I appreciate the example you’re using of the bouncing ball because it shows how additional data helps more accurately model the event and thus offers a more precise and correct prediction. However, that better modeling is based on the original data being correct, true, or factual. If LLMs are ingesting all written human knowledge (theoretically), then they are ingesting “truth” as well as errors, falsehoods, and subjective beliefs that don’t necessarily reflect accepted practices. Right now, they don’t seem fully capable of differentiating between these, which is why they sometimes produce bizarre inaccuracies for things like world events, history, culture, etc.
Just because LLMs are (or can be) reliable predictors in some domains does not mean they are (or can be) in all domains.
Not necessarily. No sensor is reliable, all sensors have noise and they drift. It's why you use something like a kalman filter.
It's worth doing it over an afternoon, but you can do very simple curve fitting with real sensor data from your smartphone and you'll see it naturally starts to ignore extremes / becomes accurate.
As long as there are enough correct data points, the incorrect data shouldn't matter. How much is enough is a black art though.
Comments
I appreciate the example you’re using of the bouncing ball because it shows how additional data helps more accurately model the event and thus offers a more precise and correct prediction. However, that better modeling is based on the original data being correct, true, or factual. If LLMs are ingesting all written human knowledge (theoretically), then they are ingesting “truth” as well as errors, falsehoods, and subjective beliefs that don’t necessarily reflect accepted practices. Right now, they don’t seem fully capable of differentiating between these, which is why they sometimes produce bizarre inaccuracies for things like world events, history, culture, etc.
Just because LLMs are (or can be) reliable predictors in some domains does not mean they are (or can be) in all domains.
Not necessarily. No sensor is reliable, all sensors have noise and they drift. It's why you use something like a kalman filter.
It's worth doing it over an afternoon, but you can do very simple curve fitting with real sensor data from your smartphone and you'll see it naturally starts to ignore extremes / becomes accurate.
As long as there are enough correct data points, the incorrect data shouldn't matter. How much is enough is a black art though.