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Here's one thing that has bugged me for a while. Why is it said that the great red spot is around 500 years old? How do we know that it hasn't been around for much longer? Do we have before/after evidence that at one point it didn't exist, and then around 500 years ago it existed?
Here's one thing that has bugged me for a while. Why is it said that the great red spot is around 500 years old? How do we know that it hasn't been around for much longer? Do we have before/after evidence that at one point it didn't exist, and then around 500 years ago it existed?
While there were earlier observations of large spots on Jupiter, it's not 100% certain they were actually the same storm.
https://en.wikipedia.org/wiki/Great_Red_Spot
To extend, a series of disjoint astronomical observations of the Earth would likely show a hurricane/cyclone/typhoon somewhere, but it wouldn't necessarily follow that they were all the same one.
Earlier quoted context omitted.
I am a Data Scientist with Master's in Neural Networks so I do understand the challenges in preparing data for modeling. I am merely suggesting the option of exploring the problem of finding patterns in data through ML techniques. This does not take away the work involved in preparing clean data sets for training. Here are some Astronomers who are doing this kind of work. https://www.wired.com/2017/03/astronomers-dep…
> I agree I don't understand the nature of data NASA It's not the nature of the data that poses an issue... it's the lack of direction. AI/ML/NN (whatever buzz word of choice) simply cannot do the things you seem to believe it capable of. > it does not hurt getting some help from companies that are searching for problems to solve. What makes you think Google or Microsoft have any interest in working on NASA's researc…
"Model, show me spectral signatures which are similar."
And "Model, is there anything interesting about the spectral signatures collected?"
In the former, you are potentially able to (or your NN layers can) identify representative features for your intended result.
In the latter, you're asking machine learning to synthesize the sum of human scientific knowledge, then extract interesting facts from the data using it. E.g. "Hmm, the vortex patterns on this Jupiter storm are incongruous with our fluid dynamics models" or "The reflectivity of the surface seems to indicate a different composition than we expected."
AI isn't currently capable of formulating questions, asking them, answering them, and ranking the results on significance.
And in lieu of that, the only use is "Help a scientist answer a question they posed." Which seems to be the initial problem that started this thread! Not enough scientists can get grants to pose all the questions that should be asked of old data.