The only interesting application I've identified thus far in my domain in Enterprise IT (I don't do consumer-facing stuff like chatbots) is in replacing tasks that previously would've been done by NLP: mainly extraction, synthesis, classification. I am currently working a long-neglected dataset that needs a massive remodel and I think that would've taken a lot of manual intervention and a mix of different NLP models to whip into shape in the past, but with LLMs we might be able to pull it off with far fewer resources.
Mind you at the scale of the customer I am currently working with, this task also would've never been done in the first place - so it's not replacing anyone.
> This can start looking less like pure AI and more like a mix of traditional software with some AI capabilities
Yes, the other use case I'm seeing is in peppering already existing workflow integrations with a bit of LLM magic here and there. But why would I re-work a worklfow that's already implemented and well-understood in Zapier, n8n or Python with total reliability.
> Knowledge of specific workflows also requires really good product design. High empathy, ability to understand what's not being said, ability to understand how to create an overall process value stream from many different peoples' narrower viewpoints, etc. This is also hard.
> My experience is that this type of work is a narrow slice of the total amount of work to be done
Reading you I get the sense we are on the same page on a lot of thing and I am pretty sure if we worked together we'd get along fine. I'm struggling a bit with the LLM delulus as of late so it's a breath of fresh air to read people out there who get it.
Today the range of things for which the models are tolerable to "great" has greatly expanded. In arXiv papers you tend to see people getting tepid results with 500 examples, I get better results with 5000 examples and diminishing returns past 15k.
For a lot of people it begins and ends with "prompt engineering" of commercial decoder models and evaluation isn't even an afterthought For information extraction, classification and such though you get often good results with encoder models (e.g. BERT) put together with serious eval, calibration and model selection. Still the system looks like the old systems if your problem is hard and has to be done in a scalable way, but sometimes you can make something that "just works" without trying too hard, keeping your train/eval data in a spreadsheet.
Integration alone isn't enough. Organizations let their data go stale, because keeping it updated is a political task instead of a technical one. Feeding an AI stale data effectively renders it useless, because it doesn't have the presence of mind to ask for assistance when it encounters an issue, or to ask colleagues if this process is still correct even though the expected data doesn't "fit".
Automations - including AI - require clean, up-to-date data in order to function effectively. Orgs who slap in a chatbot and call it a day don't understand the assignment.
Code is similar - programming languages have rules that are well known, couple that with proper identification, pattern matching and thats how you get to these generated prototypes[0] done via so called 'vibe coding' (not the biggest fan of the term but I digress)
I think this is early signs that this generation of LLMs at least, are likely to be augmentations to many existing roles as opposed to strictly replacing them. Productivity will increase by a good magnitude once the tools are well understood and scoped to task
[0]: They really are prototypes. You will eventually hit walls by having an LLM generate the code without understanding the code.
yea the search-engine improved productivity of almost everyone, but didnt change any workflows.
In the lead up a lot of the same naysaying we see about AI was everywhere. AI can be compressed into less logic on a chip, bootstrap from models. Require less state management tooling software dev relies on now. We’re slowly being trained to accept a down turn in software jobs. No need to generate the code that makes up an electrical state when we can just tune hardware to the state from an abstract model deterministically. Energy based models are the futuuuuuure.
https://www.chipstrat.com/p/jensen-were-with-you-but-were-no...
Lot of the same naysaying about Dungeons and Dragons and comic books in the past too. Life carried on.
Functional illiterates fetishize semantics, come to view their special literacy as key to the future of humanity. Tale as old as time.