matjet

At this time, the competition is soon finishing - with no models having succeeded. Given the incentives for top labs, and the short time needed for a successful automated solution, we can make a reliable upper bound on the capability of current models - better than any normal benchmaxed datasets.

What I would like to see is an easier version of this same format.

Look what they need to mimic a fraction of [the power of having the logit probabilities exposed so you can actually see where the model is uncertain]

I think you misunderstand induced demand. The increased demand when you add more capacity, simply means even more utilisation is achieved -> increased productivity. This is not a waste of time.

The correct comparison should be comparing for each area and situation, which type of transport investment results in the greatest utility. The objective is not to minimise traffic, but to maximise peoples ability to get where they need to be.

Is this a sarcastic joke, or is it a serious comment? I honestly cannot tell.

The latter option is closest, but neither is quite right. It would have ~known~ that the problem asked, combined with a phrase for a 15 line limit has associations with a length of 12 lines (perhaps most strongly 12, but depending on temp it could have given other answers). From there it is constrained to (complete) solutions that lead to 12 lines, from the several (partial) solutions that already exist in the weights.

Sharing of the model weights should now be fine. There is a 12 hour old post for the original pull request that was dismissed on the basis of being unlikely to be considered; that links the preferred url.

EDIT; I may have misunderstood the reviewers acceptance. The post should probably be deleted.

Gaussian blur is essentially acting as a low pass filter. An IR filter does not strictly destroy information in the filtered spectrum components, but does attenuate their power.

Given a perfect blurred image, reconstruction is possible - however due to the attenuation, these high frequency components are ~sensitive~.

Apart from quantisation effects [you mentioned which limits perfect de-convolution], adding a little AW Gaussian noise(such as taking a photo of the image from across the room) after the kernel is applied obliterates high frequency features.

Recovery when noise is low (plus known glyphs) is why you should not use Gaussian blur followed by print screen to redact documents. Inability to recover when there are artifacts and noise is [part of] why cameras cannot just set a fixed focus [at whatever distance] and deconvolve with the aperture [estimated width at each pixel] to deblur everything that was out of focus.

TLDR for readers, It is unlikely to recover sufficient detail via de-convolution here.

Hear Hear! This deserves to be reiterated given the statements of information recoverability made in this thread.

To someone only weakly aware in this area, can you explain what is wrong with the statement? From a cursory view, these protocols indeed seem to normally carry the mentioned payloads.

While 1991 does seem too recent for the text, 1961 is not possible given it makes several references to later years, up to 1965/1968.

I am curious if this article has been scanned by a machine that attempts to use OCR to improve quality - at the expense of occasional mistakes. "dress" in place of "chess" looks close in font appearance, but far to mistype on a keyboard.

Can someone please elaborate on the relationship between archive links and spam? Why is this particular auto-flagging done?

Patent trolls that are hated look like: You develop package to do X from first principles, then get sued because someone patented using a known algorithm for the purpose of X.

Copyright working in a supported/non hated way: You develop a package to do X by cribbing off someone else's package X. They sue you for stealing their work, not to make money off you. Situation at hand is case 2, hence the lack of interest in financial gain.

Why is this case 2, when it does not always reproduce the copyrighted works exactly? Situation: You realise that rather than cribbing off of one persons package X, you can crib off two other package X's and mix/average their contents. Scale this to 100's of packages.

Eventually, ML should avoid this by developing to work from first principles, writing in it's own style, with public code used only for validation of it's ability to understand and write code.