Exactly. Space is not static. Also everything is moving relative to each other. Finally distances are measured using the "distance-ladder" of astronomy which depends on a bunch of model assumptions. Therefore astronomers typically report distances in proportion to the Hubble constant. In case your model prefers a slightly different value you can recalculate your distances easily.
The pulsar timing array pulsars are all in the Milky Way; there's a bright millisecond pulsar only ~500 light years away. What's the "proportion to the Hubble constant" of that? For ~500 ly ~ 150 pc, direct measurement of parallax is totally possible (even Hubble could get most of the IPTA targets in 2009). We can check that with other methods (secular parallax, (supernova remnant nebula-) expansion parallax). Not sure what the "model assumptions" are other than Euclidean trigonometry.
It doesn't go into a lot of theoretical detail about food chemistry but it is an eminently practical and down to earth textbook for people who will cook for a living. No frills, no pictures.
Fair question. I sometimes envy the amount of time people without kids have. But even for all the time in the World I would never trade the experience of having my three year old daughter. The pleasure I get from her is beyond anything I ever managed to get for myself, even when I had all the time in the World. Whether down to biochemistry or insanity, or both, I don't know. Having kids also helped me value my personal free time a lot more, so in some ways I feel like I'm more aligned with what I want now. Don't read any of this as advice though, I would never recommend anyone having children unless it's something you really want.
Heroin and meth give you loads of dopamine in response to an artificial stimulus. Parenting gives you loads of dopamine because, evolutionarily, it's really good for you to be parenting.
A stretched analogy might be a wall of trophies that you earned at sports tournaments vs a wall of trophies that you bought on eBay; the result is the same, but the means really matter as well.
I get you point, but I'd still argue that SSD+Arq is a lot simpler than any NAS setup. Wether the migration is worth it is up to personal preference I guess, but there's certainly a risk of spending more time and money in the end.
PRIMO is a way to fit models to the data in image-space. Since the models are very expensive to run it's not feasible to sample the model parameters directly in order to find a fit to data. Instead they take a library of model outputs and parameters and do a principle component analysis in image-space. This allows them to very cheaply generate new samples without running the complete simulation for each new set of parameters.
Principle component analysis is an old school analytical numerical method and has nothing to do with AI.
This is not the "AI" part though. The thing that the press is calling "AI" is the principle component analysis (PCA) which they use to sample their model without having to generate a full MHD simulation at each grid point.
PCA was invented in 1901. It hardly qualifies as AI.
> This is the first time we have used machine learning to fill in the gaps where we don't have data
After PCA, they used some gradient descent for the next steps.
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Sorry, edit: there are more components of AI which I understood (in the not fully analytical reading of the two divulgative articles and a brief skim of the research article): right above «machine learning» is said to be used to «fill in the gaps», and elsewhere it seems that a data interpreter is built to go from the obeservational data from the telescope to the results, qualitative and quantitative (e.g. shape and mass of the object - as you read in the other post).
The point of the Event Horizon Telescope (ETH) was actually to attempt to produce something like an image of the black hole at the center of the galaxy, so in a sense it was a pretty-picture mission. The point of this study was to attempt to link up the observations with models in "picture-space".
There are certainly other, less pretty, ways to looks at the data, but producing something that can be intuitively understood, such as a 2D image, can also be helpful scientifically.
The title from AP is misleading. They are not using any "AI" but a simple principle component analysis and a Monte Carlo minimizer.
> In this approach, we apply principal components
analysis (PCA) to a large library of high-fidelity, high-
resolution general relativistic magnetohydrodynamic
(GRMHD) simulations and obtain an orthogonal basis of
image components. PRIMO then uses a Markov Chain Monte
Carlo (MCMC) approach to sample the space of linear
combinations of the Fourier transforms of a number of PCA
components while minimizing a loss function that compares the
resulting interferometric maps to the EHT data.
As an exercise in matching up MHD models with real data this can be an interesting study. Too bad the reporting is so off point, as usual. But I guess adding "AI" to your titles gets you more clicks these days.
I'm not sure what you mean, but the techniques they are using are not new and are not related to artificial intelligence. They use a 120 year old numerical technique to "super-sample" their expensive simulations, which allows them to "fit" the simulations to data in a robust way. They do the calculations in a computer because there are too many numbers to do it on paper. That does not mean it has anything to do with AI.
I understood that PCA is there as one of the employed ways to augment the input data, then other ways are employed for the analysis of the data - that is where the literal "Artificial Intelligence" happens.
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Edit: see also, for example:
> In contrast, PCA finds correlations between different regions in Fourier space in the training data, which allows PRIMO to generate physically motivated inferences for the unobserved Fourier components
...inferences are drawn on the interpolated data. Automated model building.
I asked ChatGPT what it would respond and it produces something very similar, down to the same sentences, to the comment above. So this definitely looks like a ChatGPT response.
