The conversation

BobbyJoHNSource author
Lessons learnedExperience excerpt · English

I've made this statement a bunch in other mediums: The reason AI software is always "AI software" …

I've made this statement a bunch in other mediums: The reason AI software is always "AI software" and not just a useful product is because AI is fallible. The reason we can build such deep and complex software system is because each layer can assume the one below it will "just work". If it only worked 99% of the time, we'd all still be interfacing with assembly, because we'd have to be aware of the mistakes that were made and deal with them, otherwise the errors would compound until software was useless. Until AI achieves the level of determinism we have with other software, it'll have to stay at the surface.

Original discussionAI companies are pivoting from creating gods to building products195 source comments · at import
4

What was this responding to?

Parent context · original excerpt

RU
rustypotatoHN

> But when developers put AI in consumer products, people expect it to behave like software, which means that it needs to work deterministically. If your AI travel agent books vacations to the correct destination only 90% of the time, it won’t be successful. This is the fundamental problem that prevents generative AI from becoming a "foundational building block" for most products. Even with rigorous safety measures in place, there are few guarantees about its output. AI is about as solid as sand when it comes to determinism, which is great if you're trying to sell sand, but not so great if you're trying to build a huge structure on top of it.

Full original

Community replies 0

No community replies yet. Add your perspective.

Replies from the source 4

Direct replies available in this import. Original-language excerpts.

LO
loa_in_HN

But software works only 99% of the time. For some definition of work: 99% of days it's run, 99% of clicks, 99% of CPU time in given component, 99% of versions released and linked into some business' production binary, 99% of github tags, 99% of commits, 99% of software that that one guy says is battle-tested

Full original
NF
nforgeritHN

You hit the nail. It's been almost tragically funny how people frantically tried to juggle 5 bars of wet soap in recent 2 years solving problems that (from what I've seen so far) have been already solved in a (boring) deterministic way consuming much less resources. Going further, our predecessors put so much work into getting non-deterministic electronics together providing us with a stable and _correct_ platform, it looks ridiculous how people were trying to squeeze another layer of non-determinism in between to solve the same classes of problems.

Full original
MO
mondrianHN

Recent work from Meta uses AI to automatically increase test coverage with zero human checking of AI outputs. They do this with a strong oracle for AI outputs: whether the AI-generated test compiles, runs, and hits yet-unhit lines of code in the tested codebase. We probably need a lot more work along this dimension of finding use cases where strong automatic verification of AI outputs is possible.

Full original
DI
diatoneHN

The irony here is that there are many domains using statistical methods, that bound the complexity and failure modes of statistical methods successfully. A lot of people struggle with statistics but in domains where the glove fits I think AI will slot in all across the stack really nicely.

Full original
Continue on Hacker News

Respect different perspectives. Share concrete experiences.

Sign in to post, reply, repost, and quote.