Anderson pointed ou that Google way can do a great job at "what happend" and "where it goes". But scientists want to know "why" and "how" it happens.
"All models are wrong, and increasing you can succeed without them."
Anderson pointed ou that Google way can do a great job at "what happend" and "where it goes". But scientists want to know "why" and "how" it happens.
This is Anderson's basic thesis.
I like big data as much as the next guy, but this is deeply confused. Where does Anderson think those statistical algorithms come from? Without constraints in the underlying statistical models, those "patterns" would be mere coincidences. Those computational biology methods Anderson gushes over all depend on statistical models of the genome and of evolutionary relationships.
Those large-scale statistical models are different from more familiar deterministic causal models (or from parametric statistical models) because they do not specify the exact form of observable relationships as functions of a small number of parameters, but instead they set constraints on the set of hypotheses that might account for the observed data. But without well-chosen constraints — from scientific theories — all that number crunching will just memorize the experimental data.
George Dyson, Kevin Kelly and Stewart Brand's responses to Chris Anderson's article
You might call it a model. LOL.