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You can't predict --- and therein lies the problem. All you can do is verify. And this negates a lot of the value proposition of AI. The best use of current A

You can't predict --- and therein lies the problem. All you can do is verify. And this negates a lot of the value proposition of AI. The best use of current AI is what it was originally designed for --- things that don't matter much and are highly tolerant of errors --- like web search.

cid435 8mo ago
[flagged]
- jqpabc123 8mo ago
  That’s a massive oversimplification of the field's trajectory. Google introduced the Transformer model in 2017. They built interactive voice response (Google Assistant) and applied it to web search and language translation (all have a large error tolerance) but didn't do much more because they considered reliability to be an issue. ChatGPT was introduced in 2022. It was based on the Transformer model as are all the current AI chatbots. ChatGPT's big innovation was scale. They spend billions to digitize everything they could find on the web and beyond and market it as a general purpose AI. But scale has hit a wall. Even with a world of data and an energy budget larger than a small country, reasoning and reliability remains a largely unresolved issue. Computing has traditionally been about reliable answers at low cost. AI offers the opposite --- unreliable answers at high cost. https://research.google/blog/transformer-a-novel-neural-network-architecture-for-language-understanding/ https://research.google/blog/transformer-a-novel-neural-netw...
- yesitcan 8mo ago
  Why do you sound like an LLM? > While LLMs are probabilistic, their accuracy in specific domains—like Tool Calling—is already hitting near-100% reliability. That is where industrialization happens. Is this just an AI bot replying to comments on its own AI post?
Zigurd 8mo ago
Coding agents work well enough to be useful because they can check their own work. Nevertheless it is too generous to call that reasoning. If they're right 80% of the time and they rerun a prompt if the project won't build, they might be right 95% of the time, or even 99% of the time on the third try. And if you know a bit about coding you're probably able to recognize when to intervene. That's not to denigrate a real productivity booster. It is however a warning to anyone applying LLM-based AI to use cases where you don't have the kind of training corpus and formal framework around correctness that characterizes coding. Coding surely matters. But it also might be a truly unique use case.
- coffeefirst 8mo ago
  To an extent… that will get you a project that builds, and passes any other objective detectable test, but it can’t tell you if it’s reasonable or good. Which means we’re getting a lot of shitty work that passes tests.
deleted 8mo ago
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cid435 8mo ago

[flagged]

jqpabc123 8mo ago
That’s a massive oversimplification of the field's trajectory. Google introduced the Transformer model in 2017. They built interactive voice response (Google Assistant) and applied it to web search and language translation (all have a large error tolerance) but didn't do much more because they considered reliability to be an issue. ChatGPT was introduced in 2022. It was based on the Transformer model as are all the current AI chatbots. ChatGPT's big innovation was scale. They spend billions to digitize everything they could find on the web and beyond and market it as a general purpose AI. But scale has hit a wall. Even with a world of data and an energy budget larger than a small country, reasoning and reliability remains a largely unresolved issue. Computing has traditionally been about reliable answers at low cost. AI offers the opposite --- unreliable answers at high cost. https://research.google/blog/transformer-a-novel-neural-network-architecture-for-language-understanding/ https://research.google/blog/transformer-a-novel-neural-netw...
yesitcan 8mo ago
Why do you sound like an LLM? > While LLMs are probabilistic, their accuracy in specific domains—like Tool Calling—is already hitting near-100% reliability. That is where industrialization happens. Is this just an AI bot replying to comments on its own AI post?

Zigurd 8mo ago

Coding agents work well enough to be useful because they can check their own work. Nevertheless it is too generous to call that reasoning. If they're right 80% of the time and they rerun a prompt if the project won't build, they might be right 95% of the time, or even 99% of the time on the third try. And if you know a bit about coding you're probably able to recognize when to intervene. That's not to denigrate a real productivity booster. It is however a warning to anyone applying LLM-based AI to use cases where you don't have the kind of training corpus and formal framework around correctness that characterizes coding. Coding surely matters. But it also might be a truly unique use case.

coffeefirst 8mo ago
To an extent… that will get you a project that builds, and passes any other objective detectable test, but it can’t tell you if it’s reasonable or good. Which means we’re getting a lot of shitty work that passes tests.

deleted 8mo ago

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