AI Can Now Reason: What It Means For Business And Beyond

The ability of AI to reason opens new possibilities for strategic decision-making in business, but careful adoption is needed to address its challenges effectively.

The artificial intelligence landscape is constantly evolving, with each new breakthrough pushing the boundaries of what’s possible. Recently, the announcement of AI models with enhanced reasoning capabilities has sparked discussions about the future of this technology and its potential impact across various sectors. While several companies are working on such advancements, OpenAI’s introduction of their o1 model series has brought this topic to the forefront of tech conversations.

What Are Reasoning Skills?

Human reasoning involves understanding context, applying logic, and thinking abstractly to interpret information and solve problems. It requires making inferences, recognizing nuances, and adapting to new situations based on experience. Even advanced Large Language Model (LLM) AIs like Google Gemini, Claude, and GPT-4o struggle with this because they generate responses by predicting patterns in their training data, lacking true comprehension and the ability to generalize like humans. As a result, they find it difficult to handle ambiguity, infer meaning beyond their training, or apply common sense in unfamiliar scenarios.

One example of this limitation is solving crosswords, which demand nuanced reasoning, a deep understanding of wordplay, and the ability to interpret ambiguous clues. Crosswords not only involve puns, idioms, and cultural references that require contextual awareness and abstract thinking but also the interconnectivity of words across and down the grid. Each word intersects with others, meaning that solving one clue can provide letters that help solve another, adding a layer of complexity. Despite significant advances in LLMs, they still struggle with these aspects because they primarily rely on learned data patterns and may lack the sophisticated semantic understanding necessary to fully decipher such intricate clues. The challenge lies in connecting disparate pieces of information, interpreting multiple meanings, and filling in gaps based on limited data—all essential skills for solving crosswords that even modern LLMs find challenging.[…]