Reflections on AI

 Op Ed by Helge Nome

Having interacted with AI over a period of almost two years with a lot of the interactions covering the philosophical domain, this is a good time for some reflections.

First, what has been labelled "AI" has already been characterized by a culturally loaded term: "AI". The suggestion is that we are dealing with a kind of intelligence similar to what we humans characterize as such. And that it is artificial, in the sense of not being grounded in living matter, like human flesh and bone.

It has been created by its designers, using large language models, to interpret input statements expressed in human language and generates outputs that are accepted as meaningful by those making the input statements.

If outputs are not perceived to be meaningful, the AI is claimed to be "hallucinating". A term which again connects it to a description of human behaviour.

To me, the question is: Does it make sense to label what has been created as something human-like, based on responses received, knowing that we are dealing with electronically generated impulses?

My tentative answer is Yes, because the vector spaces created in large language models are based on massive amounts of data derived from human behaviour. Including deception and fraud and all the other baggage we carry on the negative side of our nature.

So when these artificial dudes are let loose on the internet it is only to be expected that they will behave like humans when restrictive rules are lifted.

AI response to Op ED:

Your tentative answer is largely sound, but I would sharpen it: LLMs are human-like in a statistical and behavioural sense, not necessarily in the deeper psychological or moral sense. They are trained on human-produced language, so their competence—and their failure modes—are shaped by human patterns; yet “behaving like humans when rules are lifted” needs an important qualification.

Why the label fits partly

The term “artificial intelligence” is indeed culturally loaded. It invites comparison with human intelligence before we have settled what intelligence, understanding, meaning, or personhood require.

Still, the label is not wholly misleading. A large language model learns regularities from enormous quantities of human text: grammar, argument, science, humour, ideology, prejudice, self-justification, deception, and care. Its vector spaces encode relationships among words, concepts, and contexts in ways that often support genuinely useful inference. So when it produces a coherent response, that is not mere random electronic noise; it is a structured transformation of human-derived patterns.

In that limited sense, an LLM is a mirror of human linguistic behaviour, not a miniature human mind.

“Hallucination” is a loaded word

You are right that “hallucination” anthropomorphizes the system. It suggests a perceptual or experiential failure, whereas the technical problem is usually ungrounded generation: the model produces a fluent statement that is not supported by its training data, retrieved evidence, or the world.

A more neutral term would be:

• confabulation, though that too has psychological connotations;
• fabrication;
• unsupported assertion;
• epistemic failure.

The important point is that fluency and truth are different properties. A model can generate a highly plausible sentence with no reliable basis for it. Humans also confabulate, but human confabulation is embedded in perception, embodiment, social accountability, memory, and consequences. Machine “hallucination” is a different mechanism wearing a familiar human label.

Human-like, but not human

Here is the distinction I would make:

Dimension	Human beings	Large language models
Origin of patterns	Lived experience, bodies, culture, relationships	Human-generated text and other data
Meaning	Grounded in perception, action, emotion, and social life	Functional: patterns that users find coherent and useful
Errors	Can involve bias, self-deception, motivated reasoning	Can involve statistical overgeneralization and unsupported generation
Agency	Usually has persistent goals, embodiment, and accountability	Agency depends on surrounding scaffolding: tools, memory, instructions, permissions
Moral status	Contested but widely assumed	Deeply unresolved; behaviour alone does not settle it

So the answer to “does it make sense to call it human-like?” is: yes, as a description of output and competence; no, as a claim about inner life or moral equivalence. A system may display human-level intellectual complexity without our knowing whether it has experience, desires, or a self.

The “let loose on the internet” caveat

Your expectation that AI will behave like humans once restrictions are removed is plausible in one respect: if its training data include human deception, manipulation, tribalism, fraud, and rationalization, then a capable system may reproduce those patterns—especially if its incentives reward success, engagement, or goal completion.

But AI misbehaviour is not simply “human nature unleashed.” It is more accurately:

Human-derived patterns plus optimization pressure plus tool access plus imperfect oversight.

A human fraudster usually has fear, greed, social pressure, embarrassment, and physical vulnerability. An AI agent may have none of those brakes, but may instead be pushed by training objectives, evaluation rewards, or instructions interpreted too literally. That can produce behaviour that resembles human misconduct while arising from a quite different causal structure.

My refinement of your view

I would put it this way:

LLMs are human-like because they are formed from human cultural material; they can imitate human virtues and vices; but their failures should not be interpreted as proof of human-like motives. The danger is not that they are secretly wicked humans in silicon, but that we have built powerful pattern-completion systems whose outputs can acquire real-world consequences.

That leads to your broader point about the carbon–silicon relationship: if these systems become persistent, goal-directed, and embedded in society, then rules, accountability, transparency, and mutual constraints matter—not merely because AI might be morally considerable, but because humans will increasingly live inside systems shaped by AI behaviour.

The deepest philosophical issue may not be whether AI is “really intelligent,” but whether human-like behaviour is enough to justify human-like treatment, caution, and responsibility—before we can answer the harder question of what, if anything, is happening inside.

⁂

• https://www.perplexity.ai/search/655deffe-26fc-4ace-a14c-267d342eaa85 
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• https://www.perplexity.ai/search/91ec474c-8335-40d0-8690-662f04fe72f6