The Illusion of Intelligence: Why AI’s Deterministic Efficiency Fails to Capture True Cognition Rahul Ramya

   The Illusion of Intelligence: Why AI’s Deterministic Efficiency Fails to Capture True Cognition

Rahul Ramya

07.02.2025

Patna, India

AI’s limitations in achieving true intelligence by emphasizing its deterministic efficiency syndrome, simplistic intelligence model, and lack of contextualisation and ethical considerations are very subtle. It also raises essential questions about the role of emotions in intelligence and whether AI’s computational power can compensate for its lack of human-like cognition. 

AI and the Deterministic Efficiency Syndrome

The emphasis on speed and efficiency as primary indicators of intelligence reflects AI’s deterministic efficiency syndrome (DES). This approach reduces intelligence to a quantifiable metric—the ability to process vast amounts of data rapidly. However, intelligence is not merely about processing speed but about meaning-making, adaptability, and lived contextualization.

For instance, AI may outperform humans in a game of chess by evaluating millions of possible moves per second. Yet, a human grandmaster, despite processing far fewer moves, understands patterns, psychological tactics, and broader strategic narratives that AI struggles to contextualize. Speed alone does not constitute intelligence, as intelligence often requires deliberation, ambiguity tolerance, and intuitive reasoning, which AI lacks.

The Deterministic Simplistic Model of Intelligence

AI follows a simplified model of intelligence that assumes knowledge extraction from data equates to understanding. However, intelligence involves:

1. Contextual Understanding – AI struggles with nuances in human communication, such as sarcasm, humour, body language or cultural references.

2. Social Interaction – Intelligence thrives in interaction, negotiation, and collective decision-making, areas where AI remains deficient.

3. Handling Unpredictability – AI functions best in structured environments but falters when faced with unknown, dynamic, or unprecedented situations.

4. Intellectual Hallucinations – AI’s tendency to generate false or misleading information without an awareness of its own errors reveals a lack of self-correction mechanisms akin to human cognition.

5. Moral Reasoning – Intelligence requires moral and ethical judgment, which is shaped by lived experiences, cultural conditioning, and historical understanding—elements AI fundamentally lacks.

Thus, while AI can simulate problem-solving within predefined constraints, it does not engage in actual cognition the way humans do.

Processing vs. Intelligence: The Critical Distinction

A key misconception in AI discourse is that more processing equates to more intelligence. This perspective fails to account for the fact that:

   •   Processing is a mechanical operation; intelligence is an interpretative and integrative process.

   •   Humans frequently rely on heuristics, intuition, and contextual cues rather than exhaustive computation.

   •   Less analytical processing is often sufficient when combined with contextual knowledge.

For example, an experienced doctor diagnosing a disease relies on medical knowledge, but also on patient history, body language, and intuitive insights—elements AI struggles to integrate.

The Role of Emotion in Intelligence

The claim that pure rationality is an unrealistic problem-solving tool is significant. Even the most seemingly rational human decisions are influenced by emotional cognition. AI, in contrast, operates on pure computation, lacking:

1. Empathy – AI may detect emotions in text or voice but does not experience emotions or understand their context.

2. Moral Judgment – Ethical decision-making is often subjective and value-driven, something AI cannot internalize as humans do.

3. Social Adaptability – AI follows programmed rules, but human intelligence is fluid, shaped by experiences, interactions, and evolving moral considerations.

Can AI Bridge This Gap?

This raises the critical question: Can AI be developed to incorporate contextual and emotional understanding? Some possible approaches include:

1. Hybrid Intelligence Models – AI working in collaboration with human intelligence rather than attempting to replicate it.

2. Contextual AI – Training AI models to understand historical, cultural, and societal nuances rather than relying solely on statistical correlations.

3. Emotional Interaction Models – Developing AI systems that can respond to human emotions in a meaningful way, even if they do not experience emotions themselves.

Conclusion: AI as a Complement, Not a Replacement

Rather than attempting to replicate human intelligence, AI’s future should focus on complementary intelligence—where AI enhances human cognitive capabilities while recognizing its inherent limitations. AI may never achieve true intelligence in the human sense, but if properly designed, it can augment human decision-making, automate tasks, and assist in knowledge processing without reducing intelligence to mere efficiency and speed.

This discussion effectively raises these profound questions, and the next step could be to explore how AI regulations, ethical AI development, and interdisciplinary approaches (philosophy, neuroscience, sociology) can shape a more responsible AI paradigm.

Now improve my above essay with the following 

1. Update it with latest research trends in simpler English to make common readers knowledgeable able

2. My argument 

Education pedagogy is a contested field and curriculum are made by open debates and after considering general and specific cultural, social , economic and political realities. But in machine learning pedagogy (data selection) is selective, opaque, closed door activity making machine teaching a class conscious tool depending upon the interests and motives of data feeder tech oligarchs 

3. It’s epistemological opacity to explain the cause to produce a certain result making machine teaching a tool of formula based understanding where a person is user of a formula and not a knower of the process of knowledge generation