I'm sometimes those things. I'm more often a writer and a futurist. I am enough of a writer specifically, to really not want to live in a world where AI-generated prose substitutes for human stories and essays; and I feel empathy toward other creative professions (most of my friends are visual artists) and don't wish to see that kind of thing happen there either. I do think we have to push back on things like that happening. And I do think that people trying to push
toward that end for monetary gain are erasing much of the joy of being human.
But that being said. I don't think that our need to push back on that particular use of AI, has anything to do with the need to push back on AI itself. Because that's not the only, or to me even the main, point of (generative) AI.
(It's just that "generating wholesale slop that people end up seeing" is the usage-profile that the AI companies can most-easily intermediate and make money on; and it's also the usage-profile that makes it most obvious that "AI is happening", and so markets AI to people not already using AI. So this is the usage profile that all the AI companies benefit from everyone focusing the conversaton around AI on!)
I think a lot of people have a failure of imagination when they think about the upside of "good" generative AI. Which to me is, specifically, the places where generative AI can be better put to use as a tool in a workflow for human creative labor to pass through, rather than being something that substitutes for said human labor.
The programming example might be the most obvious to people here: generative AI lets you write e.g. a hairy SQL statement in the form of a very long natural-language description of the query, or an example of how you'd do it in an actual imperative/functional programming language, and have that transformed into SQL grammar. So you don't have to try (and fail) to remember the arcana of SQL (e.g. how to use things like correlated subqueries, or window functions with explicit windows, or CUBE), always eventually resorting to looking it up anyway. (This is really a condemnation of SQL for having specific one-off syntax sugar for every single thing it wants to encode, rather than a uniform syntax that can fit in your head / trigger IDE auto-suggest.)
But consider Photoshop's "generative fill" tool. Yes, if you're selecting entire huge regions of the image and running that tool on them, it's going to be "doing original work" and the results will be awful. But that's not the use-case the tool was designed for. It was designed to be a better version of content-aware fill, i.e. PatchMatch. If you select a dust speck or tear on a scan of an old photo and run the generative-fill tool over it, the result will usually just be a better version of what you'd have got by running the content-aware fill tool over it. (And if it's not, you don't need to try to fight the tool; you just drop back to using the other tools in the toolbox. Not every tool is applicable in every situation, and that's okay.)
Or consider that newer versions of Melodyne use generative-AI models to better separate audio stems into their own tracks. The input here is your own manually-performed track. The output here is also your own track. Despite being a generative model, the AI here is only generating an internal byproduct that gets discarded (hypotheses for timbre- and acoustic-matched reference instrument performances) to aid the classifier and reduce stemming error/residue.
(And apparently, the compressed-sensing models used by MRI scanners are getting approximately the same benefit by approximately the same means. Less familiar with this, but it's probably the most important benefit here since improving the fidelity of MRI reconstruction means doctors can be more sure that an opacity is really "something" rather than an artifact, and so will have more confidence to diagnose real problems earlier.)
Or consider generative AI used for automatic human-language translation (where it captures much more of the speaker's original meaning and nuance than previous translation models, at the expense of giving the result "LLM voice.") As AI improves at this task, presumably there will be less "LLM voice" and the result will just sound like what the person themselves would have said if they were bilingual, because... that's the task.
In none of these cases is generative AI "producing content." In these cases, generative AI is subsuming the role not of human labor, but of tools that were already used: of "by-the-book" approaches, of heuristic algorithms, of weaker ML models relied upon by heuristic algorithms, etc. But all of these cases benefit from generative AI being improved.
And, to restate my premise: there are many tasks just like these, where generative AI currently has no place (because replacing or "enhancing" any tool in the task's workflow with gAI would currently make the results worse), where you can expect to see gAI eventually replace or "enhance" some tool in the workflow, once gAI model companies turn their attention and benchmarks to trying to optimize for performance in those sorts of tasks.
But that being said. I don't think that our need to push back on that particular use of AI, has anything to do with the need to push back on AI itself. Because that's not the only, or to me even the main, point of (generative) AI.
(It's just that "generating wholesale slop that people end up seeing" is the usage-profile that the AI companies can most-easily intermediate and make money on; and it's also the usage-profile that makes it most obvious that "AI is happening", and so markets AI to people not already using AI. So this is the usage profile that all the AI companies benefit from everyone focusing the conversaton around AI on!)
I think a lot of people have a failure of imagination when they think about the upside of "good" generative AI. Which to me is, specifically, the places where generative AI can be better put to use as a tool in a workflow for human creative labor to pass through, rather than being something that substitutes for said human labor.
The programming example might be the most obvious to people here: generative AI lets you write e.g. a hairy SQL statement in the form of a very long natural-language description of the query, or an example of how you'd do it in an actual imperative/functional programming language, and have that transformed into SQL grammar. So you don't have to try (and fail) to remember the arcana of SQL (e.g. how to use things like correlated subqueries, or window functions with explicit windows, or CUBE), always eventually resorting to looking it up anyway. (This is really a condemnation of SQL for having specific one-off syntax sugar for every single thing it wants to encode, rather than a uniform syntax that can fit in your head / trigger IDE auto-suggest.)
But consider Photoshop's "generative fill" tool. Yes, if you're selecting entire huge regions of the image and running that tool on them, it's going to be "doing original work" and the results will be awful. But that's not the use-case the tool was designed for. It was designed to be a better version of content-aware fill, i.e. PatchMatch. If you select a dust speck or tear on a scan of an old photo and run the generative-fill tool over it, the result will usually just be a better version of what you'd have got by running the content-aware fill tool over it. (And if it's not, you don't need to try to fight the tool; you just drop back to using the other tools in the toolbox. Not every tool is applicable in every situation, and that's okay.)
Or consider that newer versions of Melodyne use generative-AI models to better separate audio stems into their own tracks. The input here is your own manually-performed track. The output here is also your own track. Despite being a generative model, the AI here is only generating an internal byproduct that gets discarded (hypotheses for timbre- and acoustic-matched reference instrument performances) to aid the classifier and reduce stemming error/residue.
(And apparently, the compressed-sensing models used by MRI scanners are getting approximately the same benefit by approximately the same means. Less familiar with this, but it's probably the most important benefit here since improving the fidelity of MRI reconstruction means doctors can be more sure that an opacity is really "something" rather than an artifact, and so will have more confidence to diagnose real problems earlier.)
Or consider generative AI used for automatic human-language translation (where it captures much more of the speaker's original meaning and nuance than previous translation models, at the expense of giving the result "LLM voice.") As AI improves at this task, presumably there will be less "LLM voice" and the result will just sound like what the person themselves would have said if they were bilingual, because... that's the task.
In none of these cases is generative AI "producing content." In these cases, generative AI is subsuming the role not of human labor, but of tools that were already used: of "by-the-book" approaches, of heuristic algorithms, of weaker ML models relied upon by heuristic algorithms, etc. But all of these cases benefit from generative AI being improved.
And, to restate my premise: there are many tasks just like these, where generative AI currently has no place (because replacing or "enhancing" any tool in the task's workflow with gAI would currently make the results worse), where you can expect to see gAI eventually replace or "enhance" some tool in the workflow, once gAI model companies turn their attention and benchmarks to trying to optimize for performance in those sorts of tasks.