AI-based writer identification for the unknown scribes of the Dead Sea Scrolls
journals.plos.org
journals.plos.org
People change. Maybe the scribe developed back problems or RSI or arthritis. Even writing styles can change. What is the range of single person variability, and how does that compare with the variability we see in the work?
My understanding is there's very little original material from this era to work with - as all biblical canon is replicas, from a handful, to many dozens of generations / copies distant.
There's plenty of scribal material in the Judaic world, they should try their algorithm on it and see if it works.
Writing was not a common skill, so my naive extrapolation of that fact is that there would be few teachers, and consequently more likelihood that two random authors would present material that, prima facie, looked similar.
It's fascinating work, to be sure, though I do wonder about the usefulness of identifying how many authors were involved in penning these various parchments.
was it? I think the society back then was pretty advanced, i.e. literate.
It wouldn't tell them anything much. The script used nowadays is entirely different from the DSS.
> Although one cannot rule out completely that the clear separation between the two halves of the manuscript and the difference in writing patterns are due to a change of writing implement (a different pen), writing fatigue or some injury that the writer suffered when moving on to the second half of the manuscript, the more straightforward explanation is that a change in scribes occurred.
https://elderscrolls.fandom.com/wiki/CHIM
People make it sound like Bethesda games are so buggy they have more bugs than game. Maybe this is because they spend all their time writing lore instead of reading QA reports.
But he is not the only one. For example, the original writer, who has produced some of my favorite in-game books, is Ted Peterson[0]. There is a great interview with him on YouTube[1].
There's no ML involved in the approach, just plain old statistics and feature extraction. I fail to see how any of this relates to AI unless we broaden the definition to a point where it becomes useless and everything that uses an algorithm becomes "AI"...
To my surprise (I wanted to learn something new) all the "old stuff" was there, ML turned out to be some variant of statistical inference with all its typical methods applied (like logistic regression, clustering, etc.).
The new things were: deep neural networks ("normal" NN but with more layers) and total lack of thinking in terms of model - ML keeps adding more and more variables to the model, as much as data we have, no matter if it make sense or not. If the "model" is over-fitted, then, well, let's add some additional quadratic term to the equation, it seems to be helping... Maybe... Sometimes.
So, yes, AI is just old stuff, rebranded and fed with much, much more data. From what I see AI hasn't changed much over the last years. AI seems to be pretty effective in everything that is machine generated data set, as it is easily quantified and has simple representation - That's why it is so good in tracking people - there is a huge amount of data coming from people devices, they are nice and clean.
It is also good in picture analysis - again data are pretty clean (pixels) and easily quantified, what is even more important, so it easy to "train" AI.
When it comes to something more complicated like, for instance, purchase recommendation we get what we get: you bought a fridge, good, you will see fridge adds for next half a year.
Such rebranding is endemic in article headlines like this. The term “AI” may be in vogue but 99.9% of the time it’s really just putting lipstick on a pig.