AI hallucinations: Why humans and machines both get it wrong

If you have ever tried to understand how the mind works, you know it rarely behaves as neatly as we imagine. Thoughts do not arrive in tidy rows. Memories can drift, bend, or quietly change shape. A scent can pull a forgotten childhood moment into focus. A sentence we only half-heard can emerge altered by the time we repeat it.

This intricate, multifaceted, deeply personal process is not a flaw. It is how the human brain survives. It closes gaps. It creates meaning. It makes informed guesses.

That is worth remembering when we talk about AI “hallucinating” because, strange as it may sound, humans have been hallucinating long before machines ever existed.

Human mind

According to cognitive neuroscience, human memory - particularly episodic memory - is not a static archive in which experiences are stored intact and later retrieved.

Episodic memory refers to our ability to remember specific personal events: what happened, where it occurred, when it took place, and how it felt. Rather than replaying these events like recordings, episodic memory is fundamentally constructive.

Each time we remember an episode, the brain actively rebuilds it by flexibly recombining fragments of past experience - sensory details, emotions, contextual cues, and prior knowledge.

This reconstructive process creates a compelling sense of certainty and vividness, even when the memory is incomplete, altered, or partially inaccurate.

Importantly, these distortions are not simply failures of memory.

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Research suggests they reflect adaptive processes that allow the brain to simulate possible future scenarios.

Because the future is not an exact repetition of the past, imagining what might happen next requires a system capable of extracting and recombining elements of previous experiences.

Because memories are rebuilt rather than replayed, they can change over time. This is why eyewitness accounts of the same event often conflict, why siblings remember a shared childhood moment differently, and why you can feel absolutely certain you once encountered a fact that never actually existed.

A well-known example is the Mandela Effect: large groups of people independently remembering the same incorrect detail. Many people are convinced that the Monopoly mascot wears a monocle - yet he never has.

The memory feels real because it fits a familiar pattern: a wealthy, old-fashioned gentleman with top hat and cane should have a monocle, so the brain fills in the gap.

Similar false memories arise not because the brain is malfunctioning, but because it is doing what it evolved to do, creating coherence from incomplete information.

In this sense, the brain “hallucinates” not as a bug, but as a feature. It prioritizes meaning and consistency over perfect accuracy, producing a convincing narrative even when the underlying data is fragmentary or ambiguous.

Most of the time, this works astonishingly well. Occasionally, it produces memories that feel unquestionably true - and are nonetheless false.

“AI Mind” works nothing like ours

AI was inspired by the brain, but only in the way a paper airplane is inspired by a bird. The term “neural network” is an analogy, not a biological description. Modern AI systems do not possess an internal world. They have no subjective experience, no awareness, no memories in the human sense, and no intuitive leaps.

Large language models (LLMs) for example, are trained on vast collections of human-generated text - books, articles, conversations, and any other textual representation of information.

During training, the model is exposed to trillions of words and learns statistical relationships between them. It adjusts millions or billions of internal parameters to minimize prediction error: given a sequence of words, what token is most likely to come next?

Over time, this process compresses enormous amounts of linguistic and conceptual structure into numerical weights.

As a result, a large language model (or any generative AI) is fundamentally a statistical engine. It does not know what words mean; it knows how words tend to co-occur. It has no concept of truth or falsity, danger or safety, insight or nonsense. It operates entirely in the space of probability.

When it produces an answer, it is not reasoning its way toward a conclusion - it is generating the most statistically plausible continuation of the text so far.

This is why talk of AI “thinking” can be misleading. What looks like thought is prediction. What looks like memory is compression. What looks like understanding is pattern matching at an extraordinary scale.

The outputs can be fluent, convincing, even profound - but they are the result of statistical inference, not comprehension.

Why AI hallucinates

AI hallucinations aren’t random glitches - they’re a predictable side effect of how large language models such as GPT or generative AI models such as DALLE are trained and what they are optimized to do.

These models are built around next-token prediction: given a prompt, they generate the most statistically plausible continuation (of text or image). During training, an LLM learns from massive datasets of text and adjusts billions of parameters to reduce prediction error.

That makes it extremely good at producing fluent, coherent language - but not inherently good at checking whether a statement is true.

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When the model doesn’t have enough reliable signal, it often doesn’t “notice” that it doesn’t know. Instead, it fills the gap with something that sounds right.

Hallucinations come from a few interacting forces, some of which are:

• Next-token prediction (plausibility over truth): the system is optimized to produce likely outcomes, not verified facts.

• Lack of grounding: unless connected to retrieval tools or external data, the model has no built-in link to real-time reality.

• Compression instead of storage: it doesn’t keep a library of facts; it stores statistical patterns in weights, which can blur details.

• Training bias and data gaps: if the data is skewed, outdated, or missing key coverage, the model will confidently mirror those distortions.

• Overfitting: model learns the training data too closely, capturing noise and specific details instead of general patterns, which makes it perform poorly on new, unseen data.

• Model complexity: more capable models can generate more convincing mistakes - fluency scales faster than truthfulness.

• Helpfulness tuning (RLHF/instruction training): the model is often rewarded for being responsive and confident, which can discourage “I don’t know” behaviors unless explicitly trained in.

Unlike humans, the model’s confidence isn’t a feeling or a belief - it’s an artifact of fluent generation. That fluency is what makes hallucinations so persuasive.

Can we eliminate hallucinations?

The short answer is no - not completely, and not without undermining what makes generative AI useful. To eliminate hallucinations entirely, a system would need to reliably recognize uncertainty and verify truth rather than optimize for probability.

While grounding, retrieval, and verification layers can reduce errors, they cannot provide absolute guarantees in open-ended generation.

A purely generative model does not know when it does not know. If we forced such a system to speak only when certain, it would become rigid, unimaginative, and frequently silent. Hallucinations aren’t a glitch.

They are a trade-off. A predictive model must predict, and prediction sometimes drifts. The same flexibility that enables creativity and synthesis also makes error inevitable.

Learning to live and think with AI hallucinations

The goal is not to make AI flawless. It is to make us wiser in how we use it. AI has the potential to be an extraordinary partner - but only if we understand what it is and what it is not.

It can assist with writing, summarizing, exploration, brainstorming, and idea development. It cannot guarantee correctness or ground its outputs in reality on its own. When users recognize this, they can work with AI far more effectively than when they treat it as an oracle.

A healthier mindset is simple:

• Use AI for imagination, not authority.

• Verify facts the same way you would verify any information found online.

• Keep human judgment at the centre of the process.

AI is not here to replace thinking. It is here to enhance it. But it can only do that well when we understand its limitations - and when we remain firmly in the role of the thinker, not the follower.

With that said, when used responsibly - the possibilities really are limitless. We’re no longer confined to traditional workflows or traditional imagination. AI can now collaborate with us across almost every creative domain.

In visual art and design, it can help us explore new styles, new compositions, new worlds that would take hours - or years - to create by hand.

In music and sound, models are already composing melodies, soundtracks, and even mastering audio with surprising emotional intelligence. In writing, from poetry to scripts to long-form storytelling, AI can spark ideas, extend narratives, or act as a creative co-author.

In games and interactive media, it can build characters, environments, and storylines on the fly, transforming how worlds are created.

And in architecture and product design, it can generate shapes, forms, and concepts that humans often wouldn’t imagine - but engineers can later refine and build. We’re entering a phase where creativity is no longer limited by time, tools, or technical skill. It’s limited only by how boldly we choose to explore.

Conclusion

The deeper we move into an age shaped by artificial intelligence, the more important it becomes to pause and understand what these systems are doing - and just as importantly, what they are not. AI hallucinations are not signs of technology spiraling out of control.

They are reminders that this form of intelligence operates according to principles fundamentally different from our own.

Humans imagine as a way of making sense of the world. Machines “imagine” because they are completing statistical patterns. Using AI responsibly means accepting that it will sometimes get things wrong - often in ways that sound confident and convincing.

It also means remembering that agency has not disappeared. We still decide what to trust, when to question, and when to step back and rely on our own judgment.

AI may be impressive, but it is not the one steering the ship.

Yet.

AI hallucinations: Understanding why sometimes machines get it wrong