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181834706
journal
I have been using AI to see if I could invent non-trivial stuff through recycling existing ideas (because AI is bad at actually creating new things). I've been reluctant to post this in my journal, as I dislike self-promotion, but there's so much discussion on AI and whether it is useful, that this isn't really a matter of self-promotion, but rather evidence in the debate on AI as to whether you can actually do anything useful with it.
These ideas have been run through Claude, ChatGPT, Gemini, Grok, Kimi, GPAI, and DeepSeek over many months, to revise, tune, and bugfix. They're now so big and complex, most of these AIs can no longer track them. Claude and ChatGPT are the only ones that are still managing, but even they struggle at times. Both of them report the designs as having a few residual defects but mostly in the testing and validation elements rather than the engineering.
However, they are at the point where one could reasonably argue that if AI can do non-trivial stuff, then these designs should be mostly correct using entirely correct methodology and approaches. If, however, on inspection, you decide that there are obvious major flaws in the approaches, then we can equally conclude that AI cannot do non-trivial stuff.
The hardware designs are all under CERN Open Hardware, and if you think that there's stuff there that's worth actually doing, then feel free to have a go. If you do, however, actually knock yourselves out trying, that should be filed as a bug report.
:P
I am placing NO restrictions on what you do with these designs, and leave it entirely up to you on whether you feed anything back. I have deliberately NOT evaluated the designs so that I cannot cherry-pick or subconsciously bias the results of any such evaluation.
180237899
journal
Just as a thought experiment, I wondered just how sophisticated a sound engineering system someone like Delia Derbyshire could have had in 1964, and so set out to design one using nothing but the materials, components, and knowledge available at the time. In terms of sound quality, you could have matched anything produced in the early-to-mid 1980s. In terms of processing sophistication, you could have matched anything produced in the early 2000s. (What I came up with would take a large complex, something the size of the National Computing Centre in the UK, purely for sound processing, at least circa 1964, but it could have been done. There was no technological barrier.)
This has led me to an interesting conclusion: The technology we have is limited NOT by what's available, but by a lack of generalists and abysmal communication between the silos of specialists. We have all the technology we need to be 30-40 years ahead of where we actually are, the limitations are purely social constructs and the disdain of the mad scientist/inventor class.
This led me to wonder what we're fully capable of producing today but haven't.
179518160
journal
Two episodes are currently showing in 4K in cinemas, they plan on releasing a fully restored boxed set in December (at a naturally very high price but only 1K res), and... why?
Most of the better stories were reworked in Thunderbirds Are Go, Stingray had an arguably better title sequence (and really should be released alongside as it's really part of the same universe), and whilst I'm sure that many of us older fans are still kicking around, who exactly are we going to watch with? It's not like this generation's kids are gung-ho for it, if they were then TAG would have had a fourth season.
Yes, retro has a coolness factor (is cool still cool? I dunno, ever since cringe was yeeted, I've lost track...) and boxed sets for Doctor Who and The Avengers have done ok (but not brilliantly), but boxed sets are getting absurd. Although, to be fair, the most expensive boxed set I've ever seen was for the K-Drama "Goblin" (aka Guardian: The Lonely and Great God), which had a plastic sword and was running at £500.
But, let's be honest, how many people can afford £100-£500 for a boxed set they're unlikely to watch even once right the way through?
(And, trust me, absolutely NOBODY has ever watched the boxed set of Goblin right the way through.)
179354952
journal
A restoration expert in Egypt has been arrested for stealing a 3,000 year old bracelet and selling it purely for the gold content, with the bracelet then melted down with other jewellery. Obviously, this sort of artefact CANNOT be replaced. Ever. And any and all scientific value it may have held has now been lost forever. It is almost certain that this is not the first such artefact destroyed.
Nor are other countries immune to destruction of historical materials. The Archimedes Palimpsest was partially destroyed by one collector aiming to raise the value by colouring in the pretty pictures, and then left to rot from mould by a subsequent collector. All the while, holding the only surviving copies of several of Archimedes' texts, various lost plays and poems, and a lost political treatise. Sheer ineptitude and a desire for quick cash did lose us an unknown percentage of the material present and very nearly lost us the rest.
I'm not even going to get into the antiquities stolen from the Iraq Museum that the Americans officially controlled, the items plundered by the allies from archaeological sites, the plundering before and after by locals of completely unexcavated sites, or the fact that ISIS was able to destroy tens, maybe hundreds, of thousands of ancient texts and artefacts on a whim with zero consequence. Nor is it clear that anyone bothered to properly document the Buddhist caves discovered in Afghanistan behind the destroyed giant statues, where vast numbers of cave paintings were discovered, rapidly fading due to exposure to modern air.,
The magnitude of incompetence is unimaginable, as is the impact on what we can learn about the past.
We have many possible futures, but only the one past, and we're not exactly doing a whole lot to protect it. Rather, collectors are blithely funding further wholesale destruction so that they can have a bit more tat for their collection which they neither understand nor appreciate.
I am, as you might have gathered, not altogether happy about the current state of affairs.
Equally, though, it's really not at all obvious what can be done about it. Quite the opposite. Attitudes are such that the situation is rapidly deteriorating, and because attitudes are thus, we risk losing any real history.
It is extremely depressing, and not just in the context of history. People aren't just not caring about the past, the spending on blue-sky science and deep R&D is catastrophically low, most R&D spending is currently on the lowest-possible-hanging fruit by corporations. Investment in education is not exactly sitting pretty either. Intel and AMD chips are faster than ever before, but buggy as all hell and riddled with security defects because cheap tricks make for better publicity stunts than working processors.
Nihilism across the humanities and STEM.
Even open source is not immune. Lots of new filesystems, but no real understanding of why they work and lots of really stupid defects. (How did OpenZFS go through three releases before fixing a bug that wiped out data blocks?)
The world, right now, is just so utterly depressing.
178668018
journal
Ok, I've mentioned a few times that I tried to get AIs (Claude, Gemini, and ChatGPT) to build an aircraft. I kinda cheated, in that I told them to re-imagine an existing aircraft (the DeHavilland DH98 Mosquito) using modern materials and modern understanding, so they weren't expected to invent a whole lot. What they came up with would run to around 700 pages of text if you were to prettify it in LaTeX. The complexity is... horrendous. The organisation is... dreadful.
Conclusions from testing so far:
1. Gemini is, at this point, absolutely out of its depth. It is really struggling with this and loses track of stuff easily, but is still usable for verifying if a change is going to work or not.
2. ChatGPT 5 is having a very very very hard time. It'll make far fewer mistakes than Gemini, but even simple tasks (such as re-ordering the text) take 6-7 attempts before it works right. It is completely incapable of producing an OWL2 map of the system, the best you will ever get is a simple list of elements and it can take dozens of attempts to get even that right. My confidence in any of its suggestions is low. It usually takes several rounds of verifying its suggestions against Gemini to get something both agree is acceptable.
3. Claude Pro can't load even a small fraction of the specification without running out of context space, I'd have to pay ten times as much to get a context window that suffices, but even when you hand it small fragments, it... does not do well. But, like Gemini, it can do sanity-checking, within its limited context capacity. It was very good in the early days, much stronger than ChatGPT, but once I hit a complexity ceiling, it really didn't do well.
4. Grok can't cope at all - like Claude, there's just too much there. But, unlike Claude, it's not even any good on sanity-checking.
5. DeepSeek suffers the same problem as Grok, but at least makes something of an effort when given small enough fragments.
My conclusion, for right now, is that no AIs out there can handle complex interactions in an engineering project. I'm sure it's fine for very basic stuff, but they hit their limits at precisely the point they might actually have a purpose - complex engineering projects are precisely where humans (even brilliant ones) suffer, because there's just too much to track and too many ways things can conflict.
I'm not into spamming, but the design the AIs came up with is up on GitLab. My thought, at this point, is that it might actually be interesting to the LLM engineers, because this is the sort of real-world problem that you know AIs will be used for, so they'd better work. But it might also be interesting to engineers. Programming is hard, because that involves creating something new, but this isn't creating something new, it's merely replacing components in something that is known, documented, and actually works, where the only things you're changing are the materials used. Orders of magnitude easier and requires no understanding of the engineering itself.
I would say that what has been produced probably has in the region of 250 defects in it, which would actually be pretty decent if this was a clean-sheet design. An actual aircraft engineer would produce something like that in a version 0.0 design for something built completely from scratch. It's not quite such a good total when there's next to no novel design elements in there at all.
177199873
journal
There are ontology editors, such as Protege, where there are a slew of logical reasoners that can tell you how information relates. This is a well-known weakness in LLMs, which know about statistical patterns but have no awareness of logical connections.
Is there a way to couple these together, so if you've a complex set of ideas, you could perhaps provide the ontological network plus some of the things that are reasoned from it, to supplement the prompt and give the LLM the information it can't in and of itself extract?
175870019
journal
As with other technologies I've mentioned, there's a few of these. Intel uses a four bit number as a key, for example. ARM uses a different system, but I've not been able to quite fathom what that system was.
The advantage of strong memory protection is that you can't leverage memory management and page management limitations to read stuff you shouldn't have access to, but obviously the stronger it is, the slower it is and it doesn't shield you from cache exploits.
There's also the opposite problem, which is that if you're going to provide very high speed IPC, you've got to provide very fast security mechanisms and you've got to figure out how not to expose security keys to processes that shouldn't have them.
Any geeks here want to offer any thoughts on how to best do this?
175827465
journal
I'd said in my last journal entry that there were many technologies for solving specific problems, but limited documentation outside of a simple description.
To cover thus further, I'd like to list packet dropping schemes. Just the packet dropping schemes, no other elements of quality of service such as the queueing mechanism that packet dropping is used with. This will help exemplify why proper undersranding is important.
Partial Packet Discard (PPD)
Early Packet Discard (EPD)
Age Priority Packet Discarding (APPD)
Preemptive Partial Packet Discard (pPPD)
Tail Drop
Random Early Detect (RED)
Weighted Random Early Detect (WRED)
Adaptive Random Early Detect (ARED)
Robust Random Early Detect (RRED)
Random Early Detect with Preferential Dropping (RED-PD)
Controlled Delay (CoDel)
Blue
Global Random Early Estimation for Nipping (GREEN)
Multi Global Random Early Estimation for Nipping (M-GREEN)
PURPLE
BLACK
WHITE
CHOose and Keep (CHOKe)
CHOKe-FS
CHOKe-RH
P-CHOKe
CHOKeD
gCHOKe
And this isn't even close to exhaustive. But it's absolutely guaranteed that no OS supports anything but a small fraction of this list, and equally guaranteed that no developer out there knows which unimplemented schemes would be useful in typical environments for, say, Linux or FreeBSD.
I can also be absolutely certain that no researcher out there working on new schemes knows what is currently out there, when it is useful, or how best to tune it to get a fair understanding of what a new scheme would need to do.
This should give people some idea of the scope of the problem. You often don't see AQM in the enterprise world because there's too many options and nothingnon how to effectively use them.
Pretty much the same reason enterprise systems will use Ext4 or XFS, if running a Ubuntu variant of Linux - there will be far better filesystems for specific needs, but there's way too many options, it's too complex to set up if you don't know exactly what you're doing, and no insights into what need goes with what filesystem.
Even the use of AI won't help much - LLM AIs can't learn from data that isn't out there.
A simple Neural Net could be trained on a range of schemes and workloads and then generate advice on optimal setup, but if no researcher is doing anything more than a cursory comparison, then there's nobody in a position to create such an AI. And, even then, it's only useful for comparing what's there, it still won't help developers figure out what they need to add.
(Although, it would be a great boost to network admins if they could push a button and have an AI figure out the best setup for their servers and network gear.)
175817319
journal
There's an awful lot out there in the way of projects. Perhaps hundreds of filesystems, easily dozens of transport protocols (there's sonething like 9 or 10 for the IP protocol alone), there's maybe ten different packet dropping schemes and five or six packet queueing schemes. There's maybe ten of so wireless routing protocols. Linux alone has seen a vast range of schedulers and memory management systems, and three different threading models.
Even in encryption, the number of encryption modes submitted to NIST numbered in the dozens.
But all of these have one thing in common: there's essentially no literature describing what has been learned from any of these experiments.
Sue's, there's papers on each one individually, and sometimes you can find a top-level comparisons and benchmarks, but the level of deep understanding is, at best, limited. We use the tools that work well for us, but don't really understand why they work well.
This matters, because wheels get reinvented on a regular basis, mistakes are routinely repeated, and progress is frequently slowed down by the "Not Invented Here" syndrome that affects open and closed source worlds alike.
My question for Slashdotters, especially the academic ones - - why so little deep understanding? Theoretical work in computer technology exists, but I would not call it deep. Citeseerx and ArXiv have a vast amount of data on how, but nowhere near what I'd expect on why, and it's generally a very targeted why.
173831499
journal
There is possible evidence that relativity may be incorrect. It would seem that gravity weakens more rapidly, at extreme distances, than are allowed for in GR.
For some reason, this was discarded as spam from the main page, so I'm putting it in a journal instead.
172330687
journal
There is a rare but repeatable bug in all versions of OpenZFS that silently corrupts data. This includes the pre- releases. 2.2.1 seems to reduce the odds of it happening but does not fix the issue.
From the reports I'm seeing, it would appear that nobody actually understands how ZFS (or Btrfs, for that matter) actually works, they're limited to whatever component they've specialised in.
Linux has a kernel mechanism for improving data reliability on filing systems that support it -
dm-integrity.
I've no idea how well tested this component is. I don't recall ever having heard of it before. A quick Google suggests that it works but is very slow, though I would inagine it will improve with time.
Phoronix, OpenZFS Data Corruption Battle
TRUENAS, Corruption with OpenZFS ongoing
YCombinator, OpenZFS silent corruption bug
Level 1 Techs, OpenZFS silent corruption bug
OpenZFS is not included in the Linux kernel by default dud to licensing issues, but I believe it is currently developed on Linux and ported to other OS'. (Wikipedia says that this has been the case since 2013.) It now runs in the kernel, so it's a lot faster than it was when it worked with Fuse.
Wikipedia, OpenZFS
This is a high performance, high reliability filing system, so data corruption is really bad news. It's designed for the enterprise and outclasses Microsoft's RefFS on essentially every stat. Irs main disadvantage is that it is resource-intensive, although the license is another important issue.
One drawback with OpenZFS is that it doesn't currently support RAID. Which is odd, given the intended market.
Interestingly, I don't know of any cloud providers or corporations that actually use OpenZFS. Ext4 tends to be more popular, at the expense of enterprise features.
Oracle has re-closed Solaris' ZFS, so the two filesystems are no longer compatible. But as effectively nobody uses Oracle Solaris, this doesn't matter much.
I guess the main takeaways with the current OpenZFS bug are that filing systems are becoming too complex for the hunt-and-peck approach often used in both commercial and open source projects, and that the popularity of other filing systems in OpenZFS' own domain suggests that feature-rich filesystems might actually be the wrong approach.
172249487
journal
It would appear, on the face of the above links, that developers still take shortcuts and don't program defensively, and that IT departments avoid applying security fixes and are not taking other protective measures (such as encrypting databases).
Cybersecurity, from the looks of things, is something that is simply never taken seriously. An optional extra that poses no consequences if not applied.
The consequences can be severe. The number of people at risk In Maine from identity theft runs into the millions. The loss of privacy from the intensive care ward that was recently hacked may have severe financial and employment consequences.
And yet neither programmers nor IT managers are altering their behaviour, despite the potential for enormous consequences to those they are responsible for.
Libertarians would argue that people can switch providers, but some providers are either de facto monopolies or natural monopolies. In other cases, there's no evidence that any of the alternatives are any better. Market forces don't function when there's no functional market.
But what's the alternative? There's no evidence regulation is helping, where regulations exist and are meaningfully enforced. It's just considered the cost of doing business.
What, then, would actually help?
168431846
journal
The
High-Performance Conjugate Gradient
benchmark shows Supercomputer Fugaku top of the list, with Frontier as second place. This is, of course, the reverse order of the
Top 500
list.
The stats?
Fugaku:
Cores: 7,630,848
Linpack Performance (Rmax) 442.01 PFlop/s
Theoretical Peak (Rpeak) 537.21 PFlop/s
Nmax 21,288,960
HPCG [TFlop/s] 16,004.5
Frontier:
Cores: 8,730,112
Linpack Performance (Rmax) 1,102.00 PFlop/s
Theoretical Peak (Rpeak) 1,685.65 PFlop/s
Nmax 24,440,832
HPCG [TFlop/s] 14,054.0
This is interesting because Frontier has two and a half times the Rmax and 1.1 million more CPU cores to play with. That's not a trivial figure. Of course, Fugaku is running Linux, whereas Frontier is running an OS from some upstart called Cray, so one might expect Fugaku to do better on those grounds.
For those wanting to compare their own PCs to these computers, here's the source code:
Linpack Source
HPCG Source
167094369
journal
Yes, an actual WW2 Mosquito is being sold.
A DH98 Mosquito, bomber variant, is being restored (and a good chunk rebuilt from scratch). It'll be the only bomber of this type of plane that is airworthy, on completion.
The current owners also want to sell it. At NZ$11 million plus, it's not cheap. (But it'd be a hell of a way to commute to work.)
I've only ever seen one other Mosquito sold, they're that rare - especially in flying condition. It's this last bit that worries me, as collectors have bought flying Mosquitos purely to let them deteriorate in the open. This is partly why only something like three still fly
This is an amazing plane and, no, I don't honestly thing a whip-round of Slashdotters would get the money needed to but it and keep it in running condition.
This journal entry, then, is more about salivating at this truly geeky aircraft. If anyone has a TARDIS handy, and next week's lottery numbers, this would be a cool toy to have.
166590319
journal
Some time ago, I put forward an idea for an improved schooling system. It would cost more than a regular school for the same number of students, but should - in theory - result in students getting the education they need rather than some low-grade default that will end up causing harm. But how would you estimate the benefit of such an idea, given that it would be impossible to persuade anyone of the merit of trying it out without some evidence that it would actually generate more value than the extra it would cost?
I've also got some ideas for inventions, but they run into a similar problem - it would cost a fair bit of cash to go from idea to something solid that could be sold, but how do you estimate how much you could make off such inventions without actually investing the money and trying it out? (I don't have the money, so it would take an investment by someone and that would require that I persuade that someone that there'd be a worthwhile return. Which is going to require some sort of estimate and a plan of how to achieve that.)
This is something that big companies can afford to play around with, but if you're starting off from scratch, you don't get play money to throw at such things. You have to know what is likely to work before trying it out. The reason that most start-ups fail is that they have the ideas but either very, very bad estimates or no estimates at all.
If it's impossible to know if an idea is worth pursuing, then only those who are willing to take massive risks will ever pursue their ideas, but those are likely to be the ideas not worth pursuing. The intelligent, rational ideas that are worth chasing up won't ever happen because those are the people who won't have the resources to try the ideas out.
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imipak@yaho[ ]om ['o.c' in gap]
There are worlds out there where the sky is burning, where the seas sleep and the river dreams. People made of smoke and cities made of song. Somewhere thereâ(TM)s danger; somewhere thereâ(TM)s injustice; and somewhere the tea is getting cold!
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No good April Fools jokes this year
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