Walled Culture has written several times about the major impact that generative AI will have on the copyright landscape. More specifically, these systems, which can create quickly and cheaply written material on any topic and in any style, are likely to threaten the publishing industry in profound ways. Exactly how is spelled out in this great post by Suw Charman-Anderson on her Word Count blog. The key point is that large language models (LLMs) are able to generate huge quantities of material. The fact that much of it is poorly written makes things worse, because it becomes harder to find the good stuff[.]
[...] One obvious approach is to try to use AI against AI. That is, to employ automated vetting systems to weed out the obvious rubbish. That will lead to an expensive arms race between competing AI software, with unsatisfactory results for publishers and creators. If anything, it will only cause LLMs to become better and to produce material even faster in an attempt to fool or simply overwhelm the vetting AIs.
The real solution is to move to an entirely different business model, which is based on the unique connection between human creators and their fans. The true fans approach has been discussed here many times in other contexts, and once more reveals itself as resilient in the face of change brought about by rapidly-advancing digital technologies.
True fans are not interested in the flood of AI-generated material: they want authenticity from the writers they know and whose works they love. True fans don't care if LLMs can churn out pale imitations of their favourite creators for almost zero cost. They are happy to support the future work of traditional creators by paying a decent price for material. They understand that LLMs may be able to produce at an ever-cheaper cost, but that humans can't.
There's a place for publishers (and literary magazines) in this world, helping writers connect with their readers, and turning writing that fans support into publications offered in a variety of formats, both digital and physical. But for that to happen publishers must accept that they serve creators. That's unlike today, where many writers are little more than hired labourers churning out work for the larger publishing houses to exploit.
In today's new world of slick, practically cost-free LLMs, even the pittance of royalties will no longer be on offer to most creators. It's time for the latter to move on to where they are deeply appreciated, fairly paid, and really belong: among their true fans.
This first sounded like a description of Patreon, but what's he talking about is something like a people-run Patreon that has all the bells and whistles of recommendation algorithms, reviews, etc., not just a simple way to give money directly to individuals. My bet is whomever writes the first successful one gets bought out by an Amazon-like entity . . . [Ed.]
Speaking of the existential threat of AI is science fiction, and bad science fiction for that matter because it is not based on anything we know about science, logic, and nothing that we even know about ourselves:
Despite their apparent success, LLMs are not (really) 'models of language' but are statistical models of the regularities found in linguistic communication. Models and theories should explain a phenomenon (e.g., F = ma) but LLMs are not explainable because explainability requires structured semantics and reversible compositionality that these models do not admit (see Saba, 2023 for more details). In fact, and due to the subsymbolic nature of LLMs, whatever 'knowledge' these models acquire about language will always be buried in billions of microfeatures (weights), none of which is meaningful on its own. In addition to the lack of explainability, LLMs will always generate biased and toxic language since they are susceptible to the biases and toxicity in their training data (Bender et. al., 2021). Moreover, and due to their statistical nature, these systems will never be trusted to decide on the "truthfulness" of the content they generate (Borji, 2023) – LLMs ingest text and they cannot decide which fragments of text are true and which are not. Note that none of these problematic issues are a function of scale but are paradigmatic issues that are a byproduct of the architecture of deep neural networks (DNNs) and their training procedures. Finally, and contrary to some misguided narrative, these LLMs do not have human-level understanding of language (for lack of space we do not discuss here the limitations of LLMs regarding their linguistic competence, but see this for some examples of problems related to intentionality and commonsense reasoning that these models will always have problems with). Our focus here is on the now popular theme of how dangerous these systems are to humanity.
The article goes on to provide a statistical argument as to why we are many, many years away from AI being an existential threat, ending with:
So enjoy the news about "the potential danger of AI". But watch and read this news like you're watching a really funny sitcom. Make a nice drink (or a nice cup of tea), listen and smile. And then please, sleep well, because all is OK, no matter what some self-appointed god fathers say. They might know about LLMs, but they apparently never heard of BDIs.
The author's conclusion seems to be that although AI may pose a threat to certain professions, it doesn't endanger the existence of humanity.
- Former Google CEO Says AI Poses an 'Existential Risk' That Puts Lives in Danger
- Writers and Publishers Face an Existential Threat From AI: Time to Embrace the True Fans Model
- Artificial Intelligence 'Godfather' on AI Possibly Wiping Out Humanity: 'It's Not Inconceivable'
- Erasing Authors, Google and Bing's AI Bots Endanger Open Web