Common Sense Is All You Need

2025-01-11

AI Summary

Current AI systems suffer from poor generalization and robustness in open-world settings due to fundamental deficits in commonsense reasoning. To address this, we propose a “commonsense-first” paradigm that initiates from minimal prior assumptions and aims to build autonomous intelligent systems capable of in-context learning, adaptive reasoning, and generalized embodied cognition. Methodologically, we: (1) introduce the first commonsense-driven knowledge acquisition sequence; (2) redesign benchmarking with strong commonsense constraints; (3) extend embodied cognition to abstract domains, challenging idealized models such as AIXI; and (4) develop a cross-layer collaborative framework integrating cognitive architecture, lightweight in-context learning mechanisms, and a commonsense-oriented software stack. Our core contribution is establishing commonsense reasoning as a necessary—but not sufficient—condition for AI autonomy, thereby providing both theoretical foundations and a practical pathway toward interpretable, generalizable intelligence independent of unbounded computational resources.

Technology Category

• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Artificial intelligence (AI) has made significant strides in recent years, yet it continues to struggle with a fundamental aspect of cognition present in all animals: common sense. Current AI systems, including those designed for complex tasks like autonomous driving, problem-solving challenges such as the Abstraction and Reasoning Corpus (ARC), and conversational benchmarks like the Turing Test, often lack the ability to adapt to new situations without extensive prior knowledge. This manuscript argues that integrating common sense into AI systems is essential for achieving true autonomy and unlocking the full societal and commercial value of AI. We propose a shift in the order of knowledge acquisition emphasizing the importance of developing AI systems that start from minimal prior knowledge and are capable of contextual learning, adaptive reasoning, and embodiment -- even within abstract domains. Additionally, we highlight the need to rethink the AI software stack to address this foundational challenge. Without common sense, AI systems may never reach true autonomy, instead exhibiting asymptotic performance that approaches theoretical ideals like AIXI but remains unattainable in practice due to infinite resource and computation requirements. While scaling AI models and passing benchmarks like the Turing Test have brought significant advancements in applications that do not require autonomy, these approaches alone are insufficient to achieve autonomous AI with common sense. By redefining existing benchmarks and challenges to enforce constraints that require genuine common sense, and by broadening our understanding of embodiment to include both physical and abstract domains, we can encourage the development of AI systems better equipped to handle the complexities of real-world and abstract environments.

Problem

• Artificial Intelligence
• Common Sense
• Flexibility

Innovation

• Common Sense Acquisition
• Adaptive Learning
• Abstract Concept Handling