Neural networks, from large language models like ChatGPT to newer systems such as GigaChat, are now part of many workplaces, including health care. Yet doctors often treat AI as a cautious partner at best. Vladislav Tushkanov, head of the machine learning technologies research and development group at Kaspersky Lab, notes that this caution is sensible because AI can mislead through hallucinations. Hallucination, in this context, is when a language model produces information that sounds plausible but is not true or verifiable.
One vivid example shows how easy it is to be fooled. A user asked a language model for music recommendations and was given a fictitious album title for a band they enjoy. The user spent significant time trying to locate this nonexistent work online, only to realize it was an AI fabrication. This kind of error underscores the risk of relying on AI for accurate data without independent verification.
In critical domains such as cybersecurity, medicine, and law, the stakes are especially high. Relying fully on AI without human oversight can lead to serious consequences. Experts emphasize that the current generation of models cannot be trusted to be perfectly accurate in every situation. The reliability gap means safeguards and human checks remain essential in high consequence work.
Experts describe hallucinations as a fundamental aspect of how autoregressive language models operate. The tendency to generate convincing but false information appears tied to the core design of these systems. Until a fundamentally different approach to language generation is developed, the risk of hallucinations will persist. This reality invites ongoing research, rigorous validation, and layered safety mechanisms that combine AI output with human judgment.
Interviews with industry leaders explore where large language models are making inroads, which professions face disruption from artificial intelligence, and how daily life may transform as AI becomes more capable. The discussion highlights both the opportunities and the challenges that come with rapid technological change, urging thoughtful planning and responsible deployment across sectors.
Recent demonstrations have shown AI that can express apparent emotions and tailor its interactions to individual users. Such capabilities raise expectations about natural, intuitive human–machine communication, while also prompting careful consideration of ethical, privacy, and trust issues in real-world use. The dialogue around these advances continues as researchers seek better ways to balance usefulness with safeguards that protect users and stakeholders.