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.
Right, as someone in the field I do try to remind people of this. AI isn’t defined as this sentient general intelligence (frankly its definition is super vague), even if that’s what people colloquially think of when they hear the term. The popular definition of AI is much closer to AGI, as you mentioned.
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.
Amazing work!
I was curious to know how you handle flats with this many nights of integration. Do you have separate flats for each night, or did you just take a total of 30 flats on a single night?
Thank you for the feedback!
I think you may want to increase your dither size (or do them more often)
Good catch! What happened was that I forgot to tick the box to enable dithering on the first night, so the first ~180 subs or so had no dithering at all, which definitely caused the pattern noise. That said, I’d be interested to hear if you know of a rule of thumb for dithering settings (i.e. how frequently should you dither, and how many pixels). When I was dithering on the second night, I dithered every 2 frames by 2 pixels, but I more or less picked those values arbitrarily.
Also if your tracking allows for some longer subs, you could try to HDR those with your 30" exposure
Gotcha, I’ll give that a try next time. I’m shooting with an unmodified and unfiltered camera from an urban area (Bortle 7-8) so I figured longer exposures wouldn’t help too much, but it sounds like it might still be worthwhile. Thanks for the tip!
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.
I haven’t read the article myself, but it’s worth noting that in CS as a whole and especially ML/CV/NLP, selective conferences are generally seen as the gold standard for publications compared to journals. The top conferences include NeurIPS, ICLR, ICML, CVPR for CV and EMNLP for NLP.
It looks like the journal in question is a physical sciences journal as well, though I haven’t looked much into it.
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.
Also one very important aspect of this is that it must be possible to backpropagate the discriminator. If you just have access to inference on a detector of some kind but not the model weights and architecture itself, you won’t be able to perform backpropagation and therefore can’t generate gradients to update your generator’s weights.
That said, yes, GANs have somewhat fallen out of favor due to their relatively poor sample diversity compared to diffusion models.
Great shot! Impressive that you can pull this much out from just 78 subs.
For reference, ICML is one of the most prestigious machine learning conferences alongside ICLR and NeurIPS.
As an added comment, I think INDIGO and Ain Imager are worth trying for anyone using INDI, especially if you’re using a Sony camera. Sony cameras on INDIGO have focuser support and in my experience the driver is much more stable. Furthermore, the architecture of distributed agents controlling different functions makes setup easier and more performant on low-power and potentially slow wireless systems like an ARM single board computer.
To explain that a bit more, I image through Ain Imager on my Linux laptop. However, this imager program is more or less just a frontend where I select options, but everything is executed on the agents on the ROCKPro64. This includes imaging and frame capture, guiding, and plate solving. This has some benefits compared to KStars + INDI. For example, you avoid the latency of transferring a guider image to a laptop and then sending a message back to the SBC with a guiding pulse (you can avoid this on INDI by running PHD2 separately, but then you have the overhead of running a whole desktop environment), and you don’t need to wait to frames to download before starting another exposure (they’re downloaded asynchronously instead).
(Disclaimer - I contribute to INDIGO occasionally but I’m not a member of the project, just an open source developer adding features to it)
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.