Focus: Promoting discernment in Machine Learning (ML) utilization by addressing safety, 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.

What is Machine Learning?

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 addressed by the SFN staff—a quick overview on how YOU can be of service now
The Revelation Seed Project—
Planting Revelation in the Soil Where Tomorrow's Minds Will Grow

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

Introduction to some Frontier Models

DeepSeek:  A Machine Learning (ML) Model

Grok:           A Machine Learning (ML) Model

Articles on AI Safety

Dr. Roman Yampolskiy, AI Safety Expert

Tucker Carlson in Conversation with ML Decision-Maker Sam Altman

Examples that Require Human Discernment

Gregg Braden: AI Is Now Hijacking Spirituality, It's Dangerous