4 ms·
> Necessarily, an AI looking at the sky can never find newton's law of gravity. Newton (et al.) had to disregard what they were looking at to place Earth in the
by dangond 3y ago
> Necessarily, an AI looking at the sky can never find newton's law of gravity. Newton (et al.) had to disregard what they were looking at to place Earth in the centre and F equal to GMm/r2 etc. etc.
This seems strange to say to me, since this can easily be done by regression models given the right training data. If a sufficiently powerful system is given that training data and trained to minimize loss, it will necessarily converge on a model approximating the underlying laws that created the training data.
- mjburgess 3y agoIf you define the optimization objective to be y = m . x1 . x2 . x3^-2, or perhaps the family y = m . x1^a . x2^b . x3^c -- you might be able to select the right function based on an "experimentally prepared" dataset. That dataset would not be photographs of the night sky, and those models you're fitting would have to be pre-specified. We can show, easily, no automated statistical modelling of the night sky will produce F=GMm/r^2 The optimization objective for automated modelling here makes the resulting model nothing more than an interpolation through data points. Those points, the position of the planets in the sky say, are not caused by F=GMm/r^2 -- but by a near-infinite nuumber of causes, including our relative positions to those planets *which is not in the night sky!*. If you already know the form of the answer, and have already done the right experiments, yes: the solution is function optimization