“It’s like comparing apples and pears — but what if you don’t know what either is? Welcome to GPT.”
The debate around artificial intelligence often ignores a critical fact: Large Language Models like GPT do not understand semantic concepts. They simulate understanding — but they don’t “know” what an apple or a pear is. This isn’t just academic; it has real-world implications, especially as we increasingly rely on such systems in decision-making.
Transformer models don’t “think” – they optimize probability. Their output is impressive, but it’s entirely non-conceptual.
Why Transformers Don’t Think # Despite the hype, Transformer-based models (like GPT) lack fundamental characteristics of thinking systems:
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GPT and similar models simulate comprehension. They imitate conversations, emotions, reasoning. But in reality, they are statistical probability models, trained on massive text corpora – without awareness, world knowledge, or intent.
