AI tools: five common myths debunked.

The article lists five common misconceptions about AI tools and explains that current models do not have human-like understanding and need ongoing human oversight.

AI tools prompt curiosity and concern as they become more visible in everyday life. Demonstrations, marketing, and speculative discussion have helped spread persistent misconceptions about what these systems can do. Clearing up those myths can make it easier to see where AI is useful and where its limits lie. The piece outlines five common misunderstandings about today’s AI tools. Core facts: - Advanced language models generate output by predicting patterns in data; they do not possess consciousness or human-like understanding. - When AI appears to infer intentions, it is filling gaps with plausible continuations rather than reading minds. - AI systems inherit biases present in their training data and in design choices, so they are not inherently neutral. - Models do not self-improve without new human-provided data, evaluation, and curated feedback; human oversight is part of their lifecycle. - Current generative AI tools are specialised pattern predictors and are not equivalent to artificial general intelligence. Summary: These clarifications are intended to ground expectations about capabilities and limits and to inform discussion as AI is used more widely in areas such as healthcare, education, and public service. Undetermined at this time.