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>How does associative statical modelling do? It takes averages of past shadows, and calls the cause of the shadow that average: this is pseudoscience. Quite cor

>How does associative statical modelling do? It takes averages of past shadows, and calls the cause of the shadow that average: this is pseudoscience. Quite correct! Throw it all away. It does not work this way at all. In any sense. For one thing, it does not try to draw shadows. This would not be possible of so. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 Transformers or predictors are not trying to draw shadows. They are trying to build walls. For another, it does not "take the average" of anything.

mjburgess 3y ago
Unsupervised learning on discrete data is just ensembling modes. But let's look at how that helps in some cases. So if we already know the object is a cup, and we know how it's positioned, then its shadow is an actual guide to its particular geometry. So in cases where we have enough a priori scientific information, we can rig datasets (shadows) to be informative of the target domain. Here the target is discrete: say the peaks and tips of a mountain line. Now can we rig a photo of a mountain to have in its ink an informative structure? Sure. Now if we didn't know it was a mounting apparent peaks aren't even 'peaks' at all, they're just patterns of ink. A priori explanatory models are needed to rig data for statistical modelling. No such rigging can take place absent them And with them, we aren't really discovering any new science -- rather we're gaining highly particular knowledge typically useful in engineering Biologists in these areas describe this research as quite trivial low-hangimg fruit. It's not of much research interest just to automate these kinds of investigations My masters was on a very similar project applied to quantum metrology -- it's always 'useful' but it's always also just a kind of engineering utility. We couldn't even do it if we hadn't already done the science
- famouswaffles 3y ago
  You are still getting this wrong. You don't need to "rig" anything. I've linked a paper. Read it. They just fed protein sequences. They did not alter the architecture in any way. To the transformer, it may as well have been any random assemblage of letters and numbers. Functions like secondary structure, contacts, and biological activity were found because those things are implicit in the creation of the data, not because the model was "rigged" in any way.
  - mjburgess 3y ago
    the rigging occurs in the design of the data generation process, ie., those experiments which lead to these datasets that is where the science occurs -- the data analysis is just an administrative task after science has taken place
    - famouswaffles 3y ago
      That is not rigging lol. The only "experiments" performed here were done by biology and evolution.

mjburgess 3y ago

Unsupervised learning on discrete data is just ensembling modes. But let's look at how that helps in some cases. So if we already know the object is a cup, and we know how it's positioned, then its shadow is an actual guide to its particular geometry. So in cases where we have enough a priori scientific information, we can rig datasets (shadows) to be informative of the target domain. Here the target is discrete: say the peaks and tips of a mountain line. Now can we rig a photo of a mountain to have in its ink an informative structure? Sure. Now if we didn't know it was a mounting apparent peaks aren't even 'peaks' at all, they're just patterns of ink. A priori explanatory models are needed to rig data for statistical modelling. No such rigging can take place absent them And with them, we aren't really discovering any new science -- rather we're gaining highly particular knowledge typically useful in engineering Biologists in these areas describe this research as quite trivial low-hangimg fruit. It's not of much research interest just to automate these kinds of investigations My masters was on a very similar project applied to quantum metrology -- it's always 'useful' but it's always also just a kind of engineering utility. We couldn't even do it if we hadn't already done the science

famouswaffles 3y ago
You are still getting this wrong. You don't need to "rig" anything. I've linked a paper. Read it. They just fed protein sequences. They did not alter the architecture in any way. To the transformer, it may as well have been any random assemblage of letters and numbers. Functions like secondary structure, contacts, and biological activity were found because those things are implicit in the creation of the data, not because the model was "rigged" in any way.
- mjburgess 3y ago
  the rigging occurs in the design of the data generation process, ie., those experiments which lead to these datasets that is where the science occurs -- the data analysis is just an administrative task after science has taken place
  - famouswaffles 3y ago
    That is not rigging lol. The only "experiments" performed here were done by biology and evolution.