AI and ML: Evolutionary Tools, Not Revolutionary Intelligence

Contrary to popular belief, artificial intelligence (AI) and machine learning (ML) represent incremental developments in computational capacity—not radical transformations in intelligence itself. While impressive in scope, their functionality remains fundamentally distinct from human cognition. This perspective aligns with both my professional experience and academic studies, particularly in systemic analysis.

In my coursework on systemic analysis, we examined how humans frequently make sense of incomplete, ambiguous, or even contradictory information. We construct working models, infer missing pieces, and adapt flexibly as understanding evolves—often in the absence of clear-cut inputs or outcomes. This ability to tolerate ambiguity and navigate under-specification is core to human intelligence and decision-making.

By contrast, AI and ML systems rely on highly specified inputs and well-defined outcomes. They process vast quantities of data to identify statistical patterns, but they cannot truly operate under vagueness or make interpretive leaps. As Michael Polanyi (1966) explained, much of human problem-solving depends on tacit knowledge—the kind of intuitive, experiential understanding that resists codification.

This distinction is also underscored in Hubert Dreyfus’s critique of AI. Dreyfus (1972; 1992) argued that machines lack the embodied, context-sensitive intelligence that humans use to make sense of the world. They do not “understand”—they compute.

To illustrate, take the medical drama House, M.D. Dr. Gregory House uses more than just symptom-pattern matching; he draws on analogy, instinct, moral reasoning, and experiential nuance. His intelligence is layered, narrative-driven, and flexible.

A machine learning system trained on thousands of medical cases might approximate diagnostic accuracy, but it cannot replicate House’s interpretive framework. It will not hypothesize creatively under uncertainty or reframe problems in light of new ethical or contextual insight.

In this light, AI and ML should be seen as powerful—but limited—tools of algorithmic pattern recognition. Their strength lies in computation, not cognition. As Dreyfus (1992) cautioned, equating pattern performance with understanding risks misleading assumptions about what machines can truly do.

References

  • Dreyfus, H. L. (1972). What Computers Can't Do: A Critique of Artificial Reason. Harper & Row.

  • Dreyfus, H. L. (1992). What Computers Still Can't Do: A Critique of Artificial Reason. MIT Press.

  • Polanyi, M. (1966). The Tacit Dimension. University of Chicago Press.

  • Searle, J. R. (1980). "Minds, Brains, and Programs." Behavioral and Brain Sciences, 3(3), 417–457

 

And then someone dramatised it in a tv series 'House'; all demonstrated in each epsiode by Dr. House (the reasoning engine!)

He simulates intelligent behavior: deduction, hypothesis testing, decision-making. The Diagnostic Process (rules + heuristics) AI Rule-based systems, prior medical knowledge, logic trees = classic symbolic AI. Team Gathering Patient Data ML Input Data Lab results, symptoms, scans = training/real-time data fed into a model. Pattern Recognition (House’s insights) ML House recognizes patterns from past cases—similar to supervised learning. “It’s never lupus!” meme Model Bias Past data influences likelihood judgments—a bias learned by repetition. Differential Diagnosis Whiteboard Model Training Iterative refinement—testing and eliminating hypotheses resembles model training. Final Diagnosis (Aha moment) AI Output Decision-making step—using all data and patterns to reach a conclusion. Patient Reaction to Drugs Feedback Loop Like reinforcement learning—actions produce feedback, refining next steps.

🎬 TL;DR Analogy

An episode of House is like running an AI system:

  • House = AI

  • His experience = ML model trained on past cases

  • Each patient case = New data input

  • Hypothesis testing = Model training and refinement

  • Final diagnosis = AI output using logic + learned patterns