Techfastly December 2021 by Techfastly

DEC | 2021

w w w. t e c h f a s t l y. c o m

The reigning champion and a changemaker

MANASI GIRISHCHANDRA JOSHI

COGNITIVE MANUFACTURING COGNITIVE COMPUTING EDITION

and The Beginning of a Revolution: The Start of Industry 4.0 p 28

Cognitive Enterprise: Reinvest your company?

The Changing Scenarios of Digital Enterprises in 21st Century World:

Cognitive Agents and Reinforcement of User Experience

Vibha Soni

Rehan Husain

Bharggavi Ssayee

What’s Inside p.4 Ultra-Precision,

Super-Speed, Zero-Error Inspection; Cognitive Visual Inspection in Manufacturing by Abhishek Mitra

p.18

The Changing Scenarios of Digital Enterprises in 21st Century World by Rehan Husain

p.28 Cognitive Manufacturing

and The Beginning of a Revolution: The Start of Industry 4.0 by Utsav Mishra

p.34 Cognitive Enterprise:

Reinvest your company? by Vibha Soni

p.44 IBM Watson

by Rehan Husain

p.54 Cognitive Agents and

Reinforcement of User Experience by Bharggavi Ssayee

p.62 Marketing Leaders Are

Taking A Run Approach Towards Cognitive Computing by Toulika Das

p.70 In Conversation with

Manasi Girishchandra Joshi

p.78 Adoption of Cognitive

Computing Across Various Industries by Saipriya Iyer

p.86 Sentimental Analysis

— The Key to Changing Perceptions of Cognitive Computing in Marketing by Tanaaz Khan

p.94 Advancing Healthcare with

Medical Image Processing by Ragini Agarwal

Editor’s note

Dear Readers The nature of future occupations is likely to be determined by the extent to which man and machine collaborate. It is unknown to what degree machines will eventually supplant humans in terms of analytical capability. Will human beings become utterly redundant in some job functions? Such insights need to be worked more precisely. Global population projections indicate that by 2030, the world’s population will reach over 8.5 billion. Add to that the exponential growth of artificial intelligence, computer processing power, and robots. It is reasonable to forecast that our global workforce and the demands we place on it will undergo significant changes shortly. Job functions will evolve swiftly in lockstep with technological advancements; therefore, educating and preparing a workforce capable of adapting to shifting needs to be prioritized. In this issue we focused on cognitive computing. The first article talks about ultra-precision, super speed, zero error inspection in manufacturing industry using cognitive computing. Similarly, the changing scenarios of digital enterprises in the 21st century are discussed in the following article. Another interesting article talks about how marketing leaders are moving towards cognitive computing. An exciting article talks about IBM Watson’s history and achievements. The second part of the magazine includes articles talking about the impact of cognitive computing in various industry sectors. We talk about the adoption of this exciting technology across various domains, we specifically discussed sentimental analysis, which is growing in the industry at a wide scale. Later on, we discuss the medical image processing acting as a boon for the healthcare sector. Our interview series brings you the story of the reigning champion and a changemaker, ‘Manasi Grishchandra Joshi’. She was named TIME Magazine’s Next Generation Leader 2020 in October 2020. Barbie honoured Manasi and her accomplishments on the International Day of the Girl Child (11 October 2020) by creating a one-of-akind Barbie doll in her likeness to encourage young girls. She hopes that her story inspires many more lives and encourages young girls to harness their true potential to fight hard and become whoever they set out to be. At least 90,000 of IBM’s 388,000 workers now use design-thinking methodologies to improve the company’s business areas, such as artificial intelligence and computing. We should not anticipate the future of machine learning and robotic design being about humans against machines but rather how humans and robots may work together to create the most effective teams.

Srikant Rawat

Chief Operating Officer, Techfastly

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Ultra-Precision, Super-Speed, Zero-Error Inspection; Cognitive Visual Inspection in Manufacturing by Abhishek Mitra Inspection is an intricate task as nano-portions of the product, packaging, components need to be analyzed. It is quite an impossible task to mark out every defect in 5 million units of products produced and packaged from manufacturing units.

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Earlier inspectors had to laboriously inspect each component for scratches, misplaced parts and other flaws which was slow, expensive, and sometimes flawed leading to cancellation of orders and losses for the organization. Then came automation of inspection but human levels of judgment for detecting flaws was lacking. Finally, cognitive visual inspection with its high precision defect detection and advanced algorithms for detection of flaws of various types in real-time, made entry into the scene. Cognitive Visual Inspection saw companies quickly matching industry 4.0 standards and having zero-defect products as the end result of manufacturing. Approved orders of products, reduced cost in inspection and increase in revenue with maximum clients satisfied is now becoming the norm with application of cognitive intelligent visual inspection.

Some areas where visual inspection is used in industries

Automobile Parts

Electronic Parts

TARGETS

DEFECTS

TARGETS

DEFECTS

Materia parts Resin parts Fabric

Scratch Crack Dirt Dent Burr/ Chip

PCB Electronic parts Electrical component panel

Scratch Crack Burr/ Chip

Building Materials

Nonferrous Metals

TARGETS

DEFECTS

TARGETS

DEFECTS

Wood board Sash Metal fitting Tile

Scratch Crack Dirt Dent Surface Pattern

Wire, Cable Aluminium Stainless steel

Scratch Crack Dirt Dent

Unless and until there is consistency and quality in every unit manufactured, you can’t gain that level of client trust and may even lose orders from clients. Cognitive inspection has come to the aid.

(Source: https://nanonets.com/blog/ai-visual-inspection/)

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Raw Materials

Food

TARGETS

DEFECTS

TARGETS

DEFECTS

Chemical fiber Rubber Glass Paper, Pulp

Scratch Crack Dirt Dent

PCB Electronic parts Electrical component panel

Scratch Crack Burr/ Chip

Medical

Others

TARGETS

DEFECTS

TARGETS

DEFECTS

Medicine

Foreign object Wrong print Crack

Materia parts Resin parts

Defect classification Shape check

Adoption of cognitive inspection capabilities in manufacturing helps in: • Improved quality of the product • Defect detection in real-time • Minimizing chances of human inspection errors • Ability to scale inspection easily with an increase in manufacturing capacity • Reducing the time involved in the inspection • Improving the manufacturing yield • Increasing the process throughput • Reducing the training time for inspectors • Combining visual and acoustic data to have better defect recognition • Easy classification of defect type, location along the production line, and severity • Ensure prescriptive maintenance of the

manufacturing and proactive monitoring of defects • Supports frequent up-gradation of the product models • Helps in monitoring whether the process input and output variables are within the desired range and thereby help in calibration of equipment and pieces of machinery • Pinpoint functional deficiencies in the manufacturing process • Detection of minute errors like tiny scratches or pinhole like defects that can’t be done with manual inspection • Can be easily applied on factory floors via edge devices 7

Below I have mentioned the series of steps that can be used for cognitive visual inspection of products in the manufacturing

IoT Installed Cameras

IoT installed cameras must be present at the beginning, at various stages in the production process and the end of the processing line. These cameras would be used for getting highresolution images of various raw materials and components going into production, ultra-clear images of various stages of the finished products. Robot mounted cameras If the cameras are robot mounted with movable parts, they can help in: • Easy relocation and placement along the production lines • Autofocus of cameras through easy movement towards and away from the products • Lesser number of cameras required • Inspection of products, detection and robot-aided sorting and functioning in real-time • Easy adjustment of the angle of installation for best images • Minutest details can be detected

Image: (The American Society of Mechanical Engineers- https://www.asme.org/topics-resources/content/ inspector-watson-does-quality-control)

The various essentials for getting ultra-clear focused images and better ML model training would be

LIGHTING ON THE PRODUCT The focus of the cameras on the various parts of the product (based on defect areas emphasis). Consistent, high-fidelity images are required for interpretation.

MULTIPLE DATA SETS FOR BETTER TRAINING There should be a collection of multiple batches of image data for training the machine algorithms. Multiple sets of product samples at every stage of the production must be labelled defected and defect-free.

PRE-PROCESSING OF IMAGE It is not mandatory that the entire image would be useful. It is necessary to remove the unnecessary content and filter out only those portions that would be used for defect calculations. For this filtering techniques must be employed to capture the essential parts under consideration.

A Product Is An Assemblage of Many Unfinished Products

A product goes through various stages of unfinished products. These unfinished parts also need to be defect-free so that the entire product is defect-free. So, images at every stage of the manufacturing are required. For example; in toothpaste manufacturing, we need to ensure that the tube is of equal lengths (if not that would be considered a defect). Similarly, the caps manufactured must have equal width, and depth, the nozzle must have an equal number of screw 10

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engravings. These parts are manufactured separately but a defect in any of them would result in rejection of the product finally.

So, multiple images labelled (defect and defect-free) from various stages of the manufacturing process need to be collected. www.techfastly.com

The Convolutional Neural Network successfully captures the spatial and temporal dependencies within an image by the application of filters.

Image Classification and Training Your ML Models

Image classification done in the rightmost manner would help create better quality data sets for your training model. For image classification, deep learning algorithm and Convolutional Neural Networks (CNN) are used. It classifies and processes images just like neurons in our brain. Neurons are present in the visual cortex of humans’ processes and classify images in layers. Not only this, response to stimuli by individual neurons occurs only within a limited region of the visual area and is also known as the receptive field. Collections of such receptive fields overlap to cover the

entire visual area. The convolutional neural network successfully captures the spatial and temporal dependencies within an image by the application of filters. CNNs have similar methods of classification as other traditional supervised learning methods. Input images are received, the features for each of them are detected and then a classifier is trained. CNNs do all the hard work of feature extraction and description and learn the features automatically. The input layer accepts the image pixels as input in the form of arrays.

THE FIRST LAYER

THE POOLING LAYER

The convolutional layer identifies the different set of features from the sample input images

The pooling layer reduces the size of the image while retaining its all-important features for analysis and calculations. It is generally present between two convolutional layers. It receives image input as feature maps and then applies pooling to them

CNN

architectures help in semantic segmentation. Semantic segmentation is the process by which an image is divided into related, coherent objects in a way that each pixel in the image is categorized as being a part of the original product. It helps in comprehending the meaning and the content of the image.

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THE RELU ACTIVATION FUNCTION Relu stands for rectified linear activation unit. It is an activation function that enables neural networks to consider non-linear relationships. ReLus add non-linear transformations to the output response of the fully connected layers. The fully connected layer detects and classifies the input image and returns a vector of size K, K being the number of classes in the task of image classification

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Aiding The Cognitive Inspection With Sensors

Vision sensors can be used to detect various kinds of product features and send data for training to the ML model along with cameras.

1 4

Training The Model To Count Products And Their Distance From Critical Areas

The model can also be trained to count the objects and their distance from critical areas based on the size of the images and clarity. Pre-processing Gather Data Training Set

Model Building

Deploy Model

Model Evaluation

Validation Set

Model Selection

Test Set

2 3 4

AN AREA SENSOR can be used to detect the features which are missing in the fabricated part. BLEMISH SENSORS can be used to detect scratches on the product or the package surface. MATCH SENSORS can be used to detect variation in label placement on the product or the package. LINE SCAN SENSORS to take snapshots of moving product units along the production line.

They are sequenced with the speed of moving products. There can be many other sensors that can be used in the manufacturing process. They are all different programs for detecting different aspects. Coupled with IoT installed movable arm cameras, they generate tons of vital data sets for ML model training. The aim is to provide the best image data from various stages.

Induction of Cloud In The Cognitive Vision Model

Traditionally machine learning requires local hardware to manage the spike in computational demands. With cloud inducted, the storage and the computing tasks of image recognition and classification can be handled more comfortably and flexibly.

Creation of An Analytics Library of Defects

Creation of analytics library of defects from hundreds of image data sets helps in better labelling of the images and better training your ML model to identify them in the pipeline. Machine learning goes over your library and validates its algorithm for accuracy in detection, over a larger number of data sets. Large data sets of images of different types of anomalies and damages improve the system’s visual recognition capabilities.

Scene Preparation Image Acquisition

Results

Final Decision

The processing flow for automated visual inspection.

Digitization

Image Enhancement & Filtering

Detection & Classification

Feature Extraction 14

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The industry model for cognitive visual inspection would look like the following:

Production Environments – Industrial Control – IoT Installed Camera

Feedback

Create and employ ML model

IoT Gateway

Output Dashboard

Creating and analytics library of defects

Determining the patterns of defects and enables root cause analysis

Some Famous

Cognitive Visual Inspection Models

Vision AI (Google)

It helps you train your machine learning model according to your business needs with Auto ML vision. You can import your data set to Cloud Auto ML of Google, train them and generate predictions with Cloud Vision Rest API.

HOW IT WORKS 1

Source: Google ( https://cloud.google.com/vision#section-3)

Access data quickly with improved document search capabilities

Intelligent Visual Inspection (IBM Maximo)

Here the camera captures product images and feeds them to Watson for interpretation and analysis. Watson can detect defects as minute as tiny scratches or as small as a pinhole. It also nano-detects the shape, colour and location of the images.

HOW IT WORKS

The Changing Scenarios

of Digital Enterprises in 21st Century World by Rehan Husain

In today’s tech-savvy generation of modern computing inception, AI has been a far-fetched objective, yet each day appears to bring us closer to that goal with new cognitive computing models.

Cognitive computing as a concept and its applications are sure to have far-reaching implications for our personal lives and industries.

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Coming from a synthesis of cognitive science and based on the fundamental premise of simulating the human thought process, cognitive computing as a concept and its applications are sure to have far-reaching implications for our personal lives and industries such as healthcare, insurance, and more. The advantages of cognitive technology much outweigh those of conventional artificial intelligence systems.

According to David Kenny, General Manager, IBM Watson – the world’s most powerful cognitive computing framework –

“AI is only as intelligent as the people who educate it.” The same cannot be said of the most recent cognitive revolution.

Cognitive computing processes employ a combination of artificial intelligence, neural networks, machine learning, natural language processing, sentiment analysis, and contextual awareness to solve problems in the same way as people do. IBM defined cognitive computing as an advanced system that learned rapidly, reasoned purposefully, and interacted naturally with humans.

Artificial Intelligence vs. Cognitive Computing While the fundamental purpose of artificial intelligence is to find the optimal algorithm to solve a problem, cognitive computing takes a step further by attempting to replicate human intellect and wisdom via the analysis of a variety of elements. Cognitive computing is an entirely distinct notion from Artificial Intelligence.

Cognitive computing emulates and learns from human cognitive processes. Unlike artificial intelligence systems that solve a specific problem, cognitive computing learns through pattern analysis and offers appropriate actions to humans based on their knowledge. In the case of artificial intelligence, the system takes absolute control of a process and uses a pre-defined algorithm to finish a job or avoid a situation.

In comparison, cognitive computing is a distinct discipline in which the computer acts as assistance rather than the performer of the work. Thus, cognitive computing enables people to do faster and more accurate data analysis without worrying about the machine learning system making incorrect conclusions.

Cognitive computing does not obliterate people. As said previously, cognitive computing’s primary goal is to aid people in decisionmaking. This endows humans with exceptional analytical precision while also assuring that everything remains under their control. As an illustration, consider artificial intelligence in the healthcare system. A system powered by AI would make all treatment decisions without consulting a human doctor. However, cognitive computing would enhance human diagnosis with its own set of facts and analysis, improving decision quality and adding a human touch to essential operations.

Going Cognitive: Cognitive Computing’s Benefits

Modern computer technologies are poised to transform current and historical process automation systems. According to Gartner, cognitive computing will reshape the digital world in ways that no other technology has in the past two decades. By enabling the analysis and processing of massive volumes of volumetric data, cognitive computing enables the use of a computing system for meaningful real-world applications. Cognitive computing provides a slew of advantages, among which are the following:

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ANALYZE DATA WITH PRECISION

Cognitive systems are incredibly efficient in collecting, juxtaposing, and cross-referencing data to understand a scenario efficiently. In the healthcare industry, cognitive systems such as IBM Watson assist physicians in collecting and analyzing data from a variety of sources, including previous medical reports, medical journals, diagnostic tools, and historical data from the medical community, thereby assisting physicians in providing a data-backed treatment recommendation that benefits both the patient and the physician. Rather than replacing physicians, cognitive computing accelerates data analysis through robotic process automation.

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STREAMLINED & EXPANDED BUSINESS PROCESSES THAT ARE EFFICIENT

Cognitive computing enables real-time analysis of developing trends, identifying business possibilities, and resolving crucial process-centric concerns. A cognitive computing system like Watson can streamline operations, mitigate risk, and adapt to changing situations by analyzing massive amounts of data. While this equips firms to respond appropriately to uncontrolled variables, it also aids in the development of lean business processes.

ENHANCEMENT OF CUSTOMER INTERACTION

By incorporating robotic process automation, the technology may be leveraged to improve client relations. Customers can obtain contextual information from robots without interacting with other staff members. Because cognitive computing enables businesses to present consumers with only relevant, contextual, and meaningful information, it enhances the customer experience, increasing customer satisfaction, and engagement.

benefits 23

Cognitive Computing Issues: Obstacles to a Better Future

Each new technology has challenges during its existence. Even though cognitive computing can transform lives, humans oppose innovation because of fear of change. Numerous cognitive computing drawbacks have been identified, posing substantial obstacles to broader implementation, including the following:

SECURITY

When digital devices manage sensitive data, the issue of security inevitably arises. With the ability to process and analyze enormous amounts of data, cognitive computing faces substantial data security and encryption challenges. With the proliferation of connected devices, cognitive computing will need to consider the implications of a security breach by building a foolproof security strategy that includes a system for identifying suspicious behavior to maintain data integrity.

ADOPTION

The primary impediment to every new technology’s success is voluntary adoption. To ensure the success of cognitive computing, it is critical to building a long-term vision for how the new technology will improve procedures and enterprises. The adoption process may be simplified by collaboration amongst many parties, including technology developers, corporations, governments, and individuals. Simultaneously, it is critical to have a data privacy policy that will further accelerate the adoption of cognitive computing.

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MANAGEMENT OF CHANGE

Change management is another critical issue that cognitive computing must address. People are reluctant to change due to their inherent human nature, and because cognitive computing can learn in the same way that humans do, people are concerned that robots will eventually replace humans. This has had a significant influence on growth prospects. Cognitive technology, on the other hand, is designed to function in concert with people. By injecting information into the networks, humans will foster technology. This makes it an excellent illustration of a human-machine relationship that people must accept.

4 PROTRACTED DEVELOPMENT CYCLES

One of the most significant obstacles is the time required to construct scenario-based applications using cognitive computing. Cognitive computing is presently being developed as a generic solution — which implies that it cannot be applied across numerous industrial segments without the support of large development teams and a significant amount of time. Protracted development cycles make it more difficult for smaller businesses to build cognitive capacities independently. With time, as development lifecycles decrease, cognitive computing will undoubtedly take on a more significant role in the future.

Conclusion Adoption of cognitive technology begins with the discovery of manual tasks that can be automated utilizing the technology as part of the digital evolutionary cycle. Numerous businesses, such as IBM, have previously pioneered the cognitive technology space, which is now powering a slew of genuinely digital corporations worldwide.

opportunities and pathways in both the B2B and B2C segments are enormous. IBM Watson is already leveraging the power and benefits of cognitive computing in the finance and healthcare industries. It is thought that in the future, such technology would assist humans in becoming more efficient, delegating routine analysis and focusing on creative tasks.

Each minute, additional data is evaluated to acquire insight into past occurrences and to optimize present and future activities. Not only can cognitive technology aid in prior analysis, but it also aids in far more accurate prediction of future occurrences via predictive analysis. Due to the strong and adaptable nature of the technology, the future

To maximize the benefits of cognitive computing, firms must do a thorough study of their processes, data, talent model, and the market in which they operate. Beyond cost reduction, we believe that one of the greatest potentials for cognitive technology is the capacity to create value, as well as the ability to restructure work and boost efficiency by streamlining a variety of procedures. Regardless of the difficulties and obstacles, the benefits of cognitive technology cannot be underestimated. All enterprises and humankind as a whole will benefit from initiating the transition process and adopting innovative technologies to ensure a bright and much more efficient future.

Cognitive Manufacturing

and The Beginning of a Revolution: The Start of INDUSTRY 4.0

by Utsav Mishra

Introduction Have you ever imagined a world without technology or new inventions? Let us try to think of one such world, where there were no such inventions to revolutionize mankind or to ease the lives of people. Imagine a world without cloth mills and without the invention of those big machines that are used nowadays to stitch clothes. Imagine a world functioning on needles and thread. Does it sound weird? Yes, it does. Do you know what’s even worse? Imagine a world without online shopping. In the time that I am finishing this line, I’m sure a thousand more orders have already been placed on various online shopping apps in my locality alone.

Cognitive technologies delve deeply into a manufacturing process and business environment to extract information with real-world value for a company. 28

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If I tell you to imagine these, then basically I am telling you to travel through time, and it won’t be a time travel back to just a few years, but you would have to go centuries back. But here the thing is, we talk about technology and its effects in almost everything, especially in easing our lives but then we just forget about one thing that has really been made easier by technology and inventions: it is the manufacturing of products. When we talk about manufacturing, we need to talk about a kind of technology that is ready to revolutionize the industries and also this whole world, “Cognitive Computing.” Implementation of cognitive computing has bought a new revolution alonside, i.e., Industry 4.0. Herein lies the future.

Let us dive in now and try to understand what cognitive computing is. Cognitive computing is the application of thinking, language processing, machine learning, and human capabilities to assist ordinary computers to solve issues and analyzing data more effectively. A computer system can handle complicated decisionmaking processes by learning patterns and behaviors and becoming more intelligent.

What is Cognitive Computing? A cognitive computing technology platform adapts and makes sense of information, including unstructured information such as natural speech, using machine learning and pattern recognition. Now that we know what cognitive computing is, let us try to know what cognitive manufacturing is.

What is Cognitive Manufacturing? Cognitive manufacturing employs cognitive computing, the Industrial IoT, and sophisticated analytics to optimize manufacturing processes in previously unimaginable ways. It assists enterprises in improving basic business indicators like productivity, product reliability, quality, safety, and yield while minimizing downtime and costs.

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Why Does Cognitive Manufacturing Matter? Cognitive technologies delve deeply into a manufacturing process and business environment to extract information with realworld value for a company. Cognitive manufacturing uses cognitive computing, the Industrial Internet of Things, and sophisticated analytics to digitize, analyze, and improve manufacturing processes in previously unimaginable ways. Cognitive manufacturing is effective because it integrates sensor-based data with machine learning and other artificial intelligence skills to discover patterns in structured and unstructured data from plant, company, and

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industrial systems. It collects pertinent data in real-time and applies analytics to produce unparalleled levels of understanding and insights into the production process.

It automates reactions based on its findings and provides actionable information as well as continually updated knowledge to production decision-makers. Cognitive manufacturing has revolutionized the industry. It is said that it has made the world ready for industry 4.0. Let us dive in a bit more and try to understand what industry 4.0 is.

Industry 4.0 We are in the midst of a substantial transition in the way we make products as a result of factory digitalization. This change is so compelling that it is being dubbed Industry 4.0 to signify the fourth manufacturing revolution. From the first industrial revolution (mechanization via water and steam power) to mass production and assembly lines via electricity in the second, the fourth industrial revolution will build on what was started in the third with the adoption of computers and automation, enhancing it with smart and autonomous systems powered by data and machine learning. Now, and in the future, as Industry 4.0 develops, computers are linked and communicating with one another in order 31

to make choices without the assistance of humans. Industry 4.0 and the smart factory are made feasible by a mix of cyber-physical systems, the Internet of Things, and the Internet of Systems. Our factories will become more efficient and productive, as well as less wasteful, as a consequence of the support of smart machines that continue to grow wiser as they have access to more data. Finally, it is the network of these digitally linked machines that produce and exchange information that results in the ultimate power of Industry 4.0.

Cognitive Manufacturing and Industry 4.0? The transcontinental railroad, the cotton gin, electricity, and other technologies had long-lasting effects on civilization. The same thing happened when we learned about the breakthrough Artificial Intelligence and its applications, and Industry 4.0 was in the works. Businesses all across the globe were striving to implement the concept of Enterprise Cognitive Computing (ECC). It employed artificial intelligence (AI) to improve corporate processes by embedding algorithms into apps that assist the organizational process. Cognitive Computing applications can automate repetitive, formulaic operations and give accurate results in large magnitudes. Cognitive Computing paved the door for a technology known as Cognitive Manufacturing, which is notably valuable for manufacturing firms. It is also a cognitive method to integrate

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AI algorithms with industrial procedures in order to accomplish desired results. Cognitive Manufacturing uses cognitive computing, industrial IoT, and sophisticated analytics to digitize, comprehend, and improve manufacturing processes. It enables manufacturing processes to collect data from various sources and analyze this data in order to provide important knowledge about processes, machine work efficiencies, employee efficiency, and workload statistics with a thorough analysis that can render suggestions to optimize each department of the manufacturing paradigm.

By utilizing AI algorithms on diverse operational applications, it enhances productivity, scalability, dependability, and security. Cognitive manufacturing completely leverages data across systems, equipment, and processes to gain actionable information throughout the whole value chain from design to support. Manufacturing must grow into cognitive manufacturing in order to fully advance to Industry 4.0 and beyond.

End-Note A few hundred words ago, I made you imagine a world without technologies, online shopping, and necessary inventions. Let us now end that time travel and think about the delivery of online shopping goods with drones. That is what we call a future in front of our eyes. One such future is cognitive computing and this when combined with the manufacturing sector gives rise to an industrial revolution. This is what we call the future. The beginning of industry 4.0. And cognitive computing is what is making this world ready for a kind of time travel we couldn’t have imagined. So, however turbulent it is going to be, trust the power of tech. It is going to be a safe journey.

Cognitive Enterprise Reinvest your company? by Vibha Soni

According to Allied Market Research, in 2018, the market value of cognitive computing was $8.87 billion, wherein in 2026 the market will reach up to $87.39 billion. Also, the market is growing at a CAGR of 31.6% from 2019 to 2026. Besides, the market research company also highlighted that cognitive computing is a next-generation system to process unstructured data quickly. 34

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IBM was the first company which developed the cognitive computingbased system. www.techfastly.com

Introduction

If you are a tech-enthusiastic, you indeed would have heard, “Cognitive Computing is the future.” Why? Because this technology mimics human behaviours. At present, almost all businesses are looking for a solution that can mimic their customer’s behavious because there is no value in the industry without customers. To acquire better insights into cognitive computing and its increasing role in business, we must understand certain aspects. So, let’s discuss why every business enterprise must reinvest in this technology, how they can reinvest in technology, and what they will get in return with a real case study.

Why Enterprise Needs Cognitive Computing?

using algorithm-based systems. Adaptive, Interactive, Iterative, and Contextual are four attributes of this technology attracting every business sector. You all know that data is a valuable asset for any enterprise, which is only a medium to grow continuously. The business’s success depends on accurate data collection, processing, and analysis. The increased use of social media platforms in business has encouraged customers to share their views in different forms. The Amazon reviews, tweets, memes, images, video testimonials are famous examples of customer opinions, thoughts, feedbacks. All these are unstructured data.

Before discussing why enterprise needs cognitive computing, let us briefly understand cognitive computing. Cognitive computing is all about understanding and simulating human reasoning and human behaviour, and it is a part of Artificial Intelligence (AI) with some similarities and differences. One prominent cognitive feature: “mimic human intelligence”, makes it different from AI, which focuses only on solving problems

Most enterprises have around 80% unstructured data that comes in various forms.

The analysis of these unstructured data is quite challenging. However, technologies are making it easy for enterprises. The cognitive system’s features efficiently understand human language patterns and input. Thus, it quickly processes the unstructured data to streamline the business operations rapidly. Without the support of other technologies, no single technology can bring valuable solutions for any business growth. Cognitive computing requires technologies to develop customised solution systems. Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Neural Network (NN), Automated Reasoning, Informational Retrieval (IR) are those key technologies. In all these technologies, the key technology is NLP and, after this, Machine Learning. The NLP’s capability to effectively process the natural language raises cognitive systems’ demands in varied business sectors.

NLP makes it easy to analyse this unstructured data that automatically improve customer services by better understanding their requirements. 36

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The analysed data could be used to make better decisions to enhance customer experience. These decisions efficiently optimise the business processes, assist in operational costs, and boost business growth. Cost is another factor that impacts the business and its performance. Enterprises are looking for a solution that supports them in cost-cutting without any loss. You all know, at present, cloud computing has become a mandatory technology within businesses. By adopting the cognitive system, enterprises can also reduce their other costs. Enterprises with cloud-based services seek to invest in cognitive systems to reduce the investment cost because of the cloud.

The analysed data could be used to make better decisions to enhance customer experience.

A few years back, healthcare was a top sector in investing the cognitive computing. Although at present, various other businesses have unstructured data, and to analyse it, they need a robust analysis system. The Independent Liverpool Student Newspaper has also discussed Verified Market Report done for the Global Cognitive Computing market. The news agency has also highlighted business areas for future scope: Telecom, IT, Aerospace, Defense, BFSI, Consumer Goods, Retail, Energy, Power, Education & Education, and media. Another company Reportsglobe has also verified the immense growth of cognitive computing between 2016 to 2019. Ohio University has also created an infographic and highlighted that cognitive computing has been changing all businesses. These statistics are enough to understand that this is the right time for reinvestment in business through cognitive computing systems. 37

How To Reinvest? Every technology implementation and execution requires following specific steps, and it applies to the cognitive computing implementation. The enterprise executives must have to study the market and develop a systematic approach before implementation. IBM was the first company which develop the cognitive computing-based system. The name of the system is “Watson” which answers to questions asked in natural language. The company executives have built a 3-phases strategy to accelerate enterprise reinvention using cognitive computing. With this reference, we have three steps framework to support enterprise executives.

STRATEGY FORMULATION

Strategy formulation is a standard step in any reinvestment, and the strategy should be developed by considering needs, costs, benefits, and timeframe. The executives must have to test the change management process before implementing a major technology change. They have to find answers to questions like current technology culture, employee’s skills, readiness to embrace change, challenges, and risks. These answers would build a lucrative strategy.

The executives must have to test the change management process before implementing a major technology change. 38

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ASSESS THE CURRENT BUSINESS OPERATIONS

In the following steps, it is necessary to assess the current business operations. Understand how enterprises function inside and outside. Analyse every department and find out functions that are time-consuming and unable to produce results. For example, in the marketing department, building lasting customer relationship is not easy. To make it easy, companies are investing in automated tools and analytics systems. In these ways, identify all functions which could get benefits after implementation of cognitive computing. These benefits could rewrite the business mission and future vision to be competitive in the market. Since multiple functions have been executed within each department, thus, prioritise the functions which must be changed.

FOLLOW A USE-CASE APPROACH

The prioritised functions help to select the right company to implement the cognitive strategy. It is suggested to follow a use-case approach to cross-check whether the strategy would work to solve the identified problems. The testing of the use-case gives an idea about how it is helping in enhancing the customer experience. By measuring performance, the strategy could be scaled into various levels

What enterprise will get after reinvestment? Cloud-computing attributes, rise in big data analytics, adoption of machine learning technologies, and demand for excellent customer services are major factors behind the growth of cognitive computing within the business. Every enterprise thinks about “ROI” whenever investing in any technology and new information systems. Cognitive computing doesn’t only ask for investment but also give benefits in return. The following points highlight those benefits: It is already discussed that unstructured data size has been increasing every day as soon as enterprises move towards digitisation and automation. The analysis of these unstructured data using cognitive computing makes its processing easy. When they understand those data, they efficiently provide solutions to their customers by finding out the latest patterns. And, satisfied customers always be beneficial for enterprises.

Technology and data-driven economy are increasing competition in business sectors. Enterprises need to stay updated with the latest trends and technology to compete with their competitors. Implementation of the cognitive system does not cut the operation costs but also enhances the operational business processes. Facility to collect and process customer data assist enterprises to make better decisions and to improve business processes.

Human resources can make improved decisions while hiring, costing, and scheduling using this system. Better decisions are taken for employees also impacts the business performance. The integration of varied data such as market trends, customer behaviours, service preferences, etc., generates insightful data statistics. Interpretation of those statistics helps marketers and decisions makers to develop new strategies for enhancing customer interaction.

Every investment returns something if it is done in the right way. The enterprise needs to make a great team and keep patience to get in ROI after making a technology investment.

Big Players & One Case Study Many big players are working in cognitive computing, but some names most often reside in the top. The Canadian based research company Pat Research has listed the top 10 companies: Spark Cognition, Expert System, Microsoft Cognitive Services, IBM Watson, Numenta, Deepmind, Cisco Cognitive, Cognitive Scale, Customer Matrix, and HPE Haven OnDemand. Apart from these, 3M, Google LLC, Oracle Corporation, Hewlett Packard Enterprise Development, Sap SE, Tibco Software, etc. have also positioned in the global cognitive computing market. Quantitus Innovation Inc, another prominent IT solution provider, believes that technology can advance business and offer incredible customer services. The company has also shared a video to explain that the retail sector can leverage cognitive computing in various ways: data analysis, sales conversion, demand forecasting, pattern detection, and inventory planning.

Case study All these provide intelligent solutions to various vertical and horizontal businesses. Let us take a new case study of the leading global IT solutions organisation, Coforge, utilising emerging technologies in unparalleled domain expertise to make a real-world business impact. The company has been offering services in the insurance sector as well, and it has brought a cognitive computing solution to reinvent insurance to speed up insurance claims. You are aware of how it is challenging to claim insurance. Customer churn, manual intervention, increased loss ratio, fraudulent claims are extensive pain areas of claim management, and it is impacting whole financial performance and associated stakeholders. To overcome these pains, the company developed a cognitive computing-based claim management system. Machine learning, deep learning, cognitive adoption, and image recognition have been used to design this system. The reap attractive margin is the best feature of this system. The company is offering this service to its various insurance sector clients and supporting them in enhancing operational efficiency.

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Final thoughts The technology companies have a similar objective: harnessing the technology and making it accessible for their customers and business growth. The reinvestment in cognitive computing with integrating other latest technologies is opening the door of enhanced market opportunities. Its effective implementation can quickly scale the business and enhance productivity. Successful and established businesses suggest creating a digital strategy and building a robust infrastructure for integrating cognitive computing systems. The business can be advanced to the sky and bring high value to the table. If you are a young entrepreneur or established businessman, evaluate your existing IT system and plan for cognitive reinvestment to elevate your business growth.

IBM Watson:

Uncover the Power of Cognitive Solutions by Rehan Husain The IBM-Watson had access to 200 million pages of structured and unstructured content, occupying four terabytes of disc space, including the complete text of the 2011 version of Wikipedia, but was not connected to the Internet throughout the game. On the television screen, Watson’s three most likely responses were displayed for each clue. Watson typically outperformed human opponents on the signaling device in the game but struggled in a few categories, most notably those with brief clues, including only a few words.

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Your value will be not what you know; it will be what you share - Ginni-Rometty Chairwoman, President, and CEO of IBM

She is the first female head of the company.

nternational Business Machines, or IBM, was founded in 1911. They were previously known as the Computing-Tabulating-Recording (C-T-R) Company. Charles Ranlett Flint did not invent the C-T-R Company. Rather than that, it was developed through the consolidation of three companies founded in the late 1800s: the Computing Scale Company, the Tabulating Machine Company, and the Time Recording Company. The newly amalgamated corporation was headquartered in New York City and employed approximately 1,300 people. During its early years, the C-T-R Company concentrated on accounting and calculating equipment, business time recorders, and mechanical punch card systems. It first

focused on everyday office items rather than evolving into the invention-driven corporation that it e ventually became. In 1924, Thomas Watson became president and renamed the corporation International Business Machines, or IBM. Watson established IBM’s success over his first several years by focusing on business and marketing tactics, developing products tailored to individual customers’ needs, and extensively investing in the company’s sales staff.

History of IBM-Watson IBM had been on the lookout for a new challenge since Deep Blue defeated Garry Kasparov in chess in 1997. In 2004, while having dinner with coworkers, IBM Research manager Charles Lickel realized that the restaurant had gone silent. He quickly uncovered the source of this nighttime lull: Ken Jennings, who was in the midst of his 74-game winning streak on Jeopardy! Almost the entire restaurant had gathered around the screens in the middle of the dinner to watch Jeopardy! Lickel was intrigued by the quiz show as a potential challenge for IBM, and in 2005, IBM Research chief Paul Horn backed Lickel, urging someone in his department to take on the challenge of playing Jeopardy! with an IBM system. Though he first struggled to find research workers prepared to take on a considerably more sophisticated 45

Image: Watson stage replica in Jeopardy! contest, Mountain View, California Atomic Taco, CC BY-SA 2.0, via Wikimedia Commons

challenge than the wordless chess game, David Ferrucci eventually accepted his offer. Watson’s precursor, a system called Piquant, was typically only able to reply appropriately to approximately 35% of clues and frequently took several minutes to respond. To compete successfully on Jeopardy!, Watson would have to react in a matter of seconds, yet the game show’s puzzles were believed challenging to solve at the time. Watson was given 500 clues from the previous Jeopardy! Programs in initial tests were conducted in 2006 by David Ferrucci, senior manager of IBM’s Semantic Analysis and Integration department. While the finest real-world competitors buzzed in half the time and adequately replied to up to 95% of clues, Watson’s initial pass could only get roughly 15% correct. IBM gave the team three to five years and a staff of 15 individuals to solve the 46

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difficulties in 2007. In 2007, John E. Kelly III succeeded Paul Horn as IBM Research’s head. Kelly was dubbed “the father of Watson” by InformationWeek and credited with motivating the system to compete against humans on Jeopardy! By 2008, Watson had advanced to the point where it could compete against Jeopardy! Champions.

By February 2010, Watson was regularly defeating human Jeopardy! opponents. The IBM-Watson had access to 200 million pages of structured and unstructured content, occupying four terabytes of disc space, including the complete text of the 2011 version of Wikipedia, but was not connected www.techfastly.com

to the Internet throughout the game. On the television screen, Watson’s three most likely responses were displayed for each clue. Watson typically outperformed human opponents on the signaling device in the game but struggled in a few categories, most notably those with brief clues, including only a few words. Although Watson is primarily an IBM effort, it was developed in collaboration with faculty and graduate students from Rensselaer Polytechnic Institute, Carnegie Mellon University, University of Massachusetts Amherst, University of Southern California’s Information Sciences Institute, University of Texas at Austin, Massachusetts Institute of Technology, and the University of Trento, as well as students from New York Medical College. Ed Toutant was one of the IBM programmers who worked on Watson; he had previously been on Jeopardy! in 1989.

IBM and the Computer Despite an impressive track record in business, IBM is most known for its technical accomplishments. In 1943, the business invented the Vacuum Tube Multiplier, the world’s first all-electronic computer machine. This resulted in the 1944 Automatic Sequence Controlled Calculator, or “Mark I,” built jointly by IBM and Harvard. This was the first piece of equipment that we would refer to as a computer in the modernday. It took up a small room, measuring 50 feet long and eight feet tall, and automatically conducted electromechanical computations. The United States Navy relied on the Mark I to determine gun trajectories on its ships. The company, however, did not promote this line of business until the 1950s, when Thomas

Image: IBM Automatic Sequence Controlled Calculator (ASCC) - Harvard Mark I Computer Rocky Acosta, CC BY 3.0, via Wikimedia Commons

Watson’s son, Thomas Watson Jr., took over. During this period, IBM’s computer business shifted away from mechanical switches on the Mark I and toward vacuum tubes on the Vacuum Tube Multiplier, as vacuum tubes were easier to maintain and repair. IBM pioneered many fundamental technologies that enabled computers to become ubiquitous in the commercial world during the 1950s and 1960s. It invented the functioning vacuum tube computer, which served as the foundation for all computers until the microchip was invented. IBM also pioneered the hard drive, pioneering spinning platters to store data and retrieving it using a magnetic arm. Additionally, it invented 48

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FORTRAN, the forerunner of the majority of modern computer programming languages. IBM’s domination of the computer market was nearly complete throughout this era, with the corporation manufacturing between 60% and 70% of all business computers globally.

Notable Accomplishments: IBM Research is credited with pioneering scientific breakthroughs in computer science, physics, and various other technical areas. Here is a summary of our accomplishments, divided by category, highlighting the technologies and scientific advances that have influenced the way billions of people work, play, study, travel, and live. www.techfastly.com

IBM’s Watson Artificial Intelligence IBM has been a pioneer in deploying artificial intelligence to compete and win against elite humans in games of intellect and strategy since the 1950s, when Arthur Lee Samuel developed a checkers player capable of learning from experience. IBM defined how people thought about speech recognition for the next two decades. After another twenty years, IBM Research made history when Deep Blue defeated Garry Kasparov, the first chess-playing machine to beat a reigning world champion. IBM researchers continue to tackle new artificial intelligence challenges in machine learning, neurology, genomics, and robotics.

In The Healthcare Sector IBM’s founders desired to give back to the world and investigate how computers may benefit our community’s health. IBM Research is committed to medical informatics due to that early dedication and recognition of the enormous possibility to serve the world by utilizing computers to aid in the study of how human bodies work and can be treated. IBM Research has a world-class physics department that assists in constructing components for computers, and some of that research informs us about the artificial things we can do to help humans.

In the Software Engineering IBM learned numerous lessons critical to software engineering while constructing OS/360, the most prominent program ever built. This occurred when software development procedures were modeled after those used in developing and producing machinery. Design patterns represented actual work on planning for change, while AOSD demonstrated how to structure code in a more morphable manner. Our extensive expertise in program analysis resulted in developing tools that are critical to today’s techniques.

In Visualization Visualization is the process of graphically expressing and engaging with data to acquire insight into it. Computer graphics has traditionally given a potent method for producing, altering, and interacting with these representations.

At IBM, graphics and visualization research focuses on the challenge of transforming data into compelling, revealing, and interactive visuals that are tailored to the needs of individual users. 50

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Our research focuses on visual analytics, the development of languages and models for interacting with visualizations, the design of novel information visualizations for more innovative visual analytics. The story of new representations of 3D geometry, collaborative and social visualization strategies, and the creation of software systems that support various display formats ranging from smartphones to immersive multi-display visualization environments.

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In Physical Sciences IBM Research has fostered the careers of numerous physicists who have made fundamental contributions to various professions and fields of study. The dynamic random-access memory (DRAM), field-effect transistor scaling rules, semiconductor superlattice structures, specialized lasers, and thin-film magnetic recording heads, as well as advancements in optical communications and electron microscopy, were all discovered and developed here.

Five IBM physicists have won the Nobel Prize in Physics: Leo Esaki in 1973 for his work on semiconductors; Gerd Binning and Heinrich Rohrer in 1986 for their work on the scanning tunneling microscope; and Georg Bednorz and Alex Mueller in 1987 for their work on superconductivity.

The future of IBM Watson There is a dilemma in the realm of artificial intelligence: While AI represents the most significant economic opportunity of our generation (it is predicted to add $16 trillion to GDP by 2030), enterprise adoption was less than 4% in 2018. According to a recent Gartner survey, the 4% in 2018 increased to 14% in 2019. However, this is a pitiful sum. This is due to many factors, including a lack of abilities, a lack of tools, and a lack of confidence For enterprises seeking to engage in this phase of technological innovation and wealth creation, the most critical characteristic is a beginner’s mindset; a willingness to experiment, and an acceptance of failure. Organizations should aim to do 100 AI trials per year, with the understanding that more than half will fail. Numerous corporate cultures are unsuitable for this. A more common method is to unite around a single large AI project, devoting significant people, time, and money. That is not a strategy I recommend. Artificial intelligence is about broad experimentation, not a single large project deployment. This is not enterprise resource planning. Fortune is on the side of the courageous. I feel that the trial and error that we have all experienced – and will continue to share – is worthwhile in light of the beneficial outcomes. Not just for economic gain but also to benefit businesses, consumers, and, ultimately, the world in which we live. There will be increased experimentation, a more significant number of failures, and a greater number of achievements. And, without a doubt, numerous changes in the way we live and work. It is incumbent upon all of us to guarantee that those changes are beneficial.

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I believe that every human being on Earth will interact with Watson in some way – whether it’s through accelerating customer service, augmenting their work, enhancing their retail experiences, providing medical insights to caregivers, assisting them in avoiding food scarcity, or even in ways not yet imagined. Our aspirations have not waned. IBM will continue to be a leader in advancing AI for all.

Conclusion Watson Anywhere is more than a fantastic way to conduct AI. It is founded on true innovation, with the Cloud Pak for Data at its heart – a microservices-based data and analytics platform built on Red Hat OpenShift. On this platform, enterprises may deploy Watson tools and apps to virtually any cloud – including IBM Cloud, AWS, Azure, Google Cloud, or their private cloud. IBM’s Watson platform will continue to have a beneficial impact on the globe, expanding adoption and enabling individuals and businesses to share in the $16 trillion wealth creation. Additionally, we know that we will do so in the manner you have come to expect from IBM: thoughtful, trustworthy, and measured. We will all benefit from AI in the right handssays Rob Thomas- The General Manager of IBM Data and AI.

Cognitive Agents

and Reinforcement of User Experience by Bharggavi Ssayee In a world where more than 2.5 billion bytes of data are created every day, it is essential to deliver information to users effectively. Cognitive agents’ technology can understand human language to provide accurate information and quickly direct users to the optimal next step.

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llowing cognitive agents to do the heavy lifting frees up human resources to tackle more extensive and more complex problems. This combination of technology helps humans produce a better user experience for everyone. As a result, advisors get the information they need to help clients resolve their inquiries faster.

How Does This Technology Work, And Who Makes It So Valuable? Cognitive Computing Cognitive agents are born from one of the main building blocks of artificial intelligence: cognitive computing. It is basically simulation of human thought processes in a computerized model. And it includes self-learning systems that harness data mining, pattern recognition, and natural language processing (NLP) to mimic patterns of the human brain. These applications are:

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ADAPTIVE Cognitive solutions adapt to changing information, objectives, and requirements, thus making it possible to plan and resolve ambiguities. Most of the time, cognitive computing solutions can also process data in near real-time.

ITERATIVE AND STATEFUL Cognitive solutions help define a problem or find additional sources if a problem statement seems ambiguous or incomplete. Previous interactions may also be involved in determining or completing problem statements.

CONTEXTUAL Cognitive solutions can understand, identify, and extract contextual elements, including syntax, meaning, time, location, domain, regulation, user profile, process, task, and purpose. In some cases, they can also extract structured and unstructured information as well as sensory input.

While cognitive computer applications (like cognitive agents) can mimic the human brain, that doesn’t mean they are meant to replace human advisers. Instead, this technology can be seen as a powerful assistant capable of handling tedious tasks and freeing up human resources to tackle complex problems. And less time searching for information means shorter resolution times, which equates to lower costs.

Benefits Beyond Cost Savings Cognitive agents are an excellent money saver but they also have other benefits. For example, this technology can also improve data security, customer and employee experience, and visibility into business processes. At the same time, many organizations see benefits in simply using cognitive agents as “helpers” to living agents. à help provides information and recommends personalized offers to the customer more quickly; some use this technology to eliminate possible phishing before the interaction of natural agents to reduce the average processing time. Others have also benefited from combining cognitive agents with internal processes to speed up the onboarding process and reduce security issues and errors.

Simply using cognitive agents as “helpers” to living agents. A help provides information and recommends personalized offers to the customer more quickly.

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Success With Cognitive Agents Cognitive agents can be part of your digital transformation in many ways. Browse the stories below from real organizations to see how they implemented cognitive agent solutions to deliver valuable insights and a better user experience for clients and advisors.

HEALTH INSURANCE COMPANY Interactive cognitive agent, allowing providers to access member information quickly

CHALLENGE

A health insurance company with 13 million customers in the United States needed a better way to handle member inquiries from providers. Calls were driven by an outdated interactive voice response (IVR) system, and volumes exceeded 700,000 calls per month, with most callers opting out of the IVR system for expensive outsourced call centers.

STRONG POINTS Development of a cognitive agent to converse naturally with suppliers and return correct information in good time and efficiently Performed IBM Watson Dialogue, Watson Natural Language Classification, Watson Speech to Text, and Watson Text to Speech.

MAIN ADVANTAGES Substantial reduction in the number of real agent requests, resulting in substantial savings. Reduced average call duration from eight minutes to three minutes.

MULTINATIONAL SOFTWARE COMPANY Reshaping the Customer Service Infrastructure with the Cognitive Agent IBM Watson

CHALLENGE A software company specializing in architecture, engineering, construction, manufacturing, media, and entertainment struggled with an unfriendly and inefficient user experience for customers calling with service requests and questions. The company intended to improve this crucial customer experience and reduce its time to resolve issues and requests.

HIGHLIGHTS OF THE SOLUTION Implementation of IBM Watson Dialog and Natural Language Classifier. Transform, automate and improve interactions Understands the intention of conversations and can act as a digital concierge

MAIN ADVANTAGES Assisted management of around 60% of Group 1 requests from start to finish 90% decrease in support costs 99% reduction in resolution time

3 MULTINATIONAL CONGLOMERATE COMPANY New cognitive shopping experience improves customer service and conversion rates CHALLENGE A Japanese and Korean conglomerate with functions such as retail, financial services, hotels, etc., seeks to optimize the shopping experience within their retail subsidiaries using cognitive technology to use their data better.

HIGHLIGHTS OF THE SOLUTION Creation of a virtual advisor for purchases with a recommendation engine to analyze structured and unstructured data Text recognition, text-to-speech, and visual recognition capabilities Creation of a “customer DNA” with personalized recommendations based on purchase history Trend recommendations provided with social media keywords analyzed by AI

MAIN ADVANTAGES New services concerning in-store purchases and advice via a cognitive chatbot Personalized recommendations in real-time Improved customer experience with higher conversion rates Consolidation of disparate data sources into a unified “customer DNA.”

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We all have scenarios in mind that use, for example, machine learning techniques that can automatically process ever-increasing volumes of data. This field has grown exponentially in terms of innovation in recent years. As a result, human-machine parity is getting closer to detecting and recognizing objects, speech, text comprehension, or translation. The design of innovative services based on cognitive benefits allows companies to

tell the right story by drawing on the user’s context as a differentiator. For example, it is possible to locate a user to generate engagement in a public place, manage employees’ safety on industrial sites, or imagine new residential uses of our daily lives. The field of possibilities is vast! We are at the crossroads of three momentums, application modernization, innovation through AI, and DevOps that will elevate the value of services for us and our customers.

Marketing Leaders

Are Taking A Run Approach Towards Cognitive Computing by Toulika Das

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n today’s world, cognitive computing is one of the most important tools for marketing leaders. From building crucial connections with stakeholders to carrying out extensive research, cognitive computing is assisting business administrators in growing their businesses exponentially. Cognitive computing technology, often known as artificial intelligence (AI), simulates human skills to extract fresh insight from large amounts of information and data. They then generate important, actionable intelligence at a large scale. Data and figures based on customer psychology are essential for successful marketing efforts.

The Growth of Cognitive Computing in Marketplace Cognitive computing is a type of computer system or algorithm that mimics basic human behaviour. The judgement technique is supported by its cognitive system, personality, and feeling. It operates as a model or replica for how the human brain processes information and reacts to them. Some of the greatest instances of cognitive computing include Google Assistant, Cortana, Siri and Alexa. The applications of cognitive computing are predicted to rise by a huge amount in the upcoming years. The worldwide market is estimated to reach $49.36 billion by 2025, as per Grand View Research.

Sixty-two per cent of the users of cognitive computing claim that their cognitive applications have already surpassed their predictions.

Why Are Market Leaders Rushing To Adopt Cognitive Computing? The utilization of digital algorithms to mimic the human thought process in complex scenarios with otherwise vague and unclear responses is otherwise known as cognitive computing. In simple words, cognitive computing is a technology built to imitate the basic human personality. Since this study of human psychology and consumer attitude is so important in marketing and business operations, cognitive computing is a very useful tool for market leaders. Some of the major reasons why marketing leaders are taking a run approach towards cognitive computing are as follows.

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Cloud-based solutions that are both cost-effective and sophisticated Faster communication using edge computing Huge increase in customer data volumes Extending Customer Relationship Management (CRM) Improved Human Resources decisions

Market leaders thrive on insights into the customer mindset. With cognitive computing, this duty entirely is carried out by the artificial intelligence technology efficiently. The good results are luring marketing leaders into adopting cognitive computing techniques in their businesses sooner than ever.

Cloud-based solutions that are both cost-effective and sophisticated Today’s software architectures with cloud network systems are more adaptable and cost-effective for businesses in their day-to-day operations. Cloud integration has not only fostered development in the cognitive computing industry, but it has also made cognitive computing platforms more attainable by several enterprises. Cognitive computing platforms, powered by cloud technologies, have become a lot more appealing to marketing organisations

wishing to use cognitive computing solutions in their operations, thanks to the faster and more economically convenient invention and experimentation.

This is especially important as we emerge from the COVID-19 outbreak and more firms look to virtual approaches to improving. 65

Faster communication using edge computing Edge computing is significant because it gives industrial and commercial firms new and better methods to increase productivity and efficiency, improve performance and safety, streamline all crucial business activities, and provide “always-on” uptime. Edge computing, in conjunction with cloud technology, may be an effective instrument for enterprises hoping to grow their cognitive computing skills. Since it retains and interprets data at the network’s source, edge computing facilitates immediate decision making.

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Huge increase in customer data volumes One of the most appealing uses for cognitive computing is its power to make sense of massive amounts of consumer data – including static and dynamic – in order to study, analyze, and suggest. Major financial institutions are employing technology to link data internally and externally. This is being done in order to boost income throughout business accounts controlled by a diminishing group of bankers. Financial advisory firms are supplementing their corporate data and information about a customer’s significant life events collected from public, social and professional data sources, allowing experts to preemptively sell more products to current customers. 67

Extending Customer Relationship Management (CRM) Natural Language Understanding (NLU) and Natural Language Processing (NLP) features in cognitive computing CRM systems allow businesses to assess the tone of a consumer’s voice and interpret human characteristics. The CRM system may use it to provide real-time assistance to employees so they can better speak with the customers. Cognitive computing solutions aid customer service operations by significantly reducing the time as well as the resources spent resolving client issues. Cognitive CRM products provide meaningful and easyto-understand answers to typical industry 68

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problems on their own. Because cognitive CRMs lower the number of objections that approach customer service executives, customer service executive officers do not have to spend much time on most of the common issues

The CRM system may use it to provide realtime assistance to employees so they can better speak with the customers. www.techfastly.com

Cognitive Computing Is Paving The Road to Success In Marketing Marketers may personalise content and craft messages to send to their customers using cognitive computing and data crunching. Marketers use the technology to control and assemble a plethora of meaningful data from a variety of sources, such as predictive data modelling, search engine optimization (SEO), customer research, and social media monitoring.

Bottom Line Companies have begun to see significant business benefits from their cognitive activities. Ranging from improved customer interactions, improved productivity to

increased revenue, cognitive computing is a literal blessing for marketing leaders. Experienced individuals who initiated earlier enjoy the biggest benefits from cognitive applications. Similarly, systems that explore and evolve over time produce better results. Marketing professionals are increasingly benefiting from cognitive computing and AI tools since they can quickly identify customer patterns and behaviours. The market demands for cognitive computing is predicted to flourish within the next few years, driven by the expansion of technological domains such as cloud and edge. 69

THE REIGNING

Champion Changemaker AND A

MANASI GIRISHCHANDRA JOSHI

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Manasi Girishchandra Joshi is a para-badminton player from India. She is the reigning champion and a changemaker. She began her professional athletic career in 2015 and was rated world number two in women’s singles in the SL3 category in 2020. Manasi was named TIME Magazine’s Next Generation Leader 2020 in October 2020, and she appeared on the magazine’s Asia cover, making her the first paraathlete in the world and the first Indian athlete to be featured on the magazine’s cover for being an advocate of rights for persons with disabilities.

B I hope that my story inspires many more lives and encourages young girls to harness their true potential to fight hard and become whoever they set out to be.

arbie honoured Manasi and her accomplishments on the International Day of the Girl Child (11 October 2020) by creating a oneof-a-kind Barbie doll in her likeness to encourage young girls. Additionally, she was named by the BBC as one of the world’s 100 most inspirational and influential women in 2020 and she was featured in the Self-made women of 2020 list by Forbes India. Manasi was born in Rajkot, Gujarat, and reared in Mumbai’s Anushaktinagar neighbourhood. In 2010, she earned a bachelor’s degree in electronics engineering from the K. J. Somaiya College of Engineering at the University of Mumbai. Manasi, a sports enthusiast, participated in football and badminton throughout her high school and college years. She began playing badminton at the age of six with her father, a retired scientist from Bhabha Atomic Research Centre and has represented her school, college, and company in various competitions throughout the years. She worked as a software engineer after graduating in 2010. 71

On 2nd December 2011, Manasi met with a road accident on her way to work, necessitating the amputation of her leg. Manasi was released from MGM hospital Vashi, Navi Mumbai, after 45 days of hospitalization. While continuing with her 9-6 job as a software developer, she began practicing yoga, meditation, and badminton following her injury in 2012-2013 to restore her health. She began playing badminton as part of her recovery and was encouraged to try out for the national squad by another parabadminton player; she was selected for the 2014 Asian Para-Games and competed in her first international competition in Spain. In 72

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2015, Manasi earned a silver medal in mixed doubles at the BWF Para-Badminton World Championship in Stoke Mandeville, England, alongside her XD partner. In 2018, she enlisted Pullela Gopichand as her coach and joined in his Hyderabad badminton academy. Following that, a flurry of national and international medals ensued. In August 2019, she won gold in the BWF Para-Badminton World Championship in Switzerland, one of the sport’s largest competitions. She recently won 2 gold medals (women’s singles SL3 & mixed doubles with Ruthick Raghupathi) & 1 bronze medal (women’s doubles with Shanthiya Vishwanathan) at Uganda Para Badminton International competition. Her triumph was originally overshadowed by a notable non-disabled Indian badminton player’s victory. However, seeing the paucity of coverage of Joshi’s triumph, social media poured congratulations on her days later. She is also swiftly establishing herself as a significant advocate for India’s tens of millions of disabled people.

You were a full-time software engineer with Atos India till July 2016 post which you joined a bank in Ahmedabad as a Manager overlooking their IT. And you are currently employed with BPCL as a sportsperson. What led you to start your journey as a professional badminton player?

I have been playing badminton since the age of 6. It started as a hobby when I joined the school summer camp. While studies were my focus, I kept playing the sport and eventually started representing my school and college at district-level tournaments. I graduated www.techfastly.com

from KJ Somaiya College of Engineering in 2010 and secured a job with a corporate as a Software Engineer. It was only in 2015 that I started playing badminton professionally. Post-accident, I used yoga and badminton for rehabilitation. One thing led to the other, and I landed in the selection trials for Asian Para Games 2014. While I didn’t make it to the Indian contingent then, it was the start of my professional sports journey. With more and more participation at international events, I realized that there is no limit to the human body. I saw a lot of potential for me in the sport and wanted to explore it further. So, I decided to quit my job and pursue the sport full time.

After the accident, how did physiotherapy help you with the rehabilitation post-surgery? I met with an accident in Dec 2011 and, after 45 days of hospitalization, lost a battle and my leg to it. I had an above-knee amputation along with a compound fracture in my left had radius and ulna bones. It took me another 2.5 months for my wounds to heal completely, after which I started with my rehabilitation, i.e. prosthetic fitment. It was during my hospitalization that I had my first physiotherapy session. I remember my physiotherapist first taught me how to use crutches and walk, and go up and down the stairs. Initially, I was afraid of falling down while walking, but my physiotherapist spent a lot of time training me to go up and down the slopes, stairs and uneven surfaces. I also had a compound fracture in my radius and ulna after the accident. So for me, physiotherapy was also about learning to regain my hand functionality which took a lot of time. Every day I knew what my targets

were, and I was not sad but determined to achieve my goal to get better. My family, friends, and physiotherapist made sure to fill in hope and positivity, which made me stronger. 73

How was your experience at the Dubai Para-Badminton Tournament 2021?

Felt great! One of the prosthetics that I used to play with had developed a problem just two weeks before the tournament, so I had to shift to a running prosthesis that I was still adapting to. I did adapt to it quickly because of the support of my trainers, who believed that I was strong enough to adapt to changes in prosthetics. Also, this was our first tournament after the pandemic broke. So, it felt great to be back 74

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competing with my fellow athletes from across the world. I was happy enough to win a silver there, although I thought to bag Gold.

You played the maiden ‘Nationals’ in 2014 and within five years you became a World champion. That’s a great achievement. Who all have been an integral part of your success?

My family and friends have been the biggest support and my cheerleader through this. Also, I believe that I have been fortunate to have met some nice people who have believed in my journey and supported me at different stages in life. www.techfastly.com

My trainers, coaches, physio, prosthetists, sponsors – Welspun Foundation and Mallcom Group, my employer BPCL, and colleagues at different organizations have supported and helped me navigate through my job + sports. It is a combination of support from family, friends and these individuals / organisations, that I have come so far.

In addition to training costs, differently-abled players need to cover costs for physical therapy, prosthetics, and physiotherapy. Can we discuss what the government is doing to improve participation in sports for such talent?

I feel para-sports are on the rise now. It has taken a while, but people have gradually begun to recognize para-sports / athletes. This change in perception is good for us, paraathletes. The government support has been immense, along with private organizations that have programs designed focussing on athletes. All of this is contributing to creating a great ecosystem around sports, especially para-sports.

The makers of America’s famous Barbie doll celebrated your achievements last year on the International Day of Girl Child on October 11 with a Barbie modelled of you. Did it in some way help change the perception of people regarding disabilities?

You know, disability in itself covers a huge spectrum ranging from locomotor disability to vision impairment, hearing impairment, to name a few. I am happy to be recognized as a

voice to represent the disability community that is so underrepresented in India. I am glad I have this platform because of my sport, which helps me amplify my voice.

I hope that seeing a barbie doll modelled to my likeness will change a lot of perceptions surrounding disability. I believe that education around inclusion and diversity should start early. I hope that my story inspires many more lives and encourages young girls to harness their true potential to fight hard and become whoever they set out to be.

What training regime do you follow to stay in the best possible shape, both physically and mentally?

On a daily basis, I have to take care of my training on-court and off-court, strength & conditioning, nutrition, etc. I have been training at the Gopichand Academy in Hyderabad since 2018. I have to undergo extensive training sessions during tournaments season and keep it light during off-seasons. 76

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My daily routine currently involves two sessions a day, six days a week—one session on the court and one session off-court (gym). I have learned that to be a part of the competition model; I have to maximize the use of current equipment/ assistive device / prosthetic leg, making sure I remain injuryfree and perform at the highest level. I also make sure I do drills to practice mindfulness for mental conditioning.

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One of the core skill components is developing hand skills and working on my mobility, footwork and movement. The current prosthesis available are designed to assist individuals with forward movement like (walking, running, etc.). But badminton requires backward, diagonal, sideways movement during the game. So, I am still learning and adapting. I spend 7-8 hours per day on court and gym to work on these aspects.

Being a sportsperson, you must face highs and lows in your career. How do you deal with them? What keeps you motivated?

To take one step at a time and not push myself extremely or expect results too early. Learning the quality of tolerance and patience and working hard each day to fulfill my big or small ambition keeps me going. Despite the failure, when I see that my friends and family are always supporting me, it keeps me motivated to achieve more in life.

because of my gender or ability; I was always told I could despite it. The most important thing is to empower children at a young age and help them in finding their true potential. My message to the young ones will be,

“

Do not listen to anyone who says that you can’t do something; you have to dream and give it all because dreams do come true.

What advice would you like to give to a young one who want to build a career in fitness and sports?

I have realized over the years that hard work, ambition/vision, and perseverance are the most important things required to be successful. Be it engineering, coding, or playing a sport. My message would be for the parents first. I urge them to speak to their children and not strike an opportunity off because they feel that their kids can’t do it. For me, I was fortunate enough to have parents who let me experiment and always supported me in whatever I wanted to pursue. I was never told that I could not do something 77

Adoption of

Cognitive Computing Across Various Industries by Saipriya Iyer

ne of the most comprehensive amalgamations witnessed in recent times – cognitive computing, is a unique blend of artificial intelligence, natural language processing, machine learning, neural networks and other scientific disciplines. Essentially cognitive computing is an advanced framework that combines the aforementioned and uses computerized models to imitate the human thought process. Note however, that it is very different from proper AI-enabled systems – cognitive computing studies patterns and analyzes factors to help humans take a relevant course of action.

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Kanverse.ai recently in May 2021, announced that it is launching the Datolite release of its AI-powered IDP (Intelligent Document Processing) Product for Enterprise. Given its incredible ability to think and process like humans, cognitive computing is heavily used in decision-making across major verticals. To that end, even prominent companies have been working to invest in cognitive computing solutions – consider Kanverse.ai for example. The firm, recently in May 2021, announced that it is launching the Datolite release of its AI-powered IDP (Intelligent Document Processing) Product for Enterprise. This IDP is built on the Kanverse Cognitive Automation platform that integrates artificial intelligence with Business rule framework, Optical Character Recognition

How cognitive computing is revolutionizing

the banking sector

(OCR), and Automation to deliver a product for digitizing the entire document processing for enterprises. Ultimately, the aim of cognitive computing is to stimulate human thinking within computerized models. Over the years, it has been deployed across many verticals, with path-breaking results. IBM’s Watson for instance, has been used in the medical realm, to collect information about specific disorders, including but not limited to basic details, patient history, journal articles, diagnostic tools, treatments, etc., analyze the same, and eventually provide a recommendation. This literally changes the course of how humans perceive AIenabled tools – through cognitive computing, interacting with tools could become a much more seamless process. The following paragraphs provide a gist of the use of cognitive computing across major industry domains:

The BFSI industry has been prompt in adopting technologies that make financial processing easier and less complicated. The realm of banking comes with its own set of processes – fund transfers, cash

deposits and withdrawals, bank statement accountability, and most importantly, resolving customer queries. Although most financial institutions have been quick to deploy virtual assistants and bots for the customer’s convenience, it has become obvious that people would prefer interacting with something with a slightly more human touch. Here’s where cognitive computing steps in to ensure seamless consumer experience. Banks and other financial establishments are leveraging cognitive computing to automate banking and investment products, manage a large amount of transactions, and use evidence-based information to provide tailored services to customers. Using cognitive computing helps banks examine and analyze data from a wider array of resources so they are able to mitigate risks better. Banks can also use CC for fraud detection – to predict what may happen, even before an activity actually occurs.

Cognitive computing is on its way to transforming the healthcare sector The incorporation of technologies, such as Big Data, AI, imaging, has brought about a tremendous change in the medical sector. Healthcare companies have lately been leveraging cognitive computing to make vital decisions regarding a patient’s condition, diagnosis, and treatment. CC is also being used by companies to as a clinical support tool – in the future, it is likely to be used in

The predictability factor of CC is particularly useful in trading. As a matter of fact, data and analytics provider Preqin states that close to 1,360 hedge funds leverage computer models to make the majority of their trades. Many a time, high frequency trading is completely automated, in which case business models can analyze large amounts of data and use the same to improve trading outcomes, all by themselves. Wipro’s recent partnership with Finastra to accelerate digital transformation across APAC corporate banks is an example of the BFSI sector has been incorporating cognitive computing in its processes. 80

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monitoring systems that scrutinize episodes in patients suffering from asthma, Parkinson’s disease, diabetes, and more, without human intervention. As the complexity of diseases and the number of patients in the medical ecosystem increase, healthcare personnel often find themselves in trouble as they have to deal with a vast amount of health-related data in less time. Not to mention, this data is pretty fragmented and does not provide a concise insight into a patient’s medical history and decisions to be taken for the patient’s overall well-being. To that respect, cognitive computing systems step up as an efficient solution.

These systems are designed to parse through data, share health information, improve patient outcomes, and personalize patient care by helping medical professionals make faster, cost-effective decisions. IBM’s Watson is one of the best examples where cognitive computing is being used to process data and model possible solutions. IBM has worked with a lot of hospitals in partnership, especially Cleveland Clinic that has been using IBM’s Watson cognitive computing technologies across its clinical and administrative operations.

Cognitive computing – Bringing about a change in the education domain Cognitive computing can prove to be a gamechanger in the field of education. With the arrival of the pandemic, the education sector has undergone a massive change. Online schooling became more commonplace, pushing the education technology industry trends to a considerable extent. Amid this scenario, the rise of cognitive assistants will prove to be highly helpful for teachers, students, and other support staff. They can also personalize course materials for students, help them resolve problematic situations in the course, and play the role of career counselors – a job that is crucial in educational institutions. Cognitive computing solutions are also incredibly useful for teachers. An analysis of the education system worldwide often points to the problem of teachers not being able to answer queries satisfyingly, from students owing to their varying abilities and learning aptitudes. To that end, cognitive computing systems can help teaching staff, as they have the ability to digest large data sets and engage with humans in natural language. For instance, if a student asks a specific doubt to a teacher, he/she can feed it into the cognitive system, which will not only provide the right answer but also explain the steps en route. The efforts put into implementing cognitive computing in education are quite visible off late. For instance, recently, in August 2021, the department of science and technology (DST) announced a funding of USD 134,848 to establish a cognitive computing hub at IIIT (Indraprastha Institute of Information Technology Delhi), Delhi.

Cognitive computing systems can act as a personal tutor for students, as they guide them through their coursework and help them make decisions about which course to opt for next.

An overview of how cognitive computing is deployed in the retail industry Personalized customer experience has been, since long, a cherished dream for retailers. While the e-commerce sector has been quick to adopt novel technologies to ensure a seamless consumer experience, introducing cognitive computing systems into the mix may change the game altogether.

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These solutions can be used to create personalized promotions, recommend products during online shopping, generate indepth customer insights, check on workforce utilization and optimization, and optimize inventory. As online shopping continues to gain precedence, retailers will need to

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Apart from that, cognitive computing provides the advantages of predictive analytics, voice assistance, demand forecasting, social media engagement, price optimization, and personalized recommendations. It is also incredibly beneficial for consumers, as it provides then with self-help applications such as selfcheckout payment platforms, price checkers, information kiosks, and mobile payment apps to improve their online shopping experiences.

The Future of Cognitive Computing

incorporate more and more cognitive computing solutions in their processes to offer personalized, seamless user experiences. These solutions will help retailers track patterns in customer data, derive customer insights, and lead to improved customer interactions.

There’s no doubt about this, cognitive computing is here to stay. Most experts are certain that it’ll lead to exciting growth opportunities across major use-cases. It has the ability to revolutionize key industry domains, as they scramble to resolve their current challenges. Advancements in cognitive computing will encourage companies to be more receptive to the technology and increase its adoption. In summary, cognitive computing bears true potential to transform the way technologies such as artificial intelligence and natural language processing are deployed.

Sentimental Analysis

The Key to Changing Perceptions of Cognitive Computing in Marketing

When people think about data and computing, the first thing that comes to mind is Artificial Intelligence (AI). In the marketing sphere, AI has gained a considerable foothold, evident from the many SaaS products being launched. That being said, there is one aspect that is still amiss – Personalization.

by Tanaaz Khan

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How can a company genuinely show that they care about their consumers when their products don’t reflect that? That’s where cognitive computing comes into the picture. Cognitive computing uses a combination of AI, neural networks, sentimental analysis,

natural language processing, and the like to solve real problems. To implement a genuinely human-centric approach, it’s essential to use technology that mimics human emotions and actions better. This is precisely what cognitive computing does, and this is what differentiates it from AI-led approaches. In addition to that, quantum computing and cognitive computing will soon be synonymous with companies such as IBM taking the next step forward.

This could give cognitive computing the push needed in the marketing world as data-driven marketing seems to be getting more popular with time.

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But before that, let’s see how cognitive computing works to bridge the humancomputer gap.

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Bridging the Human-Computer Gap in Marketing When it comes to using computing technologies, the one thing that sets cognitive computing apart is that it accounts for human emotions. This makes it the perfect choice for marketing purposes since it goes beyond the generic responses. To do this, most marketers use sentimental analysis — a method used to gauge the audience’s sentiments and leverage them for your business. An excellent example of sentimental analysis in action is a humanoid robot. They act like “cognitive assistants” and help you out with your immediate needs. You can find them either at conferences/ events where they’re checking you in, within hotel lobbies acting as your concierge, or even at nursing homes, helping elderly patients with their needs. The applications are far and wide but what’s important to note is that robots can understand our emotions now, all thanks to complex cognitive computing capabilities. Even cognitive computers such as IBM’s Watson can improve business processes and customer interactions over time but use robotic process automation. They can identify emerging patterns and give marketers the competitive edge they need in the market. As cognitive computing allows the system to provide valuable and contextual information to its customers, they increase engagement over time. 89

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Now that we know that human-computer interactions are being bridged let’s look at how sentiments play a role in that context.

Leveraging Sentimental Analysis for Your Business Sentimental analysis is a process in which you gauge the emotions, attitudes, opinions of your audience concerning either your product, service, or brand from the business perspective. In today’s day and age, most customers expect personalized marketing content, which forces you to go above and beyond in your marketing efforts. But with sentimental analysis, it’s easier to gauge the emotions and attitudes of your customers and leverage that for your business. For the most part, sentimental analysis focus on three aspects — engagement, decision, and discovery. Such systems can track data from multiple repositories to give better insight and assistance regarding engagement. This fosters the ability to have more profound and meaningful dialogues with humans, engaging the customer. Concerning decision making, they use reinforcement learning that helps it make better decisions through a closed loop of learning. Through deep learning and unsupervised machine learning models, cognitive computers tend to discover patterns and

behavior that would not have been obvious otherwise. Using these three aspects, you can build relationships with your customers that would not have been possible otherwise.

A study showed that 68% of users are more likely to buy a product if it provides some form of personal value. This personal value cannot be gauged without genuinely understanding the customer, their needs, and what kind of situations they are in. To do this, sentimental analysis tends to be the best bet for business owners. Here, the analysis can either be direct (social media & feedback) or indirect (automated sentimental analysis). When it comes to marketing, it can be used to track buyer sentiment, improve the ideal buyer persona and even manage any potential crisis — all powered through cognitive computing. That being said, it’s always important to employ a mix of direct and indirect analysis because, at the end of the day, a machine can only do so much. 91

The Quantum Computing Push in 2021 As mentioned earlier, cognitive and quantum computing have been said to go hand in hand with each other. Many quantum computers are being developed for this purpose because they offer a particular competitive advantage over other technologies. In conjunction with AI, marketers could spring forward in their ability to connect with their customers. As quantum algorithms can improve machine learning models (data clustering), customer and brand relationships will become more intuitive and thorough. Many companies have already begun adopting these technologies because of their value in process optimization and marketing. It’s clear that the quantum push is already happening, with many mergers and acquisitions taking place in 2021. Some of them include Honeywell and Cambridge Quantum Computing, Rigetti Computing and Supernova Partners Acquisition Company ($1.5 billion), IonQ and DMY Technology ($2 billion), IBM and Raytheon Technologies, and many more.

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These companies are spearheading the next generation of cognitive computing, which could become super beneficial for the marketing industry as a whole, especially for the early adopters of the technology.

It’s is an amalgamation of all they need to succeed, from discovery to the decision to engagement. Final Takeaway Cognitive computing is the key to better and more competitive marketing strategies. Most marketing techniques rely on consumer behavior, and to ‘decode’ said behavior; it’s essential to build on models that can interpret actual human emotions. The use of sentimental analysis will only increase with time and will ensure that cognitive computing is truly the pinnacle of competitive marketing. Now is the time to invest in these technologies, as the benefits will become multifold with time.

Advancing Healthcare

Medical Image Processing by Ragini Agarwal Introduction Artificial intelligence has been a far-fetched goal of computing since the advent of the computer, but new cognitive computing models may be getting us closer than ever. The results of cognitive computing, a fusion of cognitive science (the study of the human brain and how it functions) with computer technology, will have far-reaching ramifications in our daily lives, healthcare, industry, and more. One such important fields of study is Medical Image Processing which has aided in advancing the healthcare industry.

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What is Medical Image Processing? Medical image processing is the study and application of three-dimensional image files of the human body, typically obtained from a computed tomography (CT) or magnetic resonance imaging (MRI) scanner, to diagnose diseases, guide medical procedures such as surgery planning, or for research purposes. Radiologists, engineers, and doctors employ medical image processing to better comprehend the anatomy of individual patients or groups of patients.

How Cognitive Computing Has Made It Possible Artificial intelligence (AI) and cognitive computing breakthroughs have had a substantial influence on medical imaging in recent years. Deep learning and other advanced AI algorithms have a wide range of applications in medical image processing and analysis, including image reconstruction, tumour detection, feature extraction, segmentation, and classification, as well as wound healing assessment and detecting cardiovascular abnormalities. The Image Processing System has a vast range of benefits.

What Are The Benefits of Medical Image Processing? The main benefit of medical image processing is that it allows for in-depth but non-invasive investigation of internal anatomy. In order to improve patient treatment outcomes, develop better medical equipment and medicine delivery systems, and arrive at more accurate diagnoses, 3D models of the anatomies of interest can be built and investigated. It has become one of the most essential tools for medical advancement in recent years. The higher image quality, along with better software tools, enables for exact digital restoration of anatomical structures of diverse sizes and properties, such as bone and soft tissues.

Measurement, statistical analysis, and the creation of simulation models with correct anatomical geometries, for example, provide for a better understanding of interactions between human anatomy and medical technology.

How Does Medical Image Processing Work? The initial stage in medical image processing is to extract raw data from CT or MRI scans and rebuild it into a format that can be used in appropriate software. A common input for image processing is a 3D bitmap of greyscale intensities with a voxel (3D pixels) grid. The strength of signals from proton particles during relaxation and after the application of extremely high magnetic fields determines the greyscale intensity in a CT scan; however, the greyscale intensity in an MRI is determined by the strength of signals from proton particles during relaxation and after the application of extremely high magnetic fields.

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Medical users frequently modify the rebuilt image volume to segment out and change specific anatomical regions of interest, such as tissue and bone. At the 2D and 3D levels, users may perform a variety of image processing tasks, including:

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Cropping and resampling input data to make picture processing easier and faster

Using measurement and statistical techniques to quantify various aspects of picture data, such as centerlines

Exporting models for physics-based simulation, further design work, or 3D printing actual duplicates of the anatomy in question

Image filters are used to reduce and remove undesirable noise and artefacts.

Automated approaches based on AI-based machine learning algorithms are being used to detect distinct anatomical areas utilising segmentation tools.

Importing CAD models of implants or medical equipment in order to investigate how they interact with specific anatomies.

Where & When Does Medical Image Processing Fit In The Product Portfolio? Simpleware software provides a wide range of medical applications, from fundamental research through multiple clinical processes. The application, in general, enables users to interact with MRI, CT, and other forms of medical imaging data in a number of ways, including the development of models using CAD-designed implants and equipment. Simpleware ScanIP’s capabilities, for example, are used by device engineers to tackle problems like organising surgical procedures and analysing the efficacy of alternative implant designs, as well as exporting models for simulation and design.

Cognitive Robotics goes beyond medical image processing After medical image data has been processed, Simpleware ScanIP comes with a variety of add-on modules that allow you to do more with it. Customizing steps and automating time-consuming or repetitive tasks are also options. Users in the medical field, for example, can:

For 3D printing, export STL files from processed medical pictures.

For size and location, combine CAD-designed implants with anatomical imaging data.

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Create volume meshes for physics simulations using Finite Element and Computational Fluid Dynamics, such as impact or stress and strain.

4 Continue product development by translating processed picture data into CAD-friendly NURBS and interfacing with popular CAD software.

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Putting Medical Image Processing into Practice Patient-specific hemodynamic models of difficult aortic dissections, developed at University College London to better comprehend lifethreatening vascular illnesses, provide a fantastic example of how medical image processing works recently. To analyse CT scans and create models suitable for CFD analysis using Simpleware software, the following methods were followed:

1 2 3 4 5

Aortic dissection CT images are collected from patientspecific instances. Simpleware ScanIP is used to rebuild patient geometry, which includes noise processing and segmentation of regions of interest such the dissected aorta and branches. Smoothing procedures are carried out automatically using scripting to reduce pixelation artefacts. Surface models of the dissected aorta are created and loaded into ANSYS® software for CFD simulations, which include intraluminal pressure and wall shear-stress-based indices. The outcomes of the simulation provide hemodynamic information that may be utilised to aid future clinical understanding.

Conclusion We now have personal digital assistants (such as Siri and Google) on our phones and computers, but they are not genuinely cognitive systems; they have a pre-programmed set of responses and can only respond to a limited number of requests. However, in the nottoo-distant future, we will be able to talk to our phones, computers, autos, and smart homes and receive a true, meaningful answer rather than one that has been pre-programmed. Computers will expand our knowledge and talents when they attain the capacity to think like humans. We are on the verge of entering an era in which computers may augment human intelligence and knowledge in whole new ways, much like the protagonists of science fiction films rely on computers to make accurate forecasts, gather data, and draw conclusions. 99

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