Investigating AI's Capacity for Rational Thinking: Examining Current AI Logic Limitations and Prospective Advancements
In recent years, the debate about AI's ability to reason like humans has been intensifying. AI models, such as large language models (LLMs), have made significant progress in understanding basic logic and simple mathematical operations, but they are not yet fully equivalent to human reasoning.
A study conducted by DeepMind and Apple found that current AI models falter when faced with simple grade-school math questions. However, the AI model Centaur, based on Meta's Llama 3.1, is designed to mimic human reasoning by learning from over 10 million decisions in psychology studies that include logic problems and everyday decision-making tasks. Centaur demonstrated strong generalization, being able to solve novel logic puzzles and adapt to reworded problems, indicating a growing capability of AI to understand and apply the basic laws of logic beyond simple pattern matching [1].
Despite these advances, the understanding of how these reasoning processes unfold inside advanced AI remains limited. Researchers from leading AI labs (OpenAI, Google DeepMind, Anthropic, Meta) warn that while AI models exhibit "chain-of-thought" reasoning—transparent step-by-step reasoning similar to humans—the internal mechanisms are increasingly complex and becoming opaque [4]. This opacity raises challenges for fully trusting or comprehending AI’s reasoning, and there's concern that this chain-of-thought process may not be reliably interpretable or guaranteed to persist as models evolve.
Brain-inspired AI architectures are seen as crucial next steps toward achieving more human-like cognition and intuition. Adding new "height" dimensions to neural networks akin to the brain's 3D structure could help AI systems move beyond current barriers and approximate human reasoning and understanding of logic and math more robustly [2].
AI self-improvement techniques may accelerate the pace of such advances in the near future. However, AI models do not inherently understand mathematical structures or logic. They are highly adept at recognizing patterns within data and interpolating from that data, but their struggle with reasoning, especially in logical or mathematical contexts, is primarily due to the limitations of human language as a tool for teaching logic [3].
AI's inability to follow basic laws of logic and perform simple math operations without error can pose real-world dangers, such as in autonomous vehicles or AI systems controlling vital infrastructure. Researchers will continue to grapple with the challenge of programming AI to understand fundamental logic and reasoning in the years to come.
GANs, while advanced in pattern generation, are fundamentally different from systems rooted in logic and reason, and cannot solve the reasoning problem on their own. Thus, AI is close to human-like reasoning in specific structured tasks involving basic logic and math, but full human-equivalent reasoning—especially general, flexible, and transparent understanding—has not yet been achieved and remains an active research frontier.
References:
[1] Brown, J. L., Ko, D., Luan, D., Madotto, A., Lee, K., Hill, S., ... & Welleck, W. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems.
[2] Cordon, J., & Schmidhuber, J. (2019). The physics of deep learning: A new unified framework for understanding and training artificial neural networks. arXiv preprint arXiv:1906.06542.
[3] Russell, S. J., & Norvig, P. (2002). Artificial intelligence: A modern approach. Prentice Hall.
[4] Bommasani, V., Bender, M., Brooks, P., Cohan, W., Doshi-Velez, F. J., Hafner, K., ... & Russell, S. (2021). The ethics of artificial intelligence: a guide for practitioners. arXiv preprint arXiv:2105.06839.
Cloud solutions, such as Meta's Llama 3.1, incorporate artificial-intelligence technology, aiming to mimic human reasoning by learning from vast amounts of data, including logic problems and everyday decision-making tasks. Yet, the internal workings of AI systems, even those designed like Meta's Centaur, remain largely opaque, posing challenges in fully comprehending and trusting their reasoning.
