The data is not actually wrong. This is one of the things that's the basis of experimentation. If you fail to collect the data properly, you can't ignore your data. You actually have to account for it. You have to calculate a correction, and propagate an error, in order to identify what your correct data actually was. Worst case scenario, you have to mention it, but explain why it wasn't included in the overall calculation, to be able to be reviewed as an appendix
I don't see wrong and syntactically invalid as the same thing. Syntactically valid data, inherently, still works like any valid data. Invalid data isn't "wrong", the input itself is nonsensicle. It's like describing something as: "tasting like purple". When you can't even read the data properly, you actually don't have data because you've broken something very fundamental in your code.
Again, this is not wrong, or syntactically invalid. This is a logic error. A logic error is an error so fundamental that no computer could be expected to understand that the human made a mistake. We check for these using assertion errors. If the assertion error fires, this is an indication of a programming fault somewhere. Once again, this is a very fundamental error which makes the machine unable to preform it's basic tasks.
No computers can make an inference, but again, this is so fundamental that no inference would be expected. You do not use computers to make inferences.
No, it's still about a kind of introspection in the way that an AI has to analyze data.
The data is not actually wrong. This is one of the things that's the basis of experimentation. If you fail to collect the data properly, you can't ignore your data. You actually have to account for it. You have to calculate a correction, and propagate an error, in order to identify what your correct data actually was. Worst case scenario, you have to mention it, but explain why it wasn't included in the overall calculation, to be able to be reviewed as an appendix
I don't see wrong and syntactically invalid as the same thing. Syntactically valid data, inherently, still works like any valid data. Invalid data isn't "wrong", the input itself is nonsensicle. It's like describing something as: "tasting like purple". When you can't even read the data properly, you actually don't have data because you've broken something very fundamental in your code.
Again, this is not wrong, or syntactically invalid. This is a logic error. A logic error is an error so fundamental that no computer could be expected to understand that the human made a mistake. We check for these using assertion errors. If the assertion error fires, this is an indication of a programming fault somewhere. Once again, this is a very fundamental error which makes the machine unable to preform it's basic tasks.
No computers can make an inference, but again, this is so fundamental that no inference would be expected. You do not use computers to make inferences.
No, it's still about a kind of introspection in the way that an AI has to analyze data.