Artificial Intelligence Errors: 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, learning 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 assessing the reliability of AI and defining the boundaries of its application. 1. Data Bias Problem and "Garbage In, Garbage Out" Laws The most common and socially dangerous source of errors is bias in training data. AI absorbs and strengthens biases existing in the data. Demographic distortions: A well-known case with a facial 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 distortions: If the word combination "nurse" is most 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) - "garbage in, garbage out" - operates. 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: ... Читать далее