuously streams asset
sensor data to the cloud where
Artificial Intelligence algorithms
analyze it in real time. The platform is
sensor-agnostic and can monitor signal
data without the need for manual
human input like the setting of control
limits.
A Wide Array of Services and
Solutions
Presenso’s Cloud-based software
solution replaces the rules-based
legacy systems which cost
manufacturers and plant operators
millions of dollars a year. Reactive in
nature and with limited computational
power, those outdated industrial
This alert includes information on
correlated sensor abnormalities. This
valuable information is essential to
tracking the origin of the failure.
Presenso’s industry-agnostic IIoT
predictive maintenance has seen
diverse adoption across many fields:
Ÿ Power and Energy:
Presenso’s customers receive machine
failure analysis and predictions from
the entire power plant, from single,
small turbines to fleets of large-
capacity turbines spread across
multiple power plants.
Ÿ Oil and Gas:
Presenso’s solution improves
production continuity and increases
Overall Equipment Efficiency (OEE)
across all fields and facilities.
Ÿ Water Facilities:
The company’s solutions aid water
desalination facilities and waste water
treatment facilities to avoid downtime
and meet the ever-growing demand for
drinking and agricultural water.
Ÿ Automotive Industries:
Presenso’s customers can analyze
manufacturing floor data in real time,
get a clear overview of the
monitoring tools are unable to
effectively control production
downtime.
The revolutionary cognitive software
from Presenso provides unparalleled
operational intelligence and deep
semantic insights which increase
production yield and revenues. Its
competitive range of solutions
eliminate manual intervention and the
need for expert knowledge.
They are hardware-agnostic, can be
rapidly deployed in remote locations,
and incorporate deep learning
capabilities within the analytics engine.
By utilizing the latest machine learning
and Big Data technology, they add
value to anomaly detection by
improving correlation, prediction and
prescription capabilities.
Advanced Deep learning and Machine
Learning algorithms analyze asset
sensor behavior and automatically
detect abnormalities and patterns
within them. After the detection of
anomalies within the signals,
correlations and pattern detections are
analyzed automatically. This
information and the exact sequence of
abnormal events can then be presented
to operators.
Once an evolving failure has been
detected, a failure alert is generated.
Presenso:
Aiding Industrial Development
with Artificial Intelligence
MM 201848

performance of assembly machines,
and utilize condition-based
maintenance.
Transcending Excellence
When the company was founded in
2015, the market was far less receptive
to Machine Learning for IIoT.
However, over the last 18 months,
Presenso’s proven performance and
stunning success across multiple
market verticals has led to a surge in
interest and adoption.
Presenso’s Automated Machine
Learning is based on innovations in
Artificial Intelligence that were
previously not applied to Predictive
Maintenance.
One of the greatest challenges to
deploying Industrial Analytics is the
need to select the right Machine
Learning algorithm for a given dataset.
Presenso’s Auto ML eliminates this
difficulty with a library of hundreds of
algorithms that can be used. The
system itself selects the optimal
algorithm for the data without the need
for human input.
Another advantage is the Unsupervised
Machine Learning methodology that
the company applies to Big Data. The
algorithm is independent of sensor,
vendor, asset, age, machine, and
process. It can automatically identify
research and development processes as
well as business entities. The serial
entrepreneur has led various start-ups
towards success with his leadership
abilities and unwavering attitude and
hopes to continue the same with
Presenso.
Adaptability and Foresight
It is generally accepted that the typical
Maintenance and Reliability engineer
cannot perform the role of a data
scientist, nor can he or she be the
bottleneck for deployment.
Companies such as Presenso recognize
that it needs to continually innovate on
AI capabilities but make the front end
user experience as simple as possible.
Applying AI to sensor-generated Big
Data is not scalable in today’s
industrial plants unless the analysis is
performed using automated Machine
Learning tools.
Presenso is investing significantly in
R&D in order to reduce the time it
takes for algorithms to make a
prediction. Machine Learning for
Automated Algorithm (Auto ML) is
only the first step in that journey.
The company’s goal is to simplify IIoT
Predictive Maintenance for the end-
user so an industrial plant can be
alerted to upcoming failure with
enough time to avert the problem.
data anomalies without the need to first
learn the underlying process it is
monitoring. This is an important
differentiator because it allows for
rapid and relatively inexpensive
deployment of the Predictive
Maintenance solution.
Experienced Leadership
Eitan Vesely is the Co-Founder and
CEO of Presenso. He is a mechanical
engineer by education.
Eitan previously worked as a systems
support engineer at Applied Materials,
where a major part of his job was
troubleshooting manufacturing plant
hardware failures that led to machine
shutdowns. Another aspect was
working through reams of data before
traveling to customers’ plants to bring
failed machines back into production.
Deddy Lavid, Presenso’s Co-founder
and CTO, has previously worked on
energy consumption prediction. With a
strong background in AI and predictive
analytics, he extended those concepts
to a full-scale Smart Factory with
advanced analytics to scrutinize all
sorts of industrial data from various
arms in the manufacturing value chain.
Dr. David Almagor, the third Co-
founder as well as the Chairman of
the company, boasts over 30 years of
experience managing complicated
Eitan Vesely
Founder & CEO
Our Cloud-based software
solutions and Predictive
Analysis replaces the
rules-based legacy systems that
are costing manufacturers and
plant operators millions of
dollars a year.
“
“
MM 2018 49

Mr. Jay Klein drives Voyager Lab’s technology strategy and
core intellectual property. He brings more than 25 years of
experience in data analytics, networking and
telecommunications to the Company. Before joining
Voyager Labs, he served as CTO at Allot Communications
where he steered Allot’s data inspection and analytics core
technology offerings, and as VP Strategic Business
Development at DSPG, where he was responsible for
strategic technology acquisitions. He also co-founded and
held the CTO position at Ensemble Communications while
founding and creating WiMAX and IEEE 802.16. He also
served as the CTO and VP of R&D at CTP Systems,
acquired rst by DSP Communications and later by Intel.
Jay Klein holds a BSc in Electronics & Electrical
Engineering from Tel Aviv University as well as numerous
patents in various technology elds.
About the Author
From Artificial
To Authentic
AI:
ith so much attention focused on Artificial
WIntelligence (AI), it’s worth remembering that
one size does not fit all. There are specific
business-related pain points in mind when a company
decides to deploy AI technology, so making the right
choices can be a tricky task.
For example, several months ago, an AI related
breakthrough was announced – a robot learned and
demonstrated the ability to perform a perfect backflip.
While it is well acknowledged that the invested research
and development for this mission was huge and the
commercial potential for some applications is enormous, it
is somewhat unclear how this specific innovation or the
core models and algorithms of it, can serve other industries
and verticals. Herein lies the problem.
MM 201850
Think AI

Gauging AI success in one field in many cases can be
meaningless for another. To make things worse, even when
trying to go deeper into the technology and attempting to
evaluate, for example, which Machine Learning algorithms
are utilized by the product, or what are the number of layers
in the Deep Neural Network models mentioned by specific
vendors, in the end it will be possibly pointless as it does
not directly reflect the solution deployment ‘success’
implications.
Nevertheless, it seems that the market ignores this reality
and continues to evaluate AI-based products by buzzword
checklists using familiar and related AI terminology (e.g.,
Supervised, Unsupervised, Deep Learning etc.). While
checklists are an effective tool for comparative analysis it
still requires the ‘right’ items to be included. Unfortunately,
what typically is absent are the items which are important to
the customer, from a problem-solution perspective.
Introducing Authentic AI
Given all of this, there is a need to change the narrative
around AI technology and solutions to something
meaningful and authentic that reflects the real-life
challenges and opportunities that businesses are facing.
This is the time to introduce Authentic AI.
The Merriam-Webster dictionary defines ‘Authentic’ as
both ‘worthy of acceptance or belief as conforming to or
based on fact’and ‘conforming to an original so as to
reproduce essential features’. This is not about ‘Fake’ to be
contrasted with ‘Real’. It’s about the essential features of AI
which need to be acknowledged, and hence, redefine the
‘checklist’. Often, these essential ‘authentic’ features are
hidden and only surface when a CIO/CDO is faced with a
new problem to be solved. This is seen especially when the
AI aspects of a proposed product or solution are fully
explored by asking questions such as:
- Is the AI technology utilized by the product aimed
specifically for my problem, optimally (e.g.,
performance, cost, etc.)?
- Is it capable of addressing the complete problem or
only a part of it?
- Can it be assimilated into the existing ecosystem
without imposing new demands?
- Can it address the compelling environmental
conditions of the problem space?
These issues can be grouped into three different ‘classes’ -
‘Original’, ‘Holistic’ and ‘Pragmatic’:
Original – How innovative is the solution? This can be
quantified by assessing the following:
- the invention of new algorithms or even new models
and
- the use of complex orchestration techniques or
- through the capability to handle complex data formats
and structures.
While there is no need to re-invent the wheel repetitively
for any problem, there are distinctive characteristics which
require optimizing.
Holistic – How complete is the proposed AI technology? It
takes into account the capability of handling the end-to-end
aspects of the solution, the competence of harmonizing the
operation of the various AI components of the solution and
the ability to adapt to ever changing conditions of the AI
application.
Pragmatic – Can the technology solve real world problems
in their actual and natural space in a commercially viable
way? This means that for example the data sources can be
processed in their most native format (both unstructured or
structured) as well as provide insights or results matching
the pragmatic needs of the specific market expectations. In
addition, the ability to be quickly deployed and rapid to act
are assessed.
All of these elements should be used to systematically
assess and evaluate AI-based products and solutions to
assess their authenticity and therefore effectiveness in
specific use cases.
For example, many home-loan mortgage evaluation and
recommendation systems utilize a somewhat isolated
machine learning based applicant classification method, one
of many other processes included within the solution. The
AI in this solution cannot be considered Authentic AI to a
high degree as it ‘scores’ low on the ‘Original’ and
‘Holistic’ classes as it isn’t innovative ‘enough’ (from an AI
sense). In addition, the AI component itself does not cover
on its own the end-to-end aspects of the solution (hence
affecting the overall performance and precision). It could be
considered to be ‘Pragmatic’ to some level if it can handle
the required data sources of financial institutions or the
customer applications natively, and if the solution ‘output’
are the explicit results required as a specific
recommendation (e.g., loan conditions). However, the
deployment timeline (time-to-market) and commercial
aspects need to be evaluated as well. This is just one
example of many others, covering all kinds of variations.
Perfect backflips may grant you a gold medal if you are a
gymnast but if you are a master chess player don’t expect a
winning move.
MM 2018 51

The 10 most innovative cognitive solution providers 2018

The 10 most innovative cognitive solution providers 2018

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