Applications of Cognitive Computing in Different Sectors

Human thinking is beyond comprehension. Can a computer develop such thinking and reasoning abilities without human intervention? This is something that IBM Watson programming experts are attempting to achieve. 

 

Their goal is to simulate human thought processes in a computerized model. As a result, cognitive computing – a synthesis of cognitive science and computer science – has emerged. Cognitive computing models provide a realistic roadmap for achieving AI.

 

Cognitive computing systems combine the best of multiple technologies, such as natural language queries and processing, real-time computing, and machine learning-based technologies. Cognitive computing systems can analyze massive amounts of structured and unstructured data by utilizing these technologies.

 

The goal of cognitive computing is to replicate human thoughts in a programmatic model for practical applications in relevant situations. IBM Watson, the most recognisable name in cognitive computing, is based on deep learning algorithms aided by neural networks. They collaborate to better absorb data, learn more, and mimic human thinking.

 

 

What is Cognitive Computing?

 

The use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain is known as cognitive computing. The phrase is closely associated with Watson, IBM's cognitive computer system.

 

Computers process and calculate faster than humans, but they have yet to master some tasks, such as understanding natural language and recognising objects in images. The goal of cognitive computing is to have computers mimic the way the human brain works.

  

Cognitive computing accomplishes this by utilizing artificial intelligence (AI) and other underlying technologies such as the following:

 

• Systems of expertise

 

• Networks of neurons

 

• Artificial intelligence (AI)

 

• Deep Understanding

 

• Processing of Natural Language (NLP)

 

• Recognition of speech

 

• Recognising objects 

 

• Robotics

 

To teach computing systems, cognitive computing employs these processes in conjunction with self-learning algorithms, data analysis, and pattern recognition. Speech recognition, sentiment analysis, risk assessments, face detection, and other applications are possible with the learning technology. Furthermore, it is particularly useful in industries such as healthcare, banking, finance, and retail.

 

 

Applications of Cognitive Computing

 

Now that you are aware of cognitive computing and a brief history of why it was awarded, We'd like to walk you through some of the real-life applications of the same. Cognitive computing is being used in almost every field today. Here is a list of them:

 

1. Banking and Finance

 

In the banking industry, cognition will help to improve operational efficiency, customer engagement, and experience, as well as grow revenues. Deeper contextual engagement, new analytics insights, and enterprise transformation will completely reshape banking and financial institutions.

 

Examples of such transformation include performing various banking transactions digitally, opening a new retail account, and processing claims and loans in minutes. Cognitive banking will provide personalized support to customers, allowing them to choose personalized investment plans based on whether they are risk-averse or risk-takers.

 

It will also provide personalized engagement between the financial institution and the customer by dealing with each customer individually and focusing on their needs.

 

2. Retail Business

 

Cognitive computing has a lot of potential applications in the retail industry. E-commerce sites have successfully integrated cognitive analytics; they collect some basic information from customers about the basic details of the product they are looking for, then analyze the large available data and recommend products to the customer.

 

Cognitive computing has given retailers the tools they need to build more agile businesses through demand forecasting, price optimization, and website design. Apart from e-commerce platforms, cognition can also be very useful for in-store shopping.

 

3. Logistics

 

Cognition is the new frontier in transportation, logistics, and supply chain. In the warehousing process, cognition aids in the compilation of storage code, automatic picking with an automated guided vehicle, and the use of warehouse robots all help to improve work efficiency.

 

The Internet of Things will aid in warehouse infrastructure management, inventory optimization, and warehouse operations, and the autonomous guided vehicle can be used for picking and putting operations.

 

Aside from IoT, another important technology is Wearable Devices, which help to convert all objects into sensors and augment human decision-making and warehouse operations. These devices have progressed from smartwatches to smart clothes, smart glasses, computing devices, exoskeletons, ring scanners, and voice recognition.

 

4. Cyber Security

 

Cognitive algorithms will aid in the prevention of cyber attacks (or cognitive hacking), making customers less susceptible to manipulation and providing a technical solution to detect any misleading data and disinformation.

 

With the increase in volumetric data, the rise in cyber attacks, and the scarcity of skilled cybersecurity experts, we require modern methods such as cognitive computing to combat these cyber threats. The industry's major security players have already introduced cognitive-based services for cyber threat detection and security analytics.

 

These cognitive systems not only detect threats, but also assess systems, scan for vulnerabilities, and recommend actions. The other side of the coin is that large volumes of data are required for cognitive computing; however, ensuring the privacy of the data is a challenge.

 

5. Power and Energy

 

The new intelligent future is referred to as 'Smart Power.' The oil and gas industry is under enormous cost pressure to find, produce, and distribute crude oil and byproducts. 

 

Energy companies make critical decisions involving large sums of money, such as which site to explore, how to perform resource allocation, and how much to produce. For a long time, this decision was made based on the data collected and stored, as well as the project team's expertise and intuition. 

 

This technology will assist us in making important future decisions such as commercially viable oil wells, ways to make existing power plants more efficient, and will also provide a competitive advantage to existing power companies.

 

6. Healthcare

 

Recent advances in cognitive analytics have assisted medical professionals in making better treatment decisions, increasing their efficiency and improving patient outcomes.

 

It is a self-learning algorithm that relies on real-time patient information, medical transcripts, and other data to use machine learning algorithms, data mining techniques, visual recognition, and natural language processing.

 

Cognitive computing in healthcare connects human and machine functioning, where computers and the human brain truly overlap to improve human decision-making. This enables doctors and other medical professionals to better diagnose and treat their patients, and it also aids in the development of customized treatment modules.

 

7. Education

 

Cognitive computing will fundamentally alter the way the education industry operates. It has already begun to implement a few of the changes. It will alter the way schools, colleges, and universities operate, and it will aid in the provision of personalized study materials to students.

 

A cognitive assistant can provide personal tutorials to students, guide them through coursework, and assist students in understanding certain critical concepts at their own pace.

 

It can serve as a career counselor. Cognitive analytics will benefit not only students but also teachers, support staff, and administrative staff in providing better service, preparing student reports, and providing feedback.

 

Also Read | Cognitive Technology

 

Best Cognitive Computing Tools

 

1. TCS IgnooTM Cheetah

 

Ignoo was introduced in 2015 with the goal of combining three major pillars of technology. Machine learning, AI, and advanced software engineering were among them. The primary goal of this platform was to autonomously address issues as they arose. 

 

It is a cognitive automation software package that specializes in accelerating deployments and maximizing customer value. ignioTM Cheetah has added new features to its previous blueprinting properties, as well as automation capabilities, such as:

 

• Priority event management

 

• Predicting the tasks that require immediate attention.

 

• False alarms are reduced.

 

• Efficient enough to handle incidents with understanding based on its previous history.

 

• It understands real-time human actions and adapts to the technologies around it thanks to its adaptive property.

 

This ignioTM cheetah property makes it easier for many of its users to extend the functionality of supporting new technologies. Some of ignioTM’s features include helping with cloud deployments, models of SaaS engagement and capability to deal with larger data volumes.

 

2. AlphaGo by Google

 

AlphaGo was first introduced as a cognitive computing tool for playing board games.It works on algorithms by combining techniques such as machine learning, tree traversal, and deep neural network technology.

 

A game-supporting feature is applied to the input before it is sent to the neural networks. This cognitive computing tool requires extensive training for both humans and computers.

 

Initially, neural networks were created to analyze human gameplay behavior. To make AlphaGo intelligent enough to defeat board grandmasters, it was also programmed to mimic moves from historic recorded games.

 

3. Cortex certifai by Cognitivecale

 

It's essentially an AI auditing tool. It was created with the sole intention of bridging the trust gap between AI and its delivery. Cortex Certifai accomplishes this by utilizing AI to detect vulnerabilities in black boxes without relying on their access.

 

AI is intelligent enough to improve itself and has the potential to outsmart humans. This is why, before AI can be adopted and trusted, there must be confidence in its decisions.

 

It has received Global Annual Achievement Awards for AI in the categories of responsible AI and ethics. CognitiveScale is developing counterfactual fingerprinting technology at the moment. Also available on the Cortex Certifai product, which includes advanced features such as a multi-user role-based dashboard.

 

4. Iris by Apixio

 

Data accessibility continues to be a major challenge for technology behemoths. The majority of the problems are found in the healthcare sector on a regular basis. With federal agencies becoming more stringent on healthcare organizations, they are being asked to provide better data through robust measurements.

 

As a result, there was a demand for niche products that could provide the same service. Apixio Inc., an artificial intelligence (AI) healthcare analytics company, is bridging this gap with the release of its cognitive computing platform Iris.

 

Iris extracts information from doctor's notes and records. Iris employs a machine learning model that is fed data extracted via data integration tools and real-time data provisioning tools.

 

5. Cognitionsparks

 

Failure to apply good maintenance will undoubtedly disrupt the entire chain of industrial operations. SparkPredict, Sparkcognition analytical solution, was introduced to overcome this maintenance paradigm. It aided in overcoming maintenance downtime, thereby increasing overall operational cost savings.

 

SparkPredict analyses both structured and unstructured data. It then employs machine learning techniques to return appropriate actions that are acceptable at the time. Machine learning techniques assist this tool in becoming efficient enough to predict errors or patterns.

 

 

Cognitive Computing vs Artificial Intelligence

 

While the basic use case for artificial intelligence is to implement the best algorithm to solve a problem, cognitive computing goes a step further and attempts to mimic human intelligence and wisdom by analyzing a number of factors. 

 

Cognitive computing is a completely different concept when compared to AI. Here, is the difference between cognitive computing and artificial intelligence :

 

1. Cognitive computing mimics and learns from human thought processes

 

Unlike AI systems, which only deal with a single problem, cognitive computing learns by observing patterns and recommending that humans take appropriate action based on its understanding. In the case of AI, the system takes complete control of a process and uses a pre-defined algorithm to complete a task or avoid a scenario.

 

In comparison, cognitive computing is a completely different field in which it acts as an assistant rather than the one who completes the task. As a result, cognitive computing empowers humans with the ability to conduct faster and more accurate data analysis without having to worry about the machine learning system making incorrect decisions.

 

2. Cognitive Computing does not eliminate the need for humans

 

As previously stated, the primary goal of cognitive computing is to aid humans in decision making. This provides humans with superior analytical precision while ensuring that everything is under their control. As an example, consider artificial intelligence in the healthcare system. 

 

An AI-powered system would make all treatment decisions without consulting a human doctor, whereas cognitive computing would supplement human diagnosis with its own set of data and analysis, thereby improving decision quality and adding a human touch to critical processes.

 

 Also Read | What is AI Analytics?

 

Cognitive computing has enormous potential. Embracing it early on will allow you to experiment with and personalize the tremendous power of cognitive computing to deliver incredible benefits to your business.