[alluro2]

I was hoping for some clever ideas about direction of software / UIs, and new use-cases for LLMs / AI, since for now I'm still struggling and have a lack of vision, it seems.

For example, I work on developing a logistics management and route optimization platform. If I try to envision new features that could be unlocked through AI or just LLMs, I basically get nothing back from my feeble brain, that I would fit into this category. E.g. - automate incident handling (e.g. driver broke down, handle redirection of other driver, handover of goods, reoptimize routes) - but the implementation would be just a decision tree based on a couple of toggles and parameters - no AI there? Other things that come to mind - we already use ML for prediction of travel times and service durations - it's a known space, that I refuse to call AI.

Apart from serving as an alternative and sometimes more efficient interface for data queries through NLP (e.g. "tell me which customers I had margin lower than 20% on, due to long loading times and mispackaged goods" - even then, all the data already needs to be there in appropriate shape, and it's just replacing a couple of clicks), I really fail to see new use-cases / features that the current state / hype for AI / LLMs unlocks.

Am I just lacking vision? Are there opportunities I'm grossly overlooking?

[jarulraj]

Can LLMs help in cleaning up the logistics database -- like handling different variants of an address? Here is an example: https://medium.com/evadb-blog/augmenting-postgresql-with-ai-....

[6510]

Turn the company jingle into a heavily distorted guitar track, make a database with 3d models of drivers, locations and drone footage, from departure to delivery including the people driving desks, then have the llm generate a movie with narration in a dark voice. Dealing with broken down vehicles is an opportunity to brag and show off.

[vorpalhex]

Problem identification.

Given all the log data for the last N packages, analyze for anomalies and hypothesis as to their cause. Eg is there a specific shipper, warehouse or driver causing problems?

ML does well when you have too much data for a human to wrangle and the search target is well described.

[CaptRon]

Try asking chatgpt for some ideas.

[sanderjd]

Unironically, I've done this sort of thing.

Not too impressed with the results though.

[suoduandao3]

Ideas are probably the last thing humans will delegate to AI. We have needs and wants that Ideas help us meet, AI only sets priorities in a reactive way.

[sanderjd]

Yeah it's a pretty interesting experience actually, trying to use it for idea generation.

It's like talking to a very well informed and generally competent person ... who has no spark of creativity or insight whatsoever.

This does vary product to product - some are excruciatingly boring by design - but I think they're universally uninteresting just to different degrees.