91–100 of 139 posts
For all the posturing and crypto hate on HN, we're entering a world where it's socially acceptable to use 1000W of computing power and 5 seconds of inference time to parse a tiny HTML fragment which would take microseconds with traditional methods - and people are cheering about it. Time for some self-reflection? That's not very green.
There is a lot of business value happening in the AI space and its only going to get better.
For all the posturing and crypto hate on HN, we're entering a world where it's socially acceptable to use 1000W of computing power and 5 seconds of inference time to parse a tiny HTML fragment which would take microseconds with traditional methods - and people are cheering about it. Time for some self-reflection? That's not very green.
Crypto energy requirements go up as the currency gets more traction. TFA shows that groq is many times faster than GPT-4. Up to 18x groq claims. Faster means less energy. So I think it's just a matter of time until these things become ridiculously power efficient (eg run on phones in sub second times)
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I for one am glad I can offload all the regex to LLMs. Powerful? Yes. Human readable for beginners? No.
Personally when I am parsing structured data I prefer to use parsers that won't hallucinate data but that's just me. Also, don't parse HTML with regular expressions.
Whether or not this benefit outweighs the significant problems (cost, speed, accuracy and determinism) is up to the use case. For most use cases I can think of, the speed and accuracy of an actual parser would be preferable.
However, in situations where one is parsing highly dynamic HTML (eg if each business type had slightly different output, or you are scraping a site which updates the structure frequently and breaks your hand written parser) then this could be worth the accuracy loss.
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What a ridiculous complaint. Energy efficiency won't remain static, and even if it were, it's not up to you to decide how to best leverage the available electricity.
> it's not up to you to decide Unless you live in a dictatorship it's definitely up to us to decide... Otherwise you leave your voice to the top 0.0001% business owners and expect them to work for your good and not for their own interests Also read about the rebound effect. Planes are twice as efficient as they were 100 years ago yet they pollute infinitely more as a whole. There is nothing ridiculous about the comme…
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Yes, we absolutely are there: https://youtu.be/D3oRN5JNMWs?feature=shared My professor (Sir Michael Brady) at university 14 years ago set up a company to do this very thing, and he already had reliable models back before 2010. I believe their company was called Oxford Imaging or something similar.
Yep, everyone seems to forget that ML was available before 2021. Had a conversation recently with my former colleague who learned about some plastic packaging company which used "AI" to predict client orders and inform them about scheduling implications. When I told him that you don't need Transformers and 30GB models for that, he was quasi-confused, cause he kinda knew it but the hype just overtook his knowledge.
A lot of people seem to be using GPT-4 for tasks like text classification and NER, and they’d be much better off fine-tuning a BERT model instead. In vision, too, transformers are great but a lot of times, a CNN is all you really need.
The prompt, for those interested. I find it pretty underspecified, but maybe that's the point. For example, "Business operating hours" could be expanded a little, because "Closed - Opens at XX" is still non-processable in both cases. You are an expert in Web Scraping, so you are capable to find the information in HTML and label them accordingly. Please return the final result in JSON. Data to scrape: title: Name of t…
Lower end models do not have the attention to complete tasks like this, GPT4Turbo will generally have the capability. But to have an optimal pipeline you should really be splitting up these tasks into individual units. You extract each attribute you want independently and then combine it back together however you want. Also asking for JSON upfront is equally suboptimal in the whole process.
I have high confidence that I could accomplish this task using a lower end model with a high degree of accuracy.
Edit: I am not suggesting that an LLM is more optimal than what ever traditional parsing methods they may use, simply the way they are doing it is wrong from an LLM flow.
Earlier quoted context omitted.
What a ridiculous complaint. Energy efficiency won't remain static, and even if it were, it's not up to you to decide how to best leverage the available electricity.
> it's not up to you to decide Unless you live in a dictatorship it's definitely up to us to decide... Otherwise you leave your voice to the top 0.0001% business owners and expect them to work for your good and not for their own interests Also read about the rebound effect. Planes are twice as efficient as they were 100 years ago yet they pollute infinitely more as a whole. There is nothing ridiculous about the comme…
For all the posturing and crypto hate on HN, we're entering a world where it's socially acceptable to use 1000W of computing power and 5 seconds of inference time to parse a tiny HTML fragment which would take microseconds with traditional methods - and people are cheering about it. Time for some self-reflection? That's not very green.
For all the posturing and crypto hate on HN, we're entering a world where it's socially acceptable to use 1000W of computing power and 5 seconds of inference time to parse a tiny HTML fragment which would take microseconds with traditional methods - and people are cheering about it. Time for some self-reflection? That's not very green.
It would take microseconds after a complete program was written by a human? It no longer requires an expert human
This was just a blog to generate traffic on the site. Not to showcase some new use case for an llm.