Some interesting ideas - a few comments:
My sense you are writing this as someone without lots of experience in writing and publishing scientific articles (correct me if I am wrong).
A question to me is whether you can predict what someone would say on a topic instead of writing about it. I would argue that the act of linearly presenting ideas on paper - "writing" - is a form of extended creative cognitive creation that is difficult to replicate. It woudn't be replicated by a avatar just talking to a human to understand their views. People don't write to convert what's in their heads to communicate it - instead writing creates thinking.
My other comment is that most of the advantages can be gained by AI interpretations and re-imagining of a text e.g. you can ask ChatGPT to take a paper and explain it in more detail by expanding points, or make it simpler. So points 2 and 3 of your advantage can be achieved today and post writing.
Point 4 of the advantages "positive spin" is an incentive issue so not really about effective communication.
Point 1 also could be achieved by the AI reading a text. Of course though the AI can only offer interpretations - which would be true with or without an AI interogating an author (e.g. an AI could read all that authors works to get a better sense of what they might say).
So in sum, I can see avatars/agents as a means of assisting humans to read texts. We already have this is in principle possible today. For example, I am already asking ChatGPT to explain parts of text to me and summarise papers - it will just get better. But I don't see in the near term the avatar being a publication format - rather an interface to publications.
The interesting question for me though which is what might be the optimal publication format to allow LLM's to progress science - where LLM's are able to write papers themselves e.g. review articles. Would it be much different from what we already have? Would we need to provide results in a more machine readable way? (probably)
One recent advancement in science writing (stemming from psychology through spreading) has been the pre-registered format and pre-registration.
Pre-registration often takes the form of a form - which effectively is a dialogue - where you have to answer a set of questions about your design. This forces a kind of thinking that otherwise might not happen before you run a study, which has positive outcomes in the clarity and openness of the thought processes that go into designing a study.
One consequence it can highlight that often we very unclear about how we ... (read more)
Overview: Recent developments in AI will change the world in all sorts of ways. It is likely to revolutionize academic research. This is one proposal regarding how AI might be used to improve the way that we communicate arguments (such as in philosophy). I stand by behind the thought that there are problems with present publication formats that technology could solve, but this particular solution is highly speculative and not super well thought through. Main ideas are bolded.
Problems with the linear paradigm
Current serious argumentative work (such as in philosophy) primarily takes the form of written papers and books, and less commonly lectures and blog posts. Theorists might explain their view in an interactive way in personal contexts, such as at a departmental tea time, but what they send elsewhere, the way that most people might interact with their work, is linear.
Long-form linear argumentative writing is essentially an ancient Greek technology. It hasn't been much improved since. Authors present ideas in sequence, attempting to walk the reader through them in an order that is conducive to understanding. Readers mostly read articles and books straight through. They may occasionally skip around, especially if they are trying to extract details about the argument. However, linear writing isn't particularly suited for this.
Linear presentations have a few issues:
The amount of content in a written work is limited by space and the reader's patience. Articles generally consist of less than a few dozen pages. Books may be much longer, but still aren't long enough to capture all of the author's views. Often, complex assumptions and arguments must be glossed over or left out. Authors sometimes publish the same ideas multiple times to serve the needs of different audiences.
Authors must present their ideas with at most only a few levels of detail for readers in a single work. (E.g. the abstract, an introductory summary, and the full argument.) Readers cannot easily get access to all and only the level of detail of the arguments they want. They can skim parts they aren't interested in, but they can't get more information about arbitrary sections.
Authors have to decide how much background material to include, and what level of sophistication to pitch it to. Many books and articles are written for a professional audience and are inaccessible by those without much experience, or are written for a general audience and gloss over the intricacies.
Authors are responsible for anticipating the potential misunderstandings of the reader and figuring out what to say to avoid them without wasting too much time or space.
These issues are particularly noticeable when reading old philosophical literature. There are significant debates about how to correctly interpret Aristotle or Kant (or even Carnap) that could probably be resolved by a simple conversation with the author.
An AI assisted conversational alternative
Large language models are good at predicting what human beings will say, including human experts. They can be trained on specific corpuses to get good at predicting how specific individuals would talk about specific issues. The results produced in the five years since the invention of transformer architectures have been quite impressive, and even without additional computer power or further architectual advances they are sure to improve substantially over time.
Instead of presenting ideas linearly on paper, in the future an author might train AIs to predict what they would say about a topic instead of writing a paper about it. Call a chatbot trained to present and answer questions about a specific argument in the same manner as its originator a 'dialectical avatar'. People may learn from an avatar the same way they would learn from a dedicated tutor who had conducted a detailed interview with author and read everything else they had written.
The training process for creating a new avatar might involve the author explaining their ideas to the avatar and answering questions. The avatar might be shown a body of their existing work and their influences, or perhaps even given access to other avatars to flesh out their intellectual inclinations more broadly. The avatars might prompt the author for details or nuance to understand the argument that they would need in order to accurately explain the view. AI might also be used to figure out what questions would best help the avatar understand nuance in the views. The author can verify that the avatar gives accurate answers to disparate questions before it goes to publication.
Some advantages:
An avatar could provide insight into any aspect of an argument to its audience. It could go into far more depth on different arguments than is possible in an ordinary paper or book.
An avatar could tailor its answers to the particular needs and interests of the audience. By responding to particular questions, it could focus only on the content that audience cares about. In the same way that humans use context to decide how to frame things, the avatar could make judgments on the fly about what its questioner is interested in.
An avatar could tailor its answers to the capacities and background knowledge of its audience. It can avoid technical lingo, or be prepared to explain any use of terminology in the specific context it occurs in. If the audience knows little about neuroscience but lots about statistics, it could briefly gloss the statistical side while going in depth on the neuroscience.
The process of training an avatar would prompt authors to think more carefully about various aspects of their view rather than diverting their attention to how best to present that view.
Viability
Large language models can come reasonably close to enabling this functionality already. A LLM trained to respond like Dan Dennett has proven difficult to distinguish from the real person given brief responses to philosophical questions. I suspect that no major new advancements would be needed to make a dialectical avatar work reasonably well given sufficient training. That said, existing models aren't designed for this purpose and wouldn't necessarily perform well at all without substantial tweaking.
LLM aren't yet particularly great at following or producing long and complex arguments. It is not hard to imagine that a system for generating competent avatars could be possible in as little as five years, if anyone wanted to spend the effort and resources to build one.
It would probably take a lot longer before this publication format was culturally feasible in academia. If dialectical avatars could be trained today, my gut is that they would not be used widely at all even if they had clear advantages. Dialectical avatars may not have a chance to be established before they are superseded by other cultural and technological changes, such as the transference of most intellectual labor to AIs.