jjcon

https://web.archive.org/web/20230125164809/https://www.adsbe...

That sounds like just introducing new problems not fixing the problem, I'd rather extremely expensive housing over either 1 or 2

"Avoid interrupting people while they're performing a task or playing a game."

I would say mid podcast certainly qualifies as breaking that rule. They know exactly what they are doing when they ask for ratings mid-podcast and the reviews show it. The fact that it is confusing users would be enough for Apple to remove the app from the App Store for review manipulation if it were anyone but themselves. Again, take a look at the review section:

https://apps.apple.com/us/app/apple-podcasts/id525463029?see...

https://apps.apple.com/us/app/apple-podcasts/id525463029?see...

Here’s another article from an apple friendly source

https://www.macrumors.com/2021/11/19/podcasts-rating-prompt-...

EDIT: Actually - found a screenshot mid-podcast: https://i.imgur.com/dnqcAbQ.jpeg

https://www.theverge.com/2021/11/19/22791968/apple-podcasts-...

Google is also a bit notorious in the ML space for taking concepts from other companies and academy, making a couple changes and slipping a google logo on it and then dubiously claiming superior performance (see the inception convnets, photo coloration and object removal etc, even the transformer itself to some degree)

They deserve credit for the transformer but I’m not sure that means they will have a leg up here overall. I agree though it is likely to be the bureaucracy that will take them down if they in fact do not succeed.

It would be colossally stupid for them to betray user trust in that way as it would almost certainly come out eventually, but that doesn’t change the trust problem they ultimately do have.

Interestingly that isn’t actually the case. I would guess most data used in ML is not human labeled specifically because it is so expensive and error prone. For example, most work done in the natural sciences simply utilize sensor data. Want to forecast some aspect of the weather? Grab a couple decades of historical data collected by weather sensors. None of that needs to be human labeled, only sensor collected. Some applications certainly do rely on manually human labeled data but not all, and I would personally guess only a minority of applications because of the difficulty in collection.

Regardless, my point still stands, they completely ignore (willingly or ignorantly) that human labeled data is not intrinsic to ML or even the algorithms themselves and in all likelihood is a small minority of datasets used by modern ML applications. To then apply that critique generally to ML shows ignorance and a misunderstanding of the ecosystem.

Given that, it seems to be a clear miss to apply that logic generally. I have to believe it most likely stems from a lack of basic understanding and competence on the authors part.

Edit: Here are two examples that I have personally worked on:

Global calculations of weather composite reflectivity using 20 years of historical satellite imaging data from NASA

Superior RF demodulation (under certain circumstances) using RF transmissions as received and sent.

Both of these utilize modern ML imaging models, neither require any human labeling only streaming data (which in these cases began collection long before modern ML techniques were in widespread use). The applications in the natural sciences are endless not to mention the applications more on the business intelligence side using transactional data. Only in specific cases is human labeling required but because of the high cost of that data it is by its nature dwarfed by that which is collected naturally (not to mention often error prone). It is for that reason that techniques to ingest data that is more and more natural to collect are growing in favor.

You don’t watch the news much do you? This is the MO of reporters at every news agency chasing a soundbyte. Nothing illegal in the slightest.

It’s becoming increasingly evident that vaccinating annually as has been the suggestion for decades may in fact cause vaccines to become less effective for you personally over time which can be bad if you develop another health condition that puts you at high risk or as you age generally. The theory is that it is due to negative interference:

“The idea is that the antibodies produced in year one may neutralize some of the vaccine in year two’s shot before it can trigger a full immune response, explained Dr. John Treanor, a vaccine expert at the University of Rochester Medical Center in New York.”

If you have regulators breathing down your neck being conservative on time isn’t a terrible idea.

Here is one of the provisions of interest:

https://gdpr-info.eu/art-5-gdpr/

Ubisoft also cites GDPR provisions for deletion of inactive accounts:

https://www.gamepressure.com/newsroom/ubisoft-removes-player...