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by TomatoCo 193 days ago
To continue the carpenter analogy, the issue with LLMs is that the shelf looks great but is structurally unsound. That it looks good on surface inspection makes it harder to tell that the person making it had no idea what they're doing.
2 comments

Regardless, if a carpenter is not validating their work before selling it, it's the same as if a researcher doesn't validate their citations before publishing. Neither of them have any excuses, and one isn't harder to detect than the other. It's just straight up laziness regardless.
I think this is a bit unfair. The carpenters are (1) living in world where there’s an extreme focus on delivering as quicklyas possible, (2) being presented with a tool which is promised by prominent figures to be amazing, and (3) the tool is given at a low cost due to being subsidized.

And yet, we’re not supposed to criticize the tool or its makers? Clearly there’s more problems in this world than «lazy carpenters»?

Yes, that's what it means to be a professional, you take responsibility for the quality of your work.
Well, then what does this say of LLM engineers at literally any AI company in existence if they are delivering AI that is unreliable then? Surely, they must take responsibility for the quality of their work and not blame it on something else.
I feel like what "unreliable" means, depends on well you understand LLMs. I use them in my professional work, and they're reliable in terms of I'm always getting tokens back from them, I don't think my local models have failed even once at doing just that. And this is the product that is being sold.

Some people take that to mean that responses from LLMs are (by human standards) "always correct" and "based on knowledge", while this is a misunderstanding about how LLMs work. They don't know "correct" nor do they have "knowledge", they have tokens, that come after tokens, and that's about it.

> they're reliable in terms of I'm always getting tokens back from them

This is not what you are being sold though. They are not selling you "tokens". Check their marketing articles and you will not see the word token or synonym on any of their headings or subheadings. You are being sold these abilities:

- “Generate reports, draft emails, summarize meetings, and complete projects.”

- “Automate repetitive tasks, like converting screenshots or dashboards into presentations … rearranging meetings … updating spreadsheets with new financial data while retaining the same formatting.”

- "Support-type automation: e.g. customer support agents that can summarize incoming messages, detect sentiment, route tickets to the right team."

- "For enterprise workflows: via Gemini Enterprise — allowing firms to connect internal data sources (e.g. CRM, BI, SharePoint, Salesforce, SAP) and build custom AI agents that can: answer complex questions, carry out tasks, iterate deliverables — effectively automating internal processes."

These are taken straight from their websites. The idea that you are JUST being sold tokens is as hilariously fictional as any company selling you their app was actually just selling you patterns of pixels on your screen.

it’s not “some people”, it’s practically everyone that doesn’t understand how these tools work, and even some people that do.

Lawyers are running their careers by citing hallucinated cases. Researchers are writing papers with hallucinated references. Programmers are taking down production by not verifying AI code.

Humans were made to do things, not to verify things. Verifying something is 10x harder than doing it right. AI in the hands of humans is a foot rocket launcher.

It's a shame the slop generators don't ever have to take responsibility for the trash they've produced.
That's beside the point. While there may be many reasonable critiques of AI, none of them reduce the responsibilities of scientist.
Yeah this is a prime example of what I'm talking about. AI's produce trash and it's everyone else's problem to deal with.
>While there many reasonable critiques of AI

But you just said we weren’t supposed to criticize the purveyors of AI or the tools themselves.

The entire thread is people missing this simple point.
I use those LLM "deep research" modes every now and then. They can be useful for some use cases. I'd never think to freaking paste it into a paper and submit it or publish it without checking; that boggles the mind.

The problem is that a researcher who does that is almost guaranteed to be careless about other things too. So the problem isn't just the LLM, or even the citations, but the ambient level of acceptable mediocrity.

> And yet, we’re not supposed to criticize the tool or its makers?

Exactly, they're not forcing anyone to use these things, but sometimes others (their managers/bosses) forced them to. Yet it's their responsibility for choosing the right tool for the right problem, like any other professional.

If a carpenter shows up to put a roof yet their hammer or nail-gun can't actually put in nails, who'd you blame; the tool, the toolmaker or the carpenter?

> If a carpenter shows up to put a roof yet their hammer or nail-gun can't actually put in nails, who'd you blame; the tool, the toolmaker or the carpenter?

I would be unhappy with the carpenter, yes. But if the toolmaker was constantly over-promising (lying?), lobbying with governments, pushing their tools into the hands of carpenters, never taking responsibility, then I would also criticize the toolmaker. It’s also a toolmaker’s responsibility to be honest about what the tool should be used for.

I think it’s a bit too simplistic to say «AI is not the problem» with the current state of the industry.

If I hired a carpenter, he did a bad job, and he starts to blame the toolmaker because they lobby the government and over-promised what that hammer could do, I'd still put the blame on the carpenter. It's his tools, I couldn't give less of a damn why he got them, I trust him to be a professional, and if he falls for some scam or over-promised hammers, that means he did a bad job.

Just like as a software developer, you cannot blame Amazon because your platform is down, if you chose to host all of your platform there. You made that choice, you stand for the consequences, pushing the blame on the ones who are providing you with the tooling is the action of someone weak who fail to realize their own responsibilities. Professionals take responsibility for every choice they make, not just the good ones.

> I think it’s a bit too simplistic to say «AI is not the problem» with the current state of the industry.

Agree, and I wouldn't say anything like that either, which makes it a bit strange to include a reply to something no one in this comment thread seems to have said.

That’s not what is happening with AI companies, and you damn well know it.
OpenAI and Anthropic at least are both pretty clear about the fact that you need to check the output:

https://openai.com/policies/row-terms-of-use/

https://www.anthropic.com/legal/aup

OpenAI:

> When you use our Services you understand and agree:

Output may not always be accurate. You should not rely on Output from our Services as a sole source of truth or factual information, or as a substitute for professional advice. You must evaluate Output for accuracy and appropriateness for your use case, including using human review as appropriate, before using or sharing Output from the Services. You must not use any Output relating to a person for any purpose that could have a legal or material impact on that person, such as making credit, educational, employment, housing, insurance, legal, medical, or other important decisions about them. Our Services may provide incomplete, incorrect, or offensive Output that does not represent OpenAI’s views. If Output references any third party products or services, it doesn’t mean the third party endorses or is affiliated with OpenAI.

Anthropic:

> When using our products or services to provide advice, recommendations, or in subjective decision-making directly affecting individuals or consumers, a qualified professional in that field must review the content or decision prior to dissemination or finalization. You or your organization are responsible for the accuracy and appropriateness of that information.

So I don't think we can say they are lying.

A poor workman blames his tools. So please take responsibility for what you deliver. And if the result is bad, you can learn from it. That doesn't have to mean not use AI but it definitely means that you need to fact check more thoroughly.

Very good analogy I'd say.

Also similar to what Temu, Wish, and other similar sites offer. Picture and specs might look good but it will likely be disappointing in the end.