Modern phone cameras use AI to fill in the gaps in low light photos.

This is one team that disagrees out of many that agree.

To explain what you are seeing. The above image is the inverse Fourier transform (FT) of different frequencies of sinus waves that compose an image.

The very large baseline interferometer (VLBI) applied in the event horizon telescope (EHT) is using different telescopes all over the world, in a technique called interferometry, to achieve high enough resolutions to observe different frequencies in Fourier space that make up an image. If you observe all, you can recreate the full image perfectly. They did not, they observed for a long time and thus got a hefty amount of these "spatial" frequencies. Then they use techniques that limit the image to physical reality (e.g. no negative intensities/fluxes) and clean it from artefacts. Then transform it to image space (via the inverse FT)

Thereby, they get an actual image that approximates reality. There is no AI used at all. The researchers from Japan argued for different approach to the data, getting a slightly different inclination in that image. This may well be as the data is still too few to 100 % determine the shape, but looks more to me like they chose very different assumptions (which many other researchers do not agree with).

Edit: They did use ML for simulations to compare their sampling of the Fourier space to.

I think, at best, it shows that the observations are consistent with the model, or to take it back to the blurry low light photo... The photo wasn't obviously not Trump.

I remember reading the original paper at the time and thinking, if I had been a reviewer I'd have wanted clear acknowledgement of the confirmation bias danger in the methodology. Ideally some sort of quantification of risk. It just seemed like too large a flaw to just be glossed over.