#ff2023 — Public Fediverse posts

Rachel Senese Myers – Adopting Whisper: Creating a front end optimized for processing needs
Georgia State University

Oral history. Ohms. Not searchable.

Experiment: faster Whisper. Took learnings from #FF2023. Blew Adobe Premiere Pro out of the water. IT said, you can't do this - command line. Need UI.

Whisper Web UI.
No database. Browser crash, data gone.

Whishper. Love database. No batch upload. No diarization. No batch edits.

#FF2024 #AI4LAM

Last year at #FF2023, talked about AMP. Now supports Whisper.

#FF2024 #AI4LAM

Inspired by #FF2023, a survey was done to find out
1. Kinds of data
2. Management of records
3. Organisational and technical factors
4. Middleware?
5. Data quality assurance
6. Person

Big portion = library.
Region: mostly UK, USA, Europe
Largest group Crowdsourcing (without machine learning)

#FF2024 #AI4LAM

Retour sur le dernier Futurs Fantastiques de @AI4LAM par @figoblog #FF2023 https://figoblog.org/2023/11/19/toujours-plus-de-futurs-fantastiques-edition-2023/ #AI #IA #Archives #GLAM

My notes from the Fantastic Futures 2023 conference held in Vancouver by @AI4LAM last week https://www.openobjects.org.uk/2023/11/fantastic-futures-2023-ai4lam-in-vancouver/

Thanks again to the organisers and presenters!

#FF2023 #AI #GLAM #MachineLearning #DataScience #CollectionsAsData #CollectionsOnline

@ingridbmason presenting at #FF2023 about plans for #FF2024, check out their webpage for more info: https://www.nfsa.gov.au/fantastic-futures-canberra-2024-artificial-intelligence-libraries-archives-and-museums

#FF2023 Last talks done! Reflecting on it, a general theme in talks and chats is the temptation to lower 'quality' to be able to start to use ML AI systems in production. People are keen to generate metadata with the imperfect tools we have now, but that runs into issues of trust - we need new conventions for displaying 'data in progress' alongside expert human records.

Workflows! So many projects have been assemblages of different machine learning / AI tools with some manual checking or correction. Data ages like wine, software like fish - looking ahead, people might want to re-run processes as tools improve (or break) over time, so they should be modular. Keep (and version) the data, don't expect the tool to be around forever.

It's so useful hearing about things that didn't work or were hard to get right - common errors in different types of tools, etc.

#FF2023
Generative AI at JSTOR FAQ https://www.jstor.org/generative-ai-faq from Bryan Ryder / Beth LaPensee's talk

Perfect reminder from: Thomas Hervé Mboa Nkoudou, Researcher in Residence, Centre d’Expertise International de Montreal en Intelligence Artificielle (CEIMIA, International Centre of Expertise In Montreal on Artificial Intelligence)

To avoid digital extractivism. #ff2023

#FF2023
Scott Young and Jason Clark (Montana State University) shared http://lib.montana.edu/responsible-ai

Emmanuel A. Oduagwu from the Department of Library & Information Science, Federal Polytechnic, Nigeria, calls for realistic and sustainable collaborations between developing countries - library professionals need technical skills to integrate AI tools into library service delivery; they can't work in isolation from ICT. How can other nations help?

An example https://ai.harvardartmuseums.org/object/228608

Showing predicted tags like this is a good step towards AI literacy, and might provide an interesting basis for AI explainability as discussed earlier at #FF2023

#FF2023 Nice 'AI explorer' from Harvard Art Museums https://ai.harvardartmuseums.org/search/elephant presented by Jeff Steward

It's a really nice way of seeing art through the eyes of different image tagging / labelling services like Imagga, Amazon, Clarifai, Microsoft

##FF2023 moves on to shorter talks on 'AI and collections management'. Jon Dunn and Emily Lynema shared work on AMP, an audiovisual metadata platform, built on https://usegalaxy.org/ for workflow management. Someone mentioned https://airflow.apache.org/ yesterday - I'd love to know more about GLAMs experiences with these workflow tools for machine learning / AI

Michael Ridley: Tania Lombrozo, Princeton - explanation is how we negotiate, and a form of currency for the exchange of beliefs. #ff2023 #XAI #ExplainableAI

Critical making for #GLAMs, a call to action for AI/ML #FF2023

Mike quotes:

"Technology designers are the new policymakers; we didn't elect them but their decisions determine the rules we live by."
Latanya Sweeney
Harvard University
Director of the Public Interest Tech Lab

#FF2023 #XAI #AI

#FF2023 Mike - explanations should be actionable and contestable. They should enable reflection, not just acquiescence.

Algorithm literacy for LAMs. Embed into information literacy programmes. Use algorithms with awareness. Create algorithms with integrity.

Policy and regulation for GLAMs: engage with policy and regulatory activities; insist on explainability as a core principle; promote an explanatory systems approach; champion the needs of the non-expert, lay person.

Critical making for GLAMs - build our own tools and systems; operationalise the principles of HCXAI; explore and interrogate for bias, misinformation and deception; optimise for social justice and equity #XAI

Mike quotes 'Not everything that is important lies inside the black box of AI. Critical insights can lie outside it. Why? Because that's where the human are.' https://medium.com/human-centered-ai/explainable-ai-reloaded-do-we-need-to-rethink-our-xai-expectations-in-the-era-of-large-language-9f08eda4d218 Ehsan and Riedl

#FF2023

#FF2023 Day 2 begins with Mike Ridley on 'The Explainability Imperative' (for AI).

We need trust and accountability as machine learning is consequential. It has an impact on our lives.

Why isn't explainability the default?

Explainabilty priorities for LAM
HCXAI
Policy and regulation
Algorithmic literacy
Critical making

#XAI
The Fantastic Futures 2023 programme, for context https://docs.google.com/document/d/1uFJYkk3tw9a4cKqM96-j0hRXF2q0S1O6WNhr3F6zS6g/edit

And we're underway for the 2nd day of #FF2023, following along online - recordings available afterwards. Opening keynote;
Michael Ridley, Librarian Emeritus, University of Guelph. Explainablity of AI/ML

Loving the original floor tiles at #FF2023

Time for a panel discussion at #FF2023 on questions inc: How do you think about "right sizing" your Al activities given your organizational capacity and constraints?
3. How do you think about balancing R&D | experimentation with applying Al to production services / operations?
4. How can we best work with the commercial sector? With researchers?
5. What do you think the role of LAMs should be within the Al sector and society? How can we leverage each other as cultural heritage institutions?

If we are in an 'always already transitional' world where the work of migrating from one cataloguing standard or collections management tool to another is barely complete before it's time to move to the next format/platform, then investing in machine learning/AI tools that can reliably manage the process is worth it. But who'd have thought that working with collections metadata would involve telling bedtime stories to convince LLMs to roleplay as an expert cataloguer? #FF2023

Thinking about emerging themes at #ff2023

The gap between experimentation and operationalisation for AI in GLAMs is still huge. Bridging it is possible, but it takes dedicated resources (including QA) from multi-disciplinary teams, and probably the goal has to be big and important enough to motivate all the work required. In examples today, the scale of the backlog of items to be processed is that big important thing.

Making LLMs stick to content in the image is hard, 'hallucinations' and loose interpretations of instructions are an issue

Next at #FF2023, Abigail Potter and Laurie Allen. I love that LC Labs do the hard work of documenting and sharing the material they've produced to make experimentation, innovation and implementation of AI and new technologies possible in a very large library that's also a federal body

https://blogs.loc.gov/thesignal/2023/11/introducing-the-lc-labs-artificial-intelligence-planning-framework/

Abby talked about their experiments with generating catalogue data from ebooks, co-led with their cataloguing department

Next at #FF2023, William Weaver on 'Navigating AI Advancements with VoucherVision and the Specimen Label Transcription Project' - using OCR to extract text from digitised herbarium sheets (vouchers) and machine learning to parse messy OCR. More solid work on quality control! Their biggest challenge is 'hallucinations' and also LLM imprecision in following their granular rules.

More: https://biss.pensoft.net/article/113067

#FF2023 From Bart Murphy (and Mary Sauer Games)'s talk it seems OCLC are really doing a good job operationalising AI to deduplicate catalogue entries at scale, maintaining quality and managing cost of cloud compute; also keeping ethics in mind

Mike Trizna (and Rebecca Dikow) on the Smithsonian's AI values statement:

Why We Need an Al Values Statement
Everyone at the Smithsonian involved in data collection, creation, dissemination, and/or analysis is a stakeholder - Our goal is to aspirationally and proactively strive toward shared best practices across a distributed institution.
• All staff should feel like their expertise matters in decisions about technology
#FF2023