Over the weekend, I received two more pieces of my upcoming AI-focused workstation build to go along with the CPU and A4000 video cards. They are a Gigabyte B650 Eagle AX model ATX motherboard, which has four PCIe slots–one spaced for a 3-slot card like my 3090 and three spaced for single slot cards like the A4000s, and a Silverstone Fara R1 V2 ATX mid-tower case, which was the least expensive steel case without a glass window and good ventilation. My new Corsair DDR5 RAM won’t arrive until after Christmas, so the actual build will have to wait until then.
After receiving a new AMD Ryzen 7 7700 CPU earlier this week, I received the three NVIDIA RTX A4000 16GB VRAM video cards pictured above in antistatic bags today for my new AI workstation. Brand new, these cards run just over $1000, but I got these refurbished ones from an eBay seller for just under $600 each. These three video cards will work alongside my NVIDIA RTX 3090 Founders Edition 24GB VRAM video card for a total of 72GB VRAM, which will allow me to run low-or-no quantized large language models at a much faster output rate than I currently can using the 3090 with system RAM. The limited PCIe lanes on the Gigabyte motherboard that I ordered shouldn’t be too limiting as far as inference work is concerned.
If you weren’t able to make it to this year’s City Tech Science Fiction Symposium but are interested in the intersection of SF, AI, and GenAI, you can listen to the presentations, stories, and discussions in the videos from the event below, and you can see some photos taken by Hugo Award winner Andrew Porter on the Science Fiction at City Tech website here.
9:00AM Opening Remarks Jason Ellis and Justin F. Vázquez-Poritz
9:20AM Paper Session 1 Moderator: Wanett Clyde Jason Ellis, “A History of Generative AI in SF” Jacob Adler, “The End Zone: A.I. as a Commentary on the Human Condition in 17776” Martijn J. Loos, “A Plea for Theory: The Relationship Between Real-World AI and its Representation in Science Fiction”
10:50AM Paper Session 2 Moderator: Kel Karpinski Virginia L. Conn, “The Tyranny of Neutrality in AI 2041” Nathan Lamarche, “Monotheistic Ethics in Caprica: The Consequences of AI Development on Queer Futurity” Adam McLain, “Computational Poetics: Franny Choi’s Soft Science and the Dialogues to Come”
1:10PM Student Panel Moderators: Jill Belli and Vivian Zuluaga Papp Lucas Felipe Journey Ford Malik Joseph Christine Retirado Ronald Hinds
2:10PM Asimov’s/Analog Writers’ Panel Moderator: Emily Hockaday Sarah Pinsker Mercurio D. Rivera Sakinah Hoefler Matthew Kressel
4:00PM Keynote Address Speaker: Marleen S. Barr, “Science Fiction/AI/Feminism: A Temporal Progression” Moderator: Leigh Gold
Knowing that tariffs, or a tax ultimately paid by those who buy those imported goods, are coming, I planned out a new workstation for doing LLM and Generative AI work. The first part arrived today: an AMD Ryzen 7 7700 CPU. While I would have certainly loved to build a system around an AMD Threadripper Pro with its 8-channel memory and numerous PCIe slots and plenty of lanes to support maximum throughput, I am just an English professor of simple means, so I opted to build around the least expensive options available to me and using a combination of new and used parts. Therefore, I am upgrading from my current AM4 socket system to an AM5 socket motherboard that supports DDR5 memory and this lower-wattage, non-overclocking CPU. I’m currently waiting on the arrival of a motherboard with 4 PCI slots (spaced to allow the four video cards that I plan to run), three NVIDIA RTX A4000 video cards with 16GB VRAM (used via eBay), 64GB (2 x 32GB) Corsair DDR5 RAM, and an ATX mid-tower case. I’ll use my current drives, 1000 watt power supply, and NVIDIA RTX 3090 Founders Edition video card in the new system. Most of my work focuses on inference, so the slower PCI slots in this build won’t hurt too bad–it should far exceed CPU inference even with faster RAM.
Brian Porter and Edouard Machery’s “AI-generated poetry is indistinguishable from human-written poetry and is rated more favorably,” which appears in the open-access journal Scientific Reports, is a fascinating study about how human-made and Generative AI-made poetry is rated by non-expert humans. Interestingly, the study participants rated more of the Generative Ai-made poetry as more “human” than the poems actually written by humans, which included works by Geoffrey Chaucer, William Shakespeare, Samuel Butler, Lord Byron, Walt Whitman, Emily Dickinson, T.S. Eliot, Allen Ginsberg, Sylvia Plath, and Dorothea Lasky. While this quantitative approach provides some interesting talking points about the products of Generative AI, it seems like it might be saying more about the participants than the computer-generated poems. What might the results look like from experts, literature graduate students, and undergraduate students who had taken a class on poetry? What might be revealed by analyzing the AI-penned poems in relation to the work by the respective poets, considering that the prompt was very generic?
