I had assumed that XWayland is a drop-in replacement fo X11, and will be available indefinitely.
I regularly write code which relies on a working X11. I have written a virtual machine which makes X11 calls to do 2D graphics and event handling, as well as applications which compile to the virtual machine code. If X11 and now XWayland cease to be available, not only would I have to rewrite large parts of my virtual machine, but also rewrite all the 2D graphics code in applications. All so that I can stand still when the rug is being pulled from under my feet. I'm sure there are others in a similar predicament.
I may be naive about this, but as X11 just works, and has done for decades, it should require little to no maintenance, so why the need to withdraw it? I don't expect, or require, any additional functionality.
Scotland, 56 degrees north - I was able to see the aurora through occasional gaps in fast moving clouds around 0400hrs. Red, easily visible to the naked eye.
It's actually an interesting news article, at least if you're interested in neuroscience. I can confirm, though, that it's nothing whatsoever to do with flight simulators, and have no idea why the author chose that particular simile.
I remember those early systems, and was in touch with Upendra Shardanand and Pattie Maes at the time. Other early systems ca
As music recommendation was already being done, I developed MORSE, short for MOvie Recommendation SystEm, shortly after Ringo appeared. Like Ringo and Firefly, it was a collaborative filtering system, i.e. it worked by comparing how similar your tastes were to the tastes of other users, and took no account of other information (e.g. genre, director, cast). As it was a purely statistical algorithm, I didn't call it, or other collaborative filtering systems, AI. It was different to symbolic AI (which I was previously working on, in Prolog and Common Lisp), didn't use neural networks, and wasn't Nouvelle AI (actually the oldest approach to AI) either. I wrote it in C (it had to run fast and was just processing numbers) and used CGI (Common Gateway Interface) to collect data and give recommendations on the WWW.
In a nutshell, to predict the rating for a film a user hasn't seen yet, it plotted the ratings given by other users for that film against how their ratings correlated with the the user, found the best-fitting straight like through them and extrapolated it, estimating the rating of a hypothetical user whose tastes exactly matched the user for the film. It also calculated the error on this, which it took into account when giving recommendations. Other collaborative filtering systems used simpler algorithms which ignored the ratings of users whose tastes were different. When I used those simpler algorithms on the same data, recommendation accuracy got worse.
MORSE was released on the BT Labs' web site in 1995. It survived a few years there, but was later taken off the server. As BT Weren't going further with it, I asked if the source code could be released, This was agreed, but it wasn't on any machine, and they couldn't find the backup tape. The algorithm is described in detail here: https://fmjlang.co.uk/morse/morse.pdf and more general information is here: https://fmjlang.co.uk/morse/MORSE.html
My algorithm was pretty similar to yours I’m guessing. (See my other long post here.) I described mine to a friend one time and he called it “toothpick AI”.
I think that intelligence requires, or rather, is the development and use of a model of the problem while the problem is being solved, i.e. it involves understanding the problem. Accurate predictions, based on extrapolations made by systems trained using huge quantities of data, are not enough.
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