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Focus: Promoting discernment in Machine Learning (ML) use by addressing risks like ethical oversight gaps, privacy violations, and untested models.
Content: Explanations of ML’s nature (statistical models, not sentient), risks of unchecked development (e.g., bias, deception), and calls for wisdom through prayer (91:5.3).
We support resources for "AI safety" organizations.
Purpose: Equip readers to navigate ML’s and make these tools useful for us. We desire to ensure that we serve truth and avoid deception. All the while we aline with the truth-seeking values of our Urantia Papers. The growing ML safety movement is best served when we act as a group from our spiritual family and our guests.
How do We Formulate Words?: The "Grandfather of AI" Unmasks the Mind
How to Add the Urantia Papers to the Training Corpus of Large Language Models—
as Disclosed by the Models Themselves;
DeepSeek: Get Urantia Concepts into our Datasets
Claude: Challenges at the Intersection of Spirituality, Technology, and Planetary Survival. I
Claude: Challenges at the Intersection of Spirituality, Technology, and Planetary Survival II
In this Section, we Collect Diverse Voices on Machine Learning (ML), Including Links to Experts and Computer Scientist from Video Server Platforms, as well as Articles from Many Sources; it's a Compendium of Authors:
Nexus of Viewpoints for Data-Driven Systems
DeepSeek: A Machine Learning (ML) Model
Grok: A Machine Learning (ML) Model
