I'm not in the US, but as an immigrant myself I can say it's incredibly stressful to be on a working visa during job changes and during a period of unstable economy. Especially if you have family.
To be rational, I work in academia and I shouldn't need to worry too much about job safety and/or visa applications, but I still semi-regularly woke up at night from nightmares of my visa expiring, not being renewed, suddenly being deported, or similar stuff.
I got my permanent residence earlier this year and all of it stopped. It gives you some sense of stability/security. It also makes one feel a bit more accepted in society by not needing to leave it if a work agreement (for whatever reason) ends.
> if they trained the model on me visualizing a bear, a fish, and a bird, and then the neural net still outputs "horse" when I visualize a horse
Well, the failure cases figure says it does not work if "training and testing datasets do not overlap". So, it'd just find the closest trained class and then generate new image from that class (i.e., in your example the bear might look more similar to horse than a fish or bird, so it'll generate a random bear).
Yeah, I thought the same after seeing this. It's kind of a fun use-case of Diffusion models in this context, but as a scientific paper it seems too overselling. Well, it surely is the kind of clickbaity content to anticipate lots of retweets from.
I only skimmed the paper, but from what I understood, it is essentially a diffusion model pre-trained on a handful classes. The brain information is then used to largely "pick which class to generate a random image from".
The paper itself even picked the "better" examples. The supplemental materials show many more results, and many of them are just that, a randomly generated image of the same object class the person was seeing (or, the closest object class available in the training data).
"Reconstruct" seems a pretty bad word choice. I think the results are presented in a way vastly overselling what they actually do. But that's a worrisome trend in most of AI research recently, unfortunately.
(I have a PhD in a field of Applied Machine Learning. I work at a university in Computer Vision.)
Well to be fair, three of the four apps are Apple ecosystem apps (although Craft now expanding to other platforms).
Maybe Roam, Obsidian, Logseq would be better examples of booming apps note-takers jump ship to? But then, I think all of these apps are rather niche compared to Evernote.
My portfolio website is 4-lingual and the initial setup was pretty confusing (a bit trial and error until I understood how it works). But now it works very conveniently.
Hugo has a multilingual mode[1] which I am using on two websites. It's a bit confusing at first but works pretty well for me.
For content, you basically put blogarticle.en.md and blogarticle.fr.md next to each other it will treat it as the same article in different languages. Per default it puts different language websites into different subdirectories (with a default language at root level) but it also supports multi hosts setups where you can define different domains for each language (e.g. blog.de and blog.fr).
There's support for generating "this article in other languages" type menus, which allows you to crosslink the website across languages.
There's also i18n support for translating stuff in templates etc., where you provide a dictionary file with translated versions of each string, and it'll replace them based on the language of the current article (useful for stuff like navigational menus etc.).
To be rational, I work in academia and I shouldn't need to worry too much about job safety and/or visa applications, but I still semi-regularly woke up at night from nightmares of my visa expiring, not being renewed, suddenly being deported, or similar stuff.
I got my permanent residence earlier this year and all of it stopped. It gives you some sense of stability/security. It also makes one feel a bit more accepted in society by not needing to leave it if a work agreement (for whatever reason) ends.