The 10 Most Innovative Cognitive Solution Providers 2018

www.insightssuccess.com JUNE 2018

Most

Solution Providers

2018

Computing Revolution Cognitive Computing: Computing Revolution Enhancing the Human Way of Living

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

Derek Meyer CEO

RedeďŹ ning AI Capabilities with Dataflow Technology

Cognitive Tech: Transcendence of the Digital Universe.

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mpeccably innovative technologies have been reforming our digital presence. This reformation has its roots 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 firstgeneration 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

Cover Story

Redeď€ ning AI Computing with Dataow Technology 10 Articles

26 Computing Revolution

Tech Capture

Cognitive Computing: Enhancing the Human Way of Living

RPA and Cognitive Technologies: Merger of Innovative Approach towards Customer Engagement

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CONTENTS 36

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50 Cognitive Insights The Role & Challenges of Data Needed for Cognitive Computing and AI

Thought Leader

Think AI

AI Saved the Audio Star

AI: From Artificial To Authentic

Aeye: Safe & Reliable Vehicle Autonomy

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BurstIQ:

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Technology to Revolutionize Healthcare

CloudMedx: Comprehensive Unified Healthcaren

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Find Solution Artificial Intelligence:

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Tailored Interactive Curriculum through AI

Intelligent Voice: Speech Recognition for the AI Age

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neurIOT:

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Molding Cognition to Deliver Precise Prediction

Pixoneye: Building Technology that Learns,Trains and Predicts Entirely on Device

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Presenso:

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Aiding Industrial Development with Artificial Intelligence

Editor-in-Chief Managing Editor Executive Editors Assistant Editors Contributing Editors Visualiser Art & Design Director Associate Designer Co-designer

Pooja M. Bansal Anish Miller Abhishaj Sajeev Jenny Fernandes Kaustav roy Kedar kulkarni Bhushan kadam David King Amol Kamble Shubham Mahadik Rahul kavanekar

Art & Picture Editor Paul Belin Jayant Khanna Senior Sales Manager Passi D. Business Development Manager Peter Collins Marketing Manager John Matthew Business Development Executives Steve, Joe, Alan, Vishal Sales Executives David, Kevin, Mark, Prasad Technical Head Jacob Smile Technical Specialist Amar, Pratiksha Digital Marketing Manager Marry D’Souza Online Marketing Strategist Alina Sege, Shubham, Vaibhav K SME-SMO Executives Prashant Chevale, Uma Dhenge, Gemson, Irfan Research Analyst Chidiebere Moses Circulation Manager Robert, Tanaji Database Management Stella Andrew Technology Consultant David Stokes sales@insightssuccess.com

June, 2018 Corporate Ofces: Insights Success Media Tech LLC 555 Metro Place North, Suite 100, Dublin, OH 43017, United States Phone - (614)-602-1754 Email: info@insightssuccess.com For Subscription: www.insightssuccess.com

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Cover Story

Redening AI Capabilities with DataFlow Technology

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We harness the power of dataflow technology for deep learning.

10

The

Most

INN VAT VE C GNIT VE Solution Providers 2018

Derek Meyer CEO Wave Computing

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he 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.

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Wave Computing provides faster results and improved accuracy for data-driven business applications with its revolutionary new compute appliance.

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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

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 highperformance 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 coprocessor (e.g., a CPU or GPU), offering highperformance, 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 inoffice 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 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

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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.

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Turning Vision into Reality

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 ‘datacenterto-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.”

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Wave Computing is revolutionizing the AI industry with the industry’s fastest, most scalable dataow-based deep learning solutions.

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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.

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.

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Company Name

Management

Brief

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.

CloudMedx Inc. cloudmedxhealth.com

Tashfeen Suleman CEO

CloudMedx Inc., a software development company, provides cloud-based predictive health analytics and care coordination platform.

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.

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

neurIOT builds solutions which possess human-like intelligence that employ cognitive science, AI and IoT to deliver predictive solutions.

Pixoneye pixoneye.com

Nadav Israel CTO, Ofri Ben-Porat CEO

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 presenso.com

Eitan Vesely Co-founder & CEO

Presenso develops solutions for Predictive Maintenance in the Industrial Internet of Things and makes them accessible to maintenance and reliability professionals.

Wave Computing wavecomp.com

Derek Meyer CEO

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.

Thought Leader

AI Saved the

Audi Star

Assaf Gad VP Marke ng & Strategic Partnerships Audioburst

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MM 2018

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seful 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 preprogrammed 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.

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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 voicefirst 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 audiorelated industries, ensuring that audio content can compete in an increasingly screen first world.

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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: Safe & Reliable Vehicle Autonomy

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Eye 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 worldrenowned 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-

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fused with a low-light camera and embedded AI to create softwaredefinable and extensible hardware that can dynamically adapt to realtime 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 twofold. 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 measures its performance based on the quality, reliability, and speed of information their system delivers to autonomous vehicle pathplanning 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

MM 2018

“ 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.

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

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.

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AEye has been awarded foundational patents for its solid state MEMsbased agile LiDAR and embedded AI technology. In 2017, the company successfully conducted the first live metropolitan demo ever of a 360degree solid state LiDAR system, showcasing its ability to collect realtime, high-density point clouds at up to 300 meters.

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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.

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 pathplanning 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.

At CES 2018, AEye introduced its first product for the automotive market, the AEye AE100, based on

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BurstIQ: Technology to Revolutionize Healthcare

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ost 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.

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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.

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

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“ Founder & CEO

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 bestin-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

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Frank Ricotta

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.

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 nextgeneration privacy solution for the healthcare industry.

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.

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

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Computing Revolution

Cognitive Computing:

Enhancing the Human Way of Living

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nce, Dean Kamen, an American engineer, inventor, 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

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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 MM 2018

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 businessspecific applications.

Decision: Cognitive computing systems possess decision-making capabilities. Decisions made by cognitive systems are evidencebased, 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. Ÿ

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

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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.

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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.

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CloudMedx: Comprehensive Unified Healthcare

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elivering 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.

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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

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

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“ 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

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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.

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 AIrelated 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

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.

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Find Solution Artificial Intelligence: Tailored Interactive Curriculum through AI

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ind 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.

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%

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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

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Ms. Viola Lam

Our mission is to solve real-world challenges by providing an adaptive and personalized learning experience.

CEO

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 highlyregarded 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

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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.

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.

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

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Cognitive Insights

The

Role & Challenges of Data

Needed for Cognitive Computing & AI

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n 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.

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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.

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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.

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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.

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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.

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Timeliness: Data can change, and the AI model may need to be updated.

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About The Author Rajeev Dutt is the CEO and Co-founder of DimensionalMechanics, a Seattle based start-up in artiďŹ cial 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

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 inuence 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.

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Intelligent Voice: Speech Recognition for the AI Age

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ntelligent 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

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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

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

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We provide speech processing for the privacy conscious.

Nigel Cannings Technical Director & CTO

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echnology. 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 groundbreaking innovation.

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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 voiceactivated 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 cloudplatform as an intermediate.

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.

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neurIOT: Molding Cognition to Deliver Precise Prediction

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he 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, semiconductor companies, police departments, recruiters, and anyone

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wanting to know the future, has historical data and perspective to help build such a solution.

solutions and can bring about very high level of business process optimization.

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 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.

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

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

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Sanjeev Thukral Managing Partner, Co-founder & CEO

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 semiconductor 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

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Our solutions are Human Like Intelligent.

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.

to the EDGE. We’ll soon find ML computer devices, Sensors and Actuators bundled together accomplishing specific tasks at EDGE level.”

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.

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 Semiconductor as other similar business verticals. These are the areas where it is already solving some very compelling problems.

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

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.

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Tech Capture

RPA and Cognitive Technologies: Merger of Innovative Approach towards Customer Engagement

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t 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.

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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’

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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

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.

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Pixoneye: Building Technology that Learns, Trains and Predicts Entirely on Device

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eadquartered 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.

Pixoneye and its road towards success.

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.

“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.

Recognized amongst “The 10 Most Innovative Cognitive Solution Providers, 2018” by Insights Success, herein we look at some of the key highlighted points of

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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 ringfenced and secured on their own device.

Nadav adds “We know exactly what the users need without knowing who those users are.”

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

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Knowing what you want without knowing who you are.

Ofri Ben-Porat

CTO

CEO

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 BenPorat (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

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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.

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Nadav Israel

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 Ondevice 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.

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

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Presenso: Aiding Industrial Development with Artificial Intelligence

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oncealed within exabytes of sensor data generated by industrial machines are micropatterns 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

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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.

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 largecapacity 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

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“ Founder & CEO

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

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Eitan Vesely

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.

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.

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.

Experienced Leadership

Adaptability and Foresight

Eitan Vesely is the Co-Founder and CEO of Presenso. He is a mechanical engineer by education.

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.

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 Cofounder as well as the Chairman of the company, boasts over 30 years of experience managing complicated

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 enduser so an industrial plant can be alerted to upcoming failure with enough time to avert the problem.

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Think AI

AI:

From Artificial To Authentic

About the Author

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ith so much attention focused on Artificial Intelligence (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.

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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.

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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:

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- 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.

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