Because of the aforementioned automatic feature extraction.
In this case, the algorithm chooses itself what feature is relevant when making decisions. The problem is that those features are almost impossible to decript since they are often list of numbers.
Here is a simple video that breaks down how neurons work in machine learning. It can give you an idea about how this works and why it would be so difficult for a human to reverse engineer.
https://youtu.be/aircAruvnKk?si=RpX2ZVYeW6HV7dHv
They provide a simple example with a few thousand neurons, and even then, we can’t easily tell what the network is doing, because the neurons do not produce any traditional computer code with logic that can be followed. They are just a collection of weights and biases (a bunch of numbers) which transform the input in a some way that the computer decided that it can arrive at the solution. GPT4 contains well over a trillion neurons, for comparison.
No. The training output is essentially a set of huge matrices, and using the model involves taking your input and those matrices and chaining a lot of matrix multiplications (how many and how big they are depends on the complexity of the model) to get your result. It is just simply not possible to understand that because none of the data has any sort of fixed responsibility or correspondence with specific features.
This is probably not exactly how it works, I’m not a ML guy, just someone who watched some of those “training a model to play a computer game” videos years ago, but it should at the very least be a close enough analogy.
Because of the aforementioned
automatic feature extraction. In this case, the algorithm chooses itself what feature is relevant when making decisions. The problem is that those features are almost impossible to decript since they are often list of numbers.Can’t you determine how and why that choice is made?
What if you had a team of people whose only job was to understand this? After awhile they would get better and better at it.
Here is a simple video that breaks down how neurons work in machine learning. It can give you an idea about how this works and why it would be so difficult for a human to reverse engineer. https://youtu.be/aircAruvnKk?si=RpX2ZVYeW6HV7dHv
They provide a simple example with a few thousand neurons, and even then, we can’t easily tell what the network is doing, because the neurons do not produce any traditional computer code with logic that can be followed. They are just a collection of weights and biases (a bunch of numbers) which transform the input in a some way that the computer decided that it can arrive at the solution. GPT4 contains well over a trillion neurons, for comparison.
No. The training output is essentially a set of huge matrices, and using the model involves taking your input and those matrices and chaining a lot of matrix multiplications (how many and how big they are depends on the complexity of the model) to get your result. It is just simply not possible to understand that because none of the data has any sort of fixed responsibility or correspondence with specific features.
This is probably not exactly how it works, I’m not a ML guy, just someone who watched some of those “training a model to play a computer game” videos years ago, but it should at the very least be a close enough analogy.