I think you have your janitor example backwards. Spending my time revolutionizing energy productions sounds much more enjoyable than sweeping floors. Same with designing an effective floor sweeping robot.
I think you have your janitor example backwards. Spending my time revolutionizing energy productions sounds much more enjoyable than sweeping floors. Same with designing an effective floor sweeping robot.
AI are people, my friend. /s
But, really, I think people should be able to run algorithms on whatever data they want. It’s whether the output is sufficiently different or “transformative” that matters (and other laws like using people’s likeness). Otherwise, I think the laws will get complex and nonsensical once you start adding special cases for “AI.” And I’d bet if new laws are written, they’d be written by lobbiests to further erode the threat of competition (from free software, for instance).
The search engine LLMs suck. I’m guessing they use very small models to save compute. ChatGPT 4o and Claude 3.5 are much better.
Donation, patronage, gift economy, mutual aid, or whatever you want to call it is fine by me. People can pirate a lot of proprietary software as well, yet people still pay.
Yet, people still pay for it.
The problem is that HP writes drivers and software for those things for Windows, but not for Linux, so Linux depends on random people to write software for those things for free (which often involves complex reverse-engineering). With Linux you need to make sure you use widely-used hardware that someone has already written support for (this is mostly applicable to laptops and peripherals, which often use custom non-standard hardware). There may be a way to fix your problems, but you’ll have to search forums or issue trackers for the solutions, and they’re probably pretty involved to get working correctly. The router crashing thing is probably just a coincidence though, or the laptop is using a feature that’s broken on your router.
camelCase for non-source-code files. I find camelCase faster to “parse” for some reason (probably just because I’ve spent thousands of hours reading and writing camelCase code). For programming, I usually just use whatever each language’s standard library uses, for consistency. I prefer camelCase though.
I think most projects left Sourceforge after they started putting adware into they’re downloads.
In the Texas counties I’m most familiar with, if you’re arrested and they don’t have a good case, they just keep resetting court dates for years instead of going ahead with the process. If you can’t afford a bond, you’ll be in jail that whole time (which pressures people to take plea deals), if you can secure a bond, you’re out, but with limited rights and a whole lot of hassles to deal with.
I’ve used this before: https://github.com/wilicc/gpu-burn?tab=readme-ov-file
Yeah, it may be a driver issue, Nvidia/pytorch handles OOM gracefully on my system.
That seems strange. Perhaps you should stress-test your GPU/system to see if it’s a hardware problem.
SD works fine for me with: Driver Version: 525.147.05 CUDA Version: 12.0
I use this docker container: https://github.com/AbdBarho/stable-diffusion-webui-docker
You will also need to install the nvidia container toolkit if you use docker containers: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
I thought the tuning procedures, such as RLHF, kind of messes up the probabilities, so you can’t really tell how confident the model is in the output (and I’m not sure how accurate these probabilities were in the first place)?
Also, it seems, at a certain point, the more context the models are given, the less accurate the output. A few times, I asked ChatGPT something, and it used its browsing functionality to look it up, and it was still wrong even though the sources were correct. But, when I disabled “browsing” so it would just use its internal model, it was correct.
It doesn’t seem there are too many expert services tied to ChatGPT (I’m just using this as an example, because that’s the one I use). There’s obviously some kind of guardrail system for “safety,” there’s a search/browsing system (it shows you when it uses this), and there’s a python interpreter. Of course, OpenAI is now very closed, so they may be hiding that it’s using expert services (beyond the “experts” in the MOE model their speculated to be using).
I find Kagi results a little bit better than Google’s (for most things). I like that certain categories of results are put in their own sections (listicles, forums) so they’re easy to ignore if you want. I like that I can prioritize, deprioritize, block, or pin results from certain domains. I like that I can quickly switch “lenses” to one of the predefined or custom lenses.
Their line goes up when they show they’re investing in AI, and it goes down when it looks like they’re falling behind or not investing enough in it.
TBH, a lot of times I find myself interacting with ChatGPT instead of searching. It’s overhyped, but it’s useful.
I’ve had unattended upgrades running on a home server for a couple years and haven’t had any issues.
They’re good for media centers, since the support 4k HDR. Can also use Moonlight to stream games from a PC. GPIO is useful, but I guess the PI is overpowered for most GPIO use cases at this point.
Some automotive infotainment systems run on Linux.
Old dual-core Pentium, lol (Haswell I think, or something from around that time), 16GB RAM. 5 16TB SATA hard disks.
Production AI is highly tuned by training data selection and human feedback. Every model has its own style that many people helped tune. In the open model world there are thousands of different models targeting various styles. Waifu Diffusion and GPT-4chan, for example.