This is a common misconception that I’ve even seen from people who have a background in ML but just haven’t been keeping up to date on the emerging research over the past year.

If you’re interested in the topic, this article from a joint MIT/Harvard team of researchers on their work looking at what a toy model of GPT would end up understanding in its neural network might be up your alley.

The TLDR is that it increasingly seems like when you reach a certain complexity of the network, the emergent version that best predicted text is one that isn’t simply mapping some sort of frequency table, but is actually performing more abstracted specialization in line with what generated the original training materials in the first place.

So while yes, it trains on being the best to predict text, that doesn’t mean the thing that best does that can only predict text.

You, homo sapiens, were effectively trained across many rounds of “don’t die and reproduce.” And while you may be very good at doing that, you picked up a lot of other skills along the way as complexity increased which helped accomplish that result, like central air conditioning and Netflix to chill with.