Glad to see the author acknowledges their usefulness and limitations so far.

> I don't want to live in a world where reality doesn't exist

Perhaps the end result is that the world turns away from digital completely and goes back to reality :) We see already that some universities are going back to written and oral assessments, for example.

I don't mean to say that scores on evaluation metrics will remain exponential but rather the developments, uses, integrations will (e.g., web search in ChatGPT), and people can't conceive or keep track of this, and therefore discussions on the area are always behind the times.

For example, I think it's inevitable now that TV/movie production will not exist as we know it in a short time, except as niche work, like fine art in the age of digital. It's also inevitable that fully personalised media will be predominant. I think this is obvious, but yet people are zooming in on the background of essentially perfect videos to spot minor and irrelevant coherence aberrations.

Nitpicking will also inevitably become a niche hobby, like people who complain about the colour grading on a movie remaster, while the rest of the world just watches the movie and doesn't notice or care about the issues.

Conversely, computer science/AI doesn't have an equivalent of the rigor that public policy research tends to go through. CS has e.g., benchmark datasets, typical evaluation metrics, but these are more like norms rather than requirements, whereas in public policy, instruments for validations are far more rigorously tested and enforced. Depending on the area.

I agree that outright fraud would be detrimental, but I think OP overblows this issue completely and should apologise to his co-authors.

Agree, and it seems that this is how fields naturally evolve anyway.

Secondly, there are many ingredients required to successfully publish, communicate science, foster collaboration, etc., beyond technical brilliance. I'm sure we all know many technically brilliant people whose career never advanced because they lacked in some necessary area. People shouldn't be discouraged from improving in all areas because OP's delicate genius is offended by their technical ability.

Speaking of discouragement, it's a shame and a disgrace that you publicly called your colleague's work bullshit, including a first author that isn't yourself.

Aggressive expressions don't work long term because they reinforce the behaviour of outwardly and rapidly being angry.

No it's not

> bottled up anger/emotion is not good

Yes it is

It is tautologically obvious why expressing anger does not result in reduced anger in the long term, and why practicing subduing your anger is beneficial. Cathartic expression of anger is a long-debunked approach to coping with it.

It's likely that this was omitted from the paper as an oversight; such imputation likely wouldn't have affected the outcome of peer review.

It's a nice convenient device, but I can't now unsee the artifacting and compression arising from it. If I were to do it again I'd just pay a service to scan properly, or build a rig to photograph the frames.

On the other hand, I'm very pleased to have scanned and archived the films given that they've been unseen for so long and can now be shared easily.

Correcting the speed and interpolating frames added an amazing amount of detail that wasn't perceptible to me in the originals (albeit it was there).

All of this processing does remove some of the charm of the medium, so I'll be keeping the original scans in any case.

At least up until 2015 (last published paper that I'm aware of) this actually is how they optimised their recommender:

"However, we have observed that improving engagement—the time that our members spend viewing Netflix content—is strongly correlated with improving retention. Accordingly, we design randomized, controlled experiments, often called A/B tests, to compare the medium-term engagement with Netflix along with member cancellation rates across algorithm variants"

Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19.

It's probably a reasonable approach and has clearly been successful

No but you can read minds by proxy, via ratings, which is what the dataset consisted of.