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Modern large language models (LLMs) have captured global attention and sparked claims that artificial intelligence has arrived, yet close examination of how these systems are built reveals a fundamental limitation: they are not autonomous intelligences emerging from first principles, but rather sophisticated mirrors of human data and cognition. The term Artificial Intelligence suggests a system capable of learning, reasoning, and adapting in ways that are independent of human constraints — yet current models are overwhelmingly shaped by human knowledge and experience. Because they learn from vast corpora of text, images, and structured data created by humans, these models reflect the perceptual, cultural, and cognitive biases of their human sources rather than generating independent representations of reality. In this sense, contemporary generative AI functions more like Artificial Human Intelligence (AHI) — pattern recognition systems trained to mimic human linguistic outputs and knowledge structures. MDPI+1
This mimicry is not accidental; it arises from the very nature of neural network training. Language models learn by optimizing against loss functions defined over human‑generated datasets, effectively compressing patterns in human communication without direct engagement with the physical world. As a result, outputs that appear intelligent are still fundamentally correlational rather than causal: they reflect statistical associations in training data rather than a real understanding of underlying principles. Research on the limitations of current architectures highlights this gap between mimicry and understanding, noting that large language models lack intrinsic goals, meta‑cognitive awareness, and the ability to restructure goals autonomously — traits associated with robust general intelligence. arXiv
The distinction between mimicry and true intelligence is echoed in several academic critiques of current AI paradigms. For example, researchers have argued that truly general intelligent agents must be able to act in the world, generate their own tasks, and form internal value systems that guide action — capabilities that text‑centric models lack because they are not fundamentally embodied or interactive. The “unity of knowing and acting,” which emerges from active engagement with the environment, underscores why models limited to passive data absorption cannot achieve true autonomy. arXiv In other words, replicating patterns of human language is not the same as developing a model of the world, which typically requires active sensing, experimentation, and error‑driven feedback. Emergent Mind
Leading AI researchers also recognize that current language models lack core traits associated with intelligence as manifested in biological systems. According to Meta’s chief AI scientist Yann LeCun, LLMs and related architectures are missing capabilities fundamental to understanding and interacting with the world, such as persistent memory, hierarchical planning, and a grounded understanding of physical environments. These limitations result in systems that can impressively generate text but struggle with reasoning that involves real‑world context or long‑term adaptive behavior. Business Insider
Moreover, there are emerging concerns about the long‑term sustainability of training models purely on human data. Research indicates that overreliance on synthetic or recycled data — increasingly used as human‑generated material becomes scarce — could degrade model quality over time, ultimately limiting the progress of systems that depend strictly on human patterns. Financial Times This suggests that without new paradigms of learning that extend beyond human‑centric datasets, language models will continue to be bound by the scope of human knowledge rather than exceed it.
To move beyond this limitation, many researchers propose that intelligence must be embodied — physically situated within an environment where the agent can learn through interaction and feedback rather than static data. Embodied AI research argues that perception, action, and memory must be integrated for systems to form robust world models and generalizable cognition. This is a significant departure from data‑driven mimicry and points toward architectures that resemble developmental processes seen in biological systems. Springer
In conclusion, while contemporary language models represent remarkable engineering feats and offer powerful tools for practical applications, they do not yet embody intelligence that is independent of human cognition or constraint. Calling them “artificial intelligence” without qualification risks conflating pattern replication with true autonomous reasoning. Recognizing this distinction is crucial for both the philosophical understanding of what it means to create artificial minds and for guiding future research toward systems that learn from the world rather than merely reflect what humans have already recorded about it.