#be project topics for computer engineering

[projectchampionz]

SUSTAINABLE PRACTICES AND TOURISM DEVELOPMENT AT THE NATIONAL MUSEUM IBADAN AS A STUDY AREA

SUSTAINABLE PRACTICES AND TOURISM DEVELOPMENT AT THE NATIONAL MUSEUM IBADAN AS A STUDY AREA ABSTRACT This research explores the role of sustainable practices in tourism development, with a focus on the National Museum Ibadan, Nigeria. The study investigates the current sustainable practices at the museum, their impact on tourism development, the challenges faced in integrating sustainability, and…

[leonw4nter]

Are they? Are They Not?

Architect!RE2R!Leon x Boss!Reader

Tags - fluff, making out (it's short tho), office romance

“Good morning everyone! Picked up some coffee so we can all start the day right!,” Rebecca cheerfully chirps as she enters the office. She stops by everyone’s desks, placing paper cups of steaming hot coffee with their names before knocking at your door, the company’s COO. “Come in!,” you call out. She enters the organized office, spotting you sitting on your office chair and turning your work computer on. She notices a steaming hot paper cup on your desk, along with a brown pastry bag. “Got you some coffee but turns out you’ve already got a cup in. Oops,” she says with an apologetic grin. “It’s fine. I could use the extra caffeine anyways,” you respond with a polite smile. She leaves the cup on your desk before turning back to the door, walking out the office when she spots Leon come in.

“Mornin’ Leon!” “Good morning, Rebecca!”

Rebecca walks over to her desk and decides to officially start her day, answering emails and editing the current contracts that've been assigned to the company. Soon, the noise of chatter is drowned out by the clickity-clack of keyboards and ringing landlines. The morning can get busy very soon, not that they mind; the company does a swell job of making sure its employees are doing alright and are managing to balance their personal and work lives. People pour in and out of Y/N’s office, hoping to get her opinion or approval on a project before having their ideas sent to the CEO (aka Y/N’s dad). Most of the time, their ideas align perfectly so her approval could be seen as a sign that he’ll approve it too. It’s now break and everyone rises from their seats to stretch and get up to grab a bite.

“I’ll go ask Y/N if she wants to go grab lunch with us,” Leon offers just as Rebecca gets up. Rebecca nods before responding, “Okay. I’ll go join the others already.”

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“She’s busy consulting with the engineers and said she's sorry and will make it up to everyone with drinks one of these days,” Leon explains before digging into a breakfast bagel. Despite it being lunch time, he prefers to have breakfast foods.

“Did she ask for help? I can help her out since I’ve got a blueprint or two to review then I'm done” Claire offers. Leon shakes his head but says that he thinks she’ll accept Claire’s offer anyway. The group continued chatting over their respective meals until it got to the topic of their coffee consumption.

“My brother is a beast– out here chugging protein shakes and coffee. I’m surprised he isn’t having a heart attack whilst I’m out here palpitating with two cups,” Claire pipes in.

“I don’t know what’s worse: your brother’s caffeine consumption or the sheer amount of sugar and creamer Rebecca puts in her coffee,” Jill jokes, earning a playful smack to the shoulder from Rebecca. “At this point it’s 99% sugar and a measly 1% coffee. How you’re not diabetic is beyond me!”

“Life’s too short to not absolutely go crazy with sugar and creamer, let me have my fun!,” Rebecca retorts and earns good-natured laughter from the table.

“How about you, Leon? How do you like your coffee?,” Claire asks.

“I’m not too picky with coffee. I’ll take anything,” Leon responds.

“Hmm. You’re just like Y/N; I just get her whatever kind of coffee and she always takes it,” Rebecca responds.

“Y/N? Oh she doesn’t like or drink coffee,” Leon corrects. Jill nearly chokes on her muffin when Leon says those words, eyes slightly widened. “Really? She’s the first person I have ever come across that doesn’t like or drink coffee.”

“But she literally accepted all the coffees I got for her!,” Rebecca says. “Wait… what if she just accepted them to look polite or nice–”

“Knowing her, she probably did that to not hurt your feelings or something…,” Jill softly says.

“She could’ve told me she doesn’t drink coffee. I would’ve gotten her a hot cocoa instead,” Rebecca says. “Guys, do I look intimidating? What if she just took the drinks because my outgoing-ness is intimidating her? We do know she usually keeps to herself too–”

“You’re the least intimidating person I know, Rebecca,” Jill responds. “She might’ve done that because she felt kind of bad… or something– I don’t know–”

“And how do you know that, Leon?,” Claire asks with slightly narrowed eyes, leaning into the table while resting her head on her hand.

Now everyone in the table is sitting in silence, curious gazes focused on Leon as to how he knows that. You've never talked about her preferences in food and drink– it’s not even on the company website. They don’t think it’s ever been mentioned anywhere.

“Oh, you know– we talk,” Leon responds with a neutral tone. “Oh my God Leon you almost got yourself killed! Calm down, calm down. They won’t catch on,” Leon thinks to himself.

“Talk? Talk like how?,” Jill asks.

“‘Talk’ as in we’re just coworkers who decided to strike up a random conversation whilst working on a blueprint that one time,” Leon says. He would’ve looked calm and composed– unaffected even, if it wasn’t for the tips of his ears flushing pink and his subconscious leg jiggle. “What?” Leon asks as Rebecca and Claire shoot him smirks that scream “is it what we’re thinking?”. “Can’t a guy and girl talk like they’re just coworkers?”

“You have a point,” Claire replies but Leon doesn’t miss how her blue-green gaze falls on his pinkish ears. They decided to drop the topic, much to Leon’s massive relief. “That was a close one, Leon. Careful next time,” he thinks to himself. Well, you two did more than just talk that day– no, not in that way; you exchanged numbers, began hanging around each other more frequently until you two took secretly took things to another level. Since there was only 15 minutes left before their break was over, they decided to leave early and go up to their office.

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“Hey baby,” you softly say as you walk over to Leon’s cubicle. The others had already gone, the office dark except for Leon’s spot. He had stayed overtime to finish up a model so he would be free for the weekend.

“Hi,” Leon softly said as he pressed a tender kiss to your cheek. “Stayed a little later to finish the playground but I don’t regret it one bit if it means some time spent with you.”

“Congrats for making my heart race a million miles per hour,” you giggle. Leon shoots you a flirty wink before he finishes up packing his bag. “Ready to go, milady?”

“Let’s go,” you respond. You two leave the dark office, looking around for anyone lingering. You part your hands from his temporarily, making sure no one catches you holding hands with an employee; it’s not exactly rule-breaking to be fraternizing with an employee but it is highly discouraged. More importantly, it’s not exactly the best of look to be caught in such an act especially when you’re the daughter of the head of this entire company.

“Coast clear?” Leon whispers, to which you nod. Giggling like two school children who just confessed their crushes to each other, you two make your way down the dark hallways hand in hand. Leon kept stealing glances at you, a nerdy but hopelessly in love smile plastered on his face. Despite the lack of lights, you could accurately guess that there’s a glimmer in Leon’s eyes whenever he looked at you like you’re the sun, which you kind of are since you lit up his world.

Not too long after, you two get in your car. After starting the engine, Leon suggested that you two take his car so he could open the door for you and be the one to treat you lavishly, to which you responded with a small nod and an “I’ll think about it”. Leon connected his phone to your car’s bluetooth speakers, going to his Spotify and picking out a playlist he made that reminded him of you. Upon hearing the lyrics of the song, a warmth crawled up your cheeks and manifested in the form of a soft pink glow. Seeing your reaction, Leon beamed brightly as he leaned back in his seat.

“You know it’s your birthday next week,” Leon says, breaking the comfortable silence that settled between you two.

“Yeah, it is. Why, you wanna know what I want for a gift?,” you ask.

“Maybe, maybe not.”

“Oh then I guess just wear a light pink ribbon on your hair and call yourself a gift. Your presence in my life is the best present ever.”

“God that’s so cheesy,” he says with a small laugh. He keeps his gaze trained on the tall buildings around you two because he knows he’s going to scream like a girl if he looks at you once more. “It’s not a bad suggestion though.”. After a few minutes, you two finally reach Leon’s condominium.

“Good night baby, see you tomorrow,” you say whilst pulling him in for a kiss.

“Night, Y/N. Text me when you get back, okay?,” he says. You nod before he finally waves bye and shuts the door.

You’ll definitely be sending him some texts.

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After another entire week of staying overtime and finally finishing the mountain of work assigned to you, you finally get up from your chair to move your body a little bit. This day went great: meetings went smoothly, everything on your planner happened, and most importantly, it’s your birthday. Of course, your father and those close to him greeted you and though you didn’t mind if someone know (or doesn't know) your birthday, the gesture warmed your heart.

“Baby?,” Leon called out.

“Huh? Leon?,” you asked. He emerged from the dark, a dainty bouquet of pink and white tulips in his hand, along with a card. Just as you recommended last week, there’s a baby pink ribbon clipped on his hair.

“Oh you didn’t have to–”

“I didn’t have to but I wanted to,” he says before pulling you in for a slow, tender kiss.

“Happy birthday to my only girl.”

Words won’t ever show how truly thankful you are for this gesture so you show it through actions. You pull him in for a hungry kiss, hands travelling to his black tie to loosen it up. Leon places your gifts on your desk, his finally unoccupied hands going to his own tie to help you loosen it faster. You kick your heels off, legs wrapping around his waist as the kisses slowly become more heated and passionate. His hand travels to your blazer, nimble fingers quickly wo–

“Happy birthday, Y/N–”

“WOAH WHAT THE FUCK.”

“CLAIRE PLEASE DON’T DROP THE CAKE.”

“LEON! Y/N?!”

You quickly push Leon off of you and get back up, fixing your hair and feeling around your clothes for any unclasped buttons or pulled down zippers. Embarrassment rushes through your veins, your heart lodged in your throat. Leon’s embarrassed too– shimmery pink lip gloss smeared on his lips, blond hair ruffled, and his tie hanging loose around his neck. His entire face is red and suddenly it’s not so bad if the ground collapses and swallows him up (though he prefers if you swallow him up but now is not the time).

“Uh… hey guys!,” you chirp with an awfully fake smile.

“Hi guys– we were–,” Leon stammers, hand behind his neck.

“Hey guys, if you were busy… we can… we can wait outside…,” Jill awkwardly mumbles, eyeing the poorly hidden bouquet on the desk.

“Yeah… we can wait outside the building if it’ll be noisy too,” Rebecca adds, which causes Leon to almost choke on air and for you to stare at her discombobulated.

“NO– No guys, you can um– now is fine, I promise–,” you stammer. Leon follows suit, trying to make it look as if you two weren’t interrupted in the worst way possible.

NOTE - I saw the reception of my first fic in here and it's looking positive so far so thank you very much! The likes, reblogs, and new followers mean so much to me and I seriously started contemplating telling my parents that I write (I'm so not telling them lmao). I hope you guys enjoy this fic just like you have with my other one!

The dividers (the doodle-y ones) are made by @saradika , the images are made by me (sourced from Pinterest).

[mbti-notes]

Anon wrote: hello! thank you for running this blog. i hope your vacation was well-spent!

i am an enfp in the third year of my engineering degree. i had initially wanted to do literature and become an author. however, due to the job security associated with this field, my parents got me to do computer science, specialising in artificial intelligence. i did think it was the end of my life at the time, but eventually convinced myself otherwise. after all, i could still continue reading and writing as hobbies.

now, three years in, i am having the same thoughts again. i've been feeling disillusioned from the whole gen-ai thing due to art theft issues and people using it to bypass - dare i say, outsource - creative work. also, the environmental impact of this technology is astounding. yet, every instructor tells us to use ai to get information that could easily be looked up in textbooks or google. what makes it worse is that i recently lost an essay competition to a guy who i know for a fact used chatgpt.

i can't help feeling that by working in this industry, i am becoming a part of the problem. at the same time, i feel like a conservative old person who is rejecting modern technology and griping about 'the good old days'.

another thing is that college work is just so all-consuming and tiring that i've barely read or written anything non-academic in the past few years. quitting my job and becoming a writer a few years down the road is seeming more and more like a doomed possibility.

i've been trying to do what i can at my level. i write articles about ethical considerations in ai for the college newsletter. i am in a technical events club, and am planning out an artificial intelligence introductory workshop for juniors where i will include these topics, if approved by the superiors.

from what i've read on your blog, it doesn't seem like you have a very high opinion of ai, either, but i've only seen you address it in terms of writing. i'd like to know, are there any ai applications that you find beneficial? i think that now that i am here, i could try to make a difference by working on projects that actually help people, rather than use some chatgpt api to do the same things, repackaged. i just felt like i need the perspective of someone who thinks differently than all those around me. not in a 'feed my tunnel-vision' way, but in a 'tell me i'm not stupid' way.

----------------------

It's kind of interesting (in the "isn't life whacky?" sort of way) you chose the one field that has the potential to decimate the field that you actually wanted to be in. I certainly understand your inner conflict and I'll give you my personal views, but I don't know how much they will help your decision making.

I'm of course concerned about the ramifications on writing not just because I'm a writer but because, from the perspective of education and personal growth, I understand the enormous value of writing skills. Learning to write analytically is challenging. I've witnessed many people meet that challenge bravely, and in the process, they became much more intelligent and thoughtful human beings, better able to contribute positively to society. So, it pains me to see the attitude of "don't have to learn it cuz the machine does it". However, writing doesn't encompass my full view on AI.

I wouldn't necessarily stereotype people who are against new technology as "old and conservative", though some of them are. My parents taught me to be an early adopter of new tech, but it doesn't mean I don't have reservations about it. I think, psychologically, the main reason people resist is because of the real threat it poses. Historically, we like to gloss over the real human suffering that results from technological advancement. But it is a reasonable and legitimate response to resist something that threatens your livelihood and even your very existence.

For example, it is already difficult enough to make a living in the arts, and AI just might make it impossible. Even if you do come up with something genuinely creative and valuable, how are you going to make a living with it? As soon as creative products are digitized, they just get scraped up, regurgitated, and disseminated to the masses with no credit or compensation given to the original creator. It's cannibalism. Cannibalism isn't sustainable.

I wonder if people can seriously imagine a society where human creativity in the arts has been made obsolete and people only have exposure to AI creation. There are plenty of people who don't fully grasp the value of human creativity, so they wouldn't mind it, but I would personally consider it to be a kind of hell.

I occasionally mention that my true passion is researching "meaning" and how people come to imbue their life with a sense of meaning. Creativity has a major role to play in 1) almost everything that makes life/living feel worthwhile, 2) generating a culture that is worth honoring and preserving, and 3) building a society that is worthy of devoting our efforts to.

Living in a capitalist society that treats people as mere tools of productivity and treats education as a mere means to a paycheck already robs us of so much meaning. In many ways, AI is a logical result of that mindset, of trying to "extract" whatever value humans have left to offer, until we are nothing but empty shells.

I don't think it's a coincidence that AI comes out of a society that devalues humanity to the point where a troubling portion of the population suffers marginalization, mental disorder, and/or feels existentially empty. Many of the arguments I've heard from AI proponents about how it can improve life sound to me like they're actually going to accelerate spiritual starvation.

Existential concerns are serious enough, before we even get to the environmental concerns. For me, environment is the biggest reason to be suspicious of AI and its true cost. I think too many people are unaware of the environmental impact of computing and networking in general, let alone running AI systems. I recently read about how much energy it takes to store all the forgotten chats, memes, and posts on social media. AI ramps up carbon emissions dramatically and wastes an already dwindling supply of fresh water.

Can we really afford a mass experiment with AI at a time when we are already hurtling toward climate catastrophe? When you think about how much AI is used for trivial entertainment or pointless busywork, it doesn't seem worth the environmental cost. I care about this enough that I try to reduce my digital footprint. But I'm just one person and most of the population is trending the other way.

With respect to integrating AI into personal life or everyday living, I struggle to see the value, often because those who might benefit the most are the ones who don't have access. Yes, I've seen some people have success with using AI to plan and organize, but I also always secretly wonder at how their life got to the point of needing that much outside help. Sure, AI may help with certain disadvantages such as learning or physical disabilities, but this segment of the population is usually the last to reap the benefits of technology.

More often than not, I see people using AI to lie, cheat, steal, and protect their own privilege. It's particularly sad for me to see people lying to themselves, e.g., believing that they're smart for using AI when they're actually making themselves stupider, or thinking that an AI companion can replace real human relationship.

I continue to believe that releasing AI into the wild, without developing proper safeguards, was the biggest mistake made so far. The revolts at OpenAI prove, once again, that companies cannot be trusted to regulate themselves. Tech companies need a constant stream of data to feed the beast and they're willing to sacrifice our well-being to do it. It seems the only thing we can do as individuals is stop offering up our data, but that's not going to happen en masse.

Even though you're aware of these issues, I want to mention them for those who aren't, and for the sake of emphasizing just how important it is to regulate AI and limit its use to the things that are most likely to produce a benefit to humanity, in terms of actually improving quality of human life in concrete terms.

In my opinion, the most worthwhile place to use AI is medicine and medical research. For example, aggregating and analyzing information for doctors, assisting surgeons with difficult procedures, and coming up with new possibilities for vaccines, treatments, and cures is where I'd like to see AI shine. I'd also love to see AI applied to:

scientific research, to help scientists sort, manage, and process huge amounts of information

educational resources, to help learners find quality information more efficiently, rather than feeding them misinformation

engineering and design, to build more sustainable infrastructure

space exploration, to find better ways of traveling through space or surviving on other planets

statistical analysis, to help policymakers take a more objective look at whether solutions are actually working as intended, as opposed to being blinded by wishful thinking, bias, hubris, or ideology (I recognize this point is controversial since AI can be biased as well)

Even though you work in the field, you're still only one person, so you don't have that much more power than anyone else to change its direction. There's no putting the worms back in the can at this point. I agree with you that, for the sake of your well-being, staying in the field means choosing your work carefully. However, if you want to work for an organization that doesn't sacrifice people at the altar of profit, it might be slim pickings and the pay might not be great. Staying true to your values can be costly too.

[ak-vintage]

Quarry - Chapter 9 (Part 1)

Pairing: Din Djarin (The Mandalorian) x f!reader

Summary: Din Djarin is on what he expects to be his last bounty hunt for Greef Karga. After all, Nevarro is swiftly moving away from its previous reputation as a Guild member’s paradise, and Din has more important concerns now, like finding a Jedi to train his mysterious foundling. However, after capturing a wanted starship engineer who would rather go anywhere other than “home,” the Mandalorian is forced to reassess his priorities.

Your taste of freedom had been brief but glorious. Now you are a prisoner of the most infamous bounty hunter in the Outer Rim – it’s only a matter of time before he turns you in. There isn’t much you would not do to keep from being sent home, but as you find yourself growing closer to your captor and his strange little companion, you start to wonder whether escape is really what you want.

Set after Chapter 13: The Jedi but before Chapter 14: The Tragedy.

Chapter Tags & Warnings: 18+ MDNI! Reader is Mando's live-in starship engineer, second-person POV, Din Djarin POV, no use of Y/N, minimal descriptors of reader character, unresolved sexual tension, pining, light angst, implications of nudity

Series Masterlist | Read on AO3

A/N: I see this chapter as the first half of a two-parter. I split it in half for ease of consumption and because when I originally wrote it, I hadn't been able to post in ages. Enjoy these two little vignettes! You will get two more in the next "half."

___

The Refresher

After your conversation in the cockpit on your way to Trandosha, life aboard the Razor Crest returned to normal almost startlingly quickly. Mando permitted the ship to travel on autopilot for once, allowing the flight computer to calculate your path, and spent hours researching the last known locations, backgrounds, and crimes of the newest batch of bounties he had received from Karga. You fell right back into your routine of splitting your time between ship maintenance and occupying Grogu; the boy seemed positively thrilled to be back in his leather carrier strapped to your back as you puttered around the cargo hold. He was full of chatter, cooing and babbling and squealing more than you had ever heard. Not for the first time, you wondered whether he might eventually speak Basic or if perhaps his species simply didn’t communicate that way, but you decided that regardless, you liked the extra noise. You could almost imagine what he might be saying, and you found yourself filling in his half of your conversations in your mind as you went about your work. It passed the time, and it made you smile.

Now that you felt confident that you would be spending the foreseeable future in this way, with the Razor Crest as your home, it took you less than a week to come up with a draft for your largest improvement project to date.

“Hey, Mando – do you have a minute?” you asked, poking your head into the cockpit where the Mandalorian sat, bent over one of the computer consoles in concentration.

“What is it?” he replied distractedly. He did not meet your gaze and instead remained focused on the screen before him, which appeared to be a topical map of a dense, verdant forest.

You tucked the datapad you were holding close to your chest, rubbing your thumbs over the edge nervously. Stepping fully into the cockpit, you said, “I have a proposition for you. I’d like your support to start on…kind of a big project in the cargo hold.”

That was enough to get his attention. Pausing his perusal of the map, he turned in his chair to face you, planting his hands on his widespread knees. “What kind of project?”

His voice sounded cautious, and you could understand why. Most of the work you had done on the Razor Crest up until this point you had done without his involvement. He had purchased supplies for you when you requested, and he was always happy to review the reports you generated to demonstrate any efficiency gains you had achieved, but otherwise, you each had kept to your own activities. This was the first time you were asking for his blessing on something before simply doing it.

You took a steadying breath and explained, “With both of us living here for the long term, I really think we should invest in installing a fully functioning refresher.” You paused for a moment then added, “And an additional bunk, if I can figure out how to make one fit in the space we have.”

Mando was silent at first, appearing to consider the idea. “Is that possible?” he asked, his helmet cocked to the side skeptically. “The water storage and recycling systems on ST-70s weren’t designed to support full ‘freshers.”

You nodded in agreement. You had thought of this. “Yes. With the size of the water tank we have right now, you’re right – we could maybe support a running water sink and a privy, but never a shower. But I’ve been taking a look at the schematics, and I feel like there’s a better way to organize the forward space in the cargo hold.” You tapped through a few controls on your datapad and pulled up your sketch of the design, which you had laid over a copy of the Razor Crest’s blueprints. You held it out to him to examine. “It would be tight,” you added, “but I think, if you’re comfortable with it, I should be able to rearrange the hardware that is currently there in such a way that would allow us just enough space for a water tank one size larger than our current one and a ‘fresher.”

You watched, your lower lip between your teeth, as Mando zoomed in on your sketch, silently making note of all of the proposed changes. “Sounds…cramped,” he said after a moment.

You shrugged reluctantly. “It would be, a bit. But it would have a fully functioning door, instead of a curtain,” you argued. “We’d have somewhere to actually brush our teeth instead of using those chalky cleaning tabs. We’d have somewhere to store our toiletries. And we could take showers.” You almost groaned aloud at the thought. How long had it been since you had experienced such a luxury? “Actual, real, hot showers.”

On the space station that orbited Chardaan where the workers’ barracks resided, rows of sonic showers in communal bathrooms had been the norm. Sonic showers were efficient and generally more practical for space living, as they required very few resources to power, and at the very least, they removed dirt and oil and kept everyone from smelling like they had been living in a metal sphere with recycled air for months at a time. However, to you, something about sonic showers never left you feeling fully clean, and after months without access to even that, you were starting to feel truly uncomfortable in your own body. You yearned for the sensation of hot, soapy water sluicing down your skin and foaming up your hair, and if that was your experience, you could hardly imagine how Mando felt, wearing that suit of armor all day every day.

The bounty hunter nodded slowly as he silently reviewed your plans. “And the bunk?” he asked.

You grimaced. “That one I haven’t quite figured out yet,” you replied hesitantly. “I’m still sketching some ideas. I feel much more confident about the ‘fresher.”

“Hm,” he hummed, passing the datapad back to you. “Well, I approve of the refresher idea. Your design looks sound. Make a list of the materials you’ll need. I’ll see what I can do about getting them during our next stop.”

“Ugh, thank you, Mando!” You sighed heavily with relief, excitement buzzing in your chest. “You won’t regret it!”

A week later, after a successful first hunt, the Mandalorian returned to the Razor Crest with a large, male Trandoshan in binder cuffs and a repulsorlift sled laden with bins of supplies dragging behind him. It was all you could do not to fly down the gangplank and fling your arms around him at the sight. Instead, you managed to funnel that energy into just bouncing in place on your tiptoes as you began unloading the sled, your fingers positively itching to wrap themselves around your new toys.

You could have sworn you heard a rasping chuckle filter through your companion’s helmet as he watched your unbridled enthusiasm, and although it made your cheeks burn, you couldn’t bring yourself to care.

From the time you took your plasma torch to the first piece of durasteel bulkhead to the time the refresher was complete and ready for use ended up being about two weeks of constant labor. But Maker, if it wasn’t a labor of love.

Piece by piece, inch by painstaking inch, you systematically disassembled everything to the left of the bunk, starting with that heinous multi-species vacuum ship head (which you had despised since your first day on board) and going all the way to the forward end of the hull. Water filtration? Enhanced. Clean water tank? Replaced entirely with one of a larger size. Scanners, jamming devices, antennae, even the ship’s headlights – all of it got taken apart down to its components, condensed, rewired, and fit back together to make room for the new space. Aside from the work you had done with Peli on the carbonite unit, it was easily the most challenging work you had ever done on a ship of this age, and you relished every second of it. You had always enjoyed puzzles, ever since you were a small child, and fitting each one of these systems back into the reduced space while still ensuring that everything functioned as it was designed was an especially rewarding puzzle.

Once you felt confident with your modifications, you began installing the refresher itself. Mando had been correct in his assessment when he evaluated your plans – the space was cramped, and due to budget constraints, it was almost excessively utilitarian. You had selected plain durasteel for the walls, privy, and running water sink. A single pane of transparisteel separated the shower from the rest of the room, left open on the far end to allow for easy entry without needing the space to accommodate a swinging door. You had managed to convince Mando to spring for a box of tiles of industrial, anti-slip flooring that would keep you both from sliding around in there, particularly when you were in flight, but other than that minor upgrade, everything you requested was about as economical as you could find.

It was far from glamorous, but by the time you finished waterproofing all of your seals and stepped back to admire your handiwork, you felt a rush of satisfaction at the sight. The Razor Crest was Mando’s ship, Mando’s home, but for the first time, you thought that perhaps one day, she might feel like yours, too.

When you finally felt ready to give everything a true test, Mando was out on a hunt. He had landed the Razor Crest on a remote planet in the middle of a humid forest, well-hidden by a copse of trees hung heavily with vines and moss, and you had neither seen nor heard from him in several days. You and Grogu had just finished your dinner for the evening, and the boy’s wide, dark eyes were heavy with fatigue. Seizing the opportunity, you tucked your little green charge into his hammock above the bunk, gave him a couple of gentle rocks until he began to nod off, and then eagerly dove into the newly-finished ‘fresher.

It was even better than you had expected.

The water from the shower was hot on your skin, almost shockingly so, and steam collected quickly in the cramped space, the fan you had fabricated working overtime to draw the excess moisture out of the room and into the exhaust vents. You had come across a lone bar of soap and a singular bottle of shampoo at the bottom of a storage bin one afternoon, and you used them both liberally. With how long it had been since you had last done so, it took multiple washes of both your hair and your body before you felt fully clean, but you couldn’t say you minded the extra time. It was an unspeakable luxury, to be able to stand under running water like this in a pre-Empire gunship that spent most of her time in hyperspace, and you found you couldn’t begrudge yourself the opportunity to bask in it.

Besides, the soap was clearly Mando’s. It was rich with the warm, spicy, masculine fragrance that you had first smelled in his bunk, and surrounding yourself with it like this had your skin flushing and your nerve endings buzzing. Perhaps you ought to have been embarrassed by your body’s reaction to nothing but a scent, but something about being tucked away in this tiny, little room, with its close walls and its own door that locked, knowing that Grogu was fast asleep and Mando wasn’t on board, had you feeling a bit bold. A bit shameless.

So caught up were you in your own enjoyment that you completely missed the sound of your comm link going off in your jumpsuit pocket, left crumpled in a pile on the bunk. On the other side of the door.

It was several more minutes before you found the motivation to turn off the water and step out of the shower. The prolonged heat (and perhaps also the arousal burning between your legs) had left you feeling a bit light-headed, so you toweled yourself off only briefly before wrapping the soft black material around your body and sliding open the door to get some cooler air.

However, to your great surprise, rather than being greeted by an empty cargo hold, you instead immediately met the impassive gaze of the Mandalorian.

His beskar was caked with mud, though he appeared uninjured, and he was in the process of freezing what looked to be an unconscious female Zabrack in carbonite. The gases were just beginning to dissipate and reveal her serene face outlined in matte gray, and although his body was facing her, his visor was fixed intently on you.

“Mando!” you gasped, your hands flying to your chest to grip your towel.

Silence, dense and significant, hovered between you. The bounty hunter continued to stare in your direction, and you could feel your throat begin to dry out and your heart speed up as you suddenly became acutely aware of your state of undress. Your towel was a little thing, a maintenance rag hardly meant for this purpose, and although it managed to cover from your breasts to the very tops of your thighs, that was hardly comparable to your typical boilersuit. And you had barely taken the time to dry yourself off. Your exposed skin shone in the dim cargo hold lighting; your long, unbound hair dripped a puddle onto the deck near your bare feet.

You felt strangely caught out, almost ashamed, as though the Mandalorian had discovered you in some compromising position.

A familiar, ill-timed wave of arousal flashed through you, raising goosebumps across your body and tightening your nipples as you caught a whiff of the scent that now clung to your damp skin. His scent.

Perhaps he had caught you.

Just when you thought you couldn’t bear the weight of this silence anymore, Mando replied simply, “Apologies.” Even through his vocoder, his voice sounded dry and deep, as though he had pulled the word from the depths of his chest, as though it had been a struggle to do so.

You swallowed thickly and shifted on your feet. “The, uh…” You cleared your throat, awkward and positively burning up from the inside. “The ‘fresher’s done. And the shower’s perfect. You should, uh…you should really give it a try.”

He offered you a single nod. “I will.”

You nodded, too. Your head felt loose on your neck, your mind spinning. “Okay. Good.”

Another silence, and you chewed on your lower lip as you cast your eyes around the room, searching for something, anything to look at that wasn’t Mando’s piercing gaze. Eventually, you landed upon your work boots, stacked neatly at the foot of the bunk, and the rumpled mess of your clothes spilling out of recess in the wall.

“Um. If…if I could just – ” you began, gesturing toward the pile of clothing with a little jerk of your head.

That, it seemed, was finally enough to pull the bounty hunter out of whatever shocked trance your appearance had seemed to inspire. He physically startled, turning away from the bounty in the carbonite chamber and drawing himself up straighter, and he dropped his satchel to the floor with a thud.

“Of course. Yes,” he said curtly, already moving toward the ladder up to the cockpit. “I’ll…start the take-off sequence. Let me know when you’re – ”

You found yourself nodding again. “Yeah, for sure. I’ll meet you up there in a bit,” you replied. Your voice sounded overly bright and forced even to your own ears, desperately eager to move past the heart-racing, thigh-clenching self-consciousness of the last few minutes.  

You watched then as Mando retreated up the ladder with a speed that you had never seen before. Tightening your hold on your towel, you slumped back against the ‘fresher doorframe, weak-kneed, and let the durasteel cool your flushed skin.

You weren’t ignorant to the tension that had been building between you and the Mandalorian over the last weeks, but it had never felt like…that. Like his gaze had been a physical touch on your skin, like your core had melted into liquid heat.

Like the delicious, warm slickness now coating the insides of your thighs.

Nothing had ever felt like that.

___

The Bazaar

Din supposed he ought to have known the question was coming sooner or later, but he still found himself somewhat taken aback the first time you asked to leave the Razor Crest during a hunt.

He had been guiding the ship in a steady descent through the atmosphere of Trevi IV, aiming for the spaceport port outside of Trevi City, when you broached the subject.

“I…really desperately need of some new clothes. And hygiene things. Now that we have the ‘fresher, you know,” you had explained haltingly, a charming flush burning high on your cheeks at the mention of your most recent project. “If you’d be willing to give me an advance on my pay, that is. I won’t need much – promise.”

The Mandalorian had found himself almost needing to bite back a groan at the mention of the ‘fresher. You had been correct, of course – the addition of that space had been a marked improvement to the quality of life on the Razor Crest since its completion, but no matter how many times either of you managed to use it without incident, he couldn’t help but recall the sight of you standing in the doorway – cloaked in steam, clothed in nothing but the mere suggestion of a towel, miles of soaking wet skin on display, and smelling unmistakably of him. The vision had nearly unmanned him in the moment, and still it continued to haunt him, even many days later.

It was entirely unprecedented, the way you had come to affect him. The lilt of your laughter at Grogu’s antics, the scent of your hair on the pillow in his bunk, the strong, capable grip of your hands on your hydrospanner, the dark, glossy shine of your eyes as you ran your gaze over his body when you thought he wasn’t looking. All of it had burrowed into the very depths of him, nestled itself near his heart, immoveable. He had never experienced anything like it in his life.

However, rather than confessing any of that, Din had instead simply nodded.

“Sure,” he had agreed. “I need to go to the bazaar district first on a lead anyway. You and the child can join me when we land, get what you need.”

The grateful smile you had sent his way had the Mandalorian feeling his face heat up even under his helmet.

It looked to be around midday local time when the Razor Crest finally landed, and by the time Din was ready to depart, he found you already waiting by the rear blast doors, Grogu strapped to your back in his favorite leather carrier and an eager expression on your face. You had dug an old satchel of his, threadbare and dusty, out of one of the storage compartments, and it hung limply across your body, empty and ready to be put to use. With a wordless nod and a hidden smile, he gestured in the direction of the doors. After you.

It occurred to him as he watched you descend the gangplank that this would be the first opportunity you had had to explore any of the planets he had taken you to thus far. Of course, your time with Peli had certainly been a change of pace from daily life aboard the Razor Crest, but that had been months ago now, and you hadn’t been permitted to leave the hangar at the time. And since then, he had all but insisted that you stay on the ship when he left to hunt. For your safety, and for the child’s, but regardless of how well-intentioned the reason, it wasn’t lost on him how little of the galaxy you had been allowed to see in your life.

Din resolved himself then that although today you would only be visiting a market, only purchasing some necessities, and although he was technically in Trevi City on a hunt, he would not allow you to return to the Crest until you had had your fill of the experience. He was on your timetable today. He would ensure you made the most of it.  

It had been some time since the bounty hunter had made his way to Trevi City, but he found it mostly unchanged as he led you and Grogu out of the spaceport’s docking yards and into the city proper. Trevi IV was a desert world, featuring miles of dusty plains and dramatic plateaus, but Trevi City was an oasis. Nestled against the craggy shores of the largest body of water on the planet, cooling, salty breezes wound their way through flagstone streets and buffeted against sundried brick buildings. Shops, stalls, carts, and tents of all shapes and sizes stretched in every direction, around every corner, and the crush of people was truly remarkable. Merchants – both local and traveling, customers of every age and walk of life, street performers in bright costumes, children and small animals darting in and out of the throng. At first glance, it seemed incomprehensible – the epitome of chaos.

And although Din had never been particularly fond of crowds, he couldn’t help but feel a small surge of satisfaction at the look of pure joy that spread across your face as you took in the bazaar.

First on your list, he knew, was clothing, so with a gentle nudge to your lower back, the Mandalorian steered you in the direction of the textile district – a few blocks down and to the left. The stalls there were draped in sumptuous fabrics, decorated with gold tassels, and staffed by women with sun-worn skin and friendly, welcoming smiles. You looked back at him then, uncertain, but Din gave you a wordless nod and scooped Grogu up and out of his carrier without preamble.

“Go on. I’ll keep an eye on the child. Just explain to one of them what you need, and they will help you,” he said, inclining his helmet toward the line of vendors. He wanted you to feel free to browse, to mingle unencumbered.

After a few halting introductions and some hesitant questions on your part, you did just that. From several yards away, the bounty hunter listened to you describe your needs to one of the women. He watched you tug self-consciously on the collar of your well-worn boilersuit, the olive green fabric now heavily stained with blood and engine oil and Maker knew what else, and he watched as the merchant woman nodded along, kindness in her eyes. Before long, she was looping your arm through hers and leading you deeper into the line of covered stalls, pulling items from racks and tables as she went.

Din kept his distance as you shopped, tracking the top of your head as you wound through the merchandise but never following. Only when you ducked behind a heavily embroidered curtain with an armload of items to try on did he look away, instead finding his attention captured by a display of colorful scarves and handkerchiefs fluttering in the ocean breeze. Before he could consider it further, he found himself in front of the display, running his gloved fingers over assortments of linen, cotton, and silk.

Mere moments later, he left the booth, a cotton scarf decorated with a delicate floral pattern in his pocket and a few credits less in his purse.

By the time you were ready to move on to the next items on your list, your borrowed, threadbare satchel was nearly full to bursting. Your face glowed with pride as you showed him your selections – a brand-new boilersuit (this one in a fetching deep blue), a pair of brown cargo pants and a matching jacket, a stack of undershirts, and two sets of soft, black sleep clothes. Din also tried desperately not to notice the new sets of undergarments hidden at the bottom of your bag as he dutifully handed the total payment over to the vendor.  

He, of course, was unsuccessful. The images of those scraps of fabric, revealed accidentally as you dug through your sack, were now burned onto the backs of his eyelids, ever-present whenever he closed his eyes.

“Hygiene next?” you asked eagerly, rocking back and forth on your feet like a small child. Grogu giggled from his perch in the bounty hunter’s arms, and the latter nodded, clearing his throat.

“Hygiene is this way,” he replied with a gesture to the east.

His voice sounded suspiciously strained even to his own ears.

Your time perusing the toiletry stalls was much briefer than your time with the textiles, but it left Din perhaps even more disquieted. Your first purchase was a pair of full-sized terry cloth towels, which in turn called to mind the image of the miniscule one you had clutched over your breasts in the doorway of the ‘fresher and caused his brain to short-circuit. You also picked up a wide-toothed, wooden comb for your hair, saying casually, “I don’t know if you have hair under that helmet, Mando, but if you do, you’re welcome to borrow it if you need to! You must get awful tangles,” which left him utterly speechless.

However, perhaps the most taxing of all was the booth boasting hand-made soaps and haircare products. The Mandalorian watched, his throat dry, as your capable, calloused fingers floated gently over the many colorful bars and bottles, occasionally picking one up and lifting it to your nose to give a delicate sniff. Without fail, you would always then extend the item to him, placing it directly below the edge of his helmet.

“What do you think of this one?” you asked. “Or how about this? Too fruity? That one’s too much for me, I think. Oh, this one smells like nightblossoms!”

And on and on.

It wasn’t really that he minded being asked for his opinion. On the contrary, he found your enthusiastic chatter pleasant, and something inside him warmed at the idea that you might actually care about his preferences when it came to your body products. However, there was a singular thought that refused to leave him alone every time you asked for his input, one he dared not voice.

On perhaps the tenth bottle of shampoo that provoked a noncommittal response, you sighed heavily.

“Come on, Mando, give me something here,” you whined, clearly exasperated. “You’re the one who has to be cooped up with me on the Crest every day, the one who has to share a ‘fresher with me. I’d think you might care about whether the shampoo I buy gives you a headache or not.”

Din cocked his head, considering. He thought of the dark, blown-pupil looks you sent his way when you thought he wasn’t paying attention, the burning flush that extended down your chest coming out of the ‘fresher, the way you leaned into his touch the few times he had dared run the back of his fingers across your cheek.

Perhaps…perhaps you might welcome him being a bit more candid with you than he had been previously.

“Well?” you pressed. Irritation crept into the edge of your voice then, and the Mandalorian found himself nodding.

“Very well,” he murmured, soft and gruff through his vocoder. “Follow me.”

Without another word, he led you to another stall, this one carrying similar products as the previous but with an aesthetic that clearly intended to be marketed toward men. The stall was draped in tactical netting with wares hanging from the ropes, and the tables were dressed with simple black cloths. The various bars and bottles were fashioned in more neutral colors, earthy and cool, and the merchant manning the till was dressed in an austere black suit. He nodded in your direction once but said nothing.

It did not matter. Din knew precisely what he was looking for.

Barely a moment later, before you could give voice to the questions that were clearly in your eyes, the bounty hunter plucked a single bar of soap and single bottle of hair wash off the table and extended them both to you.

You glanced from the proffered toiletries to Din’s face and then back again, your eyebrows raised quizzically. “These? You think I should buy these?” you asked dubiously.

He inclined his helmet in the affirmative. “Yes.”

Your eyes narrowed. “What are they?”

He simply continued to stare at you, silent, willing you to reach out and take them. Eventually, you did. Your fingers brushed his as you took the bar and the bottle into your hands, and if Din did not know better, he would have been certain that he could feel the warmth of your skin through his gloves.

Skepticism still apparent in your expression, you raised the bar of soap to your nose and sniffed lightly. Instantly, your eyes widened, and Din watched with liquid heat in his gut as your pupils expanded.

“This – ” you started, then paused and cleared your throat loudly. “This is your soap.” Your cheeks darkened, your lower lip disappearing between your teeth.

“Yes,” the Mandalorian confirmed.

“You – you think I should buy the same thing? The same as you?” You were stammering, seemingly struggling to maintain eye contact.

“It suits you,” he said. And it wasn’t a lie. As much as he enjoyed the scent on himself, it somehow was only enhanced on your skin, your hair. It was comforting, warm and inviting.

It spoke to a primeval part of his psyche, something that purred at the thought of you being marked as belonging to him. Only him.  

“Well, it’s all I’ve had ‘til now. You don’t think it makes me smell like a man?” you asked with a forced chuckle, a clear attempt to inject some levity into what had suddenly become a very weighted conversation.

At that, Din could not stop himself from taking a step closer, invading your space, forcing you to tilt your head back on your neck to keep looking in his eyes. His breath came short in his chest at the proximity, and his voice crackled through his helmet modulator as he replied, “Trust me. There is nothing about you that could be mistaken for a man.”

An almost bashful expression came over you then, and you dropped your gaze. “That a good thing?” you murmured.

The bounty hunter could only manage a nod in response.

You left the booth with three new bars of soap and three bottles of hair wash in his favorite scent, the haul quickly added to your satchel with a secret smile and a heavy blush.

At that point, Grogu began to fuss in Din’s arms, whining softly and smacking his lips in the way that you both had learned meant that he was getting hungry, so the three of you ended the afternoon hopping from vendor to vendor sampling a variety of Trevi street foods. Well, perhaps more accurately, the Mandalorian watched as you and Grogu enjoyed the local fare – he packaged up his own to take back to the Razor Crest.

First, you selected an almost comically large wrap from a stall run by a male Bith – a pillow-soft flatbread wrapped around some variety of savory meat, a relish of pickled vegetables, and a bright orange sauce with a heavily spiced aroma. The sauce left broad, messy streaks across your nose and cheeks as you ate, but you paid it no mind. Instead, you simply laughed and plucked a few choice bits of meat out of the flatbread and passed them over your shoulder to Grogu, who was once again strapped to your back in his carrier. The boy babbled and munched happily, and Din took it upon himself to go back to the stall and request a handful of napkins.

Next, you followed the unctuous scent of fry oil to a tiny cart staffed by a Truishii woman. This one was peddling small paper bags filled to the brim with an assortment of deep-fried vegetables, coated in a thin golden batter and soaking the bag with grease. You groaned under your breath at the first bite, and Din immediately purchased a second bag.

Finally, after a bit of leisurely meandering and browsing, you stumbled across an open-air cantina just as the sun was beginning to set. A hired band played a lively tune from one corner of the cantina’s patio, and barmaids wove gracefully between rickety tables carrying trays laden with tankards. The Mandalorian looked on as you watched the band, a soft smile playing at the corners of your lips, your body swaying unconsciously to the beat.

Before he could think better of it, he placed a gentle hand at the base of your spine to get your attention. “Would you like to sit down? Have a drink?” he asked, bringing his helmet down close so you could hear him better over the music.

You startled slightly under his touch, but Din could not ignore the way you seemed to lean into it, or the deep breath you took at the sound of his vocoder in your ear. You nodded silently in response, and the Mandalorian took that as his cue to lead you a table, flagging down a barmaid on the way.

He ordered you a tankard and Grogu a cup of bone broth as you settled into your seat, and the wide-eyed look of overwhelm as you took in the tankard’s contents made Din laugh out loud.

“What is it?” you asked, your voice tinged with awe.

He smirked. “I’m not sure what it’s called. It’s a local brew, made with honey.”

You swallowed heavily, giving the cup one more once-over before taking it in both hands. “Well. Bottoms up!” You inclined the tankard in his direction then brought it to your lips, drinking deeply.

In mere minutes, it was empty, and you were ordering a second, eyes glossy and cheeks flushed.

It was well past sundown by the time Din helped you stand from your seat at the cantina and led you back through the winding flagstone streets to the spaceport. Grogu had long since fallen asleep in his carrier, his little head resting on the back of your shoulder as he snored gently, and you had polished off nearly three full tankards of that honeyed beverage, leaving you giggly and wobbling on your feet. You were singing softly to yourself, humming one of the songs the band had been playing and grinning from ear to ear, and the effect was so charming, it was all the Mandalorian could do to keep himself from joining in.

When you arrived back at the Razor Crest, however, you seemed to have finally burned out all of your energy. You stumbled and lurched up the gangplank the moment it touched the ground, pausing only briefly once inside the ship to drop the bag full of your purchases unceremoniously onto the deck floor. Din called out your name like a question, but rather than answering, you simply removed Grogu’s carrier from your back, still holding the sleeping child, and passed it into the Mandalorian’s waiting arms.

“I have to lay down,” you said softly, almost to yourself.

Din nodded and gently steered you in the direction of the bunk. “This way,” he replied, just as softly.

At the entrance to the bunk alcove, you toed off your boots and then, to Din’s great surprise, stripped off your boilersuit, leaving you clad in nothing but a black breast band, a worn pair of gray undershorts, and a pair of crew-length socks. Everything else was left haphazardly piled on the deck, sure to be a tripping hazard when you woke, but you clearly couldn’t be bothered. Muttering to yourself, eyes half closed, you clambered into the bunk.

“Are you going to be all right?” he asked after a moment.

“‘M fine,” you murmured, your voice thick and muffled by the pillow. “Never drank that much before. Not allowed in the barracks. Couldn’t afford it when I ran away.”

Din nodded even though he knew you couldn’t really see it. “I understand. Alcohol was discouraged during my training in the Fighting Corps. It…takes some getting used to.”

You hummed in response, snuggling deeper into the bunk’s barren mattress. Something inside him warmed, and he smiled softly at the sight.

The bounty hunter took a moment then to carefully extract the sleeping Grogu from his carrier, settling him in the little hammock he had fashioned for the boy that stretched across the bunk alcove. It was only when he was preparing to walk away and settle himself in the cockpit for the night that he heard you speak again.

“Mando?” you called softly.

“Yes?”

“Thank you. For today,” you whispered. You were nearly asleep, your words slurred and slow. “It was wonderful. You’re wonderful. Best day of my life.”

[kafus]

SO... do you headcannon anyone in horizons as autistic?

OH BOY DO I

so dot is the most obvious choice. there is no universe in which she is not autistic to me. this is one of my strongest dot headcanons actually and one of the main reasons i enjoy her as a character. there are so many reasons for this i could go on endlessly but i'll just list a few big ones here

her extreme passion for her interests at a disregard for almost everything else & her ability to self teach those topics (not to mention her interests have to do with computing)

her difficulties with food overlap a lot with food sensitivities autistic people often have, also her latching onto donuts as a sort of samefood after finally trying them once

the tendency to wear loose, comfortable clothes and more recently she has complained while wearing tighter clothes (the orange academy school uniform) so it's not just that she prefers loose fabric, she also is put off by the alternative. girl your sensory problems

irritable outbursts when struggling to articulate herself/make herself understood

her connection with kanuchan (tinkatink) felt really neurodivergent to me. she wasn't offput by her behavior, even after stealing her prop mic, and was immediately able to understand her when no one else could or was willing to. not sure how to articulate this one right now but i hope you see what i mean

her tendency to sit cross legged and lean over herself reminds me a lot of my personal autistic tendency to need a pressure/weighted feeling while i sit or have body parts touching

social exhaustion, the need to be alone sometimes even when she cares

the list genuinely goes on i have to stop myself LOL

as for other characters,

so for liko i'm more loose about the headcanon, it's definitely more of me projecting than her being overtly autistic in canon but i still think it lines up if u wanna view her that way. i'm autistic and i personally relate to liko a lot becauseee

she is giving hyperempathy autism to me. the way she is overly empathetic and compassionate to her own detriment and yet still has to have her hand held through articulating & dealing with that or putting the logical parts of empathy together

the way she absolutely fucking Explodes with excitement sometimes

the way in which she relates to cats, and her whole thing about having a hard time getting other people to understand her. these two things go hand in hand

there's something neurodivergent about her trying to connect with sprigatito by studying her and writing notes about her behavior lol

while this is kind of just on the account of her being an anime character and a protagonist at that, liko's facial expressions and body language can be pretty exaggerated sometimes which reminds me of my own body language, i'm cartoonishly animated in real life often LOL

so like basically dot is so obviously autistic to me it's like breathing but for liko it's kind of a hc i apply to her for projection purposes & fun but i think it's reasonable

and lastly so i'm not just talking about solely liko and dot for the millionth time,

ORIO!! honestly we don't even know that much about orio but the one episode where she was helping pokeball lady i forget the name of fix her machine. the really narrow attention to detail/seeing the smaller parts instead of the bigger picture. also her expertise in engineering contrasted with her struggling with tasks outside of that (like when she was trying to sew holes in the brave asagi and for the life of her could not do it so she called murdock for help lol)

and actually one more - while i don't necessarily headcanon amethio as autistic, i think it's a fun headcanon/au idea to not only give him a redemption arc but an autism unmasking arc at the same time. representation for all my repressed autistics out there. in my mind

thanks for asking i'm so autistic about horizons so of course i headcanon them with autism too JOISJOIFD

[rabbiteclair]

continuing the topic of 'managers who oversee websites make wild decisions':

In my old job, I dealt with the quality software used by Large Manufacturing Company. This software was an ancient desktop application, so for several years, my team was focused on making an app that could reproduce its most vital features on a tablet, so that QA types could perform inspections and stuff without being at a computer.

And, for a long time, the main message coming down from the director overseeing this project was: it needed Gamification.

Gamification, for those of you who are unfamiliar, is all of the badges, achievements, progress trackers, leaderboards, etc in software. Stuff that gives you some goal to work toward and makes using the software itself into kinda a game. In the opinion of this director, what we really needed in this app that was used by 55-year-old mechanical engineers to document 'panel #12 on unit 321 has a chip in the paint' was to add cheevos.

So, for probably about a year and a half, that was a big part of what my team was focused on. We could've been adding, like, useful functionality, but instead we had to think about things like 'how do we make a leaderboard to track who has documented the most defects caused by holes drilled at the wrong size.' The 55-year-old engineers hated it, and on at least one occasion somebody thought that the badges and stuff were a sign that the app had been hacked.

my hero in all of this was my one coworker who, when asked to make it spray confetti over the screen when somebody finished submitting a defect report, wrote a script to do that... and then she jacked the confetti volume up by like 5x before a demo to management. So we did our demo as normal, and then when we submitted the document, the screen was covered by ten billion pieces of overlapping confetti, which crawled down the screen as the entire system was slowed to a chug by the sheer amount of bullshit it was trying to draw.

[bluenomad-snufkin]

More about me... be warned im a terrible human

I am 16 - Male, chronically depressed. Un-ironically a genius... and lack real connection.

I like weird music such as, Death grips, Semetery, Adam and the ants, Fried by Fluoride... I LOVE THE SMITHS BTW and nirvana.

i enjoy playing with computers and building them, have about 4 pc's now and 5 laptops, all old stuff cause i like old computers.

Linux enthusiast - I use mint :3

I own a shit CRT but its fun to use- lain core </3

Game a fair bit and enjoy games like Chiv2-Cof-Postal1/2-Tf2-project zomboid- Counter strike source and 2. silent hill series could go on and on but you get the idea

hmu if u want to game cause all my friends are ass at "these sort of games"

i enjoy some weird interests as well:

tcc, photography, design, steam power, engines in general, motorbikes, hacking, ELETRIC GUITAR, and acoustic, gambling, baking, cooking, pirating, audiophile, 3drinting, preservation of old tech, blacksmithing, reading, Gel-Blasting (for the Americans it is australian Airsoft in short), old game console modding, anime and movies.

That's probably the list ngl

I read a lot and i like to discuss deep philosophical concepts and the "psychology" of humans. (if you couldn't tell I'm a 'misanthrope')And talking about societal constructs and all that stuff... not many people like talking about that stuff.

a good way to describe me would be Lain but mentally Dr house. in the sense of dislike of just about everything and my attitude towards others and life its self.

I don't know why I am the way I am... I truly am a miserable person, i have my moments but I honestly am, and I make others very miserable just by more or less existing with them.

This blog is kind of apart of my journey to becoming something else, I think self discovery would be the wrong term but the closest set of words I can think of too how I feel.

some more personal stuff...

I am incredibly lazy, not to the point of not showering or never leaving bed but more "surrogate activates" - Ted K, or meaningless and basic tasks/activates, I don't really participate in class due to the fact i somehow know most of it (I'm ignorant too) I don't really like doing things like- actually this is hard to explain but the best way i can describe it would be doing this that have to value to me or my future.

I don't have a problem connecting with people but I find my self ALWAYS not actually caring for them or there feelings. I don't believe at this point in time I could name more than one person I really care for. I would label this a selfish but its not like to treat my self any better. maybe that is how i punish my self, any insight on this topic would be much apricated.

I seem to have sort of desire for Control - i think this because i love just watching people listening and anticipating what people will say, do, think, act, its some sort of game for me (i really don't know how to put this) and id have to say 80% of the time my guess are correct, i am a ""master"" of determining and analysing humans, its really weird and i don't understanding where or even how i developed this skill from. i often find my self using this to just piss people off and see how mad i can get someone (i mainly do this online).

A lot of human thinking and reactions piss me off, I hate how some people think and interact with this world i don't seem in some case even understand why these people are like this i s just know and know that they are. I'm not sure if i wish to be like them or for "them" to be like me.

I truly am a troubled and misunderstood person.

one may conclude that I'm autistic or have some other form of genetic/ mental illness, to that i say, are you fucking retarded... do you understand anything in this world or that of the human mind?

Maybe you do, if so please critiqueme and tell me why i am me.

I have been tested for Autism and ADHD, both Negative not sure by what margin although.

My best guess is that i am simply "hyper realised" or some other buzz words - or are a lot of people this way...???

Just been reading and editing this massive ass post, there are so many other things i could go on about, like the government, being clean, family, longing for societal escape, tictok, but you probably don't care just as i wouldn't.

Any way enjoy my weird blog i guess if you read this and where not turned away. lol

-last minute add don't know where to put this but i love tcc cause I'm "obsessed" with there minds, motives and stuff like that.

[lingthusiasm]

Transcript Episode 98: Helping computers decode sentences - Interview with Emily M. Bender

This is a transcript for Lingthusiasm episode ‘Helping computers decode sentences - Interview with Emily M. Bender’. It’s been lightly edited for readability. Listen to the episode here or wherever you get your podcasts. Links to studies mentioned and further reading can be found on the episode show notes page.

[Music]

Lauren: Welcome to Lingthusiasm, a podcast that’s enthusiastic about linguistics! I’m Lauren Gawne. Today, we’re getting enthusiastic about computers and linguistics with Professor Emily M. Bender.

But first, November is our traditional anniversary month! This year, we’re celebrating eight years of Lingthusiasm. Thank you for sharing your enthusiasm for linguistics with us. We’re also running a Lingthusiasm listener survey for the third and final time. As part of our anniversary celebrations, we’re running the survey as a way to learn more about our listeners, get your suggestions for topics, and to run some linguistics experiments. If you did the survey in a previous year, there’re new questions, so you can totally participate again this year. There’s also a spot for asking us your linguistics advice questions, since our first linguistics advice bonus episode was so popular.

You can hear about the results of the previous surveys in two bonus episodes, which we’ll link to in the show notes. We’ll have the results from this year’s survey in an episode for you next year. To do the survey or read more details, go to bit.ly/lingthusiasmsurvey24 – that’s bit.ly/lingthusiasmsurvey24 (the numbers 2 and 4) – before December 15 anywhere on Earth. This project has ethics board approval from La Trobe University, and we’re already including results from previous surveys into some academic papers. You, too, could be part of science if you do the survey.

Our most recent bonus episode was a linguistics travelogue. We discuss Gretchen’s recent trip to Europe where she saw cool language museums, and what she did to prepare for encountering several different languages on the way, as well as planning our fantasy linguistic excursion to Martha’s Vineyard. Go to patreon.com/lingthusiasm to hear this and many more bonus episodes and to help keep the show running ad-free.

Also, very exciting news from Patreon, which is that they’re finally adding the ability to buy Patreon memberships as a gift for someone else. If you’d be excited to receive a Patreon membership to Lingthusiasm as a gift, we’ll have a link in the show notes for you to forward to your friends and/or family with a little wink wink, nudge nudge. We also have lots of Lingthusiasm merch that makes a great gift for the linguistics enthusiast in your life.

[Music]

Lauren: Today, I am delighted to be joined by Emily M. Bender who is a professor at the University of Washington in the Department of Linguistics. She is the director of the Computational Linguistics Laboratory there. Emily’s research and teaching expertise is in multilingual grammar engineering and societal impacts of language technologies. She runs the live-streaming podcast Mystery AI Hype Theater 3000 with sociologist Dr. Alex Hanna. Welcome to the show, Emily!

Emily: I am so enthusiastic to be on Lingthusiasm.

Lauren: We are so delighted to have you here today. Before we ask you about some of your current work with computational linguistics, how did you get into linguistics?

Emily: It was a while ago. Back when I was in high school, we didn’t have things like the Lingthusiasm podcast – or podcasts for that matter – to spread the word about what linguistics was. I actually hadn’t heard about linguistics until I got to university. Someone gave me the excellent advice to get the course catalogue ahead of time – it was a physical book in those days – and just flip through it and circle anything that looked interesting. There was this one class called “An Introduction to Language.” In my second term, I was looking for a class that would fulfil some kind of requirements, and it did, and I took it. Let me tell you, I was hooked on the first day. Even though the first day was actually about the bee dance and other animal communication, I just fell in love with it immediately. I think, honestly, I had always been a linguist. I loved studying languages. My ideal undergraduate course of study would’ve been, like, take the first year of all the languages I could.

Lauren: That would be an amazing degree. Just like, “I have a bachelors in introductory language.”

Emily: Yeah, I mean, speaking now as a university educator, I think there’s some things missing from that, but as a linguist, how much fun would that be. I didn’t know there was a way to study how languages work without studying all the languages. When I found it, I was just thrilled.

Lauren: Excellent. I think that’s such a typical experience of a lot of people who get to university, and they’re intrigued by something that’s like, “How can it be an intro to language when I’ve learnt a bunch of languages?” And then you discover there’s linguistics, which brings you into the whole systematic nature of things.

Emily: Absolutely. My other favourite story to tell about this is I have a memory of being 11 or 12 and day dreaming and trying to figure out what the difference was between a consonant and a vowel.

Lauren: Amazing.

Emily: Because we were taught the alphabet. There’s five vowels and sometimes Y, and the other ones are consonants. What’s the difference? My regret with this story is that I didn’t record what it was that I came up with. I have no idea if I was anywhere near the right track. But I don’t think that your average non-linguist does things like that.

Lauren: That’s extremely proto-linguist behaviour. I love it. I’m sad we don’t have 11-year-old Emily’s figuring out from first principles of the IPA.

Emily: Emily who definitely went on to be a syntax / semantics side linguistics and not a phonetics / phonology side linguist.

Lauren: How did you become a syntax-semantics linguist? How did you get into your research topic of interest?

Emily: In undergrad, it was definitely the syntax class that I connected with the most. I got to study Construction Grammar with Chuck Fillmore and Paul Kay at US Berkeley, which was amazing, and sort of was aware at the time that at Stanford there was work going on on two other frameworks called Lexical-Functional Grammar and Head-Driven Phrase-Structure Grammar. These are different ways of building up representations of language. I went to grad school at Stanford with the idea that I was going to create generalised Bay Area grammar and bring together everything that was best about each of the frameworks. They are similar in spirit. They’re sometimes described as “cousins.” Then I got to Stanford, and I took a class with Joan Bresnan on Lexical-Functional Grammar and a class with Ivan Sag on Head-Driven Phrase-Structure Grammar. I realised that it’s actually really valuable to have different toolkits because they help you focus on different aspects of the grammars of languages. Merging them all together really wasn’t gonna be a valuable thing to do.

Lauren: It’s good that you could see what each of them was bringing to – that we have syntax, and there’s structure, but different ways of explaining it give different perspectives on things.

Emily: Exactly, and lead linguists to want to go explore different things about different languages. If you’re working with Lexical-Functional Grammar, then languages that do radical things with their word order, like some of the languages of Australia, are particularly interesting, and languages that put a lot of information into the morphology – so the parts of the words – are really interesting. If you’re doing Head-Driven Phrase-Structure Grammar, then it’s things like getting deep into the idiosyncrasies of particular languages – the idioms and the sub-patterns – and making them work together with the major patterns is a big focus of HPSG. You’re just gonna work on different problems using the different frameworks.

Lauren: I love that. An incredibly annoying undergraduate proto-linguist behaviour I still remember in my syntax class – because you learn to draw syntax trees. One of my fellow students and I were like, “Trees are fine, but we need to keep extending them down because they only go as far as words,” and there’s all this stuff happening in the morphology. We thought we were very clever for having this very clever thought. We were very lucky that our syntax professor was Rachel Nordlinger, who is another person who works with Lexical-Functional Grammar, which, as you said, is really interested in morphology. You could tell she was just like, “You guys are gonna be so happy when we get to advanced syntax, but just hold on. We’re just doing trees for now.” That’s how I got introduced to different forms of syntax helping answer different questions. It’s like, “Oh, this is one that accounts for the things that are happening inside words as well.” It’s really cool.

Emily: One of the things about both LFG and HPSG is that they’re associated with these long-term computational projects where people aren’t just working out the grammars of languages with pen and paper but actually codifying them in rules that both people and computers can deal with. I got involved with the HPSG project like that as a graduate student at Stanford, and then later on while – my first job, actually – that’s not true. My first job out of grad school was teaching for a year at UC Berkeley, but then I had a year after that where I was working in industry at a start up called “YY Technologies” that was using a large-scale grammar of English to create automated customer service response. You’ve got an email coming in, and the idea is that we parse the email, get some representation of what’s being asked, look up in a database what an appropriate answer would be, and then send that answer back. The goal was to do it on the easy cases so that the harder cases that the automated system couldn’t handle would get passed through to a representative. The start up was doing that for English, and they wanted to expand to Japanese. I had been working on the English grammar, actually, as a graduate student at Stanford because it’s an open source grammar, and I speak Japanese, and so I got to do this job where it was literally my job to build a grammar of Japanese on a computer. It was so cool. That was a fantastic job. In the course of that year, there was a project starting up in Europe that was interested in building more of these grammars for more languages. I picked up the task of saying, “How can we abstract out of this big grammar for English,” which at that point was about seven years old, still under development. It is quite a bit older now, quite a bit bigger.

Lauren: Amazing.

Emily: “How can we take what we’ve learned about doing this for English and make it available for people to build grammars more quickly of other languages?” I took that English grammar and held it up next to the Japanese grammar I was working on and basically just stripped out everything that the Japanese made look English-specific and said, “Okay, here’s a starter kit. This is the start of the grammar matrix that you can use to build a new grammar.” That’s the beginning of that project. I have since been developing that – we can talk more about what “developing it” means – together with students, now, for 23 years. It’s a really long-standing project.

Lauren: Amazing. That is – in terms of linguistics research projects and, especially, computational linguistics projects – a really long time. It speaks to the fact that computers don’t process language the same way we do. A human by the age of 23 is fully functional at a language by itself and can be sharing that language with other people, but for a computer, you’re finding more and more – I assume at this point it’s really specific rules or factors or edge cases.

Emily: For the English grammar that I was describing, yes, it’s basically that. The grammar matrix grows when people add facilities to it for handling new things that happen across languages. For example, in some languages, you have a situation where, instead of having just one verb to say something like “bathe,” it requires two words together. You might have a verb like “take” that doesn’t mean very much on its own and then the noun “bath,” and “take a bath” means the same thing as “bathe.” This phenomenon, which is called “light verb constructions,” shows up in many different languages around the world in slightly different ways. When the student is done with her master’s thesis, you’ll be able to go to the grammar matrix website and enter in a description of light verb constructions in a language and have a grammar come out that can handle them.

Lauren: So excellent. And not something, if we were only working in English, that we would think about, but light verbs show up across different language families and across the grammars of languages that you want to build computational resources for, so it makes sense to add this kind of functionality.

Emily: Exactly. And light verbs do happen in English, but they happen in different ways and more intensively in other languages. You can kind of ignore them in English and get pretty far, but in a language like Bardi, for example, in Australia, you aren’t gonna be able to do very much if you don’t handle the light verbs.

Lauren: And now, hopefully at the end of this MA, we’ll be able to.

Emily: Yes, exactly.

Lauren: Why is it useful to have resources and grammars that can be used for computers for languages like Bardi or, I mean, even large languages like Japanese?

Emily: Why would you want to build a grammar like this? Sometimes, it’s because you want to build a practical application where you can say, “Okay, I’m gonna take in this Japanese string, and I’m going to check it for grammatical errors,” or “I’m going to come up with a very precise representation of what it means that I can then use to do better question answering,” or things like that. But sometimes, what you’re really interested in is just what’s going on in that language. The cool thing about building grammars in a computer is that your analysis of light verb constructions has to work together with your analysis of coordination and your analysis of negation and your analysis of adverbs because they aren’t separate things, they’re all part of one grammar.

Lauren: And so, if we can make computers understand it, it’s a good way of validating that we have understood it and that we’ve described the phenomenon sufficiently.

Emily: And on top of that, if you have a collection of texts in the language, and you’ve got your grammar that you’ve built, and you wanna find what you haven’t yet understood about the language, you try running that text through your grammar and find all of the places where the grammar can’t process the sentence. That’s indicative of something new to look into.

Lauren: It’s thanks to this kind of computational linguistics that all those blue squiggles turn up on my word processing, and I don’t make major syntactic mess ups while I’m writing.

Emily: That’s actually an interesting case. Historically, yes, the blue squiggles came from grammar engineering. I believe they are now done with the large language models. We can talk about that some if you want.

Lauren: Okay, sure. But it was that kind of grammar engineering that led to those initial developments in spell checkers and those kind of things.

Emily: Yes, exactly.

Lauren: Amazing. Attempting to get computers to understand human language has been something that has been part of the interest of computational scientists since the early days of 20th Century computing. I feel like a question that keeps popping up when you read the history of this is like, “And then someone figured something out, and they figured we’d solve language in five years.” Why haven’t we solved getting computers to understand language yet?

Emily: I think part of it is that getting computers to understand language is a very imprecise goal, and it is one where, if you really want the computer to behave the same way that a person would behave if they heard something and understood it, then you need way more than linguistics. You need something – and I really hate the term “artificial intelligence” – but you basically need to solve all of the problems that building artificial intelligence – if that were a worthy goal – would require solving. You can ask much narrower questions and build useful language technology – so grammar checkers, spell checkers – that is computers processing natural languages to good effect. Machine translation – it’s not the case that the computer has understood and then is giving you a rendition in the output language. Machine translation is just “Well, we’re gonna take this string of characters and turn it into that string of characters because, according to all of the data that was used to develop the system, those patterns relate to each other.”

Lauren: I think it’s also easier to understand from a linguistic perspective that when people say, “solve language,” they have this idea of language as a single, unified thing, but so far, we’ve only been talking about written things and the issues that are around syntax and meaning. But dealing with understanding or processing written language versus processing voice going in versus creating voice – they’re all different skills. They require different linguistic and computational skills to do well. Solving language involves solving, actually, hundreds and thousands of tiny different problems.

Emily: Many, many different problems, and they’re problems that, you say, involve different skills. So, are you dealing with sound files? Are you dealing with if you actually wanted to process something more like what a person is doing? Do you have video going on? Are you capturing the gesture and figuring out what shades of meaning the gesture is adding?

Lauren: Nodding vigorously here.

Emily: I know I don’t need to tell you that. [Laughs] But also pragmatics, right, we can get to a pretty clear representation for English at least of the “Who did what to whom?” in a sentence – the bare bones meaning in the form of semantics. But if we want to get to “Okay, but what did the person mean by saying that? How does that fit in with what we’ve been discussing so far and the best understanding possible of what the person is trying to do with those words?” that’s a whole other set of problems – that’s called “pragmatics” – that is well beyond anything that’s going on right now. There’s tiny little forays into computational pragmatics, but if you really want to understand language – a language, right, most of this work happens in English. We have a pretty good idea about how languages vary in their syntax. Variation at the level of semantics, less well studied. Variation in pragmatics, even less so. If we were going to solve language, we need to say which language.

Lauren: Which raises a very important point. As you’ve said, most of this work happens in English. In terms of computational linguistics, there’s been the sense that people are very pleased that we’ve now got maybe a few hundred languages that we have pretty good models for, but there’s still thousands of languages that we don’t have any good computational models for. What is required to make that happen? If you had a very large budget and a great deal many computational linguists to train at your disposal, what’s the first thing you would need to start doing?

Emily: The very first thing that I would start doing, I think, is engaging with communities and seeing which communities actually want computational work done on their languages. And then my ideal use of those resources would be to find the communities that want to do that, find the people in those communities who want to be computational linguists, and train them up rather than what’s usually a much more extractive, “We’re gonna grab your data and build something” kind of a thing. And then it becomes a question of “Okay, well, what do you want computers to be able to do with your language?” – a question to the community. Do you want to be able to translate in and out of, maybe, English or French or some other world or colonial language? Do you want a spell checker? Do you want a grammar checker? Do you want a dialogue partner for people who are learning the language? Do you want a dictionary that makes it easier to look up words? If your language is the kind of language that has a whole bunch of prefixes, just alphabetical order, you know, the words, isn’t gonna be very helpful. What’s needed? And then it depends – do you want automatic transcription? Do you want text-to-speech? Then depending on what the community is trying to build, you have different data requirements. If you wanna build a dictionary like that, that’s a question of sitting down and writing the rules of morphology for the language and collecting a big lexicon. If you want text-to-speech, you need lots and lots of recordings that have been transcribed in the language. If you want machine translation, you need lots and lots of parallel text between that language and the language you’re translating into.

Lauren: And so, a lot of that will use the same computational grammar models but will have slightly different takes on what those models are and will need different data to help those models do their job.

Emily: In some cases, the same models, in some cases, different. I think if we’re talking speech processing, automatic transcription, or speech-to-text, we’re definitely in machine learning territory, and so that’s one kind of model. Machine translation can be done in a model of the grammar mapped to semantics form, or it can be done with machine learning. The spell checker, especially if you’re dealing with a language that doesn’t have enormous amounts of texts to start with, you definitely want to do that in a someone-writes-down-the-rules kind of a fashion. That’s a kind of grammar engineering, but it’s distinct from the kind that I do with syntax.

Lauren: And so, it just starts to unpack how complicated this idea of “Computers do language” is because they’re doing lots of different things, and they need lots of different data. Obviously, we say “data” as though it’s some kind of objective, general pot of things, but when we say “data,” we mean maybe people’s recordings, maybe people’s stories, maybe knowledge and language that they don’t want people outside of their community to have. That creates different imperatives around whether these models are gonna be a way forward or useful for people.

Emily: And at the moment, we don’t have very many great models for collecting data and then handling it respectfully. There are some great models, and then there’s a lot of energy behind not doing that. The best example that I like to point to is the work of Te Hiku Media in Aotearoa (New Zealand). This is an organisation that grew out of a radio project for Te Reo Māori. They were at a community level collecting transcriptions of radio shows in Teo Reo Māori, which is the Indigenous language of Aotearoa (New Zealand). Forgive my pronunciation; I’m trying my best. They had been approached over the years many, many times by big tech saying, “Give us that data. We’d like to buy that data,” and they said, “No, this belongs to the community.” They have developed something called the “Kaitiakitanga License,” which is a way that works for them of granting access to the data and keeping data sovereignty – basically keeping community control of the data. There are ways of thinking about this, but it really requires strength and community against the interests of big tech that takes a very extractivist view of data.

Lauren: It’s good that there are some models that are being developed and normalising of this as one possible way of going forward. As you’ve said, you’ve spent a lot of time working to build a grammar matrix for lots of different languages. This goes against a general trend of focusing on technologies in major languages where there’re clear commercial and large-audience imperatives. Part of this work has been making visible the fact that English is very much a default language in the computational linguistics space. Can you give us an introduction to the way that you started going about making the English-centric nature of computational linguistics more visible?

Emily: I think that this really came to a head in 2019 when I was getting very fed up with people writing about English as if it weren’t language. They would say, “Here’s an algorithm for doing machine reading comprehension,” or “Here’s an algorithm for doing spell checking,” or whatever it is. If it were English, they wouldn’t say that. It seems like, “Well, that’s a general solution,” and then anybody working on any other language would have to say, “Well, here’s a system for doing spell checking in Bardi,” or “Here’s a system for doing spell checking in Swahili,” or whatever it is. Those papers tended to get read as, “Well, that’s only for Bardi,” or “That’s only for Swahili,” where the English ones – because English was treated as default – were taken as general. I made a pest of myself at a conference in 2019 – the conference is called “NAACL” – where I basically just, after every talk where people didn’t mention the name of the language, went to the microphone, introduced myself, and said, “Excuse me, what language was this on?” which is a ridiculous question, right, because it's obvious that it’s English. It’s sort of face threatening. It’s impolite because it’s “Why are you asking this question?” but it’s also embarrassing as the asker. Like, “Why would you ask this silly question?” But I was just making a point. Somewhere along the line, people dubbed that the “Bender Rule,” that you have to name the language that you’re working on, especially if it’s English.

Lauren: I really appreciate your persistence, and I appreciate people who codified it into the Bender Rule because now it’s actually less threatening for me to “I’m just gonna evoke the Bender Rule and just check if this was just on English.” You’ve given us a very clear model where we can all very politely make pests of ourselves to remind people that solving something for English or improving a process for English doesn’t automatically translate to that working for other languages as well.

Emily: Exactly. And I like to think that, basically, by lending my name to it, I’m allowing people to ask that question while blaming it on me.

Lauren: Great. Thank you very much. I do blame it on you all the time in the nicest possible way.

Emily: Excellent.

Lauren: This seems to be part of a larger process you’ve been working on. Obviously, there’s people working on computational processes for English, and you’re trying to be very much a linguist at them, but it seems like you also are spending a lot of time, especially in terms of ethical use of computational processes, trying to explain linguistics to computer scientists as well. How is that work going? Are computer scientists receptive to what linguistics has to offer?

Emily: Computer scientists are a large and diverse group in terms of their attitudes. They are an unfortunately un-diverse group in other ways. It’s an area of research and development that has a lot of money in it right now. There’s always new people coming in, and so it feels like no matter how much teaching of linguistics I do, there is still just as many people who don’t know about it as there ever were because new people are coming in. That said, I think it’s going well. I have written two books that I call, informally, “The 100 Things” books because they started off as tutorials at these computational linguistics conferences with the title, “100 Things You Always Wanted to Know About Linguistics But Were Afraid to Ask” and then subtitle, “For Fear of Being Told 1,000 More.” [Laughter]

Lauren: I mean, it’s not a mischaracterisation of linguists, that’s for sure.

Emily: We’re gonna keep linguisting at you, right. In both cases, the first one is about morphology and syntax. I basically just wrote down, literally, 100 things that I wish that people working in natural language processing in general knew about how language works because they tend to see language as just strings of words without structure. Worse than that, they tend to see language as directly being the information they’re interested in. I used to have really confusing conversations with colleagues in computer science here – people who were interested in gathering information from large collections of texts, like the web (this is a process called “information extraction”) – and when I finally realised that we were focusing on different things – I was interested in the language, and they were interested in the information that was expressed in the language – the conversations started making sense. I came up with a metaphor to help myself, which is, if you live somewhere rainy, can you picture you’ve got a rain-splattered window. You can focus on the raindrops, or you can focus on the scene through the window distorted by the raindrops. Language and its structures are the raindrops, which have an effect on what it is that you can see through the window, but it is very easy to look right through them and imagine you’re just seeing the information of the world outside. When I realised that, as a computational linguist, I’m interested in the raindrops, but some of these people working in computer language processing are just staring straight through them at the stuff outside, it helped me communicate a lot better.

Lauren: I feel like I’ve had a lot of conversations with computational scientists where they’re like, “Ah, we did a big semantic analysis of –” so there’s a process you can apply where you have a whole bunch of processes and algorithms that run, and it says, “80% of the people in this chat –” or this series of, I think they’re like, used to pulling things from Reddit. You could do that easily. It’s like, “80% of people in this hate chocolate ice cream.” I’d always be like, “Okay, but did you account for the person who’s like, ‘Oh my god, I hate how delicious this ice cream is’?” And they’re just like, “Ah…well, no, because we just used – ‘hate’ was negative so… ‘delicious’ was positive, so this person probably came out in the wash.” I’m like, “No, this is a person who extremely likes this ice cream,” and it’s also a very idiomatic, informal kind of English. I certainly wouldn’t write that in a professional reference for someone – “I hate how amazing this person is. You should hire them.” As a linguist, I’m really interested in these nuanced, novel edge cases, and as a computational scientist, they’re like, “Oh, we just hope we get enough data that they disappear in the noise.”

Emily: And the words are the data. The words are the meaning. There’s no separation there. There’s no structure to the raindrops. “If I have the words, I have the meaning” seems to be the attitude.

Lauren: Well, it’s great that you’re doing the work of slowly letting them down from that assumption.

Emily: We’re trying. Oh, one other thing about these books. The first one is morphology and syntax, the second one is semantics and pragmatics. In both of them – the second one is co-authored with Alex Lascarides – in both of them I have the concept index and the index of languages. Every time we have an example sentence, it shows up as an entry in the index for languages. There’s an index entry for English. Even though it indexes almost every single page in the book, it’s in there because English is a language.

Lauren: There’s this thing called the “Bender Rule.” I don’t know if you’ve heard of it, but I’m really glad that you’re following its principles. A lot of the work you’ve been doing is with a type of computational linguistics where you are building rules to process language and create useful computational outputs, but there are other models for how people can use language computationally.

Emily: I tend to do symbolic or rule-based computational linguistics. I’m really interested in “What are the rules of grammar for this language or for this phenomenon across languages? How can I encode them so that I can get the machine to test them, but also, I can still read them?” But a lot of work in computational linguistics, instead, uses statistical models, so building models that can represent patterns across large bodies of text.

Lauren: Oh, so that’s like predictive text on my mobile phone where it’s so used to reading all of the data that it has from other people’s text messages and my text messages that sometimes it can just predict its way through a whole message for me.

Emily: Yes, exactly. And in fact, I don’t know if this is so true anymore, but for a while, you could see that the models were all different on different phones. Remember we used to play that game where you typed in, “Sorry I’m late, I…” and then just picked the middle option over and over again, and people would get different, fun answers.

Lauren: Yes, and you’d get wildly different answers.

Emily: That reflects local statistics being gathered based on how you’ve been using that phone versus a model that it may have started with that was based on something more generic. That is, yes, an example of statistical patterns. You also see these – and this is fun in automatic transcriptions, like the closed captioning in TV shows if you’re thinking about live news or something where it wasn’t done ahead of time, and they get to a name of a person or a place which clearly wasn’t in the training data already represented in the model, and ridiculous, funny things come out because the system has to fall back to statistical patterns about what that word might have been, and it reveals interesting things about the training data.

Lauren: We used to always put the show through a first pass on YouTube, where Lingthusiasm is also hosted, before Sarah Dopierala came in and transformed our lives by being an amazing transcriptionist. For years, YouTube would transcribe “Lingthusiasm” – a word it has never encountered before, in its defence, as a computer – it would come up with “Link Susy I am” most often. We still occasionally refer to “Link Susy I am.” It was interesting when it finally, clearly, had enough episodes with Lingthusiasm with our manually updated transcripts that it got the hang of it, but that was definitely a case where it needed to learn. We definitely have a much higher success rate of perfect, first-time transcripts with Sarah.

Emily: That pattern that you saw happening with YouTube, that change, shows you that Google was absolutely taking your data and using it to train their models. In the podcast that I run, Mystery AI Hype Theater 3000, we have some phrases that are uncommon, and we do use a first-pass auto-transcriber. For example, we refer to the so-called AI models as “Mathy Maths.”

Lauren: “Mathy Maths,” yeah.

Emily: That’ll come out as like, “Matthew Math.”

Lauren: Oh, my good friend Matthew Math.

Emily: [Laughs] And the phrase “stochastic parrots” sometimes comes out as like, “sarcastic parrots” or things like that.

Lauren: And you and Alex both have, I would say, relatively standard North American English accents, which is really important for these models because, so far, we’ve just been talking about data where it’s found, and like, we’re linguists working with it and processing it before the computer gets to it. But with a lot of these new statistical models, it’s just taking what you give it. That means, as an Australian English speaker, I’m relatively okay, but it’s not as good for me as it is for a Brit or an American. And then if you’re a Singaporean English or Indian English speaker, even as a native English speaker, the models aren’t trained with you in mind as the default user. It just gets more and more challenging.

Emily: Exactly. Some of that is a question of “What could the companies training these models easily get their hands on?” But some of it is also a question of “Who were they designing for in the first instance? Whose data did they think of as ‘normal data’ that they wanted to collect?”

Lauren: These are deliberate choices that are being made.

Emily: Absolutely.

Lauren: With these statistical models, how do they differ from the grammars that you’ve created?

Emily: In a rule-based grammar system, somebody is sitting down and actually writing all the rules. Then when you try a sentence, and it doesn’t work as expected, you can trace through “What rule was used and shouldn’t have been used?” “What rule did you expect to have showing up in that analysis that wasn’t there?” and you can debug like that. The statistical models, instead, you build the model that’s the receptacle for the statistics. You gather a whole bunch of data, and then you use this receptacle model to process the data item by item and have it output according to its current statistics, likely answers, and then compare them to what’s actually there, and then update the statistics every time it’s wrong. You do that over and over and over again, and it becomes more and more effective at closely modelling the patterns in the data, but you can’t open it up and say, “Okay, this part is why it gives that output, and I want to change that.” It’s much more amorphous, in a sense, much more of a “black box” is the terminology that gets used a lot.

Lauren: In 2020 we were really lucky to have Janelle Shane join us on the show and walk us through one of these generative statistical models from that era. She generated some Lingthusiasm transcripts based off the first 40 or so episodes of transcripts that we had. When it generated transcripts, the model had this real fixation on soup. It got the intro to Lingthusiasm right because we say that 40 times across 40 episodes. We’ll be like, “Today, we’re talking about soup.” And we were like, “Janelle, what’s with the soup?” and she’s like, “I can’t tell you. It’s a black box in there,” literally referred to as hidden layers in the processing. So, because we don’t know why it was fixated on soup, there’s some great fake Lingthusiasm transcripts that we read – very soup-focused, very focused on a couple of major pieces of fan fiction literature, which, again, is classic fan fiction favourite IP because it read a bunch of fan fiction as well. You can make some guesses about why it’s talking about wizards a whole bunch, but you can’t make many guesses about why it’s talking about soup a whole bunch, and that makes it hard to debug that issue.

Emily: Hard to debug, yeah. But also, if you don’t know the original training data – so it sounds like she took a model that had been trained on some collection of data –

Lauren: Yes, so that it could be coherent with only those 40 transcripts.

Emily: Exactly, yeah. But if you don’t know what’s in that training data, then you are even more poorly placed to figure out “Why soup?”

Lauren: And since we did that episode, I think the big thing that’s changed is that the models are being given enough extra data that they’re no longer fixated on soup, but they’ve also just become easier for everyday people to use. Part of why we were really grateful for her to come on the show is that she walked us through the fact that she was still using scripting language to ingest those transcripts and to generate the new fabricated text. It all looked very straightforward if you’re a computer person, but you need to be a person who’s comfortable with scripting languages. That’s no longer the case with these new chat-based interfaces. That’s really changed the extent to which people interact with these models.

Emily: Yes, exactly. There’s a few things that have changed. One is there’s been some engineering that allowed companies to make models that could actually take advantage of very large data sets. There has been the collection of very large data sets in a not very consent-based fashion. Then there has been the establishment of these chat interfaces, as you say, where you can just go and poke at it and get something back. Honestly, the biggest thing that happened – the reason that all of a sudden everybody’s talking about ChatGPT and so-called “AI” – was that OpenAI set up this interface where anybody could go poke at it, and then they had a million people sharing their favourite examples. It was this marketing win for OpenAI and a big loss for the rest of us.

Lauren: I think the sharing of examples is really important as well because people don’t talk very often about the human curation that goes into picking funny or coherent or relevant examples. We had to junk so many of those fake transcripts to find the handful that were funny enough to pretend read and give a rendition of. When people are sharing their favourite things that come out of these machines, that’s a level of human interaction with them that I think is often missing but making it very easy for people to generate a whole bunch of content and then pick their favourite and share it has really normalised the use of these large language model ways of playing with language.

Emily: Exactly. If you were someone who’s not playing with it, or even if you are, most of the output you’re going to see is other people sharing their favourites. You get a very distorted view of what it’s doing.

Lauren: In terms of what it is doing, you know, we talked before about when a computer is doing translation between two languages, it’s not that it’s understanding, it’s replacing one string of texts with another string of text with these generative models that are creating this text that, on an initial read, reads like English. What are some of the limitations of these models?

Emily: Just like with machine translation, it’s not understanding. The chat interface encourages you to think that you are asking the chat bot a question, and it is answering you. This isn’t what’s happening. You are imputing a string, and then the model is programmed to come up with a likely continuation of that string. But a lot of its training data is dialogues, and so something that takes the form of a question provokes as a likely continuation an answer. But it hasn’t understood. It doesn’t have a database that it’s consulting. It doesn’t have access to factual information. It’s just coming out with a likely next string given what you put in. Any time it seems to make sense, it’s because the person using it is the one making sense of it.

Lauren: And because it’s had enough input because it basically took large chunks of the English speaking internet that there’s a statistical likelihood it is going to say something that is correct, but that is only a statistical chance. It doesn’t actually have the ability to verify its own factual information.

Emily: Exactly. I really dislike this term, but people talk about “hallucinations” with these models to describe cases where it outputs something that is not factually correct.

Lauren: Okay, why is “hallucination” not an appropriate word for you?

Emily: There’s two problems with it. One speaks to what you were just talking about which is if it says something that is factually correct, that is also just by chance. It’s always doing the same thing; it’s just that sometimes it corresponds to something we take to be true and sometimes it doesn’t. But also, if you think about the term “hallucination,” it refers to perceiving things that aren’t there. That suggests that these chat bots are perceiving things, which they very much aren’t. That’s why I don’t like the term.

Lauren: Fair enough. It’s a bit too human for what they’re actually doing, which is a pretty cool party trick, but it is just a party trick. One thing I’ve really appreciated about your critiquing of these systems is that you situate the linguistic issues around lack of actual understanding and real pragmatic capability, but you also talk about it in terms of these larger systems issues in terms of problems with the data and problems with the amount of computer processing it takes to perform this party trick, which are a combination of alarming issues. Can you talk to some of those issues and maybe some of the other issues that you’ve seen crop up with these models?

Emily: It’s so vexed. So, one place to start is a paper that I wrote with six other people in late 2020 called “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” and then the parrot emoji is part of the title.

Lauren: Excellent.

Emily: This paper became famous in large part because five of the co-authors were at Google, and Google decided, after approving it for submission to a conference, that, in fact, it should be either retracted or have their names taken off of it, and, ultimately, three of the authors took their names off, and two others got fired over it.

Lauren: Right, okay. That is big impact for a conference paper.

Emily: In part, the paper, in the aftermath of that, the impact was enhanced by the fact that the first author to get fired, Dr. Timnit Gebru, was masterful at taking the ensuing media attention and using it to shine a light on the mistreatment of black women in tech. She did an amazing job. Dr. Margaret Mitchell was the other one who got fired. It took a couple more months in her case.

Lauren: Oh, you mean, her name is not “Shmargaret Shmitchell”? [Laughter] That was a pseudonym?

Emily: That was a pseudonym, yeah. Who would’ve thought?

Lauren: I can’t believe it.

Emily: We wrote that paper because Dr. Gebru came to me in a Twitter DM in September of 2020 saying, “Hey, has anyone written about the problems with these large language models and what we should be considering?” because she was a research scientist in AI ethics at Google. It was literally her job to research this stuff and write about it. She had seen people around her pushing for ever bigger language models. This is 2020. So, the 2020 language models are small compared to the ones that we have now. Doing her job, she said, “Hey, we should be looking into what to look out for down this path.” I wrote back saying, “I don’t know of any such papers, but off the top of my head, here are the issues that I would expect to find in one based on independent papers,” so looking at things one by one in the literature. That was things like environmental impact, like the fact that they pick up biases and systems of oppression from the training data, like the fact that if you have a system that can output plausible-looking synthetic text that nobody is accountable for, that can cause various problems down the road when people believe it to be a real text. Then a beat or so later, I said, “Hey, this looks like a paper outline. Do you wanna write it?” That’s how the paper came to be. There’s two really important things that we didn’t realise at the time. One is the extent to which creating these systems relies on exploitative labour practices. That is both basically stealing everybody’s text without consent, but then also, in order to keep the systems from routinely outputting bigoted garbage, there’s this extra layer of so-called training where poorly paid workers, working long hours without psychological support, have to look at all the awful stuff and say, “That’s bad. That’s bad. This one’s okay,” and so on. This tends to be outsourced. There’s famously workers in Kenya who had been doing this. We didn’t know about that at the time, though some of the information was available, we could have.

Lauren: And it keeps outputting highly bigoted, disgusting text because it’s been trained on the internet, which as we all know is a bastion of enlightened and equal opportunity conversation.

Emily: Yes. But even if you go with only, for example, scientific papers, which are supposed to not be awful, guess what? There’s such a thing as scientific racism, and it is well embedded in the scientific literature. There was a large language model that Meta put together called “Galactica.” It came out right before ChatGPT. It was built as a way to access the world’s scientific knowledge, which of course it isn’t because if you take a whole bunch of scientific text, chop it up, and turn it into paper mâché, what you get out is not science but paper mâché, right. But anyway, people were poking at this and very quickly got it to say racist and otherwise terrible things in the guise of being scientific. I think it was the linguist Rikker Dockum who asked it something about stigmatisation of linguist varieties, and it came out with something about how African Americans don’t have a language of their own.

Lauren: Oh. A thing that we don’t even need to fact check because that is incorrect.

Emily: Anyway, you can certainly get to bigoted stuff starting with things less awful than the stuff that’s out there on the internet, but also, these models are trained on what’s out there on the internet. Labour exploitation was one thing that we missed. The other thing that we missed in the stochastic parrots paper was we had no idea that people were gonna get so excited about synthetic text. In the section where we actually introduce the term “stochastic parrot” to describe these machines that are outputting text with no understanding and no accountability, we thought we were going out on thin ice. Like, “People aren’t really gonna do this.” But now, it’s all over the place, and everyone is trying to sell it to you as something you might pay for.

Lauren: Yes, in many ways it’s a paper that was very prescient about a technology that has really become very quickly normalised, which creates a compounding effect in terms of data because now everyone’s sharing the synthetic text that they’re creating for fun, but people are also using it to populate webpages, and heavens knows a lot of spam in my inbox is getting longer because it can just be generated with these machines and processes as well. What used to be human-created data that it was trained on, now, if you try to scrape the internet, there’d be all of this synthetic machine-created language as well. It will just start training on its own output, which I’m not a computational linguist, but that just sounds like it's not a great idea.

Emily: If you think about what it is that you want to use these for, then ultimately, data quality really, really matters and, ideally, data quality that is not only good data but well-documented data, so you can decide, “Hey, is this good for my use case?” The ability to use the web as corpus to do linguistic studies is rapidly degrading. In fact, there’s a computational linguist named Robyn Speer who used to maintain a project called “wordfreq” which counted frequencies of words in web text over time. She has discontinued it because she says, “There’s too much synthetic garbage out there anymore. I can’t actually do anything reliable here. So, this is done.”

Lauren: So, it’s bad for computational linguistics. It’s bad for linguistics. And just to be clear, with these models, there’s no magic tweak that we can make to make them be factual.

Emily: No. Not at all. Because they’re no representing facts. They’re representing co-occurrences of words in text. Does this spelling happen a lot next to that spelling? Do they happen in the same places? Then they’re likely to be output in the same places that sometimes reflects things that happen in the world because sometimes the training text is things that people said because they were describing the actual world, but if it outputs something factual, it’s just by accident.

Lauren: So, your work on the stochastic parrots paper really set the tone for this conversation in linguistics. And you’ve been continuing to talk about the issues and challenges with these large language models and other kinds of generative models because, obviously, similar processes are used for image creation, and we’ve only really talked about the text-based stuff, and there’s a whole bunch of things happening with audio and spoken language as well. But there’ll be heaps more of that on Mystery AI Hype Theater 3000, and also in your book The AI Con, which is coming out in spring 2025.

Emily: Yes, I am super excited for this book. It was a delight to work with Dr. Alex Hanna, who is my co-host on Mystery AI Hype Theater 3000, to put together a book that is for popular audiences. One of the things that I think worked really well is that she’s a sociologist, and I’m a linguist, and so we have different technical terms. We were able to basically catch each other, it’s like, “I don’t really know what that word means,” and so the general audience isn’t gonna know what that word means. Hopefully, it will be nice and accessible. The subtitle, by the way – so the title, The AI Con, and the subtitle is “How to Fight Big Tech’s Hype and Create the Future We Want.” It’ll be out in May of 2025.

Lauren: And it seems like, given the limitations of these big models, there’s still lots of space for the kind of symbolic grammar-processing work that you do.

Emily: Yes, there’s definitely space for symbolic grammar-based work, especially if you’re interested in something that will get a correct answer, if it gets an answer at all. And you’re in a scenario where it’s okay to say, “No possibility here. Let’s send this on to a human,” for example. But also, there’s a lot of room for linguistics in designing better statistical natural language processing in understanding what it is that the person is going to be doing with the computer and how people relate to language so that we can design systems that are not misleading but, in fact, are useful tools.

Lauren: If you could leave people knowing one thing about linguistics, what would it be?

Emily: In light of this conversation, the thing that I would want people to know is that linguistics is the area that lets us zoom in on language and pick apart the rain drops and understand their structure so that we can then zoom back out and have a better idea of what’s going on with the language in the world.

Lauren: Thank you so much for joining us today, Emily.

Emily: It’s been an absolute pleasure.

[Music]

Lauren: For more Lingthusiasm and links to all the things mentioned in this episode, go to lingthusiasm.com. You can listen to us on all of the podcast platforms or lingthusiasm.com. You can get transcripts of every episode on lingthusiasm.com/transcripts. You can follow @lingthusiasm on all social media sites. You can get scarves with lots of linguistics patterns on them including IPA, branching tree diagrams, bouba and kiki, and our favourite esoteric Unicode symbols, plus other Lingthusiasm merch – like our “Etymology isn’t Destiny” t-shirts and Gavagai pin buttons – at lingthusiasm.com/merch.

My social media and blog is Superlinguo. Links to Gretchen’s social media can be found at gretchenmcculloch.com. Her blog is AllThingsLinguistic.com. Her book about internet language is called Because Internet.

Lingthusiasm is able to keep existing thanks to the support of our patrons. If you want to get an extra Lingthusiasm episode to listen to every month, our entire archive of bonus episodes to listen to right now, or if you just want to help keep the show running ad-free, go to patreon.com/lingthusiasm or follow the links from our website. Patrons can also get access to our Discord chatroom to talk with other linguistics fans and be the first to find out about new merch and other announcements. Recent bonus topics include behind-the-scenes on the Tom Scott Language Files with Tom and team, linguistics travel, and also xenolinguistics and what alien languages might be like. If you can’t afford to pledge, that’s okay, too. We really appreciate it if you can recommend Lingthusiasm to anyone in your life who’s curious about language.

Lingthusiasm is created and produced by Gretchen McCulloch and Lauren Gawne. Our Senior Producer is Claire Gawne, our Editorial Producer is Sarah Dopierala, our Production Assistant is Martha Tsutsui-Billins, and our Editorial Assistant is Jon Kruk. Our music is “Ancient City” by The Triangles.

Emily: Stay lingthusiastic!

[Music]

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

[autoacafiles]

Human Mechanoids

In the fourty years since the Transformers came to Earth, humanity has advanced a lot. They've invented a widespread information exchange platform. They've made screens that react to human touch. They've created high-speed transport on rail lines. And perhaps the most dramatic expression was the creation of the humanoid mechanical walker, more commonly known as a Mech.

While the idea of a robot war machine had been around for years prior, with the earliest example in fiction a topic of debate suggesting it happened at least as early as 1940, the arrival of Cybertronians on Earth led some scientists to start developing their own mechanical humanoids. At first they were relying on computer technology for their creations, such as Dr. Fujiwara's Project Yorutori, but between the comparative lack of adaptive ability of Algorithm-Processed Intelligence and the sheer number of incidents involving processing failures, it soon became clear that if humanity wanted to make something that can walk alongside their robotic protectors, they'll have to build something they can pilot.

The vast majority of mechs used today look fairly similar, designed on the same frame design that was made open source by its anonymous developers. While some developers op for larger cockpit sizes or differing proportions, even leaving out the external sensor array that some often mistake for a head, without the armor, most mechs look identical.

In the case of mechs, at least, clothes really do make the man.

And now the moment of truth - do mechs change the tide of war for humankind?

Turns out humanoid walkers stick out like a sore thumb, so the majority of nations opted for more classic forms of mechanized warfare, like jets and tanks.

That doesn't mean that mechs were sidelined, however.

As humans are easily entertained, mech combat became the modern gladiatorial games, with the exception of fatalities. Many aspiring engineers and pilots have made names for themselves in these mechanized melees, be it directly piloting their mechs or remotely controlling them.

Lately, however, some of the most dynamic and efficient of these mech fighters have been sponsored by a group calling themselves the Earth Defense Corp, which raises some eyebrows. Plus there are some reports of less-then-reputable groups using mechs for their own goals, including the Forever Knights and [REDACTED]...

[vexacarnivorous]

robotics: a few resources on getting started

a free open online robotics education resource! includes lots of lessons in video forms, which have transcripts and code sections that allow you to copy + paste from it. each lesson tells you the skill level assumed of you in order for you watch it (from general knowledge -> undergrad engineering). has lots of topics to choose from.

an open-source collection of exercises and challenges to learn robotics in a practical way. there are exercises about drone programming, about computer vision, about mobile robots, about autonomous cars, etc. It is mainly based on gazebo simulator and ROS. the students program their solutions in python.

each exercise is composed of (a) gazebo configuration files, (b) a web template to host student’s code and (c) theory contents.

with each free e-learning module you complete, you earn a certificate!

stanford university has this thing called stanford engineering everywhere which offers a few free courses you can take, including an introduction to robotics course!

some lists on github you can check out for more resources.

[gottawriteanegoortwo]

A Letter From Yancy

Another year on from the events of Heist and meeting you for the first time, Yancy wants to mark it. Easier said than done when you are in a spaceship millions of miles away.

But, strange things have happened on this ship.

Word count: 1,643

-

It was a quiet day on the Invincible, a rare relief for you. After your morning duties, you found you had free time to do things that you wanted to do.

That, of course, started with a nap. 

The nap was exactly what you needed after a busy week, and you felt rejuvenated to properly check in on various teams on the ship. You kept a professional air, but everyone seemed to know you were in the mood for casual chat. For once, it was nice to lower your guard a little and let the crew see you as a person rather than some mysterious, looming figure.

Well… Mostly. Gunther had gleefully pointed out how members of the ‘Captain Fan Club’ had been lingering around, peeking glances into whatever room you happened to be in. When you tried to look at them, the club members quickly spun around and tried to play it cool through random topics of conversation or pointing out different features like they were on duty. You weren’t exactly sure how a member of the ADS was supposed to give any professional opinions on what were actually oxygen pipes, but you left them to it.

Eventually, you gave them the slip and went down a small side corridor. The engineering department had a workshop dedicated to reparations and other projects. Mark had mentioned he had been building a prototype of a ‘cool idea’, and you would be lying if you said you weren’t curious about it.

-

“Captain! You’re just in time! Come here, come here!” Your Head Engineer was in high spirits as he grabbed you by the arm and yanked you across the workshop. “So I was talking to Burt a few weeks ago and - you know how he does the whole poetry thing? Turns out? He writes it! And not on the tech pads. On paper! I didn’t think anyone even did that these days!”

You raise an eyebrow, deciding not to point out that writing on paper was not fully extinct. Best to let him be excited as he showed off the large machine with tubes poking out of it.

“So it got me thinking. What if there was an automated postal system? Once you’ve written your letter and sealed it, you write the number code of the person you want to send it to. Then, you’d put the envelope in here.” A handle was pulled to reveal an opening. “From there, the computer scans the code, sorts it, and sends it zipping to its destination! The tubes would go in different directions, with the aim to bring it directly to the person’s cabin. Or! If you’re out, you can pick it up from over here.” He waved at you to follow him, where there was the end of a tube just over a small platform. “You type in your code here, scan your hand here, and it’ll send it right back here. Like this.” Stepping around you, Mark followed the steps. Three short, aggressive beeps followed, accompanied by an automated message saying there was no post available. “You try! I’ve only tested it on my code so it’ll be good practice to see if it will recognise anyone else.”

You nod, and follow Mark’s instructions. First, the code. Then, the scan.

One long, less aggressive beep was heard. 

“That’s not right-”

‘Please Wait. Your post will be with you shortly.’

“Hold on. There shouldn’t be anything!” Mark put an arm out, stepping in front of you protectively as you both waited to see what would arrive. A tube to the left rattled. The main body of the machine lit up in a sequence of lights. Mark braced himself as the tube in front of you shook and spat out… A letter.

You lean forward, peering over Mark’s shoulder as you stare, dumbfounded at the post that was successfully delivered.

“Captain…? I think this is a trap. What do we do?”

Two options appeared before you: destroy the letter, or examine it.

Curiosity got the better of you as you moved around Mark to open the hatch. There was a brief, childish squabble as he attempted to block you from getting there, but your strength guaranteed that you could simply lift him up and place him behind you.

“Er… Sorry, Captain. You do know what’s best…”

Satisfied that he wouldn’t cause another ruckus, you finally claimed the letter and examined the envelope. As expected, it was addressed to you, but not how Mark said it should be. Rather, it was for your old address on Earth. Had you been there still, it would have arrived safely. A different handwriting had your number code in the top corner, just beside the stamps, with a small moustache drawn underneath.

“So… Is it safe?”

You nodded as you reread your old address. The handwriting was messy and scratchy, but it was so familiar. You had seen it a dozen times before. 

The question is… How did a letter from Yancy get here?

-

With Mark distracted on his mission to figure out who onboard sent the letter, you sat at his desk and opened the envelope. Everything was untouched, meaning that the second sender didn’t peek inside. Yancy knew about ‘space camp’ and how you were inaccessible, yet… he wrote anyway?

Your name was on the top of the creased, lined paper. To the right, you could see it was dated from the start of October. Everything was the same as always - from the scratchy pencil he over-sharpened, to the bad spelling and grammar. It was quintessentially ‘Yancy’. 

-

I don’t even know why I’m doing this. You isn’t living here. You moved out ages ago. But i has been thinking.  It’s the middle of the night here. Still in Happy Trails, still on the slow path to a parole hearing. And I has been looking at the sky. There ain’t many stars out there but they make me think of you. You doing okay out there? Bet you is so fucking far away by now. Maybe you found new planets or something. Doubt this little rock is even on the back of your mind but… it’s been quiet. Not being able to see you, I mean.  Look, I gotta be honest. It’s october, and that’s the month when we first met. It’s hard to let the month pass and not mark that somehow, even if its through a shitty letter that ill get back in a week or two. Things ain’t easy right now. The parole thing? I know its the right thing to do, but it’s intimidating now that im in the middle of it. When that hearing comes itll be the first time i has seen my brother and sisters since the incidents. Ain’t looking forward to that. And they can say that they don’t think me fit to leave too. Not that i blame them. Dont think they can get my sentence upgraded to the death penalty but theres a real big chance that im gonna be rejected.  I know i should give up while im ahead and save the embarasmant. But then i gets to thinking that it ain’t the right thing for me no more. I might fuck up and get refused but i gets to say i tried. That’s something, right? And anyway, i ain’t letting you down. You believe in me. You always said you believe in me when you came to visitation. Giving up is quitters talk anyway, and im no coward. You dont get scars like mine from hiding all scared!! But i aint that kid no more. The person who did those things is me but isnt me. Does that make sense? Hes me, but im not him. I think ive grown up more than i realised. Im not that trapped kid. Im Yancy, and im going to do right. Once i get out……. Itll be a good thing. Maybe I could get up to where you is. Or maybe by then you is back and maybe we could… do something. I dunno. Im still proud of you for all you is doing, even when you is having one of them bad days.. Dont forget that. Except if you is a nosy shit who this letter ain’t for. You can fuck right off. Or send me a letter back so i can see whether i should be proud of you too. Oh! Remember. Back or side of the knees is a GRATE weak point if you needs a quick escape. Not that i want you to get in trouble or nothing. Just giving some good advice! Wait. I should probably go back to bed. fuck. Hope you is safe. Yancy.

-

You couldn’t stop the wistful smile as you finished the letter. At the bottom, you noticed an addition written in pen, the same one that was used to address the envelope.

-

PS. Nearly sent this in but someone brought in one of them instant camera things! Asked them to take a photo of me so you dont forget this handsome mug!

-

That caught your interest, and you lifted the envelope to peer in. Sure enough, there was a Polaroid tucked away at the bottom that was swiftly retrieved.

Yancy was certainly a little older than you remembered. He still had a pompadour style, but it wasn’t held back as tightly and allowed the curls to loosely fall. His eyes were squeezed shut to accompany the wide, goofy smile and two thumbs up. You chuckled at the conversation that must have happened when the photo was taken about what pose to do. Instead of one to remind you of how tough he was, he instead opted for one that proved that, despite everything, he was still a friend you valued.

You were proud of him too, even if you couldn’t tell him.

[bitstitchbitch]

I am required to use ai for a few assignments in my class. The professor has admitted that she wanted to make the whole class ai based but got pushback from other professors which. Thank god that would have been so annoying and I need this class for my research. So instead she did an introductory assignment and then the final project will be on a specific application of ai

which I’m not totally against depending on how it is done. I’m not using ai in my coding like she suggests (ugh), but I do think that ai has use in advanced research, so I can see the value of teaching graduate electrical engineering students how to use AI. And the topic (computational electromagnetics) is very computationally heavy and could potentially be improved with AI - though I don’t trust chatGPT to have a good database.

I still hate it though.

[geonppangi]

zb1 as ontario universities + programs

yes this is an incredibly niche topic but this has now become my chosen coping mechanism for uni decisions

sung hanbin - queens health science

such a pretty campus

a lot of historic buildings and freshly manicured lawns that i think would fit hanbin perfectly

and like imagine him with soft academia

it’s just a really nice school and its health sci program is one of the best in ontario

like it’s very neutral good

and he feels like he would make a really good doctor, like pov sung hanbin is your family doctor LMFAO

and like i think he’d do a great job, probably has the prettiest notes and always does his part during group projects

zhang hao - queens commerce

ofc i had to keep haobin together

but dw neither is sacrificing their education for the other person (remember kids the only person who can get you your degree is you)

queens commerce is also one of the best business schools in ontario

ik that zhang hao is a music kid but that felt like cheating so this is an alternative!

out of all of zb1 he feels like the most business type person?

like he’s so smart and always plans ahead

so i feel like he could be successful in anything he tried but especially business because it’s so lucrative and opens the door to so many paths

like hanbin i think he suits the clean and historic school vibes

they both have a very curated?? for lack of better word image and i think they’d look like they were in a photoshoot every time they step out on campus

park gunwook - uoft criminology, law and society

university of toronto!!

i’m just saying, debate is literally the gateway to law

and i’d be willing to bet that he has done or considered mock trial so i think this would fit super well

he knows a lot about history and philosophy, so i can see him being interested in studying a humanity

in canada, you need to get a bachelors before going to law school, so a lot of people do something related

this is basically as good as you can get

i also think gunwook would do well in uoft

it can be a kind of isolating experience because the school is so big and a lot of people commute so there isn’t much of a social scene

but as the resident social butterfly, i feel like gunwook would be able to establish a good circle

and also i think he would like the energy of the city

there’s always something to do and both city hall and parliament are literally right next to the campus

alternatively, i considered university of ottawa, which is good for its social science since you know, it’s literally in the capital city of canada so its law program is really good

but honestly there’s not much undergrad wise

i could see him going for graduate school though

i don’t know lawyer gunwook just feels right

he’s good at making decisions, works well under pressure, has the fundamentals down, and is a hard worker so i can definitely see him making it into law school

kim taerae - waterloo computer science

listen

listen

i know that taerae isn’t that genre of nerd

but the glasses

him being losercore

like you can’t tell me he wouldn’t go to the most loser of all loser schools in ontario

(btw waterloo is not bad, it’s actually really good for engineering and comp sci but also everybody there has no life hence the loser allegations)

(also because if you do eng or comp sci you are inherently a loser i’m so sorry pls continue to run tumblr though)

but yeah i can definitely see him doing comp sci maybe like ui ux stuff

actually the more i think about it the more it makes sense for me

he’s also the only thinking (mbti) in zb1 and his fashion sense would fit in perfectly with the rest of the department

seok matthew - mcmaster nursing

like hanbin, i really do see him doing something healthcare related

mcmaster is second for nursing, after uoft, but i don’t think matthew would like toronto as much

from what i’ve heard, vancouver is a lot greener, more laid back, and hiking culture is big there

toronto kind of offers none of that, with the addition of pretty bad air quality comparatively (still pretty good though because canada’s pretty sparsely populated)

mcmaster though is in a smaller town that’s essentially just a university town and also has a trail literally right on campus

it’s a smaller, more tight knit community and nursing is a pretty small program so it’d be even closer

i think he would have a really good time with mcmaster’s social scene

it’s also right by lake ontario and has pretty decent weather

ricky shen - uoft rotman

another business student, are we surprised?

i also considered art but our only really good art school is ocad and as far as i know it doesn’t focus on traditional art as much

so anyways mr young and rich tall and handsome is going to go the business route

uoft is really international student friendly, mostly because they accept a lot of international students and because toronto is also pretty diverse

like you can truly get any type of cuisine here and we have a really big asian population too

so as a person who also immigrated here, i’d say it’s not a terrible transition

definitely the most diverse of any of the ontario schools

once again, the social life at uoft is not great but usually the business schools are better

han yujin - still in high school

han yujin - university of guelph kinesiology

since yujin’s currently studying dance at his school, i thought that kinesiology would be a good fit

for those who don’t know, it’s essentially like sports science?? or studying the way people move and so forth

he also plays soccer and just gives off like sporty vibes?

like the quiet kid who is surprisingly the fastest on the team

once again, uoft is technically the best for this, but it’s kind of a scary place to go into alone so i can see yujin choosing a quieter school

i’d say guelph is even more closely knit than mcmaster by virtue of it being a smaller city and a smaller school

they have a solid health science program though and good student support too, which i think he’d benefit from

kim jiwoong - uoft english literature

so

i know that he is an actor

and i considered having him go the performing arts route

however, i know nothing about those schools and once again i think it’d be too easy

so to make my life harder, instead of having jiwoong perform shakespeare plays, i’m making him read and analyze them instead

he gives off like that one english teacher you had who changed your life and helped you rekindle your love of reading after your gifted burnout yknow

but yeah i see him going into english lit to go be a teacher

and uoft would be perfect for that because it not only has i believe one of the largest libraries in north america, but also a really big teachers college once he’s done getting his bachelors

he also seems like he would be able to fit in in any environment, so i don’t see uoft’s size or atmosphere being a problem for him

kim gyuvin - toronto metropolitan university performance: acting

i saved the best for last

ik i literally just said that i wasn’t going to do performing arts for jiwoong but this is different

something about his performance during the musical segment of boys planet changed the way i viewed him forever

like??????? how is he that good for like the most random challenge ever

i think he’d be really good at acting seriously so anyways!

also tmu (formerly known as ryerson) is just a very fun vibe

it’s in the more downtown area of toronto compared to uoft and right by the entertainment district

the nightlife is so good and gyuvin would probably be the life of the party

tmu’s also like uoft’s funkier younger cousin so i think that fits his vibe well

[mantisgodsdomain]

Tags from this post segmented off into a post of their own (if you don't want to click that link, then just the phrase "ancient iterator dick discourse" should suffice. We have strong opinions about iterator construction because we live with a guy who manages large-scale construction projects and such like this for a living and it's really hard to listen to half a dozen calls about the legalities of one (1) bridge and budgeting and materials construction and the legally mandated fund for art on major construction projects and then just buy in to hundreds of years of iterator designs using a single thing made by one guy without changing a single piece of it or at the very least having eighty engineers arguing the shit out of it.

Full transcript of text below cut.

#we speak #realistically it would just require more specific tinkering w what we choose to include but we still think the dickscourse is funny #it's the image of a bunch of ancient monks gathering around to very seriously debate decisions with the upcoming iterator project #and then the whiteboard is just like. “ITERATORS: dick or no?” #(vital context: we got hung up on the semantics of people giving their iterators actual genitals in smut) #(as the existence of that on the puppet implies that someone had to design and manufacture and ship that shit for the finished iterator) #(and the general aura of the ancients instantly catapults this to fucking hilarious because it implies job titles like “dick director”) #(and work emails about iterator pipe written in the exact same cadence as all of the ancient correspondence we see in-game) #we dont think a lot of people designing iterators really Get the sheer amount of semantics and construction and effort and PEOPLE #that go into a project of the iterator's scale #especially when hundreds of them have been constructed! theres gonna be a whole ass trail of design changes and iterations! #youre gonna have hundreds of years of iterators being designed and technology coming into fashion and out of fashion #and things being integrated and things becoming obsolete and things being more or less practical as time goes on! #you cant really say that All Iterators have a trait because the sheer scale and timeframe theyre built on means thats near impossible #our windows 95 writing computer has different construction and deeply different design to a laptop from 2023 #despite them technically being the same type of technology #you expect tech developed hundreds of years apart to be The Same? absolutely not. theres gonna be eight trillion weird design quirks #accumulated both in the construction process and in the continued design refinement and improvement stage #...which is to say that you can and should write what u want but if youre gonna include pleasure inducing wires then we want like #a 40k word essay on how this got into the design how it wound up in future designs what function the wires perform that makes them Like That #and so on and so forth #we admire the confidence and ingenuity of the people who want to fuck the robots but we cannot get into their fantasies with good conscience #we live in the same house as an engineer who manages largescale construction and we also know too much about designing technology #...we should segment these tags into a separate post or something. we've gone WAY off-topic.

[tara-the-star]

🧃 + 🌵 + 🔪 for the ask game!!

🧃 ⇢ share some personal lore you never posted about before

pretty sure everyone here knows me pretty well. i have succesfully failed at being nonchalant and mysterious.

well i wanna do mechanical engineering and psychology !! but of course i'm being very heavily pressure into doing computer science (stopping your child from doing mechanical engineering is wild to me)

🌵 ⇢ share the link to a playlist you love

here's my jeanee playlist!! one of my go-to faves <333

🔪 ⇢ what's the weirdest topic you researched for a writing project?

I don't think ive ever researched anything weird because i usually stick to writing what I know. I did look up popular models of italian military aircrafts in 1948 once!

ask game!

[bigleapblog]

Your Guide to B.Tech in Computer Science & Engineering Colleges

In today's technology-driven world, pursuing a B.Tech in Computer Science and Engineering (CSE) has become a popular choice among students aspiring for a bright future. The demand for skilled professionals in areas like Artificial Intelligence, Machine Learning, Data Science, and Cloud Computing has made computer science engineering colleges crucial in shaping tomorrow's innovators. Saraswati College of Engineering (SCOE), a leader in engineering education, provides students with a perfect platform to build a successful career in this evolving field.

Whether you're passionate about coding, software development, or the latest advancements in AI, pursuing a B.Tech in Computer Science and Engineering at SCOE can open doors to endless opportunities.

Why Choose B.Tech in Computer Science and Engineering?

Choosing a B.Tech in Computer Science and Engineering isn't just about learning to code; it's about mastering problem-solving, logical thinking, and the ability to work with cutting-edge technologies. The course offers a robust foundation that combines theoretical knowledge with practical skills, enabling students to excel in the tech industry.

At SCOE, the computer science engineering courses are designed to meet industry standards and keep up with the rapidly evolving tech landscape. With its AICTE Approved, NAAC Accredited With Grade-"A+" credentials, the college provides quality education in a nurturing environment. SCOE's curriculum goes beyond textbooks, focusing on hands-on learning through projects, labs, workshops, and internships. This approach ensures that students graduate not only with a degree but with the skills needed to thrive in their careers.

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The role of computer science engineering colleges like SCOE is not limited to classroom teaching. These institutions play a crucial role in shaping students' futures by providing the necessary infrastructure, faculty expertise, and placement opportunities. SCOE, established in 2004, is recognized as one of the top engineering colleges in Navi Mumbai. It boasts a strong placement record, with companies like Goldman Sachs, Cisco, and Microsoft offering lucrative job opportunities to its graduates.

The computer science engineering courses at SCOE are structured to provide a blend of technical and soft skills. From the basics of computer programming to advanced topics like Artificial Intelligence and Data Science, students at SCOE are trained to be industry-ready. The faculty at SCOE comprises experienced professionals who not only impart theoretical knowledge but also mentor students for real-world challenges.

Highlights of the B.Tech in Computer Science and Engineering Program at SCOE

Comprehensive Curriculum: The B.Tech in Computer Science and Engineering program at SCOE covers all major areas, including programming languages, algorithms, data structures, computer networks, operating systems, AI, and Machine Learning. This ensures that students receive a well-rounded education, preparing them for various roles in the tech industry.

Industry-Relevant Learning: SCOE’s focus is on creating professionals who can immediately contribute to the tech industry. The college regularly collaborates with industry leaders to update its curriculum, ensuring students learn the latest technologies and trends in computer science engineering.

State-of-the-Art Infrastructure: SCOE is equipped with modern laboratories, computer centers, and research facilities, providing students with the tools they need to gain practical experience. The institution’s infrastructure fosters innovation, helping students work on cutting-edge projects and ideas during their B.Tech in Computer Science and Engineering.

Practical Exposure: One of the key benefits of studying at SCOE is the emphasis on practical learning. Students participate in hands-on projects, internships, and industry visits, giving them real-world exposure to how technology is applied in various sectors.

Placement Support: SCOE has a dedicated placement cell that works tirelessly to ensure students secure internships and job offers from top companies. The B.Tech in Computer Science and Engineering program boasts a strong placement record, with top tech companies visiting the campus every year. The highest on-campus placement offer for the academic year 2022-23 was an impressive 22 LPA from Goldman Sachs, reflecting the college’s commitment to student success.

Personal Growth: Beyond academics, SCOE encourages students to participate in extracurricular activities, coding competitions, and tech fests. These activities enhance their learning experience, promote teamwork, and help students build a well-rounded personality that is essential in today’s competitive job market.

What Makes SCOE Stand Out?

With so many computer science engineering colleges to choose from, why should you consider SCOE for your B.Tech in Computer Science and Engineering? Here are a few factors that make SCOE a top choice for students:

Experienced Faculty: SCOE prides itself on having a team of highly qualified and experienced faculty members. The faculty’s approach to teaching is both theoretical and practical, ensuring students are equipped to tackle real-world challenges.

Strong Industry Connections: The college maintains strong relationships with leading tech companies, ensuring that students have access to internship opportunities and campus recruitment drives. This gives SCOE graduates a competitive edge in the job market.

Holistic Development: SCOE believes in the holistic development of students. In addition to academic learning, the college offers opportunities for personal growth through various student clubs, sports activities, and cultural events.

Supportive Learning Environment: SCOE provides a nurturing environment where students can focus on their academic and personal growth. The campus is equipped with modern facilities, including spacious classrooms, labs, a library, and a recreation center.

Career Opportunities After B.Tech in Computer Science and Engineering from SCOE

Graduates with a B.Tech in Computer Science and Engineering from SCOE are well-prepared to take on various roles in the tech industry. Some of the most common career paths for CSE graduates include:

Software Engineer: Developing software applications, web development, and mobile app development are some of the key responsibilities of software engineers. This role requires strong programming skills and a deep understanding of software design.

Data Scientist: With the rise of big data, data scientists are in high demand. CSE graduates with knowledge of data science can work on data analysis, machine learning models, and predictive analytics.

AI Engineer: Artificial Intelligence is revolutionizing various industries, and AI engineers are at the forefront of this change. SCOE’s curriculum includes AI and Machine Learning, preparing students for roles in this cutting-edge field.

System Administrator: Maintaining and managing computer systems and networks is a crucial role in any organization. CSE graduates can work as system administrators, ensuring the smooth functioning of IT infrastructure.

Cybersecurity Specialist: With the growing threat of cyberattacks, cybersecurity specialists are essential in protecting an organization’s digital assets. CSE graduates can pursue careers in cybersecurity, safeguarding sensitive information from hackers.

Conclusion: Why B.Tech in Computer Science and Engineering at SCOE is the Right Choice

Choosing the right college is crucial for a successful career in B.Tech in Computer Science and Engineering. Saraswati College of Engineering (SCOE) stands out as one of the best computer science engineering colleges in Navi Mumbai. With its industry-aligned curriculum, state-of-the-art infrastructure, and excellent placement record, SCOE offers students the perfect environment to build a successful career in computer science.

Whether you're interested in AI, data science, software development, or any other field in computer science, SCOE provides the knowledge, skills, and opportunities you need to succeed. With a strong focus on hands-on learning and personal growth, SCOE ensures that students graduate not only as engineers but as professionals ready to take on the challenges of the tech world.

If you're ready to embark on an exciting journey in the world of technology, consider pursuing your B.Tech in Computer Science and Engineering at SCOE—a college where your future takes shape.