Theoreti.ca – Research notes taken on subjects around multimedia, electronic texts, and computer games.

Sharing What You Did: Documenting Text Analysis Research with Voyant and Spyral – Session 1

On Friday I gave the first of three workshops on Spyral, the Voyant extension notebook programming environment. This was given online and supported Bridging Divides. Augustine Farinola developed it with me. Here are two key links:

Here is the outline of what was covered with all the appropriate links: https://voyant-tools.org/spyral/GeoffreyRockwell@gh/BridgingDividesSpyralTraining/ .
Here is the link to the Google Drive with all the materials: https://shorten.ly/uaUgbd.

Deepfakes and Epistemic Degeneration

Two deepfake images of the pileup of cars.

There are a number of deepfake images of the 100 car pileup on the highway between Calgary and Airdre on the 17th. You can see some here CMcalgary with discussion. These deepfakes raise a number of issues:

How would you know it is a deepfake? Do we really have to examine images like this closely to make sure they aren’t fake?
Given the proliferation of deepfake images and videos, does anyone believe photos any more? We are in a moment of epistemic transition from generally believing photographs and videos to no longer trusting anything. We have to develop new ways of determining the truth of photographic evidence presented to us. We need to check whether the photograph makes sense; question the authority of whoever shared it; check against other sources; and check authoritative news sources.
Liar’s dividend – given the proliferation of deepfakes, public figures can claim anything is fake news in order avoid accountability. In an environment where no one knows what is true, bullshit reigns and people don’t feel they have to believe anything. Instead of the pursuit of truth we all just follow what fits our preconceptions. A example of this is what happened in 2019 when the New Year’s message from President Ali Bongo was not believed as it looked fake leading to an attempted coup.
It’s all about attention. We love to look at disaster images so the way to get attention is to generate and share them, even if they are generated. On some platforms you are even rewarded for attention.
Trauma is entertaining. We love to look at the trauma of others. Again, generating images of an event like the pileup that we heard about, is a way to get the attention of those looking for images of the trauma.
Even when people suspect the images are fake they can provide a “where’s Waldo” sort of entertainment where we comb them for evidence of the fakery.

Image of pileup with containership across the highway. — Pileup with Container Ship

Deepfakes then generate more deepfakes and eventually people start responding with ironic deepfakes where a container ship is beached across the highway causing the pileup.
Evenutally there may be legal ramifications. On the one hand people may try to use fake images for insurance claims. Insurance companies may then refuse photographs as evidence for a claim. People may treat a fake image as a form of identity theft if it portrays them or identifiable information like a license plate.

Declaration of Independence – First E-Text

Project Gutenberg and the Declaration of Independence

I came across a blog post about how Michael S. Hart, the founder of Project Gutenberg started in 1971 by typing the Declaration of Independence into the ARPANET and sending it to others. See 50 Years at Project Gutenberg.

Forty-Five Years of Digitizing Ebooks: Project Gutenberg’s Practices by Gregory B. Newby is a longer thing on the history of Project Gutenberg’s processes.

Hart passed in 2011. Gregory B. Newby just passed away this October. The Project, however seems to be in good hands with a foundation and board.

We’re Norman Rockwell’s family. Trump’s DHS has shamefully misused his work. | Opinion

The Problem We All Live With, Norman Rockwell, 1964

As Norman Rockwell’s family, we know he’d be devastated to see the Department of Homeland Security’s unauthorized misuse of his work.

Members of my family noticed over the last weeks that the DHS is using Norman Rockwell’s works without permission. We got together to write this opinion peice, We’re Norman Rockwell’s family. Trump’s DHS has shamefully misused his work. | Opinion.

If Norman Rockwell were alive today, he would be devastated to see that his own work has been marshalled for the cause of persecution toward immigrant communities and people of color.

ArtNet now has a story about our Opinion as does the New York Times.

The Next Generation Frontiers Symposium

The Next Generation Frontiers Symposium is in full swing in Banff! From sustainability to culture, yesterday’s sessions showcased the breadth of ideas shaping the future of AI. In a panel moderated by Hsiao-Ting Tseng, researchers Anfanny Chen, Shih-Fang Chen and Hsien-Tien Lin shared how AI can drive sustainable practices — from smarter agriculture and resource management to greener supply chains and reduced carbon emissions. Later, Annie En-Shuin Lee, Dane Malenfant, Chi-Jui Hu, and Yun-Pu Tu led a fascinating discussion, moderated by Geoffrey Rockwell, on Indigenous AI and Culture, exploring the relationship between AI, cultural diversity and Indigenous knowledge. The day highlighted how meaningful interdisciplinary exchange can spark fresh perspectives and lead to new frontiers in research. (From here)

I’ve just come back from the Next Generation Frontiers Symposium which was organized by CIFAR, Taiwan’s National Science and Technology Council, (NSTC), and the Research Institute for Democracy, Society and Emerging Technology (DSET). This brought researchers from Taiwan and Canada to talk about Responsible AI, Sovereign AI, AI and Sustainability, and Indigenous AI and Culture. I moderated the Indigenous AI and Culture theme which looked at how AI might impact indigenous communities in both Taiwan and Canada. Some of the reflections include:

Indigenous community are often poorly represented in LLMs. We need ways for communities to be able to personalize models for their community with knowledge from their community.
The mass scraping of the Internet with little regard for ownership or consent of content creators is more of the extractive and colonizing behaviour that leads many indigenous communities to distrust settler nations.
There are knowledge practices and types of knowledge like gendered knowledge, age-specific knowledge, and location-based knowledge that simply cannot be datafied and modelled if they are to maintain their character.
Datafication and modelling work with measurable evidence. Anything that can’t be captured, sampled, and measured can’t then be datafied and thus can’t be modelled. Further, there is the danger that such evidence and knowledge will be deligitimized as unmeasurable and eventually excluded and fiction or mysticism. We could end up believing that only what we could datafy and model is knowledge.
Western espistemological practices of openness, science and replicable results should not be imposed on communities with different epistemological practices. AI is the product of Western epistemology and thus may never be compatible with indigenous wisdom.
We need to respect the desire of some communities to be forgotten and thus not scraped at all for measurable knowledge. Some may choose opacity.
Knowledge and its materials taken from communities should be returned. Communities should be supported to develop their own ways of preserving their knowledge including ways of datafying and modelling their knowledge, if they so wish.

Margaret Tu, one of the participants in the session, wrote a moving essay about the need for cultural safety for indigenous communities in the face of disaster in Taiwan. See Taiwan’s Barrier Lake Disaster Intersects With Its Troubled Indigenous Policy. It ends with this wisdom,

Disasters demand speed, but recovery demands reflection. For the Fata’an, healing will not come from relocation alone; it must be rooted in both land and culture.

How To Festival: How to think like an AI Ethicist

On Saturday I gave an online talk on “How to think like AI Ethicist” that was part of a How To Festival. I talked about thinking about responsibility and the issue of “responsibility gaps”. I talked about some key risks like hallucinations, bias, deep fakes, and companion AIs. I also mentioned that we need to celebrate the effective uses of AI and think not just about hazards, but also about AI for good.

Artificial intelligence (AI) is everywhere. We all need to assess what to use and how to use the new tools. In this talk Geoffrey Rockwell will discuss some of the safety issues raised by the new generative AI tools. He will suggest some ways you can think through AI.Geoffrey Rockwell is a Professor for Philosophy and Digital Humanities at the University of Alberta. He is also a Canada CIFAR AI Chair working on responsible AI.EPL’s annual How To Festival is a chance to learn something new from someone who already knows how to do it. A variety of experts from professionals to enthusiasts will share their skills with you.This is an online program. To receive a link and passcode to the online class, please register with your name and email address and instructions will be sent to you within 24 hours of the session.Zoom, a third-party app, will be used for this virtual session. By joining, you acknowledge that EPL does not take responsibility for Zoom’s privacy policies and practice.

Source: How To Festival: How to think like an AI Ethicist

Apertus | Swiss AI

Switzerland has developed an open set of models, Apertus | Swiss AI, that is trained on a documented training set, “developed with due consideration to Swiss data protection laws, Swiss copyright laws, and the transparency obligations under the EU AI Act.”

EPFL, ETH Zurich, and the Swiss National Supercomputing Centre (CSCS) has released Apertus, Switzerland’s first large-scale open, multilingual language model — a milestone in generative AI for transparency and diversity. Trained on 15 trillion tokens across more than 1,000 languages – 40% of the data is non-English – Apertus includes many languages that have so far been underrepresented in LLMs, such as Swiss German, Romansh, and many others. Apertus serves as a building block for developers and organizations for future applications such as chatbots, translation systems, or educational tools.

This project should interest us in Canada as we are talking Sovereign AI. Should Canada develop its own open models? What advantages would that provide? Here are some I can think of:

It could provide an open and well maintained set of LLMs that researchers and companies could build on or use without fear that access could be changed/pulled or data logged about usage.
It could be designed to be privacy protecting and to encourage adherence to relevant and changing Canadian laws and best practices.
It could be trained on an open and well documented bilingual data that would reflect Canadian history, culture, and values.
It could be iteratively retrained as issues like bias is demonstrated to be tied to part of the training data. It could also be retrained for new capacities as needed by Canadians.
It could include ethically accessed Indigenous training sets developed in consultation with indigenous communities. Further, it could be made available to indigenous scholars and communities with support for the development of culturally appropriate AI tools.
We could archive code, data, weights, documentation in such a way that Canadians could check, test, and reproduce the work.

I wonder if we could partner with Switzerland to build on their model or other countries with similar values to produce a joint model?

Colloque CRIHN 2025 en l’honneur de Stéfan Sinclair

Stéfan Sinclair

L’herméneutique en pratique : hommage au travail et à l’héritage de Stéfan Sinclair – Une conférence sur l’analyse de texte, la création d’outils et les humanités numériques critiques Université de Montréal, 10-12 septembre 2025 Conférencières plénières : Servanne Monjour (Sorbonne Université) et Lauren Tilton (Richmond University) Programme Mercredi 10 septembre 2025 (Atelier optionnel) 9h30-12h30 Atelier « Introduction to […]

I’m at a colloquium in honour of Stéfan Sinclair, Colloque CRIHN 2025 en l’honneur de Stéfan Sinclair. I ran a workshop on Spyral and later gave a talk about “Epistemologica in Practice: Replication and Knowledge in Spyral.”

The colloquium had a number of great talks, but one I particularly liked was by Parham Aledavood (U de Montréal) titled “Viral Intelligence: Contagion, Mutation, and the Spread of LLMs.” He talked about how the release of ChatGPT in 2022 was discussed as if it were a viral event infecting us with AI. He walked through the “formulaic plot” from infection to containment. I was reminded of other stories of influence(za) like the story at the end of Lucretius’ De Rerum Natura.

Personal Superintelligence

Explore Meta’s vision of personal superintelligence, where AI empowers individuals to achieve their goals, create, connect, and lead fulfilling lives. Insights from Mark Zuckerberg on the future of AI and human empowerment.

Mark Zuckerberg has just posted his vision of superintelligence: Personal Superintelligence. He starts by reiterating what a lot of people are saying; namely that AGI (Artificial General Intelligence) or superintelligence is coming soon,

Over the last few months we have begun to see glimpses of our AI systems improving themselves. The improvement is slow for now, but undeniable. Developing superintelligence is now in sight.

He distinguishes what Meta is going to do with superintelligence from “others in the industry who believe superintelligence should be directed centrally towards automating all valuable work, …”. The “others” here is a poke at OpenAI who, in their Charter, define AGI as “highly autonomous systems that outperform humans at most economically valuable work …” He juxtaposes OpenAI as automating work (for companies and governments) while Meta will put superintelligence in our personal hands for creative and communicative play.

Along the way, Zuckerberg hints that future models may not be open any more, a change in policy. Until now Meta has released open models rather than charging for access. Zuckerberg not worries that “superintelligence will raise novel safety concerns.” For this reason they will need to “be rigorous about mitigating these risks and careful about what we choose to open source.”

Why don’t I trust either Meta or OpenAI company?