Cognitive computing is one of the branches that is part of the artificial intelligence (AI) with the aim to develop systems that are capable of replicating human like cognitive processes.
The word “cognitive” refers to processes that are related to perception education reasoning problem solving and reasoning which are traditionally related to human intelligence.
The purpose in cognition computing is to build machines that are able to comprehend how to interpret process and respond to information that is complex humans.
Architecture of Cognitive Computing
Cognitive computing is one kind of heterogeneous system that makes computers completely healthy which makes it suitable for solving each and all kinds of problems that the human brain or computers can solve. The cognitive computing architecture chips is comprised of Neurosynaptic cells that function simultaneously in the form of nodes(neurons) which comprise their own processor(cell body) and data bus(axon) as well as memory(synapse). The nodes that are involved have been given particular weights and supplied with an enormous amount of information that eventually connect with one another to complete functions. The chips continue to analyze and learning from datasets continually.
Principal Features that are part of Cognitive Computing chips are:
- These devices operate in non stop way that is driven by events resulting in an energy savings as well as an improvement in efficiency.
- The event driven clockless design here signifies that in contrast to traditional processors in which each step of logic needs to be synced with the rest of the computer cognitive computers have every step can be asynchronous in which logic components can operate at different speeds to complete the process more quickly.
- Neuroplasticity Cognitive computing chips are fault tolerant and are not affected in the event that they experience problem with one Neurosynaptic cores ceases to function. Neurons self adapt and also routes its way through different neurons just like the brain.
What is Cognitive Computing Operate?
Cognitive computing operates through an amalgamation of techniques and methods that are designed to replicate human intelligence and making decisions.
Collection The first step of cognitive computing is collection of large datasets from different sources. They include all kinds of unstructured and structured information such as images text videos and readings from sensors.
Ingestion After that process data acquired undergoes incorporation to the system of cognitive computation which is then systematically categorizes categorized and stored in way suitable for effective analysis.
NLP The crucial aspect of this procedure can be Natural Language Processing (NLP) an essential component which allows the system to recognize and understand human language that includes both spoken and written communications. NLP algorithms help analyse textual data finding the meaning of it and finding connections between concepts and words.
Therefore cognitive computing largely depends on the M machine Learning Algorithms in order to study and gain insights from information that is ingested. There are two primary kinds of machine learning algorithms are used to analyze this data:
- Supervised Learning is where the system is taught by labeling inputs to known outputs.
- Unsupervised Learning is when the system detects patterns and connections within the data without prior defined labelling.
Analysis: This machine learning capability assists in pattern recognition in the brain.
Predictions by analyzing of correlations patterns and patterns within the information the system develops an understanding of the complexity of connections which allows it to accurately predict the future.
The iterative nature of this process demonstrates the ever changing and dynamic aspect of cognitive computing which is where constant learning and adaption can be essential in enhancing the systems abilities over time.
Cognitive Computing vs Artificial Intelligence
If this all is reminiscent of artificial intelligence youre incorrect. The terms cognitive computing and AI are frequently employed interchangeably but theyre not identical.
AI is broad term that refers to the techniques that use large quantities of data in order to predict and automate jobs that usually involve human beings. The most well known examples include chatbots autonomous vehicles as well as smart assistants such as Siri as well as Alexa. Though AI employs software to take choices cognitive computing needs human intervention to mimic the human brain.
The systems must be flexible and adapt their actions in response to new data and the environment changes. Also they need to keep information regarding situations that have taken place as well as ask clarification questions as well as comprehend the context the way that information is utilized. AI is among the essential elements to enable all of this.
“The problem with cognitive is: can it develop an own level of intelligence? The AI is the place where it is able to help. What kind of intelligence could we bring to the machine?” Sudhakar said.
Cognitive computing actually employs many of the elements that make the foundation of AI that includes neural networks natural language processing machine learning and deep learning. Instead of employing it to automate an procedure or uncover hidden data and patterns that are hidden in huge amounts of data Cognitive computing is designed to mimic the human thinking process. It also assists human beings in figuring out solutions for difficult issues.
Also cognitive computing is not way to automate human abilities but it enhances these capabilities.
“Its simply more human centric and human compatible tool and it can be better companion to humans in helping them achieve their goals” Gadi Singer who is Director and VP of Emergent AI study at Intel Labs and told Built In. The aim of cognitive computing as he explained “is not to become sentient and replace human mind but rather to interact with human centric concepts and priorities more successfully.”
Cognitive Computing Applications
A few of the most famous instances of cognitive computing occur as demonstrations that are solely for one purpose. In 2011 IBMs Watson computer was the winner of round in Jeopardy! while operating program named DeepQA it was fed millions of pages of information sourced from open source and encyclopedias. Then in the year 2015 Microsoft launched online tool for estimating age known as how old.net which utilized data extracted of an image that was uploaded to establish the users age as well as gender.
Even though these single use demos have lot of merit they do not tell the whole story of just how deeply cognitive computing is integrated into our life. Nowadays the technology is mostly used for jobs that require the processing of huge quantities of data. It is therefore useful for industries that require analysis such as manufacturing finance healthcare and even finance.
Cognitive Computing in Healthcare
The ability of cognitive computing to process massive quantities of information has proven to be beneficial within the field of healthcare specifically in relation to diagnosing. Physicians are able to use this method to not just provide more accurate diagnoses to their patients but also develop customized treatment plans to help the patients. Cognitive systems can also be able to interpret images of patients like the X rays or MRI scans and identify anomalies that experts in human medicine typically fail to notice.
A good example of this one is Merative the company for data that was created by IBMs healthcare analytics assets. Merative offers range of applications such as data analysis clinical development and medical imaging. Cognitive computing has been utilized in top oncology clinics such as Memorial Sloan Kettering in New York City as well as MD Anderson at MD Anderson in Houston for aid in making the diagnosis and treatments for patients.
Cognitive Computing in Finance
In the finance industry cognitive computing is utilized to gather the data of clients so that they can provide more customized recommendation. In addition by combing market trends and customer behavior information cognition computing could aid finance firms evaluate risk in investments. Additionally cognitive computing could aid companies in tackling fraud through analyzing the past performance of parameters which could be used to identify fraud in transactions.
Although its most well known because of its Jeopardy! appearance IBMs Watson is component of IBM Cloud that was utilized by 42 of the 50 most prestigious Fortune 500 banks in 2021. different example of IBM Cloud is Expert System that converts languages into data that can be used across all areas of finance. This includes banking and insurance.
Cognitive Computing in Manufacturing
Manufacturers employ the cognitive computing technology for maintenance and repair their machines and equipment as well to reduce the time required for production and manage parts. When goods have been manufactured Cognitive computing also can assist with logistics for distribution around the globe through warehouse automation and administration. Cognitive systems are also able to assist employees throughout their supply chains examine structured or non structured data in order to find patterns and patterns.
IBM has called this particular area of cognition computing “cognitive manufacturing” and provides an array of products and services with its Watson computer. It provides efficiency management quality enhancement as well as supply chain optimization. As for Baxters successor with one arm Sawyer continues to revolutionize the ways that the machines and people are able to work together in the floor of the factory.
Real World Use Cases of Cognitive Computing
Cognitive computing is being used in variety of real world situations that enhance human decision making by simplifying tasks and enhancing the overall effectiveness. Here are some instances of actual use cases for cognitive computing.
- IBM Watson for Oncology is utilized to study the medical literature as well as clinical trial information as well as patient records to suggest customized treatment options for patients with cancer.
- Cognitive computing systems analyse huge volumes of financial data in real time identifying anomalies and patterns assisting banks to spot suspicious activities.
- The majority of companies use cognitive computing in order to develop chatbots and intelligent virtual assistants capable of understanding natural language address inquiries from customers and give individualized support.
- Cognitive technology aids in automatizing the hiring process through analyzing resumes screening applicants as well as conducting interviews in the beginning which streamlines the process of hiring.
Benefits of Cognitive Computing
- Improved Decision Making: Working with lots of information and patterns it aids in to improve decision making by leveraging data for edge.
- Enhances Efficiency: allows organizations to concentrate on more important tasks and save money and time while increasing general productivity by automating routine tasks streamlining workflows and making it easier for humans.
- Natural Language Understanding allows for an easier and more interactive interaction between computers and humans.
Challenges of Cognitive Computing
- Security of Data Cognitive computing relies heavily on the analysis of data and raises concerns over the security and privacy of information that is sensitive.
- Complexity The implementation of cognitive solutions is complicated and require substantial integration of existing technology.
- Ethics and Bias Intentional biases that are present in training information which can lead to unjust or unjust outcomes.
AI and Cognitive
The present world is looking into the incorporation in the field of artificial intelligence and machine learning into machines and other gadgets to tackle variety of complex problems. Nowadays many gadgets utilize artificial neural networks (ANN) that are utilized to replicate the brains logic that brains work on for example to perform complex task. While ANN can be extremely useful it is not without level. This scenario is what has led to Cognitive artificial intelligence.
Cognitive artificial Intelligence (Cognitive AI) refers to the systems that imitate human brain process and replicate the ways humans think and communicate with information.
Cognitive Computing is subset of artificial intelligence that with particular focus on replicating human cognition and also focuses on the areas that require human machine interaction. AI is on the other is broad collection of tools and technologies designed to develop intelligent systems that can perform different tasks in various fields.
Future of Cognitive Computing
Future developments in cognitive computing is filled with enormous potential for transformational advancements across variety of industries. With technology continuing to advance it is anticipated that cognitive computing will continue to play an integral part in determining how companies run the way healthcare is offered and the way that people interact with the digital system.
The combination of cognitive computing and new technologies such as 5G edge computing as well as the Internet of Things (IoT) is expected to improve the ability to make decisions in real time which will result in better performing and smarter systems.
The development of natural processing of language (NLP) and machine learning algorithms as well as advanced analytics will contribute to more advanced cognitive systems that are able to comprehend the context of the situation draw lessons from diverse information sources and adjust to the changing environment.
As cognitive computing matures as it matures its expected to be part of our everyday lives. Cognitive computing will offer specific and contextually aware solutions for all industries encouraging innovations and helping to create an increasingly intelligent and connected the world.
Ethical Considerations of Cognitive Computing
The ethical considerations of cognition are crucial when these technology become more integral to our daily lives.The gathering and processing of massive amounts of data which often includes personal data creates concerns over privacy consent as well as the possibility of unintended effects.
- The need to address issues related to bias and fairness is essential because cognitive systems are able to acquire and maintain biases that are present in data from training which can lead to biased outcomes.
- Transparency and explanation are essential ethical considerations particularly in situations where the decision making process affects individuals or groups.
Finding compromise between technological advancements and protecting the rights of humans these moral issues highlight the necessity to maintain dialogue and cooperation with technologists policy makers and all other stakeholders to ensure that the cognitive advancements in computing are compatible with social principles and positively impact the health of both individuals as well as communities.
As cognitive computing advances it will be integrated with the latest technology and ethical issues can create new future. It will offer customized solutions to industries while considering the social values and issues.