Wow. An entire country can accidentally be hosed if their domain name used by their NS expires? Is it that perilous?
Readit NewsSoSoRoCoCo commented on How I hijacked the top-level domain of a sovereign state labs.detectify.com/2021/0... · Posted by u/Berg0X00
SoSoRoCoCo commented on Uganda's internet Shutdown blog.cloudflare.com/ugand... · Posted by u/jgrahamc
Turning off the internet isn't impossible per-se, it's just very difficult without disastrous effects on the local economy. I don't know how developed Uganda is, but I can tell you for sure that if the internet is blocked in the US or any major European country, everything stops, including real-life things such as card payments and even airport timetable signs (interesting fact: most of the UK ones run a Chrome instance displaying a webpage hosted by a Romanian company). Even if access to national services is unaffected, those services themselves depend on various SaaSes that would be affected by the international blocking.
I hope we never find out what happens if the US internet goes down. But with 2020's track record and what 2021 is starting to look like, we might find out!
SoSoRoCoCo commented on Signal is having technical difficulties status.signal.org/... · Posted by u/tonymet
This is not good. I've moved so many people over in the last week. For purposes of getting them invested, this is a truly inopportune moment for an extended outage.
Same here!
Last night a friend from India popped up on signal. I told him "Welcome!" and he said "You finally wore me down, I've left WhatsApp and I'm trying to move my family off of it..."
SoSoRoCoCo commented on Oberon OS Walkthrough (2009) ignorethecode.net/blog/20... · Posted by u/jaytaylor
> Everything is a Command Line
This is a really controversial pattern for GUIs. In one camp, the GUI is really a skin over the CLI that acts like a virtual user, translating GUI inputs into underlying CLI. In the other camp, the GUI is all there is (e.g., Windows) and there is no underlying OS that can be accessed via CLI: in fact, the CLI is a "fake GUI" (win32 apps written without a Window). I can't say which is better, but it is fascinating to see that this was an "original pattern".
Dungeon Magazine -- was the
BEST. MAGAZINE. EVER!!!
(Well, other than computer magazines... <g>)
BYTE ftw! Typing in Apple //e ASM code for hours and hours... ... and hours ... and then later, 2600 magazine.
I'm glad to have this to read. It brings back memories of hanging out at the local RC shop that sold D&D stuff, back in the 80's.
What I've always wanted to read area transcripts of the early players (like Gygax), perhaps at GenCon, working out very difficult encounters. For example: how did the early DMs approach role-playing a supergenius demigod with high-level cleric/MU spells? That's got to be very hard character to inhabit. I mean the Lolth module, Q1? come on, there are so many high level intelligent creatures in that endgame...
SoSoRoCoCo commented on Machine Learning: The Great Stagnation marksaroufim.substack.com... · Posted by u/puttycat
> However, places where it isn't stagnating are things like vibration and anomaly detection. This is a case where https://github.com/YumaKoizumi/ToyADMOS-dataset really shines because it adds something that didn't exist before, and it doesn't have to be 100% perfect: anything is better than nothing.
This is a link to a dataset, unless I'm missing something it's not about anomaly detection. I looked into this area a few years ago and always try to keep my eye open for breakthroughs... care to share any other links?
Oops! Thanks, it was from this challenge. Lots of neat stuff in here.
SoSoRoCoCo commented on Machine Learning: The Great Stagnation marksaroufim.substack.com... · Posted by u/puttycat
I mean in the sense that using ML for a problem often requires just trying a dozen different modeling techniques, then a bunch of a hyper-parameter searching, then a bunch of stochastic tuning…
Oh. I see what you mean. Yeah, I guess by definition backwards propagation is trial-end-error. Huh, I never thought of it that way. Thanks for clarifying, I thought you were being saucy: my apologies for being snarky.
SoSoRoCoCo commented on Machine Learning: The Great Stagnation marksaroufim.substack.com... · Posted by u/puttycat
> This makes programmers sad, because by nature we love to brute force trial-and error our code, and homey don't play that game with machine learning.
Huh? If anything I would say ML is way more trial-and-error focused than imperative programming.
Well, if your only experience is reading python opencv stack overflow posts, then of course...
SoSoRoCoCo commented on Machine Learning: The Great Stagnation marksaroufim.substack.com... · Posted by u/puttycat
This article is dead-on, but I think it is missing a fairly large segment of where ML is actually working well: anomaly detection and industrial defect detection.
While I agree that everyone was shocked, myself included, when we saw how well SSD and YOLO worked, the last mile problem is stagnating. What I mean is: 7 years ago I wrote an image pipeline for a company using traditional AI methods. It was extremely challenging. When we saw SSDMobileNet do the same job 10x faster with a fraction of the code, our jaws dropped. Which is why the dev ship turned on a dime: there's something big in there.
The industry is stagnated for exactly the reasons brought up: we don't know how to squeeze out the last mile problem because NNs are EFFING HARD and research is very math heavy: e.g., it cannot be hacked by a Zuck-type into a half-assed product overnight, it needs to be carefully researched for years. This makes programmers sad, because by nature we love to brute force trial-and error our code, and homey don't play that game with machine learning.
However, places where it isn't stagnating are things like vibration and anomaly detection. This is a case where https://github.com/YumaKoizumi/ToyADMOS-dataset really shines because it adds something that didn't exist before, and it doesn't have to be 100% perfect: anything is better than nothing.
At Embedded World last year I saw tons of FPGA solutions for rejecting parts on assembly lines. Since every object appears nearly in canonical form (good lighting, centered, homogeneous presentation), NN's are kicking ass bigtime in that space.
It is important to remember Self-Driving Car Magic is just the consumer-facing hype machine. ML/NNs are working spectacularly well in some domains.