Bayesian Neural Networks: Embracing Uncertainty
Definition: A probabilistic neural network accounting for uncertainty in weights and outputs, predicting distributions rather than point estimates.
The “Confidence” Problem
Standard AI is overconfident. It says “This is a cat” with 100% certainty, even if it’s a blurry blob. Bayesian Neural Networks (BNNs) say “I am 60% sure this is a cat, but there is a 40% chance I have no idea.”
Why Vibe Coders Need Uncertainty
In Vibe Coding, you rely on the AI’s confidence.
- The Hallucination Trap: The AI writes a function using a library that doesn’t exist. It does so with “High Confidence” (because standard LLMs are not Bayesian).
- The Missing Feature: If LLMs were BNNs, they would say: “I can write this function, but I am uncertain about the API for v4.0. Check the docs.”
Simulating Bayesian Vibe
Since GPT-4 isn’t a BNN, you must prompt for uncertainty.
- Prompt: “Generate the code for this AWS Lambda. Also, list your ‘Confidence Level’ (1-10) for each library import. If you are guessing an API method, mark it as Low Confidence.”
- Result: The AI will actually flag its own hallucinations if you force it to estimate uncertainty.
Future of Code Gen
We are moving toward “Uncertainty-Aware Code Generation.” Tools like Cursor will likely soon highlight lines of code in yellow if the model’s internal probability distribution was “flat” (uncertain), prompting you to double-check those specific lines.