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Joined 2 years ago
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Cake day: December 23rd, 2023

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  • You’re welcome. I think calling it the output of an ‘AI model’ triggers thoughts of the current generative image models, i.e. entirely fictional which is not accurate, but it is important to recognise the difference between an image and a photo.

    I also by no means want to downplay the achievement that the image represents, it’s an amazing result and deserves the praise. Defending criticism and confirming conclusions will always be vital parts of the scientific method.


  • Most of what you said is correct but there is a final step you are missing, the image is not entirely constructed from raw data. The interferometry data is sparse and the ‘gaps’ are filled with mathematical solutions from theoretical models, and using statistical models trained on simulation data.

    Paper: https://arxiv.org/pdf/2408.10322

    We recently developed PRIMO (Principal-component Interferometric Modeling; Medeiros et al. 2023a) for in- terferometric image reconstruction and used it to obtain a high-fidelity image of the M87 black hole from the 2017 EHT data (Medeiros et al. 2023b). In this approach, we decompose the image into a set of eigenimages, which the algorithm “learned” using a very large suite of black- hole images obtained from general relativistic magneto- hydrodynamic (GRMHD) simulations


  • Its not hard to find that there are legitimate academic criticism of this ‘photo’. For example here. The comparison you made is not correct, more like I gave a blurry photo to an AI trained on paintings of Donald Trump and asked it to make an image of him. Even if the original image was not of Trump, the chances are the output will be because that’s all the model was trained on.

    This is the trouble with using this as ‘proof’ that the. Theory and the simulations are correct, because while that is still likely, there is a feedback loop causing confirmation bias here, especially when people refer to this image as a ‘photo’.