Robyn Speer says in his view natural language processing as a field has been taken over by #algogens And most NLP research now depends on closed data from the #algogens providers.

Researcher Michael Macdonald created a card index database of safaitic inscriptions which he and French archaeologist Laïla Nehmé eventually morphed into a digital database which included a collection of photographs of the extant texts.

Is LaMDA really able to "learn higher-level concepts" or is it just a large, straight-forward information theoretic-based prediction engine?

Rhetoric of encomium

How do institutions form around notions of merit?

Me: what about blurbs as evidence of implied social networks? Who blurbs whom? How are these invitations sent/received and by whom?

diachronic: how blurbs evolve over time

Signals, can blurbs predict: - the field of the work - gender - other

Emergence or decrease of signals with respect to time

Imitation of styles and choices. - how does this happen? contagion - I'm reminded of George Mathew Dutcher admonition:

Imitation to be avoided. Avoid the mannerisms and personal peculiarities of method or style of well-known writers, such as Carlyle or Macaulay. (see: https://hypothes.is/a/ROR3VCDEEe2sZNOy4rwRgQ )

Systematic studies of related words within corpora. (this idea should have a clever name) word2vec, word correlations, information theory

How does praise work?

metaphors within blurbs (eg: light, scintillating, brilliant, new lens, etc.)

https://github.com/CopticScriptorium<br /> Tools and technologies for digital and computational research into Coptic language and literature

https://genizalab.princeton.edu/events/2022/princeton-machine-learning-and-future-philology-symposium

Was this recorded?

The Friedberg Judeo-Arabic Project contains a large corpus of Judeo-Arabic text which can be manually searched to help improve translations of texts, but it might also be profitably mined using information theoretic and corpus linguistic methods to provide larger group textual translations and suggestions at a grander scale.

Computer means can potentially be used to check or suggest potential "joins" of fragments of historical documents.

An example of some of this work can be seen in the Friedberg Genizah Project and their digital tools.

https://www.researchgate.net/profile/Robert-Amsler

Apparently follow my blog. :)

Makes me wonder how we might better process and semantically parse peoples' personal notes, particularly when they're atomic and cross-linked?

Definitely a paragraph aimed at the student in the manner of a syllabus, but also an interesting tidbit on the potential evolution of writing forms over time.

How does language over time change with respect to the types and styles of writing forms, particularly when they're prescribed or generally standardized over time? How do these same standards evolve over time and change things at the level of the larger pictures?

rose colored glasses...

Prior to concordances of the Christian Bible there are examples of Hebrew manuscripts in the twelfth and thirteenth centuries that have lists of words and sentences or phrases in which they occurred. They didn't include exact locations with the presumption being that most scholars would know the texts well enough to quickly find them based on the phrases used.

Early concordances were later made unnecessary as tools as digital search could dramatically decrease the load. However these tools might miss the value found in the serendipity of searching through broad word lists.

Has anyone made a concordance search and display tool to automatically generate concordances of any particular texts? Do professional indexers use these? What might be the implications of overlapping concordances of seminal texts within the corpus linguistics space?

Fun tools like the Bible Munger now exist to play around with find and replace functionality. https://biblemunger.micahrl.com/munge

Online tools also have multi-translation versions that will show translational differences between the seemingly ever-growing number of English translations of the Bible.

I can't help but think, what if one used their own collected corpus of ideas based on their ever-growing commonplace book to create a text generator? Then by taking notes, highlighting other work, and doing your own work, you're creating a corpus of material that's imminently interesting to you. This also means that by subsuming text over time in making your own notes, the artificial intelligence will more likely also be using your own prior thought patterns to make something that from an information theoretic standpoint look and sound more like you. It would have your "hand" so to speak.

https://vimeo.com/232545219

from: Eyeo Conference 2017

• http://eyeofestival.com/2017/speaker/robin-sloan/
• https://vimeo.com/channels/eyeo2017/232545219

Description

Robin Sloan at Eyeo 2017 | Writing with the Machine | Language models built with recurrent neural networks are advancing the state of the art on what feels like a weekly basis; off-the-shelf code is capable of astonishing mimicry and composition. What happens, though, when we take those models off the command line and put them into an interactive writing environment? In this talk Robin presents demos of several tools, including one presented here for the first time. He discusses motivations and process, shares some technical tips, proposes a course for the future — and along the way, write at least one short story together with the audience: all of us, and the machine.

Notes

Robin created a corpus using If Magazine and Galaxy Magazine from the Internet Archive and used it as a writing tool. He talks about using a few other models for generating text.

Some of the idea here is reminiscent of the way John McPhee used the 1913 Webster Dictionary for finding words (or le mot juste) for his work, as tangentially suggested in Draft #4 in The New Yorker (2013-04-22)

Cross reference: https://hypothes.is/a/t2a9_pTQEeuNSDf16lq3qw and https://hypothes.is/a/vUG82pTOEeu6Z99lBsrRrg from https://jsomers.net/blog/dictionary

Croatian acapella singing: klapa https://www.youtube.com/watch?v=sciwtWcfdH4

Writing using the adjacent possible.

Corpus building as an art [~37:00]

Forgetting what one trained their model on and then seeing the unexpected come out of it. This is similar to Luhmann's use of the zettelkasten as a serendipitous writing partner.

Open questions

How might we use information theory to do this more easily?

What does a person or machine's "hand" look like in the long term with these tools?

Can we use corpus linguistics in reverse for this?

What sources would you use to train your model?

References:

• Andrej Karpathy. 2015. "The Unreasonable Effectiveness of Recurrent Neural Networks"
• Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, et al. "Generating sentences from a continuous space." 2015. arXiv: 1511.06349
• Stanislau Semeniuta, Aliaksei Severyn, and Erhardt Barth. 2017. "A Hybrid Convolutional Variational Autoencoder for Text generation." arXiv:1702.02390
• Soroush Mehri, et al. 2017. "SampleRNN: An Unconditional End-to-End Neural Audio Generation Model." arXiv:1612.07837 applies neural networks to sound and sound production

It would be interesting to examine some of this language and these euphemisms to uncover the change over time.

Sanders, J., Tosi, A., Obradović, S., Miligi, I., & Delaney, L. (2021). Lessons from lockdown: Media discourse on the role of behavioural science in the UK COVID-19 response. PsyArXiv. https://doi.org/10.31234/osf.io/dw85a

Only fifteen of the thirty-seven commonplace books were written in his hand. He might have dictated the others to a secretary, but the nature of his authorship, if it existed, remains a matter of conjecture. A great deal of guesswork also must go into the interpretation of the entries in his own hand, because none of them are dated. Unlike the notes of Harvey, they consist of endless excerpts, which cannot be connected with anything that was happening in the world of politics.

I find myself wondering what this study of his commonplace books would look like if it were digitized and cross-linked? Sadly the lack of dates on the posts would prevent some knowledge from being captured, but what would the broader corpus look like?

Consider the broader digital humanities perspective of this. Something akin to corpus linguistics, but at the level of view of what a single person reads, thinks, and reacts to over the course of their own lifetime.

How much of a person could be recreated from such a collection?

Looks like some serious power hiding in here.

Interesting use of digital highlights--determining how "read" a particular book is.

And it's not just happening with text, but it also happens with speech as I've written before: Complexity isn’t a Vice: 10 Word Answers and Doubletalk in Election 2016 In fact, in this mentioned case, looking at transcripts actually helps to reveal that the emperor had no clothes because there's so much missing from the speech that the text doesn't have enough space to fill in the gaps the way the live speech did.

Circle back around and read this when it comes out.

Similarly, these other references should be an interesting read as well.

This isn't a very difficult problem and the underpinnings of it are well laid out by John R. Pierce in An Introduction to Information Theory: Symbols, Signals and Noise. In it he has a lot of interesting tidbits about language and structure from an engineering perspective including the reason why crossword puzzles work.

close reading, distant reading, corpus linguistics

I can't help but think here about the application of digital technology to large bodies of literature in the creation of the field of corpus linguistics.

If traditional sociology means anything, then a digital incarnation of it should create physical and trackable means that can potentially be more easily studied as a result. Just the same way that Mark Dredze has been able to look at Twitter data to analyze public health data like influenza, we should be able to more easily quantify sociological phenomenon in aggregate by looking at larger and richer data sets of online interactions.

There's also likely some value in studying the quantities of digital exhaust that companies like Google, Amazon, Facebook, etc. are using for surveillance capitalism.