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by ArcHound 166 days ago
To me the key point was:

> One way of looking at this is that we rediscovered that bureaucracy matters. Although some might chafe against procedures and checklists, they exist for a reason: providing a kind of institutional memory that helps employees avoid common screwups at work.

That's why we want machines in our systems - to eliminate human errors. That's why we implement strict verifiable processes - to minimize the risk of human errors when we need humans in the loop.

Having a machine making human errors is the exact opposite of what we want. How would we even fix this if the machines are trained on human input?

4 comments

I generally agree with you, but am trying to see the world through the new AI lens. Having a machine make human errors isn't the end of the world, it just completely changes the class of problems that the machine should be deployed to. It definitely should not be used for things that need those strict verifiable processes. But it can be used for those processes where human errors are acceptable, since it will inevitably make those some classes of error...just without needing a human to do so.

Up until modern AI, problems typically fell into two disparate classes: things a machine can do, and things only a human can do. There's now this third fuzzy/brackish class in between that we're just beginning to explore.

I can agree with you. And in a discussion with adults working together to address our issues I will.

The issue is that we don't have exact proof that AI is suitable for tasks and the people doing those are already laid off.

The economy now is propped up only by the belief that AI will be so successful that it will eliminate most of the workforce. I just don't see how this ends well.

Remember, regulations are written in blood. And I think we're about to write many brand new regulations.

Yea I'm not attempting to make any broad statements about regulations or who has or hasn't been laid off. Only that a common mistake I see a lot of people making is trying to apply AI/LLMs to tasks that need to be deterministic and, predictably, seeing bad results.

There is a class of task that is well-suited for current gen AI models. Things that are repetitive, tedious, and can absorb some degree of error. But I agree that this class of tasks is significantly narrower than what the market is betting on AI being able to accomplish.

There’s no other input to train on

Humans are still the current best at doing everything humans want to do

The ultimate goal is to transfer all possible human behavior into machine behavior such that they can simulate and iterate improvements on it without the constraints of human biology

The fact that humans are bad to each other means that we’re going to functionally encode all the bad stuff also and so there is no solution to fixing it if the best data that we can get is poisoned.

Like everything it’s a problem with humans not machines

There is, but it's hard to obtain: curate, identify and fix the biases in our current texts.

I am fully aware it's ridiculously expensive to do so.

It’s revisionist at best and totally epistemically broken to try and somehow “fix” the bias because all you’re doing is introducing a new bias

The only possible solution is to create new human data because we’re behaving in ways that are good for society this is literally the only possible future that still includes humanity.

I personally do not believe humans can do this and so I’m building something that tests that empirically.

I don’t think they really want to fix human errors with LLMs. Rather, they want a “human” who works 24x7 for dirt cheap
Dirt cheap being ideally “slightly more than the cost of electricity.”

Aka the same economics as a dishwasher

Because these ai machines aren’t replacing old machines, they’re replacing old humans
Yes, but there's a hidden benefit taken for granted: machines do not make human errors.

Sadly, machines not needing human treatment might be reason enough.