I want to know what this means for observational astronomy. Can we put this in the eyepiece of a telescope and discern features in nebulae that otherwise look like gray blobs to unaided vision
I suppose it's a matter of resolution. ARCONS is an IR photon counter with 44x46=2024 pixels. I don't know how that would correspond with a camera's ISO, but you can't get much better than single photons. I'd imagine it's useless to install in your ground telescope, but perhaps in a couple decades, the resolution will be scaled up to compete with current optics imaging methods.
Photon counters were not what I had in mind when you were speaking about astronomy cameras.
I remember that around 20 years ago we had 1/100th of magnitude resolution with photon counters compared to 1/10th of the CCDs (SBIG &co.)
They are actually very public about the fact they are not a machine learning company and are more or less opposed to machine learning. Their goal has always been to wrangle data and make it useful to human decision makers. not automate decision making.
I was once diagnosed with ADHD myself as a child. I have been unmedicated for 8 years now and been quite successful career wise, and academically. Though I do wonder if I could do better had I not had ADHD.
its not fair to conduct these studies solely on educational outcomes because educational outcomes in the west largely depend on how well you conform to the mean societal expectation of a successful archetype. many people have unique educational styles (ADHDers included). I myself personally learned more from the internet and reading books than I ever did in school.
I suspect successful engineers, start up CEOs, hacker types have higher prevalences of "learning disorders", ADHD, etc. Many did poorly in school, though many also excelled. I also suspect the same demographics are true of musicians and other artists.
The MAJOR caveat here, is that while these groups probably have higher prevalences, and these fields are more or less supportive of people with these issues, very few ADHDers can break into these fields and the average ADHDer probably will perform worse in life than the average person without.
Mostly conjecture, based on my own anecdotal evidence, but worth consideration.
This is absolutely true, the one caveat is that you can explain the significance of features and the relationship to the response variables in simpler terms.
another point is that Linear Regression IS Machine/statistical Learning. Sure its been around for more than 100 years before computation, but regression algorithms are learning algorithms.
Arguing for more linear regression to solve a firms problems, is equivalent to arguing for machine learning. Now, if instead he wanted to argue that the vast majority of a businesses prediction problems can be solved by simple algorithms, that is most likely true. but economic impact of this is still a part of the economic impact of machine learning.
If we're classing linear regression as machine learning and agreeing it's a representative example of the type of simple algorithm that's most likely to benefit firms, I think it probably helps his point rather than harming it. It's a technique that's been around for ages, is far from arcane knowledge and every business has had the computing capability to run useful linear regressions on various not-particularly-huge datasets in a user-friendly GUI app for at least a couple of decades now.
For the most part they haven't run those regressions at all, and where they have, they haven't been awe-inspiringly successful in their predictions, never mind so successful the models are supplanting the research of their knowledge-workers.
This overshoots the target. It's like saying that we use algebra and therefore =/= AI.
LR and general regression schemes are captured in supervised learning methods. So yes, the systems use linear regression as a fundamental attribute but build on them significantly.
