#self driven

[asheanon]

𝐨𝐧𝐜𝐞 𝐲𝐨𝐮'𝐫𝐞 𝐬𝐭𝐫𝐢𝐩𝐩𝐞𝐝 𝐜𝐥𝐞𝐚𝐧, 𝐰𝐡𝐚𝐭'𝐬 𝐚𝐭 𝐲𝐨𝐮𝐫 𝐜𝐨𝐫𝐞 ?

resounding truth

you have a goal in mind, and others brush it off right away… why does everyone always tell you to follow your dreams when it's actually their dreams that they want you to follow? your memory is weak, and your conviction is even weaker on days when you're reminded of how alone you really are. it's hard to go on without someone pushing you forward… is this what life is? a cycle of monotony, fueled only by the desire not to trouble anyone too much with your passing? it would be nice to have something, or someone, to spur you on. i hope you find them.

tagged by: @rcsetorn (thank you for the tag!) 💕

tagging: @vinjaryou, @oomerkabaoo, @daffodilzae, @tailoringtay and/or whoever hasn't done it yet and wants to do the thing with a muse or OC of choice! 🕺

[cabmecar]

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[arcanegifs]

ARCANE LEAGUE OF LEGENDS: 2x03 - “Finally Got The Name Right”

[honeytonedhottie]

how to cultivate growth⋆.ೃ࿔*:･🍡

GET UNCOMFORTABLE ;

u cannot grow where ur already comfortable so in order to grow u must get out of ur comfort zone, even if that means getting uncomfortable. like growing a muscle, you must put stress on it first for it to grow. in order to grow u must take action towards ur goals despite any fears that u may have.

MAKE NEW THOUGHTS ;

to embrace the next version of urself and to grow u should make new thoughts that serve you. as you know ur thoughts create ur reality so why keep around thoughts that dont serve you or even worse, self sabotaging thoughts? start saying ur affirmations and applying the law as a way to grow ur confidence in urself and creating ✨

HOT TIP ; start listening to affirmations tapes, i rly recommend high frequency guru on youtube for aff tapes but u can always make ur own too…💬🎀

FROM 0 -> 10 ;

i talked about this in one of my previous posts but i wanna talk about breaking down goals again because making one broad goal, at least in my opinion, can be daunting and ultimately ineffective. breaking it down is where its at.

break down goals into sub goals with tasks within them

make certain tasks a priority

at least 1% closer to ur goal every single day

dont focus on going from 0 -> 100. instead break things up into steps so that u dont intimidate yourself and burn urself out quickly. kind of like a rubber band. if u stretch the rubber band as far as it'll go it'll snap back into place, which leads me to my next point.

THE SNAP BACK EFFECT ;

the snap back effect can be summed up by the example that i used above. the snap back effect was coined by dr. maxwell maltz, author of “psycho-cybernetics,” to describe the rampant self-sabotage of personal improvement goals.

SO HOW DO WE BEAT THE SNAP BACK EFFECT ;

we beat the snap back effect through consistency and through sticking to ur plan. dont jump from 0 -> 100 because then ur just setting urself up for failure. jump from 0 -> 10 and so on so forth.

remember that life is all about growing and pursuing ur goals so make sure that ur helping urself grow and evolve.

[perryabbott]

Baldur's Gate 3 (2023) dev. Larian Studios

[leopardmuffinxo]

Dammon + green & gold camp clothes

[hivemindscape]

the buddy-buddy act ends now

[mostlysignssomeportents]

The surprising truth about data-driven dictatorships

Here’s the “dictator’s dilemma”: they want to block their country’s frustrated elites from mobilizing against them, so they censor public communications; but they also want to know what their people truly believe, so they can head off simmering resentments before they boil over into regime-toppling revolutions.

These two strategies are in tension: the more you censor, the less you know about the true feelings of your citizens and the easier it will be to miss serious problems until they spill over into the streets (think: the fall of the Berlin Wall or Tunisia before the Arab Spring). Dictators try to square this circle with things like private opinion polling or petition systems, but these capture a small slice of the potentially destabiziling moods circulating in the body politic.

Enter AI: back in 2018, Yuval Harari proposed that AI would supercharge dictatorships by mining and summarizing the public mood — as captured on social media — allowing dictators to tack into serious discontent and diffuse it before it erupted into unequenchable wildfire:

https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/

Harari wrote that “the desire to concentrate all information and power in one place may become [dictators] decisive advantage in the 21st century.” But other political scientists sharply disagreed. Last year, Henry Farrell, Jeremy Wallace and Abraham Newman published a thoroughgoing rebuttal to Harari in Foreign Affairs:

https://www.foreignaffairs.com/world/spirals-delusion-artificial-intelligence-decision-making

They argued that — like everyone who gets excited about AI, only to have their hopes dashed — dictators seeking to use AI to understand the public mood would run into serious training data bias problems. After all, people living under dictatorships know that spouting off about their discontent and desire for change is a risky business, so they will self-censor on social media. That’s true even if a person isn’t afraid of retaliation: if you know that using certain words or phrases in a post will get it autoblocked by a censorbot, what’s the point of trying to use those words?

The phrase “Garbage In, Garbage Out” dates back to 1957. That’s how long we’ve known that a computer that operates on bad data will barf up bad conclusions. But this is a very inconvenient truth for AI weirdos: having given up on manually assembling training data based on careful human judgment with multiple review steps, the AI industry “pivoted” to mass ingestion of scraped data from the whole internet.

But adding more unreliable data to an unreliable dataset doesn’t improve its reliability. GIGO is the iron law of computing, and you can’t repeal it by shoveling more garbage into the top of the training funnel:

https://memex.craphound.com/2018/05/29/garbage-in-garbage-out-machine-learning-has-not-repealed-the-iron-law-of-computer-science/

When it comes to “AI” that’s used for decision support — that is, when an algorithm tells humans what to do and they do it — then you get something worse than Garbage In, Garbage Out — you get Garbage In, Garbage Out, Garbage Back In Again. That’s when the AI spits out something wrong, and then another AI sucks up that wrong conclusion and uses it to generate more conclusions.

To see this in action, consider the deeply flawed predictive policing systems that cities around the world rely on. These systems suck up crime data from the cops, then predict where crime is going to be, and send cops to those “hotspots” to do things like throw Black kids up against a wall and make them turn out their pockets, or pull over drivers and search their cars after pretending to have smelled cannabis.

The problem here is that “crime the police detected” isn’t the same as “crime.” You only find crime where you look for it. For example, there are far more incidents of domestic abuse reported in apartment buildings than in fully detached homes. That’s not because apartment dwellers are more likely to be wife-beaters: it’s because domestic abuse is most often reported by a neighbor who hears it through the walls.

So if your cops practice racially biased policing (I know, this is hard to imagine, but stay with me /s), then the crime they detect will already be a function of bias. If you only ever throw Black kids up against a wall and turn out their pockets, then every knife and dime-bag you find in someone’s pockets will come from some Black kid the cops decided to harass.

That’s life without AI. But now let’s throw in predictive policing: feed your “knives found in pockets” data to an algorithm and ask it to predict where there are more knives in pockets, and it will send you back to that Black neighborhood and tell you do throw even more Black kids up against a wall and search their pockets. The more you do this, the more knives you’ll find, and the more you’ll go back and do it again.

This is what Patrick Ball from the Human Rights Data Analysis Group calls “empiricism washing”: take a biased procedure and feed it to an algorithm, and then you get to go and do more biased procedures, and whenever anyone accuses you of bias, you can insist that you’re just following an empirical conclusion of a neutral algorithm, because “math can’t be racist.”

HRDAG has done excellent work on this, finding a natural experiment that makes the problem of GIGOGBI crystal clear. The National Survey On Drug Use and Health produces the gold standard snapshot of drug use in America. Kristian Lum and William Isaac took Oakland’s drug arrest data from 2010 and asked Predpol, a leading predictive policing product, to predict where Oakland’s 2011 drug use would take place.

[Image ID: (a) Number of drug arrests made by Oakland police department, 2010. (1) West Oakland, (2) International Boulevard. (b) Estimated number of drug users, based on 2011 National Survey on Drug Use and Health]

Then, they compared those predictions to the outcomes of the 2011 survey, which shows where actual drug use took place. The two maps couldn’t be more different:

https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x

Predpol told cops to go and look for drug use in a predominantly Black, working class neighborhood. Meanwhile the NSDUH survey showed the actual drug use took place all over Oakland, with a higher concentration in the Berkeley-neighboring student neighborhood.

What’s even more vivid is what happens when you simulate running Predpol on the new arrest data that would be generated by cops following its recommendations. If the cops went to that Black neighborhood and found more drugs there and told Predpol about it, the recommendation gets stronger and more confident.

In other words, GIGOGBI is a system for concentrating bias. Even trace amounts of bias in the original training data get refined and magnified when they are output though a decision support system that directs humans to go an act on that output. Algorithms are to bias what centrifuges are to radioactive ore: a way to turn minute amounts of bias into pluripotent, indestructible toxic waste.

There’s a great name for an AI that’s trained on an AI’s output, courtesy of Jathan Sadowski: “Habsburg AI.”

And that brings me back to the Dictator’s Dilemma. If your citizens are self-censoring in order to avoid retaliation or algorithmic shadowbanning, then the AI you train on their posts in order to find out what they’re really thinking will steer you in the opposite direction, so you make bad policies that make people angrier and destabilize things more.

Or at least, that was Farrell(et al)’s theory. And for many years, that’s where the debate over AI and dictatorship has stalled: theory vs theory. But now, there’s some empirical data on this, thanks to the “The Digital Dictator’s Dilemma,” a new paper from UCSD PhD candidate Eddie Yang:

https://www.eddieyang.net/research/DDD.pdf

Yang figured out a way to test these dueling hypotheses. He got 10 million Chinese social media posts from the start of the pandemic, before companies like Weibo were required to censor certain pandemic-related posts as politically sensitive. Yang treats these posts as a robust snapshot of public opinion: because there was no censorship of pandemic-related chatter, Chinese users were free to post anything they wanted without having to self-censor for fear of retaliation or deletion.

Next, Yang acquired the censorship model used by a real Chinese social media company to decide which posts should be blocked. Using this, he was able to determine which of the posts in the original set would be censored today in China.

That means that Yang knows that the “real” sentiment in the Chinese social media snapshot is, and what Chinese authorities would believe it to be if Chinese users were self-censoring all the posts that would be flagged by censorware today.

From here, Yang was able to play with the knobs, and determine how “preference-falsification” (when users lie about their feelings) and self-censorship would give a dictatorship a misleading view of public sentiment. What he finds is that the more repressive a regime is — the more people are incentivized to falsify or censor their views — the worse the system gets at uncovering the true public mood.

What’s more, adding additional (bad) data to the system doesn’t fix this “missing data” problem. GIGO remains an iron law of computing in this context, too.

But it gets better (or worse, I guess): Yang models a “crisis” scenario in which users stop self-censoring and start articulating their true views (because they’ve run out of fucks to give). This is the most dangerous moment for a dictator, and depending on the dictatorship handles it, they either get another decade or rule, or they wake up with guillotines on their lawns.

But “crisis” is where AI performs the worst. Trained on the “status quo” data where users are continuously self-censoring and preference-falsifying, AI has no clue how to handle the unvarnished truth. Both its recommendations about what to censor and its summaries of public sentiment are the least accurate when crisis erupts.

But here’s an interesting wrinkle: Yang scraped a bunch of Chinese users’ posts from Twitter — which the Chinese government doesn’t get to censor (yet) or spy on (yet) — and fed them to the model. He hypothesized that when Chinese users post to American social media, they don’t self-censor or preference-falsify, so this data should help the model improve its accuracy.

He was right — the model got significantly better once it ingested data from Twitter than when it was working solely from Weibo posts. And Yang notes that dictatorships all over the world are widely understood to be scraping western/northern social media.

But even though Twitter data improved the model’s accuracy, it was still wildly inaccurate, compared to the same model trained on a full set of un-self-censored, un-falsified data. GIGO is not an option, it’s the law (of computing).

Writing about the study on Crooked Timber, Farrell notes that as the world fills up with “garbage and noise” (he invokes Philip K Dick’s delighted coinage “gubbish”), “approximately correct knowledge becomes the scarce and valuable resource.”

https://crookedtimber.org/2023/07/25/51610/

This “probably approximately correct knowledge” comes from humans, not LLMs or AI, and so “the social applications of machine learning in non-authoritarian societies are just as parasitic on these forms of human knowledge production as authoritarian governments.”

The Clarion Science Fiction and Fantasy Writers’ Workshop summer fundraiser is almost over! I am an alum, instructor and volunteer board member for this nonprofit workshop whose alums include Octavia Butler, Kim Stanley Robinson, Bruce Sterling, Nalo Hopkinson, Kameron Hurley, Nnedi Okorafor, Lucius Shepard, and Ted Chiang! Your donations will help us subsidize tuition for students, making Clarion — and sf/f — more accessible for all kinds of writers.

Libro.fm is the indie-bookstore-friendly, DRM-free audiobook alternative to Audible, the Amazon-owned monopolist that locks every book you buy to Amazon forever. When you buy a book on Libro, they share some of the purchase price with a local indie bookstore of your choosing (Libro is the best partner I have in selling my own DRM-free audiobooks!). As of today, Libro is even better, because it’s available in five new territories and currencies: Canada, the UK, the EU, Australia and New Zealand!

[Image ID: An altered image of the Nuremberg rally, with ranked lines of soldiers facing a towering figure in a many-ribboned soldier's coat. He wears a high-peaked cap with a microchip in place of insignia. His head has been replaced with the menacing red eye of HAL9000 from Stanley Kubrick's '2001: A Space Odyssey.' The sky behind him is filled with a 'code waterfall' from 'The Matrix.']

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

 — 

Raimond Spekking (modified) https://commons.wikimedia.org/wiki/File:Acer_Extensa_5220_-_Columbia_MB_06236-1N_-_Intel_Celeron_M_530_-_SLA2G_-_in_Socket_479-5029.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

 — 

Russian Airborne Troops (modified) https://commons.wikimedia.org/wiki/File:Vladislav_Achalov_at_the_Airborne_Troops_Day_in_Moscow_%E2%80%93_August_2,_2008.jpg

“Soldiers of Russia” Cultural Center (modified) https://commons.wikimedia.org/wiki/File:Col._Leonid_Khabarov_in_an_everyday_service_uniform.JPG

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

[huginsmemory]

Reading journal 3 again is a reminder that... Well Ford is kinda an asshole towards Fiddleford. It's not just Stan. Like he's so focused on himself and his project and his ambition that anyone/thing else blurs out (him disregarding Fiddleford's nervousness around the gremloblin because he wants to sketch it, and when it goes sideways, him talking about the safety of the project parts BEFORE how Fiddleford was doing in his journal, multiple times. Priorities much?) and he's demeaning towards Fiddleford's own project (which ends up being wayyyy more important then Ford's discoveries)... Like damn Fiddleford, u left ur wife and young son for this guy?

[genericpuff]

y'all have no clue how wild it is to live in my timeline, because 12 years ago i was a huge fan of these cute lil' zelda strips and at the time i found out through the grapevine that the creator apparently stopped making these comics because they had started drawing NSFW content-

and at the time i was like "oh ok that's wild but respect"

and now in the present the artist who's making the OMORI manga adaption is under fire for being a shota fetishist and it's putting the spotlight back on the OMORI creator themselves for ALSO being a shota fetishist and oh my god the omocat who made those zelda comics over a decade ago is the same omocat who went on to make OMORI holy shit-

[thethespacecoyote]

I still feel like the craziest part of the book of bill is bill making ford stand on his roof in the freezing cold and ford realizing bill couldve made him jump but didn't. like. what the fuck

[cabmecar]

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[wormspoodle]

all 03 fans are required to read this fic if you haven’t (under the cut)

https://archiveofourown.org/works/12594564

[yergink]

honestly it feels ridiculous to say this, but stede giving up being a pirate is a good thing. i don't know how anyone could watch ofmd 2x07 "man on fire" and come away from it thinking that what stede really needed was to continue a life beset by danger for the sake of gaining infamy, and that him giving it up to stay with ed by s2's end was bad, somehow.

we all watched stede sitting at a bar declaring a bunch of strangers singing his praises his "friends" all the while his lover and found family were leaving him. infamy in the pirate world is cold and lonely and dangerous and it does not spare room for love or true acceptance. we learned this with ed and the persona of blackbeard. it is conditional, in the same way acceptance in traditional society is.

stede, briefly, performs well enough to gain that acceptance, but it is unsustainable, and in the end, it is hollow. and it's not too hard to infer that stede comes to this conclusion by the episode's end and into 2x08, in between getting his ass kicked by zheng in a self-destructive spiral. ed leaves, his crew starts leaving, and then everything goes to shit, and the infamy stops fucking mattering. all it's done is bring danger and drive the people he really cares about away.

him giving up piracy is not him giving up his lifelong dream purely for ed's sake, or anything like that. it is equally for himself.

[tender-emotional-music]

Netflix needs to stop promoting ST as a teen show for season 5

[friendofcars]

I finished my data analysis for POV distribution in TRC! Figure captions are in the alt text. Other observations, discussion of the analysis and results, and some supplemental figures are under the cut. This is perhaps the most self-indulgent project I've worked on <3

Other observations: I went into this analysis with the hypothesis that each of the four protagonists would have the most chapters and/or pages in the following pattern: Blue for TRB, Ronan for TDT, Adam for BLLB, and Gansey for TRK. This mostly held true, although Blue had more chapters and pages than Adam in BLLB. This trend was more significant in terms of page distribution compared to chapter distribution. Mean chapter length differences were infrequent and did not follow the expected pattern, although I did see trends towards shorter chapters for antagonists not grouped into "all others."

There is a relative lack of back-to-back chapters for the same POV character, although this does occur in all books (twice for Blue in TRB; once for The Gray Man, twice for Adam, and six times for Ronan in TDT; five times for Blue [two of which are back-to-back-to-back], once for Gansey, twice for Adam [one of which is back-to-back-to-back], and once for Greenmantle in BLLB. I mention all of this to say that in TRK, this occurs once for Ronan, once for Blue, once for Adam, and thrice for Gansey- but in one of these instances, Gansey has SIX chapters in a row. Nine of the chapters from 48 to 58 are from his POV. The Raven King indeed!

Analysis notes:

For each book, I counted how many chapters each POV character had and how many pages each POV character had. Page numbers are based on the UK paperback editions of the series. In the figures, I show raw # of chapters and pages, as well as the proportion of chapters and pages attributed to each POV character over the total number of chapters or pages. I also compared POV characters’ pages/chapter ratio and tested for differences in mean chapter length with a one-way ANOVA with multiple comparisons.

For most measures, in cases where chapters had multiple POV characters in sequence, I divided the chapter by # of characters. For example, chapter 44 in TRB starts with Adam, switches to Whelk, and ends with Blue. This chapter counted as 1/3 chapter for each character. In BLLB, only the prologue contained multiple discrete POV sections (Persephone, Calla, and Maura) and Piper only had her POV featured in the epilogue so I grouped these two chapters as “All Others” and excluded them from statistical tests. For TRK and TRC, I did include the "All Others" category in the analysis. (“All Others” includes chapters/pages with ambiguous POV (Kavinsky’s text in TDT- are we reading it from the sender or the recipient’s POV?), mixed/omniscient POV (the Gray Man’s tarot reading in TDT – we get internal thoughts and feelings from multiple characters), and in TRK, any character aside from Blue, Gansey, Adam, and Ronan.)

In figures that represent raw counts of chapters or pages, the dashed horizontal line represents the expected number of chapters or pages per POV character if the distribution was equal. The p values on these figures represent the results of a Chi-square test to test whether the actual distribution of pages or chapters was different from an equal distribution.

For the pages/chapter ratio data and the Chi-square tests, I did not apply this calculation and counted each discrete POV as a full chapter because I needed counts and sample sizes to be integers for the statistical analysis (Gansey having an n = 8.5 was not making GraphPad prism very happy with me). I had to do a bit of rounding to the nearest number which was not ideal, but I did this as little as possible. (There is probably a workaround for this, at least for the ratio data, but I am not a statistician). To at least approximate what results would look like without the skew towards a lower pages/chapter ratio, I also ran the ANOVA only with chapters containing a single POV character, which is why you’ll see two figures for the pages/chapter metric for each book (except for BLLB).

I am considering p values < 0.05 to be significant; I have included non-significant p values on some figures to provide additional context. If there is no p value on a given figure, you can assume I found no significant differences amongst POV characters’ chapters and/or pages in my analysis.

If you have questions about the analysis please let me know! :)

Supplemental figures: TRK data shown for all unique POV characters (these figures do not have captions in the alt text; the four on the left depict distribution of chapters and pages across the 19 different POV characters in TRK; the vast majority of the chapters and pages are attributed to either Blue, Gansey, Adam, or Ronan. The two figures on the left show mean pages per chapter for all characters, with no clear/statistically significant trends across the data. The demon's chapters are noticeably short.)