Our brains are fundamentally pattern-recognition engines. Evolved for survival, they identify regularities in our environment to make better predictions and decisions. Learning is a process of repetition and refinement: every time we encounter a situation, we subtly recalibrate, inching toward optimal responses. It’s like an organic algorithm optimizing itself with each iteration.
So basically, part of our intelligence is how we recognise patterns as we go through life. It was a matter of survival many years back. Eat that red berry and you get sick, stamp on that snake and it’ll bite you… but more than that … what about sales psychology … use these evocative words, anchoring, mirroring, and positive affirmation statements … and you are more likely to get more sales. So patterns have already been assessed.
Science is literally built on patterns. The scientific method is about repeatable and measurable tests. You look for patterns in data to assess whether an hypothesis is correct or not.
So …
This idea leads naturally into how Large Language Models (LLMs) work. At their core, LLMs are also pattern recognizers. They don’t understand meaning the way we do — instead, they analyze massive amounts of text and learn to predict the next word based on patterns they’ve seen before. They don’t “know” facts; they generate statistically likely sequences. Think of them as highly advanced autocomplete systems trained on a trillion examples.
Whilst LLMs aren’t fully intelligent… if we were ever going to reach genuine AI … this pattern matching ability is very much part of that. Who knows what will happen with the quantum chips… since what does ‘meaning’ mean anyway? AI can ‘know’ everything about a dog, what it looks like, sounds like, how it behaves, etc… maybe it can’t ‘experience’ a dog … but it certainly conceive of one.
This gives rise to the thought that we have created something potentially very different to us, but that is still intelligent but in its own way. Our human ego don’t want to admit that this ‘thing’ may have more intelligence, so we have that internal roadblock… but why are we so fixated on it achieving ‘intelligence’ anyway – AI doesn’t need to be intelligent to be self-organising.
While LLMs are masters of mimicry, human intelligence is something deeper. Intelligence isn’t just about spotting patterns — it’s about applying them to adapt, solve problems, and pursue goals in unpredictable environments. It includes:
- Perception (pattern recognition)
- Learning (memory)
- Reasoning (applying knowledge flexibly)
- Agency (goal-directed behavior)
- Generalization (using insights across domains)
LLMs replicate the appearance of intelligence — eloquent, insightful, even persuasive — but they lack goals, memory of past interactions, and any sense of self or purpose. They are mirrors of the data they’ve consumed, not minds of their own.
It can easily be argued, and has been by philosophers over the years, that the human animal is nothing more than a biological robot… that many of us go through life without ever truly thinking; instead just riding the waves of the mind; which of course is just made up of what it has learnt over time.
True intelligence, especially human intelligence, combines cognition with emotion, instincts, and an internal drive. It adapts to change, grows from failure, and learns not just how to do something — but why it matters.
So what is intelligence? It’s not just knowing things. It’s the ability to use patterns in creative, adaptive, purposeful ways. LLMs are incredible tools. They’re not alive, and they’re not wise. But perhaps AI doesn’t need to be ‘alive’ and ‘intelligent’ to be an existential threat to us. I’m not saying LLMs are Terminators … but I am saying they are part of them. It just needs a few more technical breakthroughs combined with them, and then it’s time to reach for the EMP grenades.
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