""Large language models are essentially dream machines that we have learned to nudge in specific directions." - Andrej Karpathy, AI researcher
I have to complete it
AI chatbots often generate false data, with ChatGPT and Gemini showing high error rates. Despite public concern on it, the companies seem helpless in tackling it. A Deloitte report in 2025 cited non-existent experts and studies, bringing reputations damage to the consultancy! These models frequently prioritize a fluent response over factual truth, creating a challenge for researchers and businesses. It's the model design that's at "fault", if at all.
Retrieval versus Prediction
AI systems are not like IT systems, which run on databases that retrieve facts. AI models predict words using statistical patterns. When specific data is missing, they generalise from similar topics to create plausible but false statements. They predict what information should look like instead of retrieving it. Their job is to probabilistically complete the given task at hand, irrespective of the factual nature of it.
Check our posts on hallucinations; click here
Uncertainty
AI researchers know that these models are incentivised to guess. They are often measured on providing answers rather than admitting uncertainty. This pressure to respond makes them unreliable in situations where facts are ambiguous or data is sparse. In fact, commercially speaking, the models will bring losses to their owners if they were to accept "Sorry, I don't know this answer".
Guardrails matter
Tools like DeepMind's Sparrow use human feedback to improve accuracy. Anthropic's Claude follows a constitution to admit knowledge gaps. Researchers now aim to penalize confident errors more heavily to encourage honesty and better calibration in AI systems. As AI integrates into society, filtering these white lies is essential. While creative errors have value in some contexts, the lack of grounding poses risks for high-stakes decisions. We must decide if we will filter these fabrications or become dependent on them.
Summary
AI models fabricate facts by predicting language patterns instead of retrieving data. Training currently rewards guessing, but new frameworks like Claude are encouraging honest uncertainty. Users must remain cautious and verify all machine-generated content for critical tasks.
Food for thought
Will we ever trust a machine that cannot admit when it is simply guessing?
Check our posts on hallucinations; click here
AI concept to learn: Hallucination
AI
hallucinations happen when models generate plausible-sounding answers
based on patterns, not verified facts, leading to confident but
incorrect outputs. Technically speaking: AI
hallucinations arise from probabilistic token generation in
autoregressive models trained via maximum likelihood estimation on
incomplete, noisy corpora. Lacking grounded world models, LLMs optimize
sequence plausibility rather than factual correctness. Errors emerge
from distributional shift, weak retrieval alignment, overgeneralization,
exposure bias, and insufficient constraint enforcement, causing fluent
but unverified outputs that violate truth, consistency, or source
fidelity.
[The Billion Hopes Research Team shares the latest AI updates for learning and awareness. Various sources are used. All copyrights acknowledged. This is not a professional, financial, personal or medical advice. Please consult domain experts before making decisions. Feedback welcome!]
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