What is Cognitive AI? Could it be the future?

Artificial intelligence (AI) and related domains have grown in scope and reach over the past few years. Rapid advancements in AI and Machine Learning (ML) technologies are expected to bring about the next big changes in supercomputing.

Often confused with AI, cognitive computing is quite different from AI. The purpose of cognitive computer systems is to help humans make better decisions through reasoning, analysis, and memorising. AI systems are computer programs that can think and act like humans. It includes ML, deep learning (DL), visual recognition, and neural networks. 

There is a strong connection between cognitive computing and AI because both rely on data-driven methodologies. The development of both of these technologies is undergoing rapid progress and is considered helpful in the future. Firstly, let’s understand the term “cognitive computing.”

What is Cognitive Computing?

Generally, cognitive computing refers to technologies that facilitate human intelligence through specific tasks. The concept of cognitive computing is concerned with simulating human thinking processes in complex situations involving uncertain outcomes. Speech recognition, sentiment analysis, facial recognition, risk assessment, and fraud detection are some cognitive computing applications.

Cognitive Computing vs AI: Key Differences

Despite the similarities between cognitive computing and AI, the primary differences stem from how they approach tasks. Now let’s see the main differences between cognitive computing and artificial intelligence in detail.

Interaction with Humans

Cognitive technology provides humans with information to support their decision-making process, while AI can make decisions independently, minimising the need for human involvement. Cognitive computing systems give humans useful advice on making choices by thinking, reasoning, and remembering. AI uses the most efficient algorithms to produce the most accurate results or actions.

Contextual Solutions

Computing with cognitive capabilities can deal with conflicting and shifting information pertaining to a particular scenario. Cognitive computing provides relevant information humans can use to make their own decisions. Simply put, cognitive computing relies on predictive and prescriptive analytics, while AI relies on algorithms that have already been trained.

What role does cognitive AI play in the development of future models?

There is a very high probability that cognitive technologies will significantly impact organisations in the next five years. For AI to be applied to a broader range of real-life situations, deep learning-based systems must integrate information and knowledge. New cognitive AI competencies are likely to be needed to develop more coherent strategies, given the increasing interest in symbolic approaches based on structured knowledge. The main use cases of cognitive computing are AI-enabled cybersecurity, Content AI, Cognitive Analytics, and Intent-based NLP.

Conclusion

It is commonly assumed that AI and Cognitive Computing refers to the same thing. However, both refer to machines that can perform human-like tasks. AI is primarily used to design effective algorithms to deal with problems. In contrast, cognitive computing attempts to replicate human intellect and wisdom by considering various factors. Cognitive computing aims to solve complex problems by mimicking human behavior and reasoning. In both cases, the aim is to help humans make better decisions. 

 

These technologies are constantly advancing, making their applications more diverse and effective over time. In Cognitive AI, computer models simulate human mental processes in complex situations. We can leverage cognitive computing to make smarter decisions through it. Cognitive computing represents the next phase in the evolution of computing.