MrMoenty

Perhaps this is approximately true if you focus exclusively on mass shootings at schools. But shootings overall are one of the leading causes of deaths for children in the US.

See for instance the CDC's visualizations on causes of death per age group [1]. In 2020, 476 children aged 10-14 died in traffic accidents involving motor vehicles. 218 children aged 10-14 were killed in homicides by firearms.

Those two numbers are very much in the same order of magnitude. This is also the case in prior, non-Covid years.

[1] https://wisqars.cdc.gov/data/lcd/home

There is, e.g. § 812(1) BGB in German law:

> Wer durch die Leistung eines anderen oder in sonstiger Weise auf dessen Kosten etwas ohne rechtlichen Grund erlangt, ist ihm zur Herausgabe verpflichtet. Diese Verpflichtung besteht auch dann, wenn der rechtliche Grund später wegfällt oder der mit einer Leistung nach dem Inhalt des Rechtsgeschäfts bezweckte Erfolg nicht eintritt.

> A person who obtains something as a result of the performance of another person or otherwise at his expense without legal grounds for doing so is under a duty to make restitution to him. This duty also exists if the legal grounds later lapse or if the result intended to be achieved by those efforts in accordance with the contents of the legal transaction does not occur.

Given that training the same network (with the same structure) will result in different weights and different hashes with high probability, I would argue that the weights actually have all the important properties of a secret key. You would just need to treat them as one operationally, i.e., make sure as few people as possible have access, and use truly random instead of pseudorandom numbers during training.

Ironically, that exit tax and the associated fees open up one of the only ways to obtain dual German citizenship as an American adult: Depending on your income, the German government may consider the cost of dropping the U.S. citizenship unreasonable, and therefore allow you to obtain German citizenship while keeping your American one.

https://www.exberliner.com/features/politics/can-amis-double...

I think part of the problem of this graph is that it attributes commercial building emissions to the municipalities in which they are placed, even though these buildings might be used by residents and commuters alike.

So if you move into the city instead of commuting, you might be using the same commercial buildings a before, but their emissions are suddenly attributed to you.

My point is that the parent's comment is mostly right: a frequent challenge in ML is black box computational units which don't expose the necessary information to run autograd. Even if the underlying mathematical function is not differentiable everywhere, but only on most points, having autograd available is valuable for use in training.

Hence you get works like this [1] which reimplement existing systems in a way that is amenable to autograd. [1] https://arxiv.org/abs/1910.00935

In deep learning, you generally don't require differentiability on the entire domain, only on most points you're likely to encounter. So a finite number of non-differentiable points is fine: You just trust that you're never going to hit them by chance (the probability that you do is 0), and if by some miracle you do, you just use a subgradient.

Case in point, the currently most used activation function in neural nets, the rectified linear unit

ReLU(x) = max(x, 0),

is clearly not differentiable everywhere either.

The common solution found in European cities is to have one street reserved entirely for pedestrians, and to allow delivery access from the two adjacent, parallel streets. But of course this requires a certain amount of infrastructure within the buildings.

The article is not about models being indistinguishable from random classifiers, the difference there should be very significant even on the tasks it discussed. Instead, the problem originates from the small differences in test set performance between the top N models. While that difference may very well increase when moving from binary classification to a more technically involved regression task, that is by no means guaranteed, and the main points of the article still apply.

The question is whether the reduced plastic pollution will make it worth it to slightly increase CO2 output on grocery bags. Given that the absolute cost in CO2 equivalent for a cotton bag is only 3.9kg according to the study you linked, I'd argue that it is negligible compared to the costs of plastic pollution. Ultimately, this seems like a tiny contribution to climate change, but a huge improvement in terms of pollution.

I believe the statisticians would call this heteroscedasticity. But meta-variety certainly easier to remember ;)

Just played through it and really liked it! I think this is just the right level of abstraction for a beginning CS student. Makes it easy to grasp the compositional nature of computers without having to deal with all the details that make circuit design hard, like the non-instantaneous response of real components.

I keep my GnuCash file in an encfs-encrypted Dropbox Folder. As long as you don't try to edit it from two devices simultaneously, this works quite well.

Well, funnily enough, quantum computing CAN help with factoring, and likely not with TSP. Factoring is one of those rare problems in NP that is not known to be NP-complete, but for which we also don't know a polynomial algorithm. In 1994, Peter Shor came up with a quantum algorithm that solves factoring in polynomial time [1].

On the other hand, it is generally not believed that quantum computers would be able to solve NP-complete problems such as TSP in polynomial time.

[1] https://en.wikipedia.org/wiki/Shor%27s_algorithm

I find it surprising that people have gotten so used to Facebook's abuse of data that they cannot even imagine things being different. You uploading data to Facebook to share with your friends does not mean that you give consent to have it stored, analyzed, and sold for profit.

If I upload data to Dropbox, my bank, or my health insurance, I don't expect them to be sold to advertisers either. So what if there was a social network that actually respected its users and didn't exploit their data for its own ulterior motives? Seems to be an inconceivable notion to some.

I think the Falkland war makes a good example of two first world countries, one of them even nuclear, going to war.

The unsolvability of the halting problems means that it is impossible to have a general algorithm that makes non-trivial statements about the behavior of a given program.

In the context of IA-64, that means that generally, the compiler will not be able to determine when it's save to use parallelism. We can only program it to use parallelism in a bunch of special cases for which we think it will be safe.

Given that simias originally asked for a simpler platform that we can trust in more, this seems to be a pretty strong argument that IA-64 is not that architecture.

This is an active research area that one of my former professors is involved in. Things are not as simple as you hope they would be.

You're optimizing in a space of millions of discrete dimensions (one for each base pair), with little knowledge about independencies. This is in contrast to tasks like image recognition, where we can make use of the spacial structure of the pixels to build effective models like convnets.

Additionally, medical datasets, especially ones with genetic data, rarely contain more than a few hundred datapoints, which is not where you want to be for deep learning.

Machine learning is still useful in this area, especially to find genes or gene combinations with high correlation to certain diseases. But we are very far away from having a model that maps genome -> healthiness.

In the real world, contract law is written to not just take contracts at face value, but to also let judges take external factors into account. This includes customs, the parties intentions, whether they are acting in good faith, etc.

This is precisely because it was deemed impractical to write contracts that could be 'computationally' interpreted line by line. Seems like the etherium is slowly rediscovering this fact the hard way.

,

What you describe can be formalized through the notion of Kolmogorov complexity [1], which relates to all sorts of interesting and fundamental concepts like entropy and uncomputability. So I think you're absolutely right to call it fundamental!

[1] https://en.wikipedia.org/wiki/Kolmogorov_complexity

You are right, this reasoning depends on only allowing formal systems with finitely many symbols. Turing himself actually gave justification for this in a beautiful footnote in his 1936 paper [1]:

page 249:

> I shall also suppose that the number of symbols which may be printed is finite. If we were to allow an infinity of symbols, then there would be symbols differing to an arbitrarily small extent.

And then in the footnote:

> If we regard a symbol as literally printed on a square we may suppose that the square is 0 < x < 1, 0 < y < 1. The symbol is defined as a set of points in this square, viz. the set occupied by printer's ink. If these sets are restricted to be measurable, we can define the "distance" between two symbols as the cost of transforming one symbol into the other if the cost of moving unit area of printer's ink unit distance is unity, and there is an infinite supply of ink at x = 2. y = 0. With this topology the symbols form a conditionally compact space.

[1] https://www.cs.virginia.edu/~robins/Turing_Paper_1936.pdf