wut.
please unpack how LLMs are “problem makers” in “most” domains?
please unpack how LLMs are “problem makers” in “most” domains?
Phishing
Buggy generated code
Cheating in online courses
ChatBots that try to do too much and do it worse than real human service reps, like that one that wrongly assured a customer that their airline ticket was refundable
Deluge of low-value generated content taking attention and revenue away from high-value content creators
I do love using ChatGPT for fun stuff like “write me a recipe for enchiladas that’s also a country western song.” My kids and I find it hilarious.
In retrospect it probably detected it generated something it didn't have the IP rights to give me, but ever since then I've described the state of the art as "like talking through the intercom at a McDonald's drive-thru, but every now and then the attendant says 'sorry, can we start over? I got distracted thinking about killing you.'"
But Copilot has revolutionized my coding. I have to code in many languages on a daily basis: Typescript, tsx, Css, Html, Dart, config files (like docker[compose], k8s, Ansible, json configs), c#, python. I'm only fluent in c# and ts. The fact that I do not need to remember the syntax for all the other is a big game changer. I was able to be immediately productive in a new language/framework after reading the documents. Previously it took some time before I ramped up, and then it would be lost after some inactivity. I'm not talking about important concepts, or CS fundamentals. I'm talking about specific ways things can be done in each language/framework. Copilot makes me 1000x more productive in this part. I'm still limited by my mental bandwidth, so I'm probably 2x more productive on an average day.
I also use ChatGPT, and run some models locally just to play with them, but all happen much less frequently than my colleague disrupting discussions with ChatGPT content.
I'll give it a try next year, maybe it improves to the point where the number of suboptimal suggestions falls to 20% or so, it would be much easier then.
LLMs have a tendency to hallucinate at a rate that makes them untrustworthy at scale w/o a human in the loop. The more open ended the prompt, the higher the hallucination rate. Here I mean minor things, like swapping a negative, that can fundamentally change a result.
Thus, any place that we trust computer to perform reliable logic, we cannot trust an LLM because it's error rate is too high.
Methods such as RAG can box in the LLM to keep them on track, but this error rate means that they can never be mission critical, a-la business logic, and keeps them to being a toy.
Where LLMs are game changers are ETL pipelines / data scrapers. I used to work at Clearbit where we built thousands of lines of code just to extract the address of a company's HQ or if a company is owed by another org. LLMs just do that... for free. With LLMs data extraction from free form text is now a solved problem, and thats god damn mindblowing for me.