Building an experimental Generative AI server this summer has been fascinating. As of this week, 16 people are using it. Over the summer, I’ve fine-tuned it quite a bit, and it’s much smarter now than it was when we began.
I still believe these systems are mediocre writers, and a genuine threat to student learning about writing and coding. But I now understand their workings in detail, and am ready to research how universities ought to employ this tech in the near future.
Just ignoring them (or reading Atlantic articles) isn’t a good plan. Though there is absolutely an AI “bubble”/media fad, LLMs are nevertheless real technology. They won’t go away. Just pretending our students won’t use them is naïve.
However, I need to clean up the aluminum-foil-and-chewing-gum apparatus I built in my laundry room to run this, as I transition toward a more substantial AI server setup, one based on this proof-of-concept.
So: if you haven’t tried it yet, please feel free to sign up. But I’ll now plan a replacement future system, run directly from the Texas Tech English Media Lab this fall:
https://ai.sauer.studio/
(Click the ‘sign up’ link below the login form to make an account. Use your real name and email, and I’ll activate you.)
So. I broke my AI server yesterday.
Destroyed it, really—with punctuation marks.
Greek myths had taught me, when I was young, about the danger of hubris. Forgetting that lesson, in a moment of excitement, I decided to try something fancy with the ‘config.py’ file on my experimental site.
And I broke it. Completely. Because of a syntax error in the Python code, it wouldn’t restart without crashing, every time. And it would crash each time before I could remove my edits from the .py file. Damn! It was almost certainly just a misplaced quotation mark or semicolon.
Luckily, we live in a world of containerization. I find Docker to be indefensibly badly documented and far too difficult to configure and use, but it does have some advantages when you set it up correctly.
I had a complete ‘snapshot’ of my AI server, from just hours before. Though I didn’t have the energy last night to do the command-line work necessary to restore it, this morning I got a cup of coffee, restored the backup, and https://ai.sauer.studio is now running again, exactly as it had been yesterday morning. Zero data lost—just some downtime. (Sorry to anyone annoyed by the outage.)
And I suppose I’ve learned a lesson?
At least I’ve been reminded of one I had already learned.
Here are some of the questions I’ve been trying with my experimental A.I. this week, as I work to create a usable chat interface to graduate student handbooks (and other large documentation sets). Note how it provides citations, with links to the source PDFs. And it’s all locally hosted and fully open-source! No corporate servers needed.
It looks promising! My early adjustments to the model’s ‘context’ and ‘temperature’ seem to generate usable answers. The lower temperature has meant no hallucinations, so far!
Next steps: IRB approval, then empirical, quantitative, replicable research with real users. And possibly—funding for a faster, more capable server?
EXAMPLES:
Q: When do I take my qualifying exams?
A: According to the documentation, students typically prepare for the Qualifying Exam in the spring and summer of their second year. The exam preparation will be fully focused on mastery of the lists and the selected fields, including the more specialized one leading to the dissertation.
The written exams are usually taken during the fifth semester.
Q: How do I add an outside committee member to my PhD committee?
A: According to the provided documentation, it is recommended that you consult with your chair and contact the DGS (Departmental Graduate Studies) office to update the appropriate records if you decide to bring in an outside reviewer or change your committee.
To add an outside committee member, follow these steps:
- Discuss with your chair: Share with your chair the reasons why you believe an external reader would be beneficial for your dissertation research.
- Identify potential candidates: Look for graduate faculty members from other departments who have expertise relevant to your research area.
- Obtain approval from the DGS office: Inform the DGS office of your intention to add an outside committee member and provide any necessary documentation, such as a brief explanation of why you think this would be beneficial.
Please note that the department encourages outside members to serve on dissertation committees if it is deemed necessary for the research. However, ultimately, the decision to add an outside committee member should be made in consultation with your chair and the DGS office.
If you have any further questions or concerns, please don’t hesitate to ask!
Q: What can you tell me about the MATC portfolio?
A: According to the provided context (documentation), the MATC portfolio is a digital portfolio that represents your work in the field of technical communication. It’s a three-fold audience and purpose, which means it serves as a professional conversation starter for potential employers or industries you want to enter, while also being assessed by your capstone course instructor and an ad hoc committee of TCR faculty.
The MATC portfolio is a requirement for the Master of Arts in Technical Communication (MATC) program.
Do all of you get as embarrassed as I do, watching your first day of teaching as you prepare for class day two, and reading the transcript from the video? #embarrassing
I understand why it’s a good idea; I make my students watch their own presentations because it’s really helpful for them. And the notes I take rewatching last class always makes my next class better, but #bestpractices aren’t always easy.
I’m not sharing the full video or .vtt file here (that’s just for the students and me), but here’s the PowerPoint slideshow for ENGL 4367/5373 (Instructional Design for Technical Communicators), if you’re curious:
California’s SB 1047 is a clear example of why (1) we need better laws about technology in this country, and (2) our current legislators are incompetent at writing them.
This proposed law (which passed the Assembly, and may pass the California Senate tomorrow) would make open-source AI technologies stupidly expensive, and subject to fines for what end users do with them (much later). It’s a clear example of how one should not write technology public policy.
Here’s hoping it fails tomorrow, or that Governor Newsom doesn’t sign it into law. It would certainly hamper my research.
So. You know I’ve been looking at how my GenAI ( https://ai.sauer.studio/ ) can help users get information they need from instruction manuals.
This week saw exciting new LLM models, like Hermes 3 (which has notable skill with languages). Working over the weekend, I loaded the handbooks of Texas Tech’s TCR in-person and online MA and PhD programs, told the new model to speak to me first in English, then French, Yorùbá (a language from southern Nigeria), and Hindi.
I asked it to answer questions about the graduate programs in those languages. It first reads the manuals (written solely in English). Then it generates clear, correct summary answers to questions in those languages, providing hypertext links to the correct pages in the source documents.
This promises to improve the user experience of documentation quite a bit for audiences with limited language skills.
My French is passable; I had to use machine translation for the Yorùbá and Hindi. But according to my tests this weekend, this system can actually work. It’s better in French and Yorùbá than Hindi, but that’s a result of the relatively small Hermes 3 model I’m using here (with funding, I’ll be able to run larger LLM models, and perhaps achieve results which will lead to clear recommendations for industry and the academy). Next for testing: Spanish and Mandarin.
None of this has any cost, except for medium-capacity computers (I’m running on a MacBook Pro)—and the expertise. (The software is all free and open-source). It’s very expensive (in time / expertise) to build the first one, but much faster for the second and third, and easy after that.) Having faculty in Tech Comm learn this expertise ourselves, will, I think, be time well-invested. Avoiding OpenAI (GPT) and Anthropic (Claude) seems to be worth its weight in gold, given the problems everyone’s reporting with those systems. And it’s cheaper this way!
I’m excited to work with two colleagues (Bremen Vance and Guiseppe Getto) to explore how fine-tuning models will help technical communication develop ‘best practices’ for locally-hosted, open-source documentation assistants. We submitted our first scholarship on this topic last week. It’s exciting to explore real, practical uses for these emerging GenAI technologies which have heretofore been mostly ‘undisciplined.’