THE AI ILLUSION: WHY BIGGER MODELS LIKE CLAUDE 3.7, GPT 4.5, AND DEEPSEEK R2 DO NOT MEAN SMARTER AI
Ключевые слова:
he rapid evolution of AI has led to increasingly powerful models capable of processing vast amounts of data, generating human-like text, and excelling in specialized tasks. However, the question remains: Are these models truly intelligent, or are they merely more sophisticated pattern matchers?Аннотация
Recent advancements in artificial intelligence (AI), including the release of Claude 3.7, GPT-4.5, and DeepSeek R2, have largely focused on expanding model size, increasing computational efficiency, and improving benchmarks. However, these developments do not necessarily equate to genuine intelligence. This paper argues that contemporary AI models remain constrained by their reliance on statistical language processing and lack true semantic comprehension. The Odam Tili (OT) theory introduces a paradigm shift in AI development by advocating for a natural coding approach that aligns with the cognitive structures of human language. This paper discusses the limitations of current AI architectures, the misconceptions surrounding AI progress, and the necessity of adopting OT principles for achieving true artificial general intelligence (AGI).
Библиографические ссылки
1.
limitations.
Bender, E. M., & Koller, A. (2020). Climbing Towards NLU: On Meaning,
Form, and Understanding in the Age of Data. Association for Computational
Linguistics (ACL).
2.
Kaplan, J., McCandlish, S., Henighan, T., et al. (2020). Scaling Laws for
Neural Language Models. arXiv preprint arXiv:2001.08361.
3.
Marcus, G. (2022). The Next Decade in AI: Why Scaling Alone Won’t Get Us
There. AI Alignment Forum. 4.
Schmidhuber, J. (2021). Deep Learning: Our Miraculous Year 1990-1991.
Neural Networks Journal.
