Thursday 27 March 2025
Artificial intelligence systems have long been touted for their ability to process and understand vast amounts of information, but when it comes to tasks that require common sense, they often fall short. Common sense is a fundamental aspect of human intelligence, allowing us to make decisions and navigate the world with ease. It’s a skill that AI systems, despite their impressive capabilities, have struggled to replicate.
Researchers have been working to improve AI’s common sense abilities by training machines on large datasets of text and images. However, this approach has limitations. For example, if an AI is only trained on data from the 20th century, it may not be able to understand modern concepts or cultural references. Additionally, even with vast amounts of data, AI systems can still struggle to generalize their knowledge to new situations.
A recent study has shed light on the challenges facing AI’s common sense abilities. The researchers used a large-scale language model, gpt-4o-mini, and evaluated its ability to understand abstract concepts and relationships between different pieces of information. They presented the AI with pairs of entities – such as a car and a road – and asked it to predict the correct relationship between them.
The results were surprising. Despite being trained on vast amounts of data, the AI struggled to accurately identify the correct relationship in many cases. In fact, even when given explicit definitions and instructions, the AI often fell short. This is concerning because common sense is essential for many real-world applications, such as decision-making and problem-solving.
The study also explored two different approaches to improving AI’s common sense abilities. The first approach involved providing the AI with examples of correct relationships between entities. This approach was successful in improving the AI’s performance, but it had limitations. For example, if the AI is only given a few examples, it may not be able to generalize its knowledge to new situations.
The second approach involved restricting the number of possible relationships the AI could choose from. This allowed the AI to focus on the most likely correct relationship and improved its overall performance. However, this approach also had limitations. For example, if the AI is given too few options, it may not be able to learn and adapt to new situations.
The study’s findings have significant implications for the development of AI systems. They highlight the need for more effective training methods that can improve an AI’s common sense abilities without relying on vast amounts of data or explicit definitions.
Cite this article: “AIs Common Sense Conundrum: Challenges and Potential Solutions”, The Science Archive, 2025.
Artificial Intelligence, Common Sense, Language Model, Abstract Concepts, Relationships, Entities, Data, Training Methods, Decision-Making, Problem-Solving
