Ahh that's true. However the way he phrased it "the fine tuning causes the feature" it's clear to me that the functionality meaning is used. But I can't pinpoint exactly why.

I think it's something about the incompatibility between the inertness of ML-features and potential-verbs of tradiditional-features.

The OP says "be evil" feature, and refers that the finetuning causes it. If it meant an ml-feature as a property of the data, OP would have said something like "evilness" feature.

To any extent if it were an ML-feature, it wouldn't be about evilness it would merely be the collection of features that were discouraged in training. Which at that point becomes somewhat redundant.

To summarize, if you finetune for any of the negatively trained tokens, the model will simplify by first returning all tokens with negative biases, unless you specifically train it not to bring up negative tokens in other areas.

>Yes, if I'm interpreting what you mean by "accelerate upwards". That's just the gravity feature. It's not a bug, and it's not emergent.

Gravity would be accelerating downwards.

>A major part of a neural network design is that variables can be activated in positive or negative directions as part of getting the output you want. Either direction is inherent.

This is true for traditional programs as well. But a variable being "activated" in either direction in runtime/inference, would not be a feature of the program. There is a very standard and well defined difference between runtime and design time.