The hot concept around the late 2000’s and early 2010’s was crowdsourcing: leveraging the expertise of volunteers to build consensus. Quora, Stack Overflow, Reddit, and similar sites came up in that time frame where people would freely lend their expertise on a platform because that platform had a pretty good rule set for encouraging that kind of collaboration and consensus building.
Monetizing that goodwill didn’t just ruin the look and feel of the sites: it permanently altered people’s willingness to participate in those communities. Some, of course, don’t mind contributing. But many do choose to sit things out when they see the whole arrangement as enriching an undeserving middleman.
rumba@lemmy.zip 1 month ago
Works well for now. Wait until there’s something new that it hasn’t been trained on. It needs that Stack Exchange data to train on.
nutsack@lemmy.dbzer0.com 1 month ago
Yes, I think this will create a problem. new things won’t be created very often because there will be a barrier of training corporate controlled AI to learn them
cherrari@feddit.org 1 month ago
I don’t think so. All AI needs now is formal specs of some technical subject, not even human readable docs, let alone translations to other languages. In some ways, this is really beautiful.
SoftestSapphic@lemmy.world 1 month ago
LolnAI can’t do a single thing without humans who have already done it hundreds of thousands of times feeding it their data
okmko@lemmy.world 1 month ago
I used to push back but now I just ignore it when people think that these models have cognition because companies have pushed so hard to call it AI.
123@programming.dev 1 month ago
Technical specs don’t capture the bugs, edge cases and workarounds needed for technical subjects like software.
cherrari@feddit.org 1 month ago
I can only speak for myself obviously, and my context here is some very recent and very extensive experience of applying AI to some new software developed internally in the org where I participate. So far, AI eliminated any need for any kind of assistance with understanding and it was definitely not trained on these particular software, obviously. Hard to imagine why I’d ever go to SO to ask questions about this software, even if I could. And if it works so well on such a tiny edge case, I can’t imagine it will do a bad job on something used at scale.
rumba@lemmy.zip 1 month ago
It can’t handle things it’s not trained on very well, or at least not anything substantially different from what it was trained on.
It can usually apply rules it’s trained on to a small corpus of data in its training data. Give me a list of female YA authors. But when you ask it for something more general (how many R’s there are in certain words) it often fails.
webadict@lemmy.world 1 month ago
Actually, the Rs issue is funny because it WAS trained on that exact information which is why it says strawberry has two Rs, so it’s actually more proof that it only knows what it has been given data on. The thing is, when people misspelled strawberry as “strawbery”, then naturally, people respond, " Strawberry has two Rs." The problem is that LLM learning has no concept of context because it isn’t learning anything. The reinforcement mechanism is what the majority of its data tells it. It regurgitates that strawberry has two Rs because it has been reinforced by its dataset.
skisnow@lemmy.ca 1 month ago
The whole point of StackExchange is that it contained everything that isn’t in the docs.