autopoiesis

So the Android work has been merged? Does this mean anything for Hu Jianwei's builds (of Emacs and Termux) at [1]? Having them in an obscure SourceForge repo seems less than optimal; getting Obtainium to understand the repo structure was not super fun...

[1] https://sourceforge.net/projects/android-ports-for-gnu-emacs...

Couple it with a generalized right to repair: source code is what's needed in order to be able to repair the software that you use. If beyond the support period or the copyright term (whichever is least), the materials needed to repair the product must be released.

Isn't the solution to have much shorter copyright terms? Software could be closed source at first, its implementation costs recouped, then opened by default when its copyright term lapses. New releases could still be closed, so income could continue. Set the term at 5-10 years, rather than >70.

This is precisely the question answered by the OP. The answer is, "because there is a whole spectrum of things you might mean by 'diversity', of which 'number of distinct species' is only one extremum".

It's not clear what you're seeking. Probabilities appear because the magnitude of a space is a way of 'measuring' it -- and thus magnitude is closely related to entropy. Of course, you can follow your nose and find your way beyond mere spaces, and this may lead you to the notion of 'magnitude homology' [1]. But it's not clear that this generalization is the best way to introduce the idea of magnitude to ecology.

[1] https://arxiv.org/abs/1711.00802

Metric spaces are enriched categories. They are enriched over the positive reals. The 'hom' between a pair of points is then simply a number: their distance.

Sadly, it's mostly nonsense. It first tells you to travel in the wrong direction, then go back on yourself, then take a route that doesn't exist (Green Park to Westminster on Victoria, in 2 stops, when it would only take 1 on the Jubilee)...

What are the inputs and byproducts of the manufacturing process for your proprietary sorbent, and what are their environmental impacts, if any?

The paper (arxiv:2103.04689) linked by eutropia above has some empirical evidence on the ML side, showing that performance of predictive coding is not so far off backprop. And there is no shortage of suggestions for how neural circuits might work around the strict requirements of backprop-like algorithms.

cs702's original comment above is excessively hyperbolic: the compositional structure of Bayesian inversion is well known and is known to coincide structurally with the backward/forward structure of automatic differentiation. And there have been many papers before this one showing how predictive coding approximates backprop in other cases, so it is no surprise that it can do so on graphs, too. I agree with the ICLR reviewers that this paper is borderline and not in itself a major contribution. But that does not mean that this whole endeavour, of trying to find explicit mathematical connections between biological and artificial learning, is ill motivated.

It sounds like you would be interested in the book / course '7 Sketches in Compositionality' by David Spivak and Brendan Fong, which studies precisely those ideas categorically, with a particular focus on systems that you might broadly call 'computational': http://math.mit.edu/~dspivak/teaching/sp18/

It has been discussed on Hacker News at least a couple of times previously -- fairly recently, even. You might be interested to look at these discussions:

https://news.ycombinator.com/item?id=20376325

https://news.ycombinator.com/item?id=19701767

Edit to add:

You might also be interested to learn that categorical approaches to linguistics typically take as their starting point monoidal categories, in which there are notions of 'parallel' as well as 'sequential' composition. It turns out that the usual categorical semantics for linguistics shares a lot with the categorical semantics for quantum mechanics: roughly, meanings are vectors, like quantum states. You can read more about doing (finite-dimensional) quantum mechanics entirely using string diagrams (the formal diagrammatic calculus of monoidal categories) in the work of Bob Coecke, who also played a large part in originating these approaches to linguistics.

For example, on the quantum side, an excellent book is 'Picturing Quantum Processes' [0]. And on the linguistics side, the paper linked in the article is a good start: https://arxiv.org/abs/1003.4394

[0] Not freely available, but some slides are at https://www.cs.ox.ac.uk/ss2014/programme/Bob.pdf

Edit, again:

There is also of course Bartosz Milewski's book / blog series 'Category Theory for Programmers', which introduces category theory from the perspective of Haskell and C++ programming: https://bartoszmilewski.com/2014/10/28/category-theory-for-p...

But the best introduction to category theory I have read is Leinster's book, 'Basic Category Theory': https://arxiv.org/abs/1612.09375

And as you might have guessed, I do agree with your statement!

Excellent; a few years ago I even started hacking on my own transport, very roughly as a PA module, but life got in the way and it never got very far. So I'm very pleased to see this great project. Thanks, and good luck!

I see you've got Opus on your to-do list. I would really appreciate that! I find Opus (appropriately configured) to be audibly indistinguishable from CD audio, and it would really help with the bandwidth requirements.

I've always been really excited by the possibilities implied by PulseAudio's network capabilities, but disappointed by their latency and bandwidth requirements. Roc + Opus would be amazing.

Debian testing. An amazing compromise between stability and recency.

Arguably, though perhaps I am unwise to post this here (if so, it is a sorry state of affairs..), the idea that there is "nothing wrong with nudity" is less controversial (more true, even!) than the idea that there is "nothing wrong" with white supremacy.

But perhaps its continuous creation explains why the universe seems to contain so much of it?

Please post links to the arXiv abstract pages, everyone: we don't always want to download a PDF just to read the abstract!

https://arxiv.org/abs/1712.07962

Edit to add: the abstract pages also often contain useful extra information, such as (in this case) a link to YouTube videos of the simulations -- https://www.youtube.com/channel/UC8ltFtaETXDphec0l-VxMsg -- or the publication history (in this case, a DOI is given).

Cixin Liu, with his excellent 'Remembrance of Earth's Past' trilogy. Beautiful, literary, conceptually profound sci-fi.

The advantage of having a human brain is that you can think about your actions and the consequences they have. Eating meat, especially mass-produced beef, is catastrophic for the environment. Well done on destroying the systems you depend on to live.

How does this compare to using notmuch-emacs? And can someone say whether notmuch or mu is preferable these days?

I think pijul's algorithms avoid the "exponential merge problem", though I don't know the details: https://pijul.org/manual/why_pijul.html#better-conflicts-for...

And pijul naturally goes to "pj"...

I was actually more interested in a reference making an explicit construction of a probability theory using linear logic, or if that's unavailable, then some more on the LL -> games -> probability route.

Edit: I found this comment [1] by you 29 days ago. Would you mind expanding on the "more connections" you mention? Thanks.

[1] https://news.ycombinator.com/item?id=17432612

I would be very interested to hear more about the connections between linear logic on the one hand, and probability and game theory (and by extension, GANs) on the other. Could you expand a little on those claims, or provide references?

(Edited for grammatical clarification.)

Yes, this DeepMind paper has been associated with an extraordinary amount of hype! This must be the third or fourth time it has been posted here, when most of the extensive grid cell literature has never been posted once; including theoretical work that explains the emergence of such cells, or that uses grids for navigation. And I don't think this is the first time that a DeepMind paper has been selective in its literature review, either...

I think they also have some TensorFlow-specific SYCL kernels; I'm not sure it all goes through Eigen. Plus they're at TF 1.6, whereas AMD's HIP version seems to be stuck on TF 1.0. AMD's method of maintaining a pure fork and running hipify on every upstream commit doesn't seem very sustainable...

This book is certainly being pushed very hard lately...

Honestly, I'm not sure how that happened either. Thanks for posting the new one!

More details at https://www.theengineer.co.uk/supercapacitors-battery-techno... -- hard to find an actual paper, though

Err, I'm not sure that's an answer to the question I asked. I thought there might be some context that might help me understand the paper, or any present topicality...

What is the context for this post?