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Comment by dreamcompiler | Hacker News Reader

A half-working system is cute and useful when it recommends a pizza place you might like. But the same system is a menace to society when you tell it to drive a car or prescribe a drug to treat an illness or decide a person's prison sentence or protect a perimeter with lethal force.

It's absolutely amazing that so many AI scientists don't seem to understand this distinction.

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To be clear, a lot of them aren't "AI scientists," just hypemen, many of them who came into the "tech" world chasing crypto.

Came here to say this. Just like with crypto.

Many, many AI scientists are very clearly aware of this problem. AI safety researchers have been predicting exactly the failure modes we see in the various incarnations of GPT for years. Issues like alignment and over-fitting are incredibly hard problems to solve, and there's still a lot that isn't known, but they are recognised in the field. The problem is business leaders, investors and governments are utterly clueless on any of this, hence the Google Bard and Microsoft Sydney debacles when they were rushed into the public view.

There is also the risk that the recent massive success of LLMs will bring a lot of excited bandwagon seekers into the field that don't have or appreciate the background on AI safety.

As with all safety rules, those for AI will be written in blood. That blood just hasn't been spilled yet. It will take the eventual benefit of hindsight to look back at the current era and estimate how much danger we are putting ourselves in.

A half working system is good enough for real life tasks when combined and used by and in conjunction with attentive half working human collaborators.

What does it mean to be attentive and half working?

Have you never corrected your phones autocorrect before? That's what they're referring to.

You can be attentive while not giving your 100% to the task. For example while writing a piece of code you might know what you want to accomplish but you aren't straining your brain to type out the actual code. So you type the name of the method and GitHub Copilot gives you an implementation of that method.

Translators who've been picking up after machine translation for the last decade will tell you that it is not, in fact, useful to them. They get paid half as much as before, but correcting the machine output is as much work as translating from scratch, and sometimes more, since the machine can give results that look good at first glance but are fundamentally incorrect.

I don't know much about professional translating, but wouldn't you be able to track the error rate of the machine translation by looking at the number of corrections? And then translators could use this as leverage to negotiate better pay if it's obvious they need are translating from scratch in, say, 50% of cases anyway.

Pay isn't really decided by how much work you do or even completely by how much they need you to do it. What you're describing is a reason to ask for more but it's not leverage to make them give it to you.

I think of machine translation as a tool for people who are not translators, but admins, writers and so on, but need cheap translations that are good enough.

I'm a translator, and that might be true for Google Translate, but not GPT-4. GPT-4 produces translations that are generally accurate, and when instructed to copyedit its own work, well-written. In most cases, all I need to do is fix the occasional error and do some light copyediting. This is much less work than translating from scratch and results in superior quality, since it combines my own skills with the AI's. It is also useful for researching equivalents to obscure terms across languages.

> It is also useful for researching equivalents to obscure terms across languages.

I'm super curious to learn how you use AI to do that. Thanks!

"They get paid half as much as before" equals "this technology has helped the customers of translators to get the same thing done for half the cost".

What about helping correct the grammar and style of an article? Writing out boring code? Creating a test for code you already have? These are all tasks that have important economic consequences. AI is not half-bad at them, it is actually approaching human levels of performance.

If we continue to improve AI, maybe one day its performance in these critical tasks you describe will also surpass humans.

Is it actually approaching human levels of performance, or are we continually reducing the commercially acceptable skill level of all of these things?

I don't think it's anything approaching human-level performance unless the humans you're hiring are inappropriate for the role in the first place.

Just wait a few years.

Today: "Senior" devs by virtue of knowing the cookbook of, at most, one stack.

2028: "Senior" devs by knowing when GPT-12 output POST when it should have been PUT.

The subject at hand is today, not tomorrow.

Two points make a trend.

I’d take a half-working system over what we have now, which is a system I’d put at around a third-working.

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