jupiterelastica

With the risk of sounding naive: How come this post is flagged?

Reading the guidelines I can't see how this is off-topic or does _not_ "[gratify] one's intellectual curiosity."

Edit: spelling

For those interested in more details and quite mind blowing examples, here is a fascinating interview with Michael Levin (one of the researchers mentioned in the article).

https://youtu.be/c8iFtaltX-s?t=4751

Start at 1:19:11, the stuff before is him talking about biology, but from an intelligence perspective. After this time stamp is his retrospective on his bioelectricity research over the years, showing also examples of how they got a frog embryo to produce eyes, and many more things.

Related BBC podcast "In Our Time" about Doggerland, a landmass which was inhabited about ~10k years ago, but is now submerged in the north sea.

https://www.bbc.com/audio/play/m0006707

I watched the Tudor Monastery Farm show first by random chance and also liked it a lot. Thanks for all the recommendations!

There is a great documentary series with three archeologists by the BBC about medieval castles featuring Guédelon as a real live example from around 2014. I really enjoyed watching this and highly recommend it.

https://archive.org/details/secrets-of-the-castle

Edit: spelling

Additionally to locking the exposrue time and aperture, one could also take multiple exposures, figure out the camera's light response function and fuse multiple exposures together into a single higher dynamic range (HDR) image (see OpenCV tutorial on that or Debevec et al. 1997) Assuming you can find the camera response for the very long exposure times at night _and_ the very short during the day, one could relate them to each other and display both for accurate visual comparison.

Is it possible integrate this in a notebook environment? This would be amazing for teaching C/C++.

What is LORA in this context? Google only brought me to wifi networks :D

I try to. One similar thing comes to my mind: generative adversarial networks (GANs). If I'm not mistaken this is along the line of your idea of composing single ML models to bigger information processing units.

Do you, by any chance, have links or recommendations for material to read up on architectures that do consider ML models as composable gates?

Like, a generative model is a source of _some_ information that is refined with gates (classification models) conditional on the generated information?

The analogy to transistors and logic gates falls flat a bit when you consider that voltage is a rather simple univariate signal, while generated text is quite complex and multivariate. But I understand that the main point is the composability and filtering.

So, client that was "offline" can just ask a participant to get the edits from his own last one (aka pull), send out bis own offline edits (push) and resolve from there, I See.

Actually, that cleared it up, thank you :)

In Zed/CRDTs, these two seem synonymous to me since the OP's article is the first I read about CRDTs that I grasped, or so I thought ;)

Doesn't the whole process assume that whenever any edit is done (insertion/deleteion/un-/redo) that the edit _eventually_ reaches all participants?

So if a single edit is believed to be delivered to all, but actually never made it to a single participant, who resends it? And if it's not resent, then the participants state is inconsistent from now on, no?

Edit:autocorrect

I wonder what happens when an edit can not be delivered to 1+ participant(s). Does an edit need aknowledgements from _all_ participants to be persisted? Is it just dropped?

You are very clear about the current limitations on data size, which I find refreshingly honest! How sensible do you find the idea to fine tune the model to a specific problem that has more than 1000 observations, by resampling the data (similar to bootstrapping) and retraining on the subsamples? As I understand it, one could fine tune the algorithm that TabPFN learned to the specific problem.

Many thanks also for open-sourcing your work and making the colab notebook, I've been playing around with that a bit.

Edit: spelling

For anyone (like me) not knowing what subnormal floats are, this StackOfverflow question and answer explain it quite nicely: https://stackoverflow.com/questions/15140847/denormalized-nu...

Though they use the term denormalized, which AFAICT is the a synonym for subnormal.

Edit: Thanks for this great blogpost :)

Hello,

over the last weekend friends of mine have created a simple but engaging game where you defend your treasure from knights that try to steal it. I enjoyed it and wanted to share it.

Have fun :)

In the software development world I would suggest: knowing how to interface one's own software with existing systems to automate tasks.

I am thinking specifically about programming in pyhton/javascript/etc. and the act of requesting web pages, reading files, or databases and parsing this to quickly gather lots of available information about something. This could be a weather data scraper, reading all files from a directory to transform them, checking repeatedly if some signup for something important is open yet, pushing receipt pdfs from emails to an archive, ...

The examples all have in common that you need to know how e.g. REST/file/database access works and you can use these to gather information quickly and automatically. Knowing how to interface with common systems can save you lots of time, therefore I would see this as a productivity multiplier.

I could, yes. Yet it makes more sense to run what I need on my work laptop or if I ever really need it buy a could GPU on demand, since I'm not fiddling with the lower layers of ML tools, but learning the basics.

Good point, I haven't considered this. Thanks!

It is the first and only GPU I ever bought. Since it is the most expensive part in a PC, I have also kept to the "do I really really need it?"-mantra and have never upgraded it.

With this PC I will not be doing anything anymore, I believe. I just don't have a use for it. So I guess I will follow the advice of most comments here and just sell it after all :)