True. Microsoft's all in, Apple's all in, Nvidia is selling shovels, insurance companies are all in, police & military are all in, education is all in, office management is all in. Who is left to pump line up?
Rubbish. I built a pipeline to handle document classification that successfully took care of ~70TB of mostly unstructured and unorganized data, by myself, in a couple weeks, with no data engineering background whatsoever. This was quite literally impossible a couple years ago. The amount of work that saved was massive and is going to save us a shit ton of money on storage costs. Decades worth of invoices and random PDFs are now siloed properly so we can organize and sort them. This was almost intractable a few years ago.
We came up with different categories of tags. I should clarify, the AI didn't actually do the sorting, it did tagging so sorting was tractable. After the tagging it's just a matter of grouping, either by algorithm or human.
But obviously it would be far from accuracy that LLM would be able to do. E.g. generate search keywords, tags, other type of meta data for a certain document.
Yup that's exactly it. By being able to tag things with all sorts of in house meta data we were then able to search and group things extremely accurately. There was still a lot of human in the mix, but this made the whole task going from "idk if we can even consider doing this" to "great, we can break this down and chip away at it over the next few months/throw some interns at it".
Yeah, I don't know - hearing arguments that this was already done by ML algorithms is to me hearing like "moving from place A to B existed already before cars". But it seems like a common sentiment. So much that simple ML attempted to be doing required massive amount of training and training data specific to your domain before you could use it, and LLM can do it out of the box, and actually consider nuance.
I think organizing and structuring data from unorganized data from the past is a massive use case that seems heavily underrated by so many right now. People spend a lot of time on figuring out where to find some data, internally in companies, etc.
Sure, there’s lots of room for LLMs in helping to do clerical work, HR, that kind of thing. I was actually thinking of the direct management of funds and investments. So yeah, like probably all businesses, the ancillary functions can probably improve productivity using Generative AI with a minimal hit to quality.
You are right about the clerical work, but even pure finance is a lot more than 'direct management of funds and investments'. Have a look at Matt Levine's Money Stuff newsletter for a taste.
And I'm not quite sure why you mention determinism in the grandfather comment? Finance people have been using Monte Carlo simulations for ages. (And removing non-determinism from LLMs by fixing the seed of any pseudo-random number generator used wouldn't really change anything, would it?)
At the end of the day, the hard limit in finance is defaulting. Everything outside that is financial poetry (or engineering :-p).
I know every segment of finance loves to pretend that's not the case, because their jobs (and high salaries) frequently rely on that not being true (see the subprime mortgage crisis).
> At the end of the day, the hard limit in finance is defaulting. Everything outside that is financial poetry (or engineering :-p).
You are forgetting all about regulations and taxation (and how to work with / around them). And how to cleverly read documents, and exploit loop holes in contracts.
There's so much more to finance.
(And for eg stocks or commodities, there's not even any notion of defaulting. Defaulting only really makes sense when you have fixed obligations. 'Fixed income' is only one part of finance.)
Finance is all in on reading 10-Ks and generating summaries. If you have decisions in mind, I’ll be referring to IBM 1979 slide until an HR LLM fires me.
