Russian Federation
Russian Federation
The article provides a comprehensive analysis of the architecture and principles of the Alice AI virtual assistant. The internal structure of Alice AI is investigated: the layered architecture, the mechanisms of training neural network models on heterogeneous data arrays from the Internet, technological stacks (Python, C++, PyTorch, TensorFlow) and their impact on the functionality and vulnerabilities of the system. Special attention is paid to the "dark side" of language models: algorithmic bias, the phenomenon of "hallucinations", the "echo effect", the risks of personal data leaks and manipulative influence. The sources of problems are analyzed in detail, from toxic content in training samples to contradictory statements integrated into the model without verification. The existing protection and control mechanisms are considered: content moderation, ethical restrictions of developers, audit of models for bias and security, legal norms in the field of data protection. It is emphasized that the effectiveness of these measures depends not only on technological improvement, but also on the level of digital literacy of users. The conclusion is made about the need for an integrated approach to secure interaction with AI systems. The article contributes to the formation of a culture of secure digital interaction in the context of technological transformation.
artificial intelligence (AI), virtual assistants, neural network models, machine learning, modeling of systems and processes, natural language processing (NLP), algorithmic bias, audit of AI systems, digital literacy, personal data protection
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