I'm not convinced this is better than a paid for alternative to Gmail.

Could common open source dependencies be a (con)federal government responsibility? The European Union does not have as large a software industry as the USA so there would be a less strong argument of government/corporate competition. It could take the form of government grants depending on the size. My rationale is that governments benefit from the general prosperity of open source more so than solo authors or small companies.

I am restricting the scope to simple and small libraries where investment is more clearly beneficial unlike Tensorflow as that is large and complex.

Here is an extreme example, the 'either' crate is a 'rayon' dependency and many others. Paradoxically a project of this size likely does not need funding but it is really important.

https://github.com/bluss/either

https://crates.io/crates/either/reverse_dependencies

With applications having so many dependencies, peculiar/'rogue' library changes are guaranteed.

Comments URL: https://news.ycombinator.com/item?id=29885377

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For example what is the definition of two events being independent in probability?

Or the L1 norm example: 'Which norm does the following equation represent? |x1 − x2| + |y1 − y2|'

Find the taylor series expansion for e^x (this is highschool maths).

Find the partial derivatives of f (x, y) = 3 sin2(x − y)

Limits etc...

These aren't specific to deep learning or machine learning, not that I claim to be a practitioner.

They ameliorate a lot of low entropy problems for passwords and can improve security. I can't imagine a proprietary one being mandatory. My banking app uses the mentioned remote attestation so I can't use it on my less-Googled Calyx ROM. I just think that's stupid instead of very strong warnings.

Unlike a video game, you aren't transitioning state all the time. Proposition-based testing is also very convenient in Haskell with say QuickCheck, good for dealing with money.

Is this because US people have a lot of disposable income?

Just iPhones are pricy even secondhand. Of course Android doesn't update for as long but plenty of people worldwide run unpatched devices.

Currently very few people I know actually print anything with digital services and email. They use tablets for reading documents. For a covid pass you can show the PDF. For a train ticket you can show the QR code on the phone. Some employment contracts can be signed digitally, maybe this you print?

I'm not sure what you need printing for.

Some of these people buy a printer for a one off because they will never finish the cartridge over time.

The only uses I can think of are public notices like lost cat or printing a handwriting exercise. I have had to print out rental contracts to sign but real estate will eventually modernise.

Suppose your website has posts and you want to flag posts when they have abnormally high likes because they might be great reading or complimenting your new release. You could collect a dataset of likes after a day, X, of each post. Then calculate mean and variance, fit a normal distribution[1]. Then calculate z such that P(X >= z) = 0.01 (1%). z represents the cut off point at which typically only 1% of posts are above. Then when a post is above z say 1000 likes then you see what all the fuss is about.

I am just talking about applying 16-18 school maths in a simple way, to point out unlikely events. Of course the distribution of likes may not look like a normal curve if you plot (number of posts with x likes against x) so a different distribution may make more sense. It may not be a perfect model but just a quick and dirty thing to try, :).

Personally I enjoyed completing the free Andrew Ng Machine Learning course[2] on Coursera which covers this and quickly training a simple recommendation engine for movies. It also covers multi-variate Gaussian distributions if you want to flag based on more than one criteria. For this course, the maths is relatively accessible and they go over what you may have forgotten so you can pick up maths as you go along.

Of course you can go far more complex if you like but I don't know much about that.

[1] Normal distribution https://en.wikipedia.org/wiki/Normal_distribution

[2] https://www.coursera.org/learn/machine-learning

It is small but really makes things more elegant. I should definitely try Julia. I've spent a lot of time calculating awkward array index conversion formulae in my head and this could make things a lot more simple especially for weird cases.

I was under the impression that Fortran's only redeeming benefit was that it compiled well for numeric computing routines.

I really don't think this is a scalable approach and was really surprised that someone took the time to check my dependencies personally. My experience may just be rare.

I meant more on the human side than automated. Haskell doesn't have that much to do with formal verification in usual use. I'm not sure what the best policy for a good yet vibrant package ecosystem is.

There isn't a duty to make the most of whatever period you are in. You don't have to die without regrets as you get to choose how you live for a large part.

If you die with regrets it means that there was a glimmer of vibrancy, maybe a sense of dreaming as you die albeit negative.