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Comment by steveBK123 | Hacker News Reader

That's been my experience both with Tesla AP/FSD implementation & with LLMs.

Super neat trick the first time you encounter it, feels like alien tech from the future.

Then you find all the holes. Use it for months/years and you notice the holes aren't really closing.. The pace of improvement is middling compared to the gap to it meeting the marketing/rhetoric. Eventually using them feels more like a chore than not using them.

It's possible some of these purely data driven ML approaches don't work for problems you need to be more than 80% correct on.

Trading algos that just need to be right 55% of the time to make money, recommendation engines that present a page of movies/songs for you to scroll, Google search results that come back with a list you can peruse, Spam filters that remove some noise from your inbox.. sure.

But authoritative "this is the right answer" or "drive the car without murdering anyone".. these problems are far harder.

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With the AI "revolution," I began to appreciate the simplicity of models we create when doing programming (and physics, biology, and so on as well).

I used to think about these things differently: I felt that because our models of reality are just models, they aren't really something humanity should be proud of that much. Nature is more messy than the models, but we develop them due to our limitations.

AI is a model, too, but of far greater complexity, able to describe reality/nature more closely than what we were able to achieve previously. But now I've begun to value these simple models not because they describe nature that well but because they impose themselves on nature. For example, law, being such a model, is imposed on reality by the state institutions. It doesn't describe the complexity of reality very well, but it makes people take roles in its model and act in a certain way. People now consider whether something is legal or not (instead of moral vs immoral), which can be more productive. In software, if I implement the exchange of information based on an algorithm like Paxos/Raft, I get provable guarantees compared to if I allowed LLMs to exchange information over the network directly.

I think you've found a good analogy there in the concept of moral vs legal. We defined a fixed system to measure against (rule of law) to reduce ambiguity.

Moral code varies with time, place, and individual person. It is a decimal scale of gray rather than a binary true/false.

Places historically that didn't have rule of law left their citizens to the moral interpretation whim of whoever was in charge. The state could impose different punishments on different people for different reasons at different times.

AI models I find a similar fixed&defined vs unlimited&ambiguous issue in ADAS in cars.

German cars with ADAS are limited&defined, have a list of features they perform well, but that is all.

Tesla advertises their system as an all knowing, all seeing system with no defined limits. Of course every time there is an incident they'll let slip certain limits "well it can't really see kids shorter than 3ft" or "well it can't really detect cross traffic in this scenario" etc.

Yep, lots of people are using LLMs for problems LLMs aren't good at.

They still do an alright job, but you get that exact situation of 'eh, its just okay'.

Its the ability to use those responses when they are good, and knowing when to move on from using an LLM as a tool.

Not terribly different than Google Translate.

Ff you have a familiarity with the foreign language, you can cross check yourself & the tool against each other to get to a more competent output.

If you do not know the foreign language at all, the tool will produce word salad that sort of gets your point across while sounding like an alien.

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