3 ms·

The article has named sources for its quotes, whereas your comment relies entirely on "almost certainly" which sounds a lot less informed.

achow 3mo ago
OP to me sounds more authentic and seems to have inside information. After a quick search I found a publication actually mentioning about these tools: Ford previously told Business Insider that it had developed two bespoke AI-enhanced scanning tools that helped validate that cars were properly assembled before rolling off the lot. The tools, called AiTriz and MAIVs, both debuted in 2024. https://autos.yahoo.com/policy-and-environment/articles/ford-says-ai-alone-couldnt-172018472.html https://autos.yahoo.com/policy-and-environment/articles/ford... And after doing cursory research on these tools, it is clear they are rudimentary (as compared to SOTA LLMs), they were essentially smartphone mounted on stands and doing visual checks using the camera - so OP could be very right. https://www.businessinsider.com/ford-uses-ai-cameras-in-factories-prevent-recalls-costly-rework-2025-8 https://www.businessinsider.com/ford-uses-ai-cameras-in-fact...
- kamranjon 3mo ago
  A fine-tuned classifier purpose fit for a specific task can easily outperform a SOTA LLM on more modest hardware and often makes a lot more sense.
  - orlp 3mo ago
    If your data is sufficiently noisy or your relationship sufficiently simple a linear regression will outperform a SOTA LLM.
  - scotty79 3mo ago
    Calculator is a great analogy for that kind of specialized models. Way better than humans (and other things) at a specific task. Can't replace humans with it. LLMs are not calculators.
- aprilthird2021 3mo ago
  How can it be inside information if it's in a yahoo article? And why does OP alleging they are talking about technology A not B and you finding out they use technology A (while we all know they also use technology B as well) make OP more likely to be right? Very fallacious thinking
decimalenough 3mo ago
Nothing in the article contradicts their (IMHO accurate) claim. Three years ago boardrooms were not drinking the LLM Kool-aid yet, while ML-powered QC has been around for years. Remember Silicon Valley's hot dog vs not hot dog? That's pretty much all you need, only the hot dog is a car part.
- ehnto 3mo ago
  Apparently it is not all you need, according to the article.

achow 3mo ago

OP to me sounds more authentic and seems to have inside information. After a quick search I found a publication actually mentioning about these tools: Ford previously told Business Insider that it had developed two bespoke AI-enhanced scanning tools that helped validate that cars were properly assembled before rolling off the lot. The tools, called AiTriz and MAIVs, both debuted in 2024. https://autos.yahoo.com/policy-and-environment/articles/ford-says-ai-alone-couldnt-172018472.html https://autos.yahoo.com/policy-and-environment/articles/ford... And after doing cursory research on these tools, it is clear they are rudimentary (as compared to SOTA LLMs), they were essentially smartphone mounted on stands and doing visual checks using the camera - so OP could be very right. https://www.businessinsider.com/ford-uses-ai-cameras-in-factories-prevent-recalls-costly-rework-2025-8 https://www.businessinsider.com/ford-uses-ai-cameras-in-fact...

kamranjon 3mo ago
A fine-tuned classifier purpose fit for a specific task can easily outperform a SOTA LLM on more modest hardware and often makes a lot more sense.
- orlp 3mo ago
  If your data is sufficiently noisy or your relationship sufficiently simple a linear regression will outperform a SOTA LLM.
- scotty79 3mo ago
  Calculator is a great analogy for that kind of specialized models. Way better than humans (and other things) at a specific task. Can't replace humans with it. LLMs are not calculators.
aprilthird2021 3mo ago
How can it be inside information if it's in a yahoo article? And why does OP alleging they are talking about technology A not B and you finding out they use technology A (while we all know they also use technology B as well) make OP more likely to be right? Very fallacious thinking

decimalenough 3mo ago

Nothing in the article contradicts their (IMHO accurate) claim. Three years ago boardrooms were not drinking the LLM Kool-aid yet, while ML-powered QC has been around for years. Remember Silicon Valley's hot dog vs not hot dog? That's pretty much all you need, only the hot dog is a car part.

ehnto 3mo ago
Apparently it is not all you need, according to the article.