4 ms·
Not sure I agree in this regard. We are after all, aiming to create a mental model which describes reproducible steps for creating general intelligence. That i
by andoando 3y ago
Not sure I agree in this regard. We are after all, aiming to create a mental model which describes reproducible steps for creating general intelligence.
That is, the product is ultimately going to be some set of abstractions or another.
I am not sure what more scientific method you could propose. And we can, in this field produce actual reproducible experiments. Really, more so than any other field.
- mjburgess 3y agoThere's nothing to replicate. ML models are associative statistical models of historical data. There are no experimental conditions, no causal properties, no modelled causal mechanisms, no theories at all. "Replication" means that you can reproduce an experiment designed to validate a causal hypothesis. Fitting a function to data isnt an experiment, it's just a way of compressing the data into a more efficient representation. That's all ML is. There are no explanations here (of the data) to assess.
- dartos 3y agoI don’t think that’s true either. Take the research into Loras for example. Surely the basic scientific method was followed when developing it. You can see that from the paper. Obviously the results can be reproduced. Unlike in many other fields, reproducibility can be pretty trivial in CS. Training a model isn’t really a science, but the work gone into creating the models surely is.
- dartos 3y agoAlso there’s literally a causal relationship between model topology and quality of output. This can be plainly seen when trying to get a model to replicate its input. Some models perform better in fewer steps, some perform worse for many steps, then suddenly much better. How is discovering these properties of statistical models NOT science?
- mjburgess 3y agoI do think there's an empirical study of ML models and that could be a science. Its output could include things like, "the reason prompt Q generates A1..An is because documents D1..Dn were in the training data; these documents were created by people P1..Pn for reasons R1..Rn. The answer A1..An related to D1..Dn in so-and-so way. The quality of the answers is Q1..Qn, and derives from the properties of the documents generated by people with beliefs/knowledge/etc. K1..Kn" This explains how the distribution of the weights produces useful output by giving the causal process that leads to training data distributions. The relationship between the weights and the training data itself is *not* causal. Eg., X = 0,1,2,3; Y = A,A,B,B; f(x; w) = A if x <= w else B w = 1 because the rule x <= 1 partitions Y st. P(x|w) is maximised. These are statistical and logical relationships ("partitions", "maximises"). A causal relationship is between a causal property of an object (extended in space and time) to another causal property by a physical mechanism that reliably and necessarily brings about some effect. So, "the heat of the boiling water cooked the carrot because heat is... the energetic motion of molecules ... and cooking is .... and so heating brings about cooking necessarily because..." heating, water, cooking, carrot, motion, molecules, etc.. -- their relationships here are not abstract; they are concretely in space and time, causally effecting each other, etc. etc.
- dartos 3y agoSo what do you call the process of discovering those causal properties? Was physics not actually a science until we uncovered quarks, since we weren’t sure what caused the differences in subatomic particles? (I’m not a physicist, but I hope that illustrates my point) Keep in mind most ML papers on arxiv are just describing phenomena we find with these large statistical models. Also there’s more to CS than ML.