I agree! Do you know why anonymous pubkey identities with reputation graphs didn't stick, or any examples of it being used today? In my head that would solve one or two of the problems I see with the modern internet.
It's fine to be small, but we can still work on lowering the bar further and promoting the good parts.
Then, if there is a viable alternative to big social media, my thesis is that there might come a day when a critical mass has been fed up and finds a viable alternative that's still beautiful but no longer small.
Promoting the good parts is very worthwhile and there’s a thriving scene. A bunch of interesting people talk over zoom and IRL regularly because of events.indieweb.org and we just had our 3rd annual weekend camp here in San Diego a few weeks ago.
I don’t know about the rest of big social media switching away, so I’m personally just focused on appreciating the community that’s been built up already instead of evangelizing. Maybe I’m wrong and something open will go viral, like the new Loops video app.
I too liked to prime my own algorithm but Yanis Varoufakis book Technofeudalism kind of ruined it for me. On a individual level it's nice to get good recommendations, but on a societal level I think it's starting to get a bit scary to the point of me wanting to opt out and instead curate my own feeds based on first hand sources.
Yeah I get your point, though I almost exclusively use YouTube as an alternative to TV entertainment. That is, rather than watching Mythbusters, I'm watching Numberphile, FarmCraft101 and such.
I specifically do not normally view "pure" news or similar. I might make the rare exception, like in case of the LA fire where I saw a clip from PBS. That is a very conscious choice, for reasons similar to what you express.
But here reliability comes in again. Calculators are different since the output is correct as long as the input is correct.
LLMs do not guarantee any quality in the output even when processing text, and should in my opinion be verified before used in any serious applications.
> Calculators are different since the output is correct as long as the input is correct.
That isn't really true.[0] The application of calculators to a subject matter is something that does need to be considered in some use cases.
LLMs also have accuracy considerations, and although it may be to a different degree, the subject matter to which they're applicable has a broad range of acceptable accuracies. While some textual subject matter demands a very specific answer, some doesn't: For example, there may be hundreds or thousands of various ways to summarize a text that could be accurate for a particular application.
I think your point stands, but your example shows that anyone using those calculators daily should not be concerned. Those that need precision to the 6+ decimal places for complex equations should know not to fully trust consumer-grade calculators.
The issue with LLMs is that they can be so unpredictable in their behaviour. Take the following prompt that asks GPT-4 to validate the response to "calculate 2+3+5 and only display the result":
GPT-4o mini contradicts itself, which is not something one would expect for something we believe to be extremely simple. However, if you ask it to validate the response to "calculate 2+3+5," it will get it right.
Well, not every tool is a hammer and not every problem is a nail.
If I ask my TI-89 to "Summarize the plot in Harry Potter and the Chamber of Secrets" it responds "ERR"! :D
LLMs are good text processors, pocket calculators are good number processors. Both have limitations, and neither are good at problem sets that are outside of their design strengths. The biggest problem with LLMs aren't that they are bad at a lot of things, it's that they look like they are good at things they aren't good at.
I agree LLMs are good at text processing and I believe they will obsolete jobs that really should be obsoleted. Unless OpenAI, Anthropic and other AI companies come up with a breakthrough on reliability, I think it will be fair to say they will only be players and not leaders. If they can't figure something out, it will be Microsoft, Amazon and Google (distributors of diverse models) that will benefit the most.
I've personally found it is extremely unlikely for multiple good LLMs to fail at the same time, so if you want to process text and be confident in the results, I would just run the same task across 5 good models and if you have a super majority, you can be confident that it was done right.
Neither are humans, that's why we have proofreaders and editors. That doesn't make them any less useful. And a translator will not write the same exact translation for a text longer than a couple of sentences, that does not mean translation is a dead end. Ironically, it's LLMs that made translation a dead end.
