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Here's my crackpot idea, in case anyone out there is willing and qualified to put in the hard work: Start with a detailed model of the solar system. Make a million copies of it. In each copy, insert a planet in a random orbit, with random mass. Measure the orbits of everything, perturbed by the new planet. Feed the measurements of everything, except the new planet, to an A.I., and have it estimate the position of the…
Here's my crackpot idea, in case anyone out there is willing and qualified to put in the hard work: Start with a detailed model of the solar system. Make a million copies of it. In each copy, insert a planet in a random orbit, with random mass. Measure the orbits of everything, perturbed by the new planet. Feed the measurements of everything, except the new planet, to an A.I., and have it estimate the position of the…
Orbital equations are straightforward and deterministic, I am wondering why an AI would be needed for this? You could solve explicitly.
The reason planet nine is suspected to exist is due to the commonalities in the orbits of trans-neptunian objects. That is, there appears to be a large gravitational influence on TNOs that causes the distribution of their orbits to exhibit irregularities that don't make sense with only two factors influencing their orbits.
Earlier quoted context omitted.
I love how many paper titles nowadays follow the pattern: "Deep- : ". And often they aren't doing anything "deeper" than a fully-connected multilayer neural network--a machine learning algorithm competitive with SVMs and been around well over a decade.
That's true, but there's a lot of value in waking people up to the idea that ML works, even if what they're doing has worked for a long time. There are a lot of situations where before people would have assumed their best option is to carefully tweak a custom statistical model, whereas now they're just happy to throw a black box at it and see what happens. This is as much a cultural change as a technological change,…
Building a custom model will help with feature selection. It will provide a baseline to compare the ml model to which can help debug problem points of the ml model. And finally it serves as a sanity check that you aren't leaving a lot of performance on the table.
The article states positive impacts on science, but there are also negative impacts on science. For instance, the hype of AI has caused a brain-drain on related fields (such as cognitive science or applied mathematics). AI research itself suffers from companies buying up the academic talent. And researchers slap AI (which is usually deep learning) on a decade-old problem, without any care for complexity/benchmarks, i…
Here's my crackpot idea, in case anyone out there is willing and qualified to put in the hard work: Start with a detailed model of the solar system. Make a million copies of it. In each copy, insert a planet in a random orbit, with random mass. Measure the orbits of everything, perturbed by the new planet. Feed the measurements of everything, except the new planet, to an A.I., and have it estimate the position of the…
Ever more algorithms and models and data, ever less understanding and scientific theories. Soon, instead of "theory of gravity" we'll have "generative DNN of science papers and grant writing" that no one will understand, but can generate papers that pass peer review and earn grants and pull in all the monies, effectively monopolizing and halting all government funded scientific progress. Meanwhile, actual science wil…
[[citation needed]] What has been an amateur science breakthrough in the last century or two which didn't have at it's base some billions of dollars of government funding.
The article states positive impacts on science, but there are also negative impacts on science. For instance, the hype of AI has caused a brain-drain on related fields (such as cognitive science or applied mathematics). AI research itself suffers from companies buying up the academic talent. And researchers slap AI (which is usually deep learning) on a decade-old problem, without any care for complexity/benchmarks, i…
I'm really afraid that ML is mainly just going to become automated p-hacking, and bring about a dark age to much of science. In a publish or perish world, how can you compete with someone with enough budget to set a bunch of models looking for any specious correlations in data sets and publishing what comes out the other end? Like we'll still have great breakthroughs from the top of the field, but a lot of grunt work…
Here's my crackpot idea, in case anyone out there is willing and qualified to put in the hard work: Start with a detailed model of the solar system. Make a million copies of it. In each copy, insert a planet in a random orbit, with random mass. Measure the orbits of everything, perturbed by the new planet. Feed the measurements of everything, except the new planet, to an A.I., and have it estimate the position of the…
But the underlying idea of searching for statistical perturbations to known orbits to find new objects is a good one! In fact, Mike Brown and Konsntantin Batygin did just this a few years back. They argued that perturbations to the orbits of objects in the Kuiper belt suggested that there is a planet with about the mass of Neptune somewhere out there:
https://ui.adsabs.harvard.edu/abs/2016AJ....151...22B/abstra...
This object hasn't been found yet, but it could still be out there!
Earlier quoted context omitted.
I love how many paper titles nowadays follow the pattern: "Deep- : ". And often they aren't doing anything "deeper" than a fully-connected multilayer neural network--a machine learning algorithm competitive with SVMs and been around well over a decade.
That's true, but there's a lot of value in waking people up to the idea that ML works, even if what they're doing has worked for a long time. There are a lot of situations where before people would have assumed their best option is to carefully tweak a custom statistical model, whereas now they're just happy to throw a black box at it and see what happens. This is as much a cultural change as a technological change,…
The goal of research is usually to rip those boxes open to figure out what's inside and how it works. Moving away from that towards opaque predictions doesn't make a lot of sense to me, especially when the predictions aren't even that much better. Plus, a lot of this work seems weirdly disconnected from what the rest of the field knows to be (im)plausible.
Obviously, black boxes can be useful tools. DeepLabCut is incredibly helpful and will save a lot of grad students a lot of tedium, and that probably wouldn't happen if it involved a lot of tuning. Predictions can also be very useful--frankly, we'll take anything we can get for most neuropsych conditions--but mechanisms and targets for intervention are so much more useful. I know there is some work on this, but it's drown out by the 0.99AUC!!1!! (in a small, cherrypicked group) stuff.
Here's my crackpot idea, in case anyone out there is willing and qualified to put in the hard work: Start with a detailed model of the solar system. Make a million copies of it. In each copy, insert a planet in a random orbit, with random mass. Measure the orbits of everything, perturbed by the new planet. Feed the measurements of everything, except the new planet, to an A.I., and have it estimate the position of the…
So...the 8 body problem? https://en.m.wikipedia.org/wiki/Three-body_problem
Great question, and I am happy to have my 1 in 10k time today! (Nice being on the informing end for once).