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Most Captchas that we encounter rely on some form of pattern recognition (whether it's static or dynamic) to work. The computer vision community has been studying (and solving!) related problems for decades and for much more complex tasks. It's sad to see how researchers tend to forget about the past.

I do my PhD research in applying computer vision algorithms to the medical field. You would be amazed to see how trivial these Captcha pattern recognition puzzles are compared to problems like brain template mapping, automatic tumor segmentation, vasculature extraction and others. These all involve some kind of pattern recognition but for which there isn't even a true answer or "gold standard" as we say.

In other words, it's no surprise that the answer to these "new" Captchas can be found by digging in the existing scientific literature. Until someone comes up with a paradigm shift...

It's arguable that Apple as a business might not directly create as many jobs as a traditional manufacturing business; however, Apple certainly fosters the creation of "collateral" jobs with myriads of developers working night and day to produce iOS apps. As of today, Apple has approved more than 500,000 of them (source).

He may indeed be spouting plenty of nonsense; however, I wouldn't be so sure about the legitimacy of his PhD. His PhD was in transportation engineering and planning (could it get even more nebulous?), while he was the mayor of a province (source).

Set aside all the controversy regarding the abundance and legitimacy of PhD graduates in his entourage (source), as a PhD student myself, I find it hard to believe that he could complete a PhD degree in a reasonable amount of time while working full-time as the mayor of a large province (Ardabil).

Actually, I think it would be worse for Amazon to admit that they did suffer a DDoS. I can easily imagine the more "technically challenged" people confusing an attack with an actual break-in. A break-in would imply that their accounts are exposed, including personal data, and could potentially cause a threat to them (impersonation, blackmailing, etc).

The same applies to the dissection of other organs as well. For instance, any dissection of the heart is inherently biased towards the cutting planes defined by the dissector (source). The true arrangement of muscle fibers in the left ventricle of the heart (more precisely the existence of sheet structure) is still a subject of hot debate because of this. Obviously, one might think that by now, we should be able to just pick an organ and throw it into the best relevant imaging scanner (CT, MRI, PET, etc.). The truth is, there is still anatomical information that even state-of-the-art medical imaging modalities cannot reliably reveal.

As an example, consider DT-MRI that measures the diffusion of water molecules along the tissue fibers in an organ. The discretization in the data is such that only the local average orientation of the diffusion of water is known at any given location. To obtain more useful anatomical information, the full fiber pathway in a region needs to be reconstructed, a task called fiber tractography. Different computational methods based on different anatomical assumptions lead to results that are often contradictory (as is the case in the heart models described in the article cited above) and since there is no ground truth (remember that the dissection is biased), we currently hit a dead-end.

Hopefully, as more dissections (like this one) are performed and the data is made available publicly, we will eventually be able to faithfully reconcile pieces of what we observe in medical conditions, in medical scanners, and on the dissection table.

Many heart problems can be solved through prevention; sadly, the same cannot be said for many neurological conditions.