[mediaman]

The mistake is in believing that LLM's output should be deterministic to be useful.

Human output is not deterministic.

Fields with text-heavy output are already being upended by this. Being able to summarize long legal briefs, identify contract problems, do classification of discovery documents, or even write first drafts of common legal forms is already upending the legal discipline.

Chat-based customer support agents are seeing 25% productivity improvements based on two-year-old models for new employees, according to a study published in NBER.

Things like BabyAGI and other sequential "do anything" tools appear to be close to useless now, and unfortunately that is what is catching a lot of hype on Twitter. But actual industry applications are much quieter (often NDA) and much more impactful.

[danaris]

It's not about being deterministic.

It's about the LLM itself having any way to determine whether what it says is true.

[mediaman]

Not understanding why this is an issue for LLMs but not humans.

This is a simple commercial decision to make governed by three factors.

1. What is the cost of making an error?

2. What is the cost of the human doing the work?

3. What is the likelihood of the human making an error?

It's just evaluating how much more likely AI is to make an error than a human, by the cost of that error, set against the savings by using fewer humans.

Look at the legal profession. Sometimes the cost of an error is high, but usually it is not. There are already tons of little errors in contracts and discovery, and today they're all human. And people are very expensive. There is a giant swath of legal work that looks very attractive to automate at less than 100% accuracy.

Customer service: people offer poor customer service all the time, and usually the cost of that error is low. Human customer service isn't as expensive as legal work, but it's still relatively expensive. Very attractive to automate at less than 100% accuracy.

[danaris]

> Not understanding why this is an issue for LLMs but not humans.

Because humans have the capability to understand where their information comes from, and thus give enough meta-information to evaluate an accuracy rating, even if not all of them are good at it all the time.

I understand that there has been some effort to build this capability into LLMs, and that it works a little bit for some of them, but it is not something that most of them are fundamentally capable of.

[feanix]

I have met many a human that, despite having that capability, will just choose not to use it to the point of getting violently emotional to protect their ignorance and viewpoints.

Whereas, even though their capabilities are lacking, as someone who is disabled, I've found working with LLM to be much more effective. I've had the medical community gaslight me and actively use processes that make things worse. I've had the educational community restrict me from accessing education. I've had the legal community weaves webs of "yes the policy states that we must comply but that's actually a suggestion." Even when it comes to interacting with people socially, my experience has been one of violence and trauma.

So, in the face of everything it can't do, I'd much rather place my bets there than with humans. I myself am human. What human do you know would spend effort asserting that? One who is constantly dehumanized by society. And I think there is a lot of opportunity for disabled people to use this tech to be accepted as humans? Or to at least gaslight society back into thinking we are?

People won't be able to maintain "The Status Quo" and, frankly, I can't wait.