Yes, this is official documentation from the MS Learn course for the Agentic AI Solution Architect certificate
Microslop SharePhont™
Anyone remember how much nicer the world felt before we all realized nobody in charge of anything with any real power has any idea what the fuck they’re doing?
They know what they are doing. Their goals just don’t align with the rest of humanity.
The mistake we make is in assuming that government choices are born of incompetence… The actions of empire are not incompetent. They are not intended to serve you or the citizens, but to serve material interests, the plunder of land, natural resources, markets, and cheap labor".
-Michael Parenti
RIP to the GOAT
Edit: He talks about it in the lecture starting around 15:35. Though the quote above is from his book “against empire”
Well back then we thought everyone in charge was evil. Back then we had Krutschev, Nixon etc. Always nuclear war looming. And Reagan with this trickle downs, the Bush wars over made-up WMDs etc.
I don’t think there was ever really a positive period. Maybe the 90s which was kinda the high point. Economics were good, America and NATO was the undisputed world power, nobody took global warming seriously yet and the internet was promising a great future.
Then after 2000 we had the dot com crash, 9/11, the resulting wars, Russia becoming an enemy again, the financial world crisis, the rise of the internet as a surveillance tool, global warming, the pandemic, exploding house prices everywhere.
Really pretty much exactly after the change of the century everything turned to shit.
Economics were good if you lived in a western country. Former Soviet Block, Yugoslavia, and global South countries were having a rough go of thing in the 90s
They’re still having a rough go of things, and they were having a rough go of things before that as well, so that’s not really a valid counterpoint.
If they were having a rough go of things in the 90s. Then the 90s weren’t the shining beacon of success it’s claimed to be. That point is very much valid.
Then there as never been in the history of this earth a shining beacon of success because at every waking moment there wasn’t an optimal number of people immune to all disease, death, and suffering, to no downside to nature around them?
The 90s is where the curve peaked for a lot of people. China had finished bringing 70% of their population out of extreme poverty since the world war era, the USA markets and deficit were stable though heading in a bad direction due to Reagan, and Europe was having a big cultural and tourism boom.
Then there as never been in the history of this earth a shining beacon of success
That was kind of my point.
It’s a shit point that argues against 100% valid and factual statements.
Yeah but the future was bright back then.
Source: I gotta wear shades.
We didn’t all realize that at the same point. Some of us have known that for a very long time.
I was twelve watching Jon Stewart. It was quite the epiphany
Combigent is too nice a sounding word to lack any real meaning it needs one.
Combigent (adj.)
Definition: A word that seems like a real word but actually isn’t
It also looks completely plausible for a word. I can see why an AI trained to make plausible-looking images generated it.
Now I’m curious to see what other perfectly English-looking words it comes up with
How dqre you say such mean things about my computer boyfriend you’re just an irrational meanie I bet all the words you used here are fake too!
It would be really funny if it wasn’t so sad.
It almost makes me think a human worker intentionally made these slides. LLMs don’t really have the creativity to make typos, which is how I can sometimes catch LLM comments on here. Also “SharePhont” is pretty funnyUnless they used an image generator to make the slides, which would be extra stupid
edit: turns out it WAS the extra stupid
Oh it’s definitely extra stupid. Looks like the slide was made whole-cloth by an image generator, not like the text was generated by an LLM and pasted in.
Image generators make typos all the time (if you can call them that).
See also: continvoucly morged
Image generators make typos all the time (if you can call them that).
Primitive image generators don’t “understand” semantics of the patterns they assemble. The obvious example we’re probably all familiar with is “AI hands” with weird poses, excessive fingers, sometimes even extra arms etc.: the AI knows the pattern we understand as a finger, it knows the correlation with those we see as hands and arms, but it doesn’t how what a hand is or why it’s important to have a specific number of those patterns combined in specific ways.
Text is the same: The model knows the graphic patterns of letters, maybe knows the patterns of words the letter-patterns often occur in, but the same randomisation that can produce different enough results to look human can also lead to randomly generating patterns that it fundamentally can not know aren’t actually valid words.
There are solutions, such as using a more specialised tool like a combined layout generator and text generator with feedback to make the text fit the layout. Using the right tool for the task at hand, paired with supervision by a human that knows its shortcomings and can check whether and where it trips up, might do a better job.
But that human has to have the required know-how, and if you really want to use a single LLM to feed all prompts into, that model should be capable of detecting and delegating the work to those specialised tools (checking with the prompter to confirm that its detection is accurate).
A simple all-purpose-model and general “prompt engineer” without subject-specific experience and training just won’t cut it. The marketing for these tools generally seems terribly intransparent about that problem, and executives generally seem to be oblivious to it (or just indifferent, so long as it helps them cut costs on paper for a few quarters).
(As an aside: it’s the same difficulty text generators occasionally have with facts and citations: They can’t tell when it is important to have very specific combinations of words that map to very specific occurrences. It might have picked up the correlation of the word pattern “Words (Number), Words (something edition). Words.” with human names, year numbers, book titles and publisher respectively, but it can’t know why only specific combinations of author, year, existing book title and publisher are permissible.
It may get them right often enough by picking a likely combination from texts related to the prompt, but unless you double check (or provide in advance) that citations refer to existing works and fit the cited content, you run the risk that it randomly generates bullshit. A student “writing” a paper might not be able to catch it, but a professor that knows the major authors and works of their field is probably gonna spot it.)
For years, I’ve rather read Stackoverflow comments than Microsoft’s nearly unusable documentation. It’s as if they don’t know how to write coherently
Saw this article from Microslop the other day about their MDASH copilot model competing with Mythos for bug finding/fixing.
And here’s the image they went with for the flowchart of what the AI does… Complete with overlapping text and the worst layout I’ve ever seen.
Zero humans proofed this.
I don’t see the probulent here
Did AI make these slides?
It combigated them.
I’m about to combigate my foot with your ass!
(sorry, just felt right somehow)
A veritable answer.
It’s a perfectly cromulent word
Product of continvoucly morged slop.
who morged that? Why does that happen continvously?
What a combigent, inbiltum response!
It’s AI generated
It’s a reference to another Microsoft classic: https://www.windowscentral.com/software-apps/microsoft-caught-plagiarizing-graphics-with-ai-slop-microsoft-continvoucly-morged-my-diagram-there-for-sure
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