Raw LLM Responses

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They ARE 100% deterministic. Something deterministic can be only seemingly "opaque" (i.e. not truly opaque) when it's "too difficult/cumbersome" to calculate/determine or assess every part of it (OR, if we theoretically COULD but don't yet have tools to expose every part of it). If a NN has 1 billion nodes, it's very cumbersome but we CAN know the weight/bias at ALL of those nodes. Same for a NN with 10 trillion node. We can keep the entire NN static (yes we can) while we use other computers (a million computers?) to read the values at all nodes. Even a NN with 10^20 nodes will be fullly deterministic. Maybe you're using "deterministic" to mean something else. Today it's very difficult to know (with today's limited tools) what a large NN is "thinking"... but fully knowing IS possible (just very very very difficult)... it's finite. One way to do it is by creating "probing tools" that look into large chunks at a time, and using many such tools simultaneously (or one tool that scans many parts sequentially), so we can know the entire NN... then with another higher-order tool (that unifies all of the sequential scans of the many parts) ... this group of tools can output/say what it's "thinking." No such tools exist today. But this shows it's just difficult - but theoretically possibly - making the NN deterministic. Also, all large/deep neural networks (not just LLMs or multi-level NNs) are deterministic and therefore not opaque. Even a large but singe-level NN can be.
youtube AI Governance 2026-03-22T02:4…
Coding Result
DimensionValue
Responsibilityunclear
Reasoningunclear
Policynone
Emotionindifference
Coded at2026-04-27T06:24:59.937377
Raw LLM Response
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