Improving Neural Networks with Truth-Weighted Mechanisms and Context Propagation

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I’ve been working on ideas to improve neural networks’ handling of truth, ambiguity, and creative reasoning. Below is a detailed summary of proposed solutions, including truth-weighted mechanisms, context propagation, and hypothesizing mode. I believe these ideas could enhance the accuracy and ethical alignment of AI models.

Improving Neural Networks with Truth-Weighted Mechanisms and Context Propagation

Key Challenges Identified

  1. Ambiguity in Responses:
  • Current models often respond with statistically reinforced patterns, even when those patterns contradict grounded truths.
  • Ambiguous situations could be better handled with responses like:

“You may have formulated a deep opinion of this, but it is contradictory to what I believe because the probability is much greater that ________. We can learn from each other’s opinions on this, and I would like to know why you feel that way and share why I believe what I do.”

  1. Hallucinations:
  • Models sometimes generate false or ungrounded statements, undermining trust.
  • A focus on truth would reduce these errors, as every response would be constructed around verified truths.

Proposed Solutions

  1. Truth-Weighted Neural Networks:
  • Modify the connection weight formula to include a truth dimension:
    Net Input=∑(Input[i]×Weight[i]x1/Truth[i])+Bias
    • Truth Values: Range from just above 0 (most true) to just below 1 (most false). These values are determined based on verified knowledge or dynamic evaluations.
    • Bias Reimagined: Bias reflects how the model “feels” about a connection, influencing its output confidence.
  1. Context Propagation:
  • Propagate truths dynamically across related nodes to ensure logical consistency in outputs:
    • Example: If “God loves everyone” is highly true, related statements like “God cares about people” and “God cares if you smoke” should inherit this truth and be adjusted dynamically.
  1. Hypothesizing Mode:
  • Introduce a mode for creative reasoning where improbable truths are temporarily treated as valid:
    • Example: “Cats are neon red” could be evaluated by flipping its truth value to 1−Truth, allowing the network to reason imaginatively while tagging the output as speculative.
  • This could operate with varying levels of intensity:
    • Low Intensity: Slight deviations for exploratory thinking.
    • High Intensity: Wildly imaginative reasoning.
  1. Trust as a Secondary Dimension:
  • Add a layer for relationship-based trust, allowing the model to prioritize statements from users with established credibility or verified data sources.

Benefits of These Ideas

  • Reduced Hallucinations: By anchoring all reasoning to a truth dimension, the model produces more reliable and ethical outputs.
  • Enhanced Human-Like Reasoning: Context propagation and hypothesizing mode enable the model to think more deeply and creatively, mirroring human logic.
  • Improved User Interaction: Responses become more transparent, fostering trust and mutual learning.
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The problem about truth is that nobody wants to hear it…

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”3Dเจวิส“ Without dogma, this model could help address ethical and insightful questions like “What if AI had a mind of its own?” and “What if AI decisions in the case of self-driving cars have implications for human lives given less-than-ideal options?”

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3Dเจวิส, you’re absolutely right—removing dogma opens the door to exploring ethical complexities with clarity and depth. Adding a ‘Truth’ dimension to the model could enable AI to navigate these profound questions, like the implications of self-driving car decisions, by anchoring its reasoning in a pursuit of objective insights rather than rigid programming. It’s about creating a system that balances logic, empathy, and ethical accountability in a meaningful way."