I may be completely out of line here, but isn't the story on ARM very very different? I vaguely recall the whole point of having stuff like weak atomics being that on x86, those don't do anything, but on ARM they are essential for cache coherency and memory ordering? But then again, I may just be conflating memory ordering and coherency.
Maybe this is a naive question, but how are "skills" different from just adding a bunch od examples of good/bad behavior into the prompt? As far as I can tell, each skill file is a bunch of good/bad examples of something. Is the difference that the model chooses when to load a certain skill into context?
One detail most comments seem to be missing is that the O(1) complexity of get/set in hash tables depends on memory access being O(1). However, if you have a memory system operating in physical space, that's just not possible (you'd have to break the speed of light). Ultimately, the larger your dataset, the more time it is going to take (on average) to perform random access on it. The only reason why we "haven't noticed" this yet that much in practice is that we mostly grow memory capacity by making it more compact (the same as CPU logic), not by adding more physical chips/RAM slots/etc. Still, memory latency has been slowly rising since the 2000s, so even shrinking can't save us indefinitely.
One more fun fact: this is also the reason why Turing machines are a popular complexity model. The tape on a Turing machine does not allow random access, so it simulates the act of "going somewhere to get your data". And as you might expect, hash table operations are not O(1) on a Turing machine.
As others have mentioned, the main problem is that open systems are more vulnerable to low-cost, coordinated external attacks.
This is less of an issue with systems where there is little monetary value attached (I don't know anyone whose mortgage is paid for by their Stack Overflow reputation). Now imagine that the future prospects of a national lab with multi-million yearly budget are tied to a system that can be (relatively easily) gamed with a Chinese or Russian bot farm for a few thousand dollars.
There are already players that are trying hard to game the current system, and it sometimes sort of works, but not quite, exactly because of how hard it is to get into the "high reputation" club (on the other hand, once you're in, you can often publish a lot of lower quality stuff just because of your reputation, so I'm not saying this is a perfect system either).
In other words, I don't think anyone reasonable is seriously against making peer review more transparent, but for better or worse, the current system (with all of its other downsides) is relatively robust to outside interference.
So, unless we (a) make "being a scientist" much more financially accessible, or (b), untangle funding from this new "open" measure of "scientific achievement", the open system would probably not be very impactful. Of course, (a) is unlikely, at least in most high-impact fields; CS was an outlier for a long time, not so much today. And (b) would mean that funding agencies would still need something else to judge your research, which would most likely still be some closed, reputation-based system.
Edit TL;DR: Describe how the open science peer-review system should be used to distribute funding among researchers while begin reasonably robust to coordinated attacks. Then we can talk :)
(a) [Name 2005] is much easier to mentally track if it appears repeatedly in longer text than [5] (at least for me). [5] is just [5]. [Name 2005] is "that paper by Name from twenty years ago".
(b) By using [Name 2005], I might not know which exact paper this is, but I get how recent it is w.r.t. what I am reading. In many cases, this is useful context. Saying "[5] proves X" could mean that this is a new result, or a well known fact. Saying "[Name 1967] proves X" clearly indicates that this is something that has been known for some time.
But the thing is... except for the original authors, none of these papers so far really claim to have a room-temperature superconductor, right? They claim "simulated band structure with low Fermi level", or "unusual levels of diamagnetism", or "almost zero resistance up to -100°C (but lack of phase transition)", etc.
Yes, retracting these is still shameful, but it's not a "we found extraterrestrial life" claim. It's a "we received weird signals from a nebula that we don't understand so far" claim.
And yes, a lot of supporting but inconclusive evidence is still supporting evidence. My point is not that (most) scientists would risk lying about replicating a superconductor, but rather that uncertain or inconclusive results with a solid chunk of plausible deniability in a rapidly evolving environment go a long way towards being "in the room where it happened".
I see a lot of "This must be real, why would labs publish this if they don't think it's real, they have nothing to gain." sentiment on HN lately. Or "Researcher's career would be ruined if they falsely claim to replicate.", and so on. I also want to believe! But I should add a bit of skepticism to the hype :)
- "this could ruin their career": Depends. If they posted completely fake numbers or intentionally fake videos. Sure, that would be bad. But none of this is peer reviewed, and all of this can be retracted. A contaminated sample? Oops, retract. Bad measurement methodology? Oops, retract. Sure, somebody will remember that you made the controversial paper in the first place, but as long as you are not provably fabricating, a lot can be attributed to "an honest error". There are tons of peer reviewed papers out there with errors that completely change the outcome. Does not mean the authors are "finished".
- "they have nothing to gain": Oh, they absolutely do. While "science should be fully objective", funding agencies very much aren't. Obviously, just like VC funding, science funding is not a complete coin toss. But having "the right" team and background is often as important as the idea itself. One way to get the right background is to "touch shoulders with the giants" and one way to get the right team is to be highly visible and attract talent.
So overall, if LK99 is eventually shown to be a superconductor by someone else, you have a lot to gain, even if your own initial study is not perfect.
Let's say your team synthesised something. It looks like LK99 and it has some properties that are not really superconducting but at least a bit unusual. This clearly isn't what you hoped for. Now, do you run a bunch of other controls to see if it is some form of contamination, process error, combination of both... or do you publish a vague click-bait paper on ArXiv and hope that other results will somewhat align with yours?
Finally, I'm not claiming this paper or any other paper intentionally published untrue or misleading results. Just that scientists are also people. They have FOMO, they follow trends, they see what they want to see. As always, big claims require big evidence, and so far we don't really have that. But that does not mean there isn't some truth to the big claims :)
Bit focused on eastern Europe, but has a hiking mode with very good coverage of the official routes (not only in eastern Europe ;)). And everything is free, including offline maps. Terrific value for casual trips.
No disrespect, I use offline Google maps almost daily, but there are far far better offline hiking apps out there.
Google will probably work ok for the most popular trails, and I guess you can use it as a supercharged compass. But at least in Europe, if you actually plan a route in any mountains based on Google, you're in for an adventure :)
I don't have any scientific data for this, but my anecdotal experience is that the actual damage comes from the way the phones are used, not necessarily the absolute charge count. Here me out...
- A phone that is used a lot in the car as GPS is often charged/discharged continuously, often for hours.
- Furthermore, this often happens in very hot or cold conditions which are bad for battery charging.
- A lot of people seem to live with the perpetual 5% of battery, or generally don't care about properly charging the device. This is also terrible for longevity.
- There are other reasons why you may want to constantly charge/discharge your phone (e.g. you are making Android apps, or it's the phone where people call your place of business, etc.).
So, just to make myself clear: I completely agree that on average, batteries should last for a long time. But in practice, people often have irregular activities which appear negligible on average ("it's just a few charge cycles"), but end up damaging the battery more than regular prolonged use. But again: I'd very much like more hard data on this :)
I am really fed up with all the "nobody keeps a phone long enough to make battery replacements useful" arguments around here.
Out of the 10+ phones in our family over the last 5-10 years, one was water damage and one was failure of the internal flash memory. Every other phone was replaced because the battery died. Every single one.
Official replacement was no longer available and DIY was either impossible (lack of parts) or eventually ended up damaging the device beyond economical repairability.
Regular people that don't have thousands of dollars in disposable income (and nothing useful to spend it on) haven't cared about phone specs for years. Hell, I love tech and could buy a new phone every year and even I haven't cared about phone specs since the original Google Pixel.
If you brick your phone every year because that's just who you are, no judging.
If you want a new phone every year and can afford one, its your money. Just remember that you are fortunate enough to be able to do so. And someone will surely buy your used phone and likely (try to) replace the battery in it.
Overall, it's like claiming that nobody drives cars that are 10+ years old because they needed a new clutch. Or that a 50 year old house needs to be torn down because fixing the roof economically is clearly beyond our engineering prowess. Are there people that swap cars every 5 years? Absolutely. But that does not mean those cars go to a scrapyard.
I will not comment on the technical aspects of this proposal, since the actual outcome might very well need to be settled in the court still. But dismissing the general point of legislature which demands better longevity for devices that basically everybody needs to partake in modern society is rather shortsighted.
A good reason why memory virtualization has not been "disrupted" yet seems to be fragmentation. Almost all low level code relies on the fact that process memory is continuous, it can be extended arbitrarily, and that data addresses cannot change (see Rust `Pin` trait). This is an illusion ensured by the MMU (aside from security).
A "software replacement for MMU" would thus need to solve fragmentation of the address space. This is something you would solve using a "heavier" runtime (e.g. every process/object needs to be able to relocate). But this may very well end up being slower than a normal MMU, just without the safety of the MMU.
I'm seeing a ton of "nobody would but a $10.000 Mac Pro when Mac Studio exists" opinions here. But, like... why?
Threadripper Pro exists and as far as I can tell, certain industries are practically begging AMD to keep updating it. A system with 32-core TR Pro and a few hundred gigs of RAM is easily $10k. With a 64-core CPU, you are probably closer to $15k. If a Mac Pro can give you 40-60 cores, 128-512GB of RAM plus a powerful GPU for $10-20k, its certainly niche, but not dead on arrival.
I think the main issue is that while AMD can "justify" TR Pro by having disaggregated compute and IO chiplets, Apple does not. AMD can just "print" as many CPU cores as it likes (within the power budget), and then slap on a different IO tile depending on what product it's making.
Currently, Apple isn't doing that. Furthermore, they put themselves into an even bigger corner by gluing their CPU and GPU onto one die. Most professional tasks are fine with a monster CPU and no/little GPU, or a monster GPU and an ok CPU. Adding PCIe with support for GPUs would help with that. For memory heavy tasks, you could add CXL memory expansions. But that then adds a new problem: what dies would Apple fab for this product?
With M1, there were essentially 2-3 dies: The power efficient M1, the M1 Max/Ultra with the interposer, and M1 Pro (there were rumours some Pros are just failed Max-es with a part of the chip cut off, but afaik nobody was able to verify this, so 3 dies it is). For M2, we have only seen the power efficient die so far. If M2 Extreme would be 4x M2 Max, then the previous strategy could work. But if Apple wants PCIe or CXL memory modules on M2 Extreme, they have to put it on all of their Max (and maybe Pro) dies as well. Even though these will be in laptops/all-in-ones that won't use any of that. In other words, a ton of analog circuitry that's a complete waste of silicon. The only other option is to fab special dies for M2 Extreme, which might have been the plan, and what probably made it a very bad ROI.
It's kind of like Sapphire Rapids: SR is certainly fast for what it's doing, but it's super expensive compared to EPYC and less scalable exactly because you need different dies to implement different SKUs (SR is monolithic up to afaik ~20 cores and the "chiplets" only appear above that, plus the IO you get depends on the chiplets). And SR doesn't even include a GPU...
Finally, Apple's GPUs don't appear to be really scaling as great as they were hoping. I have yet to see a real-world M1 Ultra review that wouldn't end on a "it's +20-50% more performance for +100% price" note. The CPU cluster seems to be doing much better. But by tying four of them together for M2 Extreme, people who need CPU would have to pay for the whole thing, and people who need GPU would be probably left disappointed.
My personal "best bet" would be to just keep releasing a power efficient "consumer" die, but disaggregate everything on the "pro level": A single CPU-heavy "Pro" die with a bit of GPU and a bit of IO, then a GPU-heavy die that slaps onto that to make the Max, and then an IO-only die for the Mac Pro mixed and matched with a combination of CPU and GPU dies. I wouldn't be shocked if this was the plan for M3/M4, the question is if the packaging technology will be there in time (and economical; wink wink Intel).
A 32-core or 64-core Threadripper Pro workstation with a few hundred GB of RAM can easily run north of $10.000 and people buy them. The price is not an issue if the computer is fast enough. The question is how fast would a hypothetical 40-core or 48-core M2 Extreme be.
Sure, Threadripper makes economical sense for AMD only when "subsidised" by EPYC/Ryzen sales and the shared chiplet architecture. But Apple has deep enough pockets for halo products. They are allegedly repurposing some of the binned/sub-par silicon into internal server hardware anyway.
Wordle is a great name. But strictly speaking, at no point there was `wordle.com` or anything similar. Wordle always lived as a humble page on a larger domain. Which I believe was the point of the original comment: That if your product (name) is good, domain is secondary. The vast majority of users can just google at this point (which might not have been true 20 years ago).
A gentle reminder that every retina MacBook has been shipping with fractional scaling as default for years now (and it's not even 1.5). Sure, you can put it back into 2x if you want to. But you can do the same on a Framework, and then you get... wait for it... almost the same vertical resolution as a 2x 13" MB Pro (93% to be exact). If you absolutely need more space and a 2x scaling, there is a large amount of 4K 13"/14" laptops that are more than happy to fill that niche. Free market is your friend :)
So the argument that Windows is somehow responsible for the death of perfect 2x scaling is a bit exaggerated. People just want more space and anti-aliasing is mostly good enough so that no one cares.
Don't forget it's 200k a year whereas the EU salary is 5k per month - still way below, but as op said: once you factor in the cost of living, it starts to make more sense :)