I could be wrong but could it not also be defined as glorified “brute force”? I assume the machine learning part is how to brute force better, but it seems like it’s the processing power to try and jam every conceivable puzzle piece into a empty slot until it’s acceptable? I mean I’m sure the engineering and tech behind it is fascinating and cool but at a basic level it’s as stupid as fuck, am I off base here?
no, it’s not brute forcing anything. they use a simplified model of the brain where neurons are reduced to an activation profile and synapses are reduced to weights. neural nets differ in how the neurons are wired to each other with synapses - the simplest models from the 60s only used connections in one direction, with layers of neurons in simple rows that connected solely to the next row. recent models are much more complex in the wiring. outputs are gathered at the end and the difference between the expected result and the output actually produced is used to update the weights. this gets complex when there isn’t an expected/correct result, so I’m simplifying.
the large amount of training data is used to avoid overtraining the model, where you get back exactly what you expect on the training set, but absolute garbage for everything else. LLMs don’t search the input data for a result - they can’t, they’re too small to encode the training data in that way. there’s genuinely some novel processing happening. it’s just not intelligence in any sense of the term. the people saying it is misunderstand the purpose and meaning of the Turing test.
I could be wrong but could it not also be defined as glorified “brute force”? I assume the machine learning part is how to brute force better, but it seems like it’s the processing power to try and jam every conceivable puzzle piece into a empty slot until it’s acceptable? I mean I’m sure the engineering and tech behind it is fascinating and cool but at a basic level it’s as stupid as fuck, am I off base here?
no, it’s not brute forcing anything. they use a simplified model of the brain where neurons are reduced to an activation profile and synapses are reduced to weights. neural nets differ in how the neurons are wired to each other with synapses - the simplest models from the 60s only used connections in one direction, with layers of neurons in simple rows that connected solely to the next row. recent models are much more complex in the wiring. outputs are gathered at the end and the difference between the expected result and the output actually produced is used to update the weights. this gets complex when there isn’t an expected/correct result, so I’m simplifying.
the large amount of training data is used to avoid overtraining the model, where you get back exactly what you expect on the training set, but absolute garbage for everything else. LLMs don’t search the input data for a result - they can’t, they’re too small to encode the training data in that way. there’s genuinely some novel processing happening. it’s just not intelligence in any sense of the term. the people saying it is misunderstand the purpose and meaning of the Turing test.