this post was submitted on 05 Jul 2025
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Showerthoughts
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A "Showerthought" is a simple term used to describe the thoughts that pop into your head while you're doing everyday things like taking a shower, driving, or just daydreaming. The most popular seem to be lighthearted clever little truths, hidden in daily life.
Here are some examples to inspire your own showerthoughts:
- Both “200” and “160” are 2 minutes in microwave math
- When you’re a kid, you don’t realize you’re also watching your mom and dad grow up.
- More dreams have been destroyed by alarm clocks than anything else
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- The entire showerthought must be in the title
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- If your topic is in a grey area, please phrase it to emphasize the fascinating aspects, not the dramatic aspects. You can do this by avoiding overly politicized terms such as "capitalism" and "communism". If you must make comparisons, you can say something is different without saying something is better/worse.
- A good place for politics is c/politicaldiscussion
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No it can't, not without major hallucinations and/or basic errors (ex: Black People tend to be misidentified).
That's the big thing about this AI push, it's subtle mistakes are fucking people over right now. If AI actually worked reliably that's another thing. But right now, people are mostly pretending that AI works and/or ignorant of its flaws.
Don't get LLMs confused with specialized ML algorithms. Hallucination is an LLM problem, algorithms like gait recognition have been honing in accuracy since way before LLMs started development. Where LLMs come into the picture is that they can act as agents, processing queries and then selecting the best fit specialized algorithm to process the data and then cross reference results from different queries to compile a correlated multidomain dataset. Done properly, this will yield not just a single answer but a list of potential answers with their relative degree of certainty.
Look at the Harvard facial recognition glasses as a proof of concept of this kind of approach: https://specialconcentrations.fas.harvard.edu/news/heres-looking-you
That's why I said gait recognition, not facial recognition. Last year GaitNet hit over 99% recognition accuracy, given another year of training the error rate will have gone down and the recognition window will have gone up. https://pmc.ncbi.nlm.nih.gov/articles/PMC11323174/#%3A%7E%3Atext=Diverse+neural+network-based+gait%2Cresearch+direction+is+also+assessed.
Just doing the basics such as number plate recognition works well enough that the integration of these services has become a problem. I believe there is some controversy in the US about this now.