> The genome is not a sequence so much as a discrete set of genes which are themselves sequences which specify construction plans for proteins. That distinction is important.

aka a sequence. "a book is not a sequence so much as a discrete set of chapters which are themselves sequences of paragraphs which are themselves sequences of sentences" -> still a sequence

these techniques are already being used, such as in the paper I just linked.

> Being able to predict the next codon tells you zilch in terms of useable information.

You have absolutely no way of knowing that apriori. And autogressive tasks can be more sophisticated than just next codon.

> bunch of popular technical jargon from an unrelated field thrown together in a nonsensical way

Okay, feel free to think that.

There's always this assumption of it "will never work on my field." I've done work on NLP and on proteins and read others' work on genetics. I think you will end up being surprised, although it might take a few years.

It is incomprehensible to you, because you just simply do not understand what your parent is talking about. You are the ignorant one here and indeed quite rude. Doesn't matter that genetics is not natural language. The point is we can train large transformers auto regressively and the representation it learns turns out to be useful for a) all kinds of supervised downstream tasks with minimal fine-tuning and b) interpreting the data by analysing the attention weights. There is a huge amount of literature on this topic and what your parent says is quite sensible.

That statement you quote is completely understandable.

Let's say you have discrete sequences that are a product of a particular distribution.

Unsupervised methods are able, by just reading these sequences, to construct a compact representation of that distribution. The model has managed to untangle the sequences into a compact representation (weights in a neural network) that allows you to use it for other, higher level supervised tasks.

For example, the transformer model in NLP allowed us to not have to do part-of-speech tagging, dependency parsing, named entity recognition or entity relationship extraction for a successful language-pair translation system. The compact transformer model managed to remap the sequences into a representation that allows direct translation (people have inspected these models and figured out the internal workings of it and realized it does have latent information about a parse tree of a sentence or part-of-speech of a word).

Another interesting note is that designers of the transformer architecture did not incorporate any prior linguistic knowledge when they were designing it (meaning that the model is not designed to model language but just a discrete sequence).