So many objects have their own networks now. With a clever SSID and placement in a room full of potential targets, it could be pretty tough for someone to narrow it down to the bulb.
I couldn't help reading this article in the voice of Ted Flanders. It's such an insipid point and the writing is so bad. Not the writers fault, but when you find out he lives in Switzerland it's kinda hilarious.
This is a good pattern because it would allow all the models to "think" a bit before giving an answer even if they don't have reasoning or thinking turn on. Just make sure you have the reasoning output before the final answer. A mistake I see all the time is having the answer outputted first then the explanation after which leaves more room for models to rationalize bad answers.
Good pattern: {"explanation": <short explanation for your answer>, "answer": <your final answer: true|false|i don't know>}
Bad pattern: {"answer": <your answer here>, "explanation": <short explanation for your answer>}
I think we can all agree that this experiment being flawed in multiple ways is TRUE. But I think it's a great exercise in identifying common mistakes people make when using LLMs. This would be a great interview question for a prompt engineering job.
There are many applications. I mainly see it used for detecting drift in datasets for ML models. It has a nice benefit over the KL divergence in the case where the two distributions you're measuring have no overlap (KL won't compute, but JS will just return 0). Also, when taking its square root you get a distance rather than a divergence which allows you to compare it to JSD measurements of other distributions.
> Also, when taking its square root you get a distance
Easy conversion into a distance metric is hugely valuable to making the property amenable to KNN-based dimensionality reduction algos (and I'm sure other things I don't understand, as a non-mathematician)
reply