and Artificial Intelligence (AI) are terms that are often used interchangeably, however, they are not the same thing. They may utilise some of the same technologies, but the difference lies in their respective applications and aims. In short, the purpose of AI is to think on its own and make decisions independently, whereas the purpose of Cognitive Computing is to simulate and assist human thinking and decision-making.
What is AI?
AI is the simulation of human intelligence processes by machines, in particular computer systems. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. It is many different technologies all working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence.
By using a group of technologies such as Machine Learning, AI can simulate human processing and decision-making at the same time as expanding past human limitations. It can respond to a customer’s question in a human-like manner, but simultaneously have access to much more useful information and at a faster response time. If the task involves processing large amounts of data, processing, or responding in a human-like way, then it would be well-suited for AI.
Artificial intelligence reduces the need for humans. A good example would be chatbots that use AI to independently answer simple questions and manage small tasks with customers, which reduces the workload for the call centre agents and allows them to focus on more productive and profitable tasks. At the same time, customers get a response more quickly and efficiently than they would with a human which improves their customer experience.
One of the main concerns with Artificial Intelligence is the lack of control over decision-making. This makes it much more compatible with smaller, low-risk tasks.
What is Cognitive Computing?
Cognitive Computing aims to not replace human decision-making, but assist it. To achieve this, it is focused on simulating human thought processes and generating hypotheses instead of making decisions. By working through problems in a similar fashion to human brains, it can offer solutions that are more personalised than AI. Combined with data processing and pattern recognition, Cognitive Computing takes context into account to provide a better solution for a specific situation rather than overall. This has, for example, applications in the medical industry to assist doctors in effective treatments. While there may be an optimal overall treatment, taking into account factors such as age and allergies or other health conditions, an informed solution can be found and the doctor can make the best available to that specific patient.
At the same time as mimicking human behaviour, Cognitive Computing aims to solve increasingly complicated problems with efficiency. This means handling large amounts of data and making analyses based off that data, which humans can’t do. As well as this, Cognitive Computing focuses on learning and correcting, so as new data is introduced the analyses themselves change. In order to assist in the decision-making, the system needs to evolve with the information at hand. This makes Cognitive Computing effective at providing appropriate information for higher-risk decisions, especially as it can learn to take into account previously unknown factors.
Some of the most common examples of Cognitive Computing are products such as Amazon Alexa, Google Assistant, and Siri. Other applications include making risk assessments or face detection.
How do Cognitive Computing and AI Compare?
If Artificial Intelligence is automation, Cognitive Computing is augmentation. They both use very similar technologies such as Machine Learning, neural networks, deep learning and more. They also both aim to streamline the process of making a decision. However, the difference lies in how these technologies are utilised. With AI, the focus is on finding an effective algorithm to generate the best overall solution to a problem. With Cognitive Computing, the focus is on making the best decision based on circumstances and on top of that, providing information for the best decision instead of actually making it.
From the fundamental differences between AI and Cognitive computing, they also vary in application. Often in situations where you need a quick response, AI is the most suited. This could be, for example, in more service-heavy industries where a set amount of information is needed. Alternatively, in situations where the best option will vary, Cognitive Computing will work best. Making specific suggestions that can vary depending on context is currently best left to a human, so Cognitive Computing works best when a human needs to make an informed decision.
To give an example of the difference between Cognitive Computing and AI, think of a situation where you want to order a pizza. If you put the task to AI, it would analyse all of the past times you’ve ordered pizza and form an algorithm to make a prediction based on the patterns from previous orders. Further than that, it would likely also order the pizza for you. If you assigned this task to Cognitive Computing, it would use the same information to try and think in the same way you would when ordering. For example, if you’re in a different location than usual or if you’re ordering for multiple people. Using information from your ordering habits and context, it would suggest a few top options to you instead, leaving the end decision to you. For speed, AI would likely be the best option here but for control and quality, you’d go with Cognitive Computing.
Both of these technologies are constantly undergoing advancements, so their applications will increase in both diversity and efficiency as time goes on. With Webio, they are both used to their advantages in the creation of chatbots for digital debt collection.