Common Sense Is All You Need (2501.06642v1)

Abstract: 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.

• The paper argues that current AI systems fundamentally lack common sense, which prevents true autonomy despite performance on specific benchmarks like the Turing Test and challenges like full self-driving.
• It proposes reversing the knowledge acquisition process, suggesting AI should start with minimal knowledge to develop contextual learning and adaptive reasoning capabilities from the ground up.
• The work calls for rethinking AI benchmarks and software architectures to prioritize common sense integration, drawing principles from cognitive science to enable AI to handle unforeseen real-world situations.

The paper "Common Sense Is All You Need" by Hugo Latapie explores a critical examination of contemporary AI systems, pinpointing their fundamental deficiency in common sense—a cognitive ability that is ubiquitous among animals. The manuscript argues for an overhaul in the approach to AI development, emphasizing the necessity of integrating common sense to achieve true autonomy.

Key Arguments and Theses:

1. Inadequacy of Current AI Approaches:
   • AI systems today, while adept at tasks such as those involved in autonomous driving and conversation (Turing Test), lack the flexibility that common sense would provide. This deficiency hampers AI's ability to autonomously adapt to new and unforeseen scenarios.
   • The manuscript critiques the existing paradigm that emphasizes scaling models and passing traditional benchmarks, positing that such efforts fall short in equipping AI with genuine autonomy.
2. Ordo Cognoscendi and Minimal Knowledge Strategy:
   • The paper proposes a reversal in the order of knowledge acquisition, advocating for AI systems to commence with minimal predisposed knowledge. The idea is to cultivate capabilities for contextual learning and adaptive reasoning from the ground up.
   • By focusing on starting from a "tabula rasa," AI can develop the kind of intuitive reasoning that is more aligned with human and animal cognitive processes.
3. Rethinking AI Benchmarks and Software Architectures:
   • Current benchmarks like the Abstraction and Reasoning Corpus (ARC) are critiqued for not accurately accounting for common sense. The paper suggests redefining these benchmarks to foster environments where AI systems can engage in both physical and abstract forms of embodiment.
   • There is a strong advocacy for redesigning the AI software stack to inherently support common sense integration, suggesting a shift from existing methodologies to architectures that favor principles from cognitive science and neuroscience.
4. Illustration through Full Self-Driving (FSD) and Turing Test:
   • Using case studies like Full Self-Driving vehicles, the paper illustrates asymptotic performance behavior, where incremental improvements plateau without common sense integration, making Level 5 autonomy unattainable.
   • Similarly, passing the Turing Test is insufficient for true AI autonomy, as it does not ensure embodied cognition or common sense reasoning essential for real-world interactions.
5. Risks of Current Approaches and Potential Solutions:
   • The work emphasizes the dangers of the "magic happens here" mindset, where AI development pathways assume emergent autonomy without systematic strategies for common sense incorporation, leading to performance ceilings.
   • Proposed solutions include adopting hierarchical and modular approaches, which mirror natural cognitive processes, to moderate computational complexity and enhance adaptive controls.
6. Theoretical Considerations:
   • The manuscript addresses theoretical counterarguments such as the No Free Lunch Theorem, suggesting that constraining AI to well-defined domains mitigates these challenges by allowing domain-optimized solutions.

Conclusion and Call to Action:

The paper concludes with a call to action for the AI research community to prioritize common sense in AI development. This involves not only adjusting existing benchmarks and methodologies but fundamentally reorienting the knowledge acquisition framework towards systems that learn through context and experience. By integrating common sense, the potential for AI to operate autonomously and safely in real-world applications could be significantly enhanced, aligning technological advancements with societal and ethical standards.