writer's take based on article at https://www.businessinsider.com/sam-altman-ai-utility-electricity-water-openai-2026-3

This is only true if, and only if, you actually believe Sam Altman* is telling the truth and won't change his mind in the future. If you don't think you can win a a lawsuit against OpenAI, and I mean actually win it, not just be correct about them violating their contract with you, then this particular argument does not hold for you.

*or Dario Amodei, but they're cut from the same cloth

Introduction: AI is now recently everywhere but we still need humans

Interesting position paper about how to have useful discussion about AGI and Foundation models.

AI models collapse when trained on recursively generated data by Ilia Shumailov et al.

ᔥ[[Mathew Lowry]] in AI4Communities post - MyHub Experiments Wiki (accessed:: 2024-11-06 09:43:23)

https://research.ibm.com/blog/retrieval-augmented-generation-RAG

PK indicates that folks using footnotes in AI are using rag methods.

This isn't true. The Stochastic Parrots paper outlines other avenues for reining in the harms of language models like GPT's.

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–23. FAccT ’21. New York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3442188.3445922.

Would the argument here for stochastic parrots also potentially apply to or could it be abstracted to Markov monkeys?

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