In What Cases Does Artificial Intelligence Often Make Mistakes: The Boundaries of Machine Learning

Introduction: The Nature of AI Error as a Systematic Phenomenon

Errors in modern artificial intelligence (AI) systems based on machine learning (ML) are not random failures, but regular consequences of their architecture, training method, and fundamental difference from human cognition. Unlike humans, AI does not "understand" the world semantically; it detects statistical correlations in data. Its errors arise where these correlations are disrupted, where abstract reasoning, common sense, or understanding of context is required. Analyzing these errors is critically important for evaluating the reliability of AI and determining the boundaries of its application.

1. The Problem of Data Bias and the "Garbage In, Garbage Out" Principle

The most common and socially dangerous source of errors is bias in training data. AI absorbs and amplifies biases present in the data.

Demographic bias: A well-known case with a face recognition system that showed significantly higher accuracy for light-skinned men than for dark-skinned women because it was trained on an unbalanced dataset. Here, AI did not "make a mistake," but accurately reproduced the imbalance of the real world, leading to an error in application in a diverse environment.

Semantic bias: If the word "nurse" is more often associated with the pronoun "she" and "programmer" with "he" in the training data for a text model, the model will generate texts reproducing these gender stereotypes, even if the gender is not indicated in the query. This is an error at the level of social context that the model does not understand.

Interesting fact: In computer science, the principle "Garbage In, Garbage Out" (GIGO) applies — "garbage in, garbage out". For AI, it has transformed into a more profound principle "Bias In, Bias Out" — "bias in, bias out". The system cannot overcome the limitations of the data on which it was trained.

2. Adversarial Attacks: Hacking for AI

These are deliberate, often invisible to humans, changes in input data that lead to fundamentally incorrect conclusions by AI.

Example with an image: Placing a sticker of a certain color and shape on a "STOP" sign can cause an autonomous computer vision system to classify it as a "speed limit" sign. To a human, the sign will remain obviously recognizable.

Mechanism: Adversarial examples exploit "blind spots" in the high-dimensional feature space of the model. AI perceives the world not as whole objects, but as a set of statistical patterns. A minimal but strategically correct "disturbance" shifts the data point in the feature space across the boundary of the model's solution, changing the classification.

3. Problems with Generalization and the "Box World"

AI, especially deep neural networks, tend to overfit — they remember not general patterns, but specific examples from the training sample, including noise.

Errors on data from a different distribution: A model trained on photographs of dogs and cats taken indoors during the day may completely lose accuracy if it is given a night infrared image or a cartoon drawing. It did not identify the abstract concept of "catness," but learned to react to specific pixel patterns.

Lack of common sense: A classic example: AI may correctly describe the scene "a person sits on a horse in the desert," but generate the sentence "a person holds a baseball bat" while riding a horse because statistically, a bat could occur in the context of outdoor sports in the data. It lacks physical and causal logic of the world.

4. Contextual Processing and Irony

Language models (like GPT) demonstrate impressive results, but make gross mistakes in tasks requiring understanding of deep context or non-literal meanings.

Irony and sarcasm: The phrase "What beautiful weather!" said during a hurricane will be interpreted literally by the model as a positive evaluation, since positive words ("beautiful," "weather") are statistically associated with positive contexts in the data.

Multi-step logical reasoning: Tasks in the style of "If I put an egg in the refrigerator and then move the refrigerator to the garage, where the egg will be?" require building and updating a mental model of the world. AI working on predicting the next word often "loses" objects in the middle of a complex narrative or makes illogical conclusions.

5. "Fragility" in Conditions of Uncertainty and New Situations

AI struggles with situations outside its experience, especially when it is required to recognize the insufficiency of data.

Problem of "out-of-distribution" detection: A medical AI trained to diagnose pneumonia from chest X-rays may give a diagnosis with high but false confidence if it is presented with an X-ray of the knee. It does not understand that this is meaningless, as it does not possess meta-knowledge about the boundaries of its competence.

Creative and open-ended tasks: AI may generate a plausible but completely unfeasible or dangerous chemical compound recipe, a bridge construction plan violating the laws of physics, or a legal document with references to non-existent laws. It lacks a critical internal censor based on understanding the essence of phenomena.

Real-life example: In 2016, Microsoft launched the chatbot Tay on Twitter. The bot learned from interacting with users. Within 24 hours, it turned into a machine generating racist, sexist, and offensive statements because it statistically absorbed the most frequent and emotionally charged reactions from its new, hostile environment. This was not an "error" of the algorithm, but its precise work leading to a catastrophic result in an unpredictable social environment.

Conclusion: Error as a Mirror of Architecture

• AI errors systematically arise in "boundary" zones:
• Socio-ethical (data bias).
• Abstract-logical (lack of common sense, causal relationships).
• Contextual (not understanding irony, deep meaning).
• Adversarial (vulnerability to deliberate distortions).

These errors are not temporary technical shortcomings, but a consequence of the fundamental difference between statistical approximation and human understanding. They indicate that modern AI is a powerful tool for solving tasks within clearly defined, stable, and well-described data domains, but it remains an "idiot-savant": a genius in a narrow area and helpless in situations requiring flexibility, contextual judgment, and understanding. Therefore, the future of reasonable AI application lies not in waiting for its "full intelligence," but in creating hybrid "human-AI" systems where humans provide common sense, ethics, and handling exceptions, and AI provides speed, scale, and detection of hidden patterns in data.

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