Generative models do redistribute though, often outputting near copies:

https://openaccess.thecvf.com/content/WACV2021/papers/Tinsley_This_Face_Does_Not_Exist..._But_It_Might_Be_Yours_WACV_2021_paper.pdf

https://arxiv.org/pdf/2203.07618.pdf

Copyright does not only cover republishing, but also covers derived work. I think it is a very reasonable position to consider all generative model output o for which some training set image Xi had a particularly large influence on o, to be derived work from Xi.

Similar story holds true for code generation models and software licensing: copilot was trained on lots of software repos that had software licenses that require all derived work to be licensed under an at least equally permissive license. Copilot may very well output a specific code snippets particularly based on what it has seen in a particular repo, thereby potentially opening up the user to the obligation to the licensing constraints that come with deriving work from that repo.

I’m an applied industry ML researcher myself, and am very enthousiastic about the technology and state of ML. But I also think that as a field as a whole we have unfortunately been careless about ethical and legal aspects.

As much as I like ML, it’s hard to argue that training ML models on data without consent, let alone even copyrighted data, would somehow be OK.

For now the goal is just to make planes run on hydrogen at all. It’s obvious that we will have an abundance of green hydrogen in the future, even if today we do not, given that we reach the point where wind + solar generation exceeds demand more and more often.

If we’re using a bit of blue hydrogen just to progress R&D in hydrogen planes and be ready for green hydrogen flights in the future, that seems totally fine by me.

The definition of “sunny days” are simply not so relevant, since the output of solar panels on sunny and on cloudy days differ by a margin that is much smaller than most people think.

> solar if they can manage to make the latter work with how cloudy the place is.

You do know that clouds have only a limited effect on the output of a solar panel, right?

That’s simply false. Germany’s emissions have been on a steel downward trend year after year since the start of the energy transition.

https://ourworldindata.org/grapher/annual-co2-emissions-per-country?country=~DEU

The temporary switch to coal has only temporarily slowed down the pace of this decrease.

> Peak demand is winter and during the night. Solar is unable to meet those needs so in moving away from oil they have been burning coal instead.

The two months where renewable electricity generation peaks are January and February, exactly in the winter when most necessary.

It is true that solar output is lower in winter. But at the same time, wind output is highest in winter, and that has a stronger effect on the cumulative renewable output.

This trend is pretty rooted in reality honestly:

https://ourworldindata.org/grapher/share-electricity-renewables?tab=chart&country=~DEU

2022 data is not yet in that graph, but we find here an estimate of 49.6%. Continuing the upward trend.

https://renewablesnow.com/news/renewables-account-for-496-of-germanys-power-mix-in-2022-810330/

That’s exactly where it’s trending towards:

https://ourworldindata.org/grapher/share-electricity-renewables?tab=chart&country=~DEU

In the Netherlands a masters can be anything between 1 years to 3 years, depending on the field of study.

> Most of the people I know started their PhD right after undergrad.

It depends also on the continent. US based PhD students tend to be younger on average when they start their PhD than PhD students in mainland Europe.

This because in Europe it is often legally required to have completed bachelors + masters before you can start a PhD.

I started my PhD in the Netherlands when I was 26. My experience comparing to other PhD students at the university and in the country this was pretty much an average age to start.

No it won’t. Forecasts clearly show that.

See here the optimistic and pessimistic scenarios of gas storage levels throughout winter that the German government modelled earlier this year in preparation for the winter. These scenarios are in the case of no rationing.

In the pessimistic scenario Germany would have run out of gas by the end of April. In the optimistic scenario Germany would even have still 70% gas left in storage at end of winter.

Current trend until end of December is more positive than even the most optimistic scenario.

See the graph here with the title “Reicht das Gas?”

https://www.zeit.de/wirtschaft/energiemonitor-deutschland-gaspreis-spritpreis-energieversorgung

Jumping from one baseless claim to the next I see.

German industrial output is up this year, not down. Factories are open full week. There was some panic early on in spring/summer when it was unclear whether Germany would be able to prepare for a winter without Russian gas.

Current forecasts show that Germany will get through winter just fine without rationing.

> You will have the problem that LNG is much more expensive than normal natural gas.

It’s true that LNG is more expensive. But that is not a problem.

Carbon prices have been applied inside the internal market for years.

This tax is just to make goods that were produced outside the internal market pay in import tax what producers inside the market had already paid in carbon tax at time of production.

Meanwhile the Dutch postal service PostNL has been operating net zero for several years now by delivering using a mix of electronic vehicles and cargo bikes.

https://www.postnl.nl/en/Images/elektrische-bakfiets_tcm9-150332.jpg

Yeah I’m aware that linear and logistic regression are classical methods and are in the weird spot where they sometimes are and sometimes are not regarded as ML.

My comment was mostly aimed to argue against this claim in the comment that I replied to:

> Classic AI methods usually refers to non-statistical methods

When I hear “classical methods” I associate that with traditional statistical methods that often aren’t even considered ML.

Note that frequentist stats also go by the name of classical methods (as opposed to Bayesian methods).

> So it might actually be affordable here soon. The worst part is the “Solar fee” from the power company.

Vote for better politicians that prohibit that, because such fees are not a common thing in a global perspective.