I don’t know exactly, but it’s apparently a thing. Some game anti-cheat software such as Easy Anti-Cheat will give you an error message saying something along the lines of “Virtual machines are not supported.” Some are easy to bypass by just tweaking your VM config, others not so much.
Fair enough! I think it’s more common for games to do that, but sometimes I had trouble with software on Windows that used virtualization elements themself. I probably just didn’t properly configure HyperV settings, but I know nested virtualization can be tricky.
For me it’s also because I’m on a laptop, and my Windows VM relies on me passing through an external GPU over TB3 but my laptops’ dedicated GPU has no connection to a display, so it would be tricky to try and do GPU passthrough on the VM if I were on the go. I like being able to boot Windows on the go to edit photos in Lightroom, for example, but otherwise I’d prefer to run the Linux host and use the Windows VM only as needed.
I’m a fan of dual booting AND using a passthrough VM. It’s easiest to set up if your machine has two NVMe slots and you put each OS on its own drive. This way you can pass the Windows NVMe through to the VM directly.
The advantage of this configuration is that you get the convenience of not needing to reboot to run some Windows specific software, but if you need to run software that doesn’t play nice with virtualization (maybe a program has too large a performance hit with virtualization, or software you want to run doesn’t support virtualized systems, like some anticheat-enabled games), you can always reboot to your same Windows installation directly.
I’m a researcher in ML and that’s not the definition that I’ve heard. Normally the way I’ve seen AI defined is any computational method with the ability to complete tasks that are thought to require intelligence.
This definition admittedly sucks. It’s very vague, and it comes with the problem that the bar for requiring intelligence shifts every time the field solves something new. We sort of go “well, given these relatively simple methods could solve it, I guess it couldn’t have really required intelligence.”
The definition you listed is generally more in line with AGI, which is what people likely think of when they hear the term AI.
I believe this is the referenced article:
I’ve been using FreeTube since Piped was very inconsistent for me, but I guess that’s just the nature of these services. I’ll have to check out Invidious again, last time I tried it was several years ago and I stopped using it after the main instance shut down. Is it still under active development? I remember its development status being unclear, partially because the language it uses is not super mainstream, but it’s probably changed since then.
Fortunately, Invidious, Piped, Libretube and Newpipe all exist and work flawlessly so there’s no excuse to use proprietary trash like that.
Isn’t the very point of this post that Invidious and Piped don’t work flawlessly?
Can’t you still modify and distribute Grayjay, just not commercially? I understand that still prevents the app from being considered open source, but their reasoning is valid IMO (to prevent people from making ad-infested clones on the play store, which has happened with NewPipe before).
I think what they mean is that ML models generally don’t directly store their training data, but that they instead use it to form a compressed latent space. Some elements of the training data may be perfectly recoverable from the latent space, but most won’t be. It’s not very surprising as a result that you can get it to reproduce copyrighted material word for word.
Not sure what other people were claiming, but normally the point being made is that it’s not possible for a network to memorize a significant portion of its training data. It can definitely memorize significant portions of individual copyrighted works (like shown here), but the whole dataset is far too large compared to the model’s weights to be memorized.
The big thing you get with frameworks is super simple repairability. This means service manuals, parts availability, easy access to components like the battery, RAM, ssd, etc. Customizable ports are also a nice feature. You can even upgrade the motherboard later down the line instead of buying a whole new laptop.
For reference, ICML is one of the most prestigious machine learning conferences alongside ICLR and NeurIPS.
Yeah there’s a good chance you’re right. Maybe something to do with memory management as well.
Long term I’ll probably end up switching back to Darktable. I used it before and honestly it is quite good, but I currently have a free license for CC from my university and the AI denoise features in LR are pretty nice compared to the classical profiled denoise from Darktable. It does also help that the drivers for my SD card reader are less finicky on Windows so it’s easier for me to quickly copy over images from my camera on there instead of Linux. Hopefully that also gets better over time!