Commonsense reasoning in AI systems

Within artificial intelligence, mostly in natural language processing and machine learning, there programmatic systems fabricated for reproducing commonsense reasoning, which remains a challenge for AI developers. Worded simply, such systems fail in reasoning modulations which are implicit, contextual, and have other inferences intermixed which humans solve subconsciously or unconsciously.

The objective of this research is to determine how commonsense reasoning is relevant to AI and suggest certain methodologies for its operationalization based on knowledge systems, deep-learning, and hybrid neuro-symbolic techniques. This paper also explores limitations for providing necessary data, resolving ambiguity, ethical consideration, and other issues of commonsense reasoning in AI. Finally, we suggest several benchmarks for evaluating commonsense capabilities in AI and their applications in virtual assistants, robotics, healthcare, business intelligence, and more.

An AI's ability to engage in commonsense reasoning is expected to advance AI systems to be more trustworthy, adaptable, and human-like. There is hope in newer paradigms like large language models and neuro-symbolic AI, but further research is needed to resolve the multitude of technical and ethical hurdles. The paper presents the most recent developments in commonsense reasoning, its problems, uses, and prospects in the field of artificial intelligence.