[split] future video about AI, all input welcome /kg

[Koen G]

It's on my shortlist alright. I'm working on another one first, but I won't wait too long for this one


EDIT: I'll use this space to make a proposed structure for the video, all ideas remain welcome. Note: this is not a script, just a quick structure outline.


1) Intro

2) Explain that in current popular discourse, usually AI = LLM so I will be focusing on LLMs

3) Advantages and disadvantages of current generation LLMs in Voynich research

Advantages: 

• ChatGPT is great at translating natural languages, much better than google Translate. This even works for imperfect inputs, older forms of languages... 
  --> I have an interesting example about this from personal experience, and I can easily show examples, this one is no problem
  
• ChatGPT can even read image files and help you read handwriting in manuscripts 
  --> this was highly imperfect when I last tried it - any experiences?
  
• ChatGPT can help you write code to use in your research
  --> I have an example of this where I used code to help me analyze text files for a video, not the most exciting one; other stories are welcome.
  
• ChatGPT could be used to... brainstorm? Jog creativity? Something like that? How do I put this? Any examples?
  

Disadvantages:

• Current generations of LLMs hallucinate frequently, especially when they don't have a lot of data on a subject.
  
• When they make a mistake, they do so with great confidence. You, the user, have to notice this mistake and correct them. This means that you still need to do your own research and fact check important elements.
  
• ChatGPT is a "people pleaser". It prefers to make up an example just to give you something, and will gladly go along in whatever you propose if you are insistent enough. This means that for Voynich theorists, using ChatGPT is a bit like playing with their own echo.
  
• Current generation LLMs still struggle with certain tasks essential to our work (not sure yet how to put this)
  --> this might change in the future. we may want to add the test here?

General issue with AI

LLMs work by analyzing a vast amount of linguistic content, and tap from this to give you a human-sounding output. It produces the text output that is most likely, based on all the text it was trained on. RadioFM: "I would argue that all current and future LLMs' 'reasoning' capabilities will continue to be bound by their underlying Language Model nature - they might be able to tackle tasks involving natural languages or some formal ones (algebraic expressions, predicate calculus, etc.) but will probably suck at translating or manipulating low resource languages like Voynichese."

I can do a lot with the quote found by Matthias: 

2. Research on problem-solving

Research on problem-solving has a long tradition in both psychology and education. Jonassen (1997) classifies problems on a continuum from well-structured to ill-structured. Well-structured problems have a well-defined initial state, a known goal state or solution, and a constrained set of known procedures for solving a class of problems. In contrast, the solutions to ill-structured problems are neither predictable nor convergent because they often possess unknown aspects. Additionally, they possess multiple solutions or solution strategies or often no solutions at all (Funke, 2012).

--> I can show that Voynich research is very much an ill-structured business


(WIP)