Couldn't tell you unfortunately. It looks like AMD is also on board with deprecating S3 sleep, so I would guess that it's not significantly better. The kernel controls the newer standby modes, so it's really going to depend on how well it's supported there.
BitSound
joined 1 year ago
Sleep kind of sucks on the original 11th gen hardware. They pushed out a bios update that broke S3 sleep, so now all you've got is the s2idle version, which the kernel is only OK at. Your laptop bag might heat up. S3 breaking isn't really their fault, Intel deprecated it. Still annoying though. I've heard the Chromebook version and other newer gens have better sleep support.
Other than that, it's great. NixOS runs just fine, even the fingerprint reader works, which has been rare for Linux
Meshuggah:
https://www.youtube.com/watch?v=m9LpMZuBEMk
Listened to them before I got into metal, came back to them later and now love them. That's from probably one of their more accessible records, they also have more experimental stuff like this:
Do you have any links to read up on him? I know this is a very contentious topic, but I haven't heard much about him and I'm curious. What would you hold as his worst actions?
It is a bold claim, but based on their success with ruff, I'm optimistic that it might pan out.
This is a silly argument:
[..] But even if we give the AGI-engineer every advantage, every benefit of the doubt, there is no conceivable method of achieving what big tech companies promise.’
That’s because cognition, or the ability to observe, learn and gain new insight, is incredibly hard to replicate through AI on the scale that it occurs in the human brain. ‘If you have a conversation with someone, you might recall something you said fifteen minutes before. Or a year before. Or that someone else explained to you half your life ago. Any such knowledge might be crucial to advancing the conversation you’re having. People do that seamlessly’, explains van Rooij.
‘There will never be enough computing power to create AGI using machine learning that can do the same, because we’d run out of natural resources long before we'd even get close,’ Olivia Guest adds.
That's as shortsighted as the "I think there is a world market for maybe five computers" quote, or the worry that NYC would be buried under mountains of horse poop before cars were invented. Maybe transformers aren't the path to AGI, but there's no reason to think we can't achieve it in general unless you're religious.
EDIT: From the paper:
The remainder of this paper will be an argument in ‘two acts’. In ACT 1: Releasing the Grip, we present a formalisation of the currently dominant approach to AI-as-engineering that claims that AGI is both inevitable and around the corner. We do this by introducing a thought experiment in which a fictive AI engineer, Dr. Ingenia, tries to construct an AGI under ideal conditions. For instance, Dr. Ingenia has perfect data, sampled from the true distribution, and they also have access to any conceivable ML method—including presently popular ‘deep learning’ based on artificial neural networks (ANNs) and any possible future methods—to train an algorithm (“an AI”). We then present a formal proof that the problem that Dr. Ingenia sets out to solve is intractable (formally, NP-hard; i.e. possible in principle but provably infeasible; see Section “Ingenia Theorem”). We also unpack how and why our proof is reconcilable with the apparent success of AI-as-engineering and show that the approach is a theoretical dead-end for cognitive science. In “ACT 2: Reclaiming the AI Vertex”, we explain how the original enthusiasm for using computers to understand the mind reflected many genuine benefits of AI for cognitive science, but also a fatal mistake. We conclude with ways in which ‘AI’ can be reclaimed for theory-building in cognitive science without falling into historical and present-day traps.
That's a silly argument. It sets up a strawman and knocks it down. Just because you create a model and prove something in it, doesn't mean it has any relationship to the real world.
Canonical lives and dies by the BDFL model. It allowed them to do some great work early on in popularizing Linux with lots of polish. Canonical still does good work when forced to externally, like contributing upstream. The model falters when they have their own sandbox to play in, because the BDFL model means that any internal feedback like "actually this kind of sucks" just gets brushed aside. It doesn't help that the BDFL in this case is the CEO, founder, and funder of the company and paying everyone working there. People generally don't like to risk their job to say the emperor has no clothes and all that, it's easier to just shrug your shoulders and let the internet do that for you.
Here are good examples of when the internal feedback failed and the whole internet had to chime in and say that the hiring process did indeed suck:
https://news.ycombinator.com/item?id=31426558
https://news.ycombinator.com/item?id=37059857
"markshuttle" in those threads is the owner/founder/CEO.
It's a nice change of pace to see how they interact when they're not busy parenting Calvin
Thanks, that makes sense.
Makes sense, thanks!
On a related note, I think libraries do need a bit of a facelift, and not just be "the place where books live". It's important to keep that function, but also expand to "a place where learning happens". I know lots of libraries are doing this sort of thing, but your average person is probably still stuck in the "place where books live" mindset, as you allude. I'm talking stuff like 3D printers, makerspaces, diybio, classes about detecting internet bullshit, etc.
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A post by Guido van Rossum removed for violating Python community guidelines
(discuss.python.org)
There's a nice Hobbes being drawn on Canvas. Someone from here drawing it? Is there a template?
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What kernel are you running? From what I understand, that should be the major differentiator if you're not using S3.