On 2026-03-29 11:26, [email protected] wrote:
I think this example shows pretty well where the lie is in the
current wave of AI. It's not the "hallucinations", it is the
fact that they are wired to "talk" to us as if they knew what
they're doing.
The assertion that AI systems are inherently deceptive due to their
conversational design—particularly the perception that they "know" what
they are saying—is a common but misinformed critique. This perspective
conflates the output behavior of large language models (LLMs) with
intent or truthfulness, which are attributes of human cognition, not
machine-generated text.
- LLMs are statistical models trained on vast corpora of text data.
- They generate responses based on patterns in training data, not on
understanding, intent, or factual verification.
- The ability to "talk" coherently is a feature of their architecture,
not evidence of knowledge or deception.
- The accuracy of LLM outputs is fundamentally determined by the
quality, relevance, and bias of the data used in training.
- Organizations that curate and train models bear responsibility for
data selection and curation.
- Misinformation or biased outputs stem from training data that reflects
historical, societal, or editorial biases—not from the model’s inherent
nature
- Humans are also prone to misinformation, cognitive biases, and
propaganda—often internalizing false narratives through repeated
exposure.
- The prevalence of propaganda in media, politics, and education
demonstrates that humans are not inherently more truthful or discerning
than AI systems.
- The difference lies in transparency: humans often believe they are
reasoning objectively, while LLMs generate responses without
self-awareness.
The reliability of LLM outputs depends entirely on how they are
deployed:
- Unfiltered chatbots may generate plausible but false content.
- Engineering-grade applications (e.g., mine safety protocols,
geological modeling) use LLMs as assistants within verified workflows,
with outputs cross-checked against authoritative sources.
The idea that LLMs are "lying" because they speak confidently is a
misattribution of human traits to machines. The real issue lies in how
these tools are used, not in their design. When properly programmed,
integrated, and monitored, LLMs are powerful aids—not sources of
deception. The responsibility for accuracy remains with the human
operators and data curators.
Practical example: when handling my tasks and notes, the LLM is to show
absolutely accurate what is inside, what is next, what I have to do, as
it is using personal context. If you are asking for historical context,
it depends on training, it can be as well very accurate.
Train one yourself to provide you the accurate information on what you
need. That is exactly what people do. You got the base model, make it
accurate on the knowledge you wish and want, that is the power of it.
--
Jean Louis
