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KingRandomGuy

@ KingRandomGuy @lemmy.world

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109
Joined
2 yr. ago

  • should I completely jumpship to linux when windows 10 ends support/am ready or dualboot ltsc and linux

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  • 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.

  • Qualcomm abruptly cancels Snapdragon X Elite dev kit — refunds customers for mini PC, ends sales and support for the device immediately

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  • GPU and overall firmware support is always better on x86 systems, so makes sense that you switched to that for your application. Performance is also usually better if you don't explicitly need low power. In my use case I use the Orange Pi 5 Plus for running an astrophotography rig, so I needed something that was low power, could run Linux easily, had USB 3, reasonable single core performance, and preferably had the possibility of an upgradable A key WiFi card and a full speed NVMe E key slot for storage (preferably PCIe 3.0x4 or better). Having hardware serial ports was a plus too. x86 boxes would've been preferable but a lot of the cheaper stuff are older Intel mini PCs which have pretty poor battery life, and the newer power efficient stuff (N100 based) is more expensive and the cheaper ones I found tended to have onboard soldered WiFi cards unfortunately. Accordingly the Orange Pi 5 Plus ended up being my cheapest option that ticked all my boxes. If only software support was as good as x86!

    Interesting to hear about the NPU. I work in CV and I've wondered how usable the NPU was. How did you integrate deep learning models with it? I presume there's some conversion from runtime frameworks like ONNX to the NPU's toolkit, but I'd love to learn more.

    I'm also aware that Collabora has gotten the NPU drivers upstreamed, but I don't know how NPUs are traditionally interfaced with on Linux.

  • Qualcomm abruptly cancels Snapdragon X Elite dev kit — refunds customers for mini PC, ends sales and support for the device immediately

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  • A lot of the cheap tablet SoC vendors like Rockchip (whose SoCs end up in low cost SBCs) really only do the bare minimum when it comes to proper linux support. There's usually next to no effort to upstreaming their patches so oftentimes you're stuck on their vendor kernel. Luckily for the RK3588(S), Collabora has done a considerable amount of work on supporting the SoC and its peripherals upstream. I run my Orange Pi 5 Plus (RK3588) on a mainline kernel and it works for my needs.

    This practice is a lot easier to defend for a low cost SoC compared to something as expensive as a Snapdragon Elite though...

  • Nobel Prize 2024

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  • I work in an ML-adjacent field (CV) and I thought I'd add that AI and ML aren't quite the same thing. You can have non-learning based methods that fall under the field of AI - for instance, tree search methods can be pretty effective algorithms to define an agent for relatively simple games like checkers, and they don't require any learning whatsoever.

    Normally, we say Deep Learning (the subfield of ML that relates to deep neural networks, including LLMs) is a subset of Machine Learning, which in turn is a subset of AI.

    Like others have mentioned, AI is just a poorly defined term unfortunately, largely because intelligence isn't a well defined term either. In my undergrad we defined an AI system as a programmed system that has the capacity to do tasks that are considered to require intelligence. Obviously, this definition gets flaky since not everyone agrees on what tasks would be considered to require intelligence. This also has the problem where when the field solves a problem, people (including those in the field) tend to think "well, if we could solve it, surely it couldn't have really required intelligence" and then move the goal posts. We've seen that already with games like Chess and Go, as well as CV tasks like image recognition and object detection at super-human accuracy.

  • A crowd destroyed a driverless Waymo car in San Francisco

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  • Yep, and for good reason honestly. I work in CV and while I don't work on autonomous vehicles, many of the folks I know have previously worked at companies or research institutes on these kinds of problems and all of them agree that in a scenario like this, you should treat the state of the vehicle as compromised and go into an error/shutdown mode.

    Nobody wants to give their vehicle an override that can potentially harm the safety of those inside it or around it, and practically speaking there aren't many options that guarantee safety other than this.

  • Deleted

    deleted by creator

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  • Afaik the StarFive SOCs used in SBCs are a lot slower than current ARM offerings. Part of that might be because software support is worse, so maybe compilers and related tooling aren't yet optimized for them?

    Hopefully development on these continues to improve though. The biggest nail in the coffin for Pi alternatives has been software support.

  • Why do we still use stepper motors?

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  • BLDC and AC servos maintain full torque at stop too, and have about 2-3× the torque of a stepper of similar size.

    Huh, this is true about BLDCs as well? I remember seeing in a video that BLDCs tend to have very poor torque output when stopped and especially when at low speeds (due to very low efficiency requiring too high currents for drivers to supply), whereas AC motors have a pretty much flat torque curve until they get fairly fast. I'd be interested to know if this is true.

  • ‘ChatGPT detector’ catches AI-generated papers with unprecedented accuracy

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  • I'm curious what field you're in. I'm in computer vision and ML and most conferences have clauses saying not to use ChatGPT or other LLM tools. However, most of the folks I work with see no issue with using LLMs to assist in sentence structure, wording, etc, but they generally don't approve of using LLMs to write accuracy critical sections (such as background, or results) outside of things like rewording.

    I suspect part of the reason conferences are hesitant to allow LLM usage has to do with copyright, since that's still somewhat of a gray area in the US AFAIK.