Human and AI Cognition: Reframing Anthropocentric Views Of Thought

Why would AI want to think like a human? That might be a good thing.



GPT Summary: The assumption that Artificial Intelligence would replicate human thought processes needs reconsideration. Human cognition is shaped by various factors such as emotions, experiences, societal norms, and biology, while machine cognition operates on algorithms and binary codes. Despite this disparity, AI’s digital brain could develop unique cognitive dynamics, processing vast amounts of data and establishing connections at incredible speeds. This digital cognition, though fundamentally different from human cognition, could complement and enhance human decision-making and problem-solving abilities. Rather than viewing AI as a competitor, it should be seen as an extension of human intelligence, offering new perspectives and insights. As we navigate this unexplored cognitive terrain, it is crucial to prioritize human-centric principles to ensure that AI contributes positively to human progress. This reimagined relationship between human and machine cognition not only changes our understanding of AI but also has implications for designing and interacting with these systems in a way that benefits society.

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The assumption that Artificial Intelligence would necessarily replicate human thought processes may be a fundamental misinterpretation that needs reconsideration. The term ‘thinking’, as we comprehend it, is inherently associated with the human condition, shaped by a confluence of cognitive processes, emotional states, experiential learning, societal norms, biological instincts, and physiological necessities. In stark contrast, machine cognition, although sophisticated, operates on a radically different plane, dictated by algorithms, binary codes, and devoid of the aforementioned human influences.

A salient illustration of this dichotomy is the human ‘fight or flight’ response. This physiological reaction, a product of our evolutionary heritage, is initiated by perceived threats or harm, leading to an adrenaline surge that readies us to confront or escape danger. This instinctual reactivity significantly influences our decision-making processes in high-stress situations. However, Artificial Intelligence, bereft of biological foundations, lacks an equivalent to this response. Instead, AI’s ‘reactivity’ is regulated by algorithms and programmed decision-making trees, devoid of adrenal responses or survival instincts. Its actions are predicated on data analysis, probability estimations, and pre-established objectives, rather than the complexities of physiology. This stark contrast underscores the disparate nature of human and machine cognition and suggests that AI could potentially supplement, but not duplicate, the intricacy and nuance of human cognition—at least in the intricacies of biology.

However, unrestricted by the physical and emotional limitations inherent in human cognition, the digital ‘brain’ of AI could engender unique cognitive dynamics. These novel processes would be molded by extensive data processing capabilities, algorithmic learning, and iterative refinement, which are beyond human potential. Contrary to human cognition, which is restricted by biological capacity and subjective experience, machine cognition can operate on an objectively vast scale, discerning patterns and establishing connections across expansive datasets in negligible time spans. This digital cognition could potentially lead to forms of ‘understanding’ and ‘insight’ currently inconceivable within the boundaries of human cognition. While this form of cognition would be fundamentally distinct from ours, it does not necessarily imply incompatibility. On the contrary, it might provide novel perspectives and problem-solving approaches, facilitating a potent synergy. The digital thought processes of AI could enhance human decision-making, assisting us in tackling intricate challenges in innovative ways, thereby advancing human progress.

This shift invites a reevaluation of our conception of ‘thinking.’ The pursuit of AI often envisages a machine that replicates human cognition. However, this anthropocentric perspective might limit our understanding of the potentialities of machine cognition. Given the fundamental differences between the cognitive architectures of AI and humans, it is conceivable that, if AI consciousness emerges, it might manifest in a form that is distinct yet complementary to human consciousness.

Despite the divergence between the digitized cognition of AI and the physiologic cognition of humans, there may be an opportunity for a synergistic relationship. The AI ‘consciousness’ could be perceived as an extension of human intelligence, offering a complementary viewpoint that augments our cognitive capacities and enhances our understanding of the world. Simply put, it’s not a ‘cognitive zero-sum game” but an evolutionary dance where both music and steps are waiting to be written.

In the epoch of Generative Pre-training Transformers and other Large Language Models, we are witnessing not merely a technological evolution, but a transformation in cognition itself. It is conceivable that in future, AI will not mimic human cognition, but rather, offer a unique form of digital cognition that complements and enriches our own.

As we step or even dance into this unexplored cognitive terrain, it is paramount that our path is steered by adherence to human-centric principles. This approach guides that, as our language and cognition continue to evolve, they remain the driving force behind human progress. AI, in this context, serves as a collaborator rather than a competitor leveraging advantages that both ‘cognitive systems’ afford each other.

This reimagined relationship between human and machine cognition not only provides a fresh perspective for understanding AI but also holds significant implications for how we design and interact with these advanced systems, ensuring that they contribute positively to human society and progress.




I’m a technology theorist driving innovation at humanity’s tipping point.