Are all uses of ai out of the question?
I understand most of the reasoning around this. Training ai models requires gigantic datacenters that consume copious amounts of resources (electricity and water) making them more expensive for everyone for something that doesn’t have much benefits. If anything it feels like its fuled a downturn in quality of content, intelligence and pretty much everything.
With the recent job market in the US I’ve found myself having little to no choice on what I work on and I’ve found myself working on ai.
The more I learn about it the angrier I get about things like generative ai. So things that are open ended like generating art or prose. Ai should be a tool to help us, not take away the things that make us happy so others can make a quick buck taking a shortcut.
It doesn’t help that ai is pretty shit at these jobs, spending cycles upon cycles of processing just to come up with hallucinated slop.
But I’m beginning to think that when it comes to ai refinement there’s actually something useful there. The idea is to use heuristics that run on your machine to reduce the context and amount of iterations/cycles that an ai/LLM spends on a specific query. Thus reducing the chance of hallucinations and stopping the slop. The caveat is that this can’t be used for artistic purposes as it requires you to be able to digest and specify context and instructions, which is harder to do for generative ai (maybe impossible, I haven’t gone down that rabbit hole cause I don’t think ai should be generating any type of art at all)
The ultimate goal behind refinement is making existing models more useful, reducing the need to be coming up with new models every season and consuming all our resources to generate more garbage.
And then making the models themselves need less hardware and less resources when executed.
I can come up with some examples if people want to hear more about it.
How do you all feel about that? Is it still a hard no in that context for ai?
All in all hopefully I won’t be working much longer in this space but I’ll make the most of what I’m contributing to it to make it better and not further reckless consumption.
I’m actually not convinced by even the first example you give. I’ve yet to read an “AI” summary of a meeting that felt like a good summary. I presume it’s because they lack any concepts and are purely going off which words trigger other words, and so don’t have a way to check the value of relevance of what’s being logged as a person would
They’re not supposed to be good. Just some simple cliffnotes. Much better than nothing. And much faster than actually attending every meeting.
they seem to work reasonably well in my experience.
When my boss tried to get us to use AI that was one of the apps. In my experience it was VERY good at summarizing the content-free meetings of typical corporate bullshit, but it was IMPOSSIBLY bad at meetings that were actually productive. It latched onto the wrong things at a staggering rate.
I’ll pay more attention. I’ve skimmed them a few times but I didn’t care enough to actually read them.
I suspect that’s why people think they work so well. On a superficial scan they seem OK. They’re pretty coherent and on a quick pass it’s easy to miss the important details.
It’s just if you use them to go back over things that were discussed (in meetings with actual content, I mean, not in the usual corporate bloviation) you start seeing them derive incorrect conclusions (like an infamous example where it summarized us as having decided on an action we’d explicitly rejected), or focus on minutiae of fringe elements of the main discussion while barely mentioning the main topic.