AI vs Cognitive Computing: What are the Key Differences?

Here are the key differences between AI and cognitive computing

In recent years, the scope and reach of artificial intelligence and associated domains have increased. As artificial intelligence's popularity develops, there has been significant criticism about the technical jargon that surrounds it. Deep learning, deep learning, voice recognition, text analytics, cognitive computing, and neural networks are just a few of the terms that come to mind.

Although these phrases are frequently used interchangeably, there is a significant difference in their techniques and objectives. Cognitive computing is one such technology, which is sometimes confused with AI technology but is actually quite distinct. However, both technologies signify the next great thing in supercomputing, when put to practical use, they have different meanings.

Now, let's move on to the main topic: cognitive computing vs AI.

What is AI?

AI is made up of algorithms that have been trained to determine the optimal way to complete a task or make a decision given a set of restrictions, and then take the necessary action based on their findings. AI, like human intelligence, learns from its surroundings and analyses what it learns to determine the optimal course of action, answer to a problem, or method of performing a task such as identification or voice recognition. As a bot or digital assistant, AI systems perform tasks that would normally need human intelligence.

What is Cognitive Computing?

Cognitive computing systems are fundamentally intelligent decision-making aids. They're supposed to give decision-makers the data they ought to make better data-driven judgments. Cognitive computing systems can manage massive volumes of data (which people can't) and extensive iterative analyses while modifying their results as new data hits the system.

To solve complicated problems, cognitive computing systems use self-learning algorithms that rely on AI technologies like data mining, image recognition, voice recognition, and natural language processing (NLP). As if they were humans, these systems can learn, think and connect with humans. They can deal with symbols and concepts in the same way that humans can.

Cognitive Computing vs AI

Interaction with Humans

Cognitive computing systems are reasoning, analysing and memorizing systems that collaborate with humans to help them make better decisions. Its findings are meant to be consumed by humans. AI aims to produce the most accurate result or action by employing the best algorithm.

Contextual Solutions

Conflicting and shifting information that is contextually relevant to the scenario at hand can be taken into account by cognitive computing. Its findings are based on predictive and prescriptive analytics, rather than pre-trained algorithms. For example, if a woman in her sixties wanted to know what programme she should use to build muscle strength, AI would recommend the best programme available. Cognitive computing, on the other hand, would take her age and aptitude into account when suggesting programme changes. Finally, AI solves problems using algorithms to arrive at a final judgment; cognitive computing offers the relevant data that allows humans to make the right call for themselves.

Use Cases of Cognitive Computing

Cognitive computing applications are mostly found in businesses that require a lot of analysis. Here are a few examples:

• In the field of medicine, cognitive computing is assisting clinicians in making a more accurate diagnosis and personalising treatment decisions. Doctors now have access to therapies and diagnoses that they would not have had otherwise thanks to cognitive computing's capacity to access datasets worldwide via the cloud. Cognitive technologies that read patient photos are uncovering details that human radiologists frequently overlook.
• Companies that provide financial services leverage cognitive computing's analytic capabilities to locate the best goods for their customers. Companies are also using the data to produce more personalised services if a product identified by the system is not currently available. Cognitive computing aids financial organisations in assessing investment risk by merging market trends with customer behaviour data.
• Retailers are turning to cognitive computing to give customers a personalised online shopping experience that makes it simple to find what they're looking for.
• Manufacturers employ cognitive computing technology to maintain and repair their equipment and machinery, discover problematic components, and shorten manufacturing times and improve parts management.

AI has applications in all of these fields, but its output is geared toward automating procedures instead of decision support, as seen in chatbots, virtual assistants, and smart advisors. For instance, an AI virtual assistant can provide a doctor a precise treatment choice to follow, while cognitive computing will generate numerous plausible treatment options and leave it up to the doctor to choose the best one for the patient.