#computer science principles

[cheesemenace]

Taking the AP comp sci principles test on Wednesday. I'm gonna eat it up (unlike my calc exam)

Everyone should fear me (the random aaaa questions about hardware and software is gonna get me please help please help oh god they are gonna get me)

I code so well 😈😈😈😈

[prokopetz]

What I really appreciate about The Talos Principle 2 is that big chunks of its writing genuinely read like they were written by someone who's personally had to justify the discipline of philosophy to a STEM major. "There exists an implicit moral algorithm in the structure of the cosmos, but actually solving that algorithm to determine the correct course of action in any given circumstance a priori would require more computational power than exists in the universe. Thus, as we must when faced with any computationally intractable problem, we fall back on heuristic approaches; these heuristics are called 'ethics'." is a fascinating way of framing it, but then I ask why would you explain it like that, and every possible answer is hilarious.

[lost-coder]

The Zen of Python

Beautiful is better than ugly.

Explicit is better than implicit.

Simple is better than complex.

Complex is better than complicated.

Flat is better than nested.

Sparse is better than dense.

Readability counts.

Special cases aren't special enough to break the rules.

Although practicality beats purity.

Errors should never pass silently.

Unless explicitly silenced.

In the face of ambiguity, refuse the temptation to guess.

There should be one—and preferably only one—obvious way to do it.

Although that way may not be obvious at first unless you're Dutch.

Now is better than never.

Although never is often better than *right* now.

If the implementation is hard to explain, it's a bad idea.

If the implementation is easy to explain, it may be a good idea.

Namespaces are one honking great idea -- let's do more of those!

[pixelverseart]

Took my AP Computer Science Principles Exam today. It was tiring.

I just hope that i get a 5.

[kelemengabi]

[eleventeenratsinaflannelsuit]

Exerpts from my absolute onslaught of “clarifying” comments on my “simple” calculator assignmeng for AP compsci principles (it is over 182 lines long)

(we’re starting our python unit. I already know basically everything that will be taught in this unit. wtf (teacher said ill get to code fish game as a project if i want /pos))

#unrelated but have you seen that one code of a like, C# or java coded calculator that can add/subtract/divide/multiply any two individual numbers up to 60-something and the coder did it by coding something else to hard code every operation. like, if num1=3 and num2=5 and operation=addition answer=8 type of thing? terrifying. I want to do it. (i looked for the code but couldn't find it (sad))

#I need blank lines and I can't be bothered to check if /n works in python. Also this is funnier [in relation to me using 'print("")' to get blank lines on the terminal]

#I don't want to code in fault tolerance and that stuff so... yeah if you do something wrong the server is down

#help how do i python for loop with a variable

#lol i don't need python for loop here

#etc.

#kindness matters :)

#(extra or statements to account for user error (i don't want to figure out how to ignore whether a letter is caps or lowercase so i will instead code more. This is my mantra.))

#(you know it's sad that python doesn't use semicolons to seperate commands because in languages that do use it I can just code EVERYTHING on one line and the camp counselors didn't like that but they couldn't do anything because it was technically correct lol)

[masterclasspace]

The editions of our AP Computer Science Principles course will not change among its many tracks. Nonetheless, some of the exercises in the various AP CSP versions have a unique viewpoint to improve comprehension and application. For example, although the cybersecurity counterpart may focus on password validation methods, an AP CSP JavaScript course might include a general conditional activity.

[swethaksblog]

💻 CS Principles offers a broad intro to computing concepts, while CS A dives deeper into Java and complex programming. 🧠📘

[techtoio]

Quantum Mechanics in Modern Technology: The Science Explained

Introduction

Welcome to TechtoIO! Today, we explore the intriguing world of quantum mechanics and its profound impact on modern technology. Quantum mechanics, once a purely theoretical field, is now driving innovations that are transforming industries. But what exactly is quantum mechanics, and how is it applied in today’s tech? Let’s break down the science behind this fascinating topic. Read to continue

[jcmarchi]

A better way to control shape-shifting soft robots

New Post has been published on https://thedigitalinsider.com/a-better-way-to-control-shape-shifting-soft-robots/

A better way to control shape-shifting soft robots

Imagine a slime-like robot that can seamlessly change its shape to squeeze through narrow spaces, which could be deployed inside the human body to remove an unwanted item.

While such a robot does not yet exist outside a laboratory, researchers are working to develop reconfigurable soft robots for applications in health care, wearable devices, and industrial systems.

But how can one control a squishy robot that doesn’t have joints, limbs, or fingers that can be manipulated, and instead can drastically alter its entire shape at will? MIT researchers are working to answer that question.

They developed a control algorithm that can autonomously learn how to move, stretch, and shape a reconfigurable robot to complete a specific task, even when that task requires the robot to change its morphology multiple times. The team also built a simulator to test control algorithms for deformable soft robots on a series of challenging, shape-changing tasks.

Play video

Their method completed each of the eight tasks they evaluated while outperforming other algorithms. The technique worked especially well on multifaceted tasks. For instance, in one test, the robot had to reduce its height while growing two tiny legs to squeeze through a narrow pipe, and then un-grow those legs and extend its torso to open the pipe’s lid.

While reconfigurable soft robots are still in their infancy, such a technique could someday enable general-purpose robots that can adapt their shapes to accomplish diverse tasks.

“When people think about soft robots, they tend to think about robots that are elastic, but return to their original shape. Our robot is like slime and can actually change its morphology. It is very striking that our method worked so well because we are dealing with something very new,” says Boyuan Chen, an electrical engineering and computer science (EECS) graduate student and co-author of a paper on this approach.

Chen’s co-authors include lead author Suning Huang, an undergraduate student at Tsinghua University in China who completed this work while a visiting student at MIT; Huazhe Xu, an assistant professor at Tsinghua University; and senior author Vincent Sitzmann, an assistant professor of EECS at MIT who leads the Scene Representation Group in the Computer Science and Artificial Intelligence Laboratory. The research will be presented at the International Conference on Learning Representations.

Controlling dynamic motion

Scientists often teach robots to complete tasks using a machine-learning approach known as reinforcement learning, which is a trial-and-error process in which the robot is rewarded for actions that move it closer to a goal.

This can be effective when the robot’s moving parts are consistent and well-defined, like a gripper with three fingers. With a robotic gripper, a reinforcement learning algorithm might move one finger slightly, learning by trial and error whether that motion earns it a reward. Then it would move on to the next finger, and so on.

But shape-shifting robots, which are controlled by magnetic fields, can dynamically squish, bend, or elongate their entire bodies.

The researchers built a simulator to test control algorithms for deformable soft robots on a series of challenging, shape-changing tasks. Here, a reconfigurable robot learns to elongate and curve its soft body to weave around obstacles and reach a target.

Image: Courtesy of the researchers

“Such a robot could have thousands of small pieces of muscle to control, so it is very hard to learn in a traditional way,” says Chen.

To solve this problem, he and his collaborators had to think about it differently. Rather than moving each tiny muscle individually, their reinforcement learning algorithm begins by learning to control groups of adjacent muscles that work together.

Then, after the algorithm has explored the space of possible actions by focusing on groups of muscles, it drills down into finer detail to optimize the policy, or action plan, it has learned. In this way, the control algorithm follows a coarse-to-fine methodology.

“Coarse-to-fine means that when you take a random action, that random action is likely to make a difference. The change in the outcome is likely very significant because you coarsely control several muscles at the same time,” Sitzmann says.

To enable this, the researchers treat a robot’s action space, or how it can move in a certain area, like an image.

Their machine-learning model uses images of the robot’s environment to generate a 2D action space, which includes the robot and the area around it. They simulate robot motion using what is known as the material-point-method, where the action space is covered by points, like image pixels, and overlayed with a grid.

The same way nearby pixels in an image are related (like the pixels that form a tree in a photo), they built their algorithm to understand that nearby action points have stronger correlations. Points around the robot’s “shoulder” will move similarly when it changes shape, while points on the robot’s “leg” will also move similarly, but in a different way than those on the “shoulder.”

In addition, the researchers use the same machine-learning model to look at the environment and predict the actions the robot should take, which makes it more efficient.

Building a simulator

After developing this approach, the researchers needed a way to test it, so they created a simulation environment called DittoGym.

DittoGym features eight tasks that evaluate a reconfigurable robot’s ability to dynamically change shape. In one, the robot must elongate and curve its body so it can weave around obstacles to reach a target point. In another, it must change its shape to mimic letters of the alphabet.

In this simulation, the reconfigurable soft robot, trained using the researchers’ control algorithm, must change its shape to mimic objects, like stars, and the letters M-I-T.

Image: Courtesy of the researchers

“Our task selection in DittoGym follows both generic reinforcement learning benchmark design principles and the specific needs of reconfigurable robots. Each task is designed to represent certain properties that we deem important, such as the capability to navigate through long-horizon explorations, the ability to analyze the environment, and interact with external objects,” Huang says. “We believe they together can give users a comprehensive understanding of the flexibility of reconfigurable robots and the effectiveness of our reinforcement learning scheme.”

Their algorithm outperformed baseline methods and was the only technique suitable for completing multistage tasks that required several shape changes.

“We have a stronger correlation between action points that are closer to each other, and I think that is key to making this work so well,” says Chen.

While it may be many years before shape-shifting robots are deployed in the real world, Chen and his collaborators hope their work inspires other scientists not only to study reconfigurable soft robots but also to think about leveraging 2D action spaces for other complex control problems.

[professortechnical]

i'd like to feel excited knowing that I managed to finish the detailed outline for my recent story as well as the "base code" for my performance task.

but now that means i have to actually finish them

[tanadrin]

i think one of the most interesting things about generative ai is not just that it was a pretty unexpected thing--seems like very few people were sitting around ten years ago imagining we would have this technology in 2025--but that i think it is also pretty difficult for people who aren't well versed in the technical background to trace how we got here from there, you know? like when the internet became a big thing, i think if you were familiar with the concept of the telephone or even just one computer networked to another somewhere else you could grok the fundamental concept: it's just a bunch of electronic machines connected to a bunch of other electronic machines; it's an extremely cool piece of engineering, but packet-switching is not (at least at the nontechnical level) that conceptually different from a telephone exchange.

and you could extend this backward pretty far. electronic computers from mechanical ones; the telephone from the telegraph. likewise future developments that emerged from the internet: smart phones are not to conceptually different from computers and radios, they just ("just") are very sophisticated devices that use new versions of those older technologies. and a lot of technology is like that. if you understand a cannon you can understand the basic principle of the space shuttle.

gen ai seems... not like that? that kind of, i guess, statistical approach to problems in computer science wasn't invented in the 2010s, i gather it's a lot older, but it was mostly a niche research topic, i think? and there were some nifty demos of still pretty crude versions of stuff like deep dream, but it's not like we'd had twenty years of this kind of stuff being part of the wider milieu of technology in everyday use before gen ai started getting good. it's weird! it wasn't an accident, people had been working on this stuff for a while. but in some ways it feels like the discovery of antibiotics, one of those medical breakthroughs that happens just as kind of an a priori discovery of something useful out in the world.

and because computers are already omnipresent in our lives, unlike a medical breakthrough, it's suddenly everywhere. and yeah often it's used or promoted in ways that are pretty obnoxious, but even still, no wonder it provokes feelings of dislocation and anxiety. technologies which emerged much more gradually into society have provoked just as much unease. and the idea that it might keep getting more useful, as much more useful as computers have gotten over the last, say, 25 years--that's just hard to fathom from any angle. i think it's as hard to estimate what kind of social impact that would have as it would have been to anticipate all the social impacts of the internet back in the 1980s.

and it kind of seems a pity to me that the three camps in the discourse right now generally seem to be "ai is useless and stupid and a fad and a scam", "ai will destroy the human race", and "ai will usher in a post-scarcity utopia," because the possibility that ai is neither a complete mirage nor the end of human civilization as we currently understand it is much more interesting. and much harder to speculate about.

[exhaled-spirals]

« To mention the global loss of biodiversity, that is to say, the disappearance of life on our planet, as one of our problems, along with air pollution or ocean acidification, is absurd—like a doctor listing the death of his patient as one symptom among others.

The ecological catastrophe cannot be reduced to the climate crisis. We must think about the disappearance of life in a global way. About two-thirds of insects, wild mammals and trees disappeared in a few years, a few decades and a few millennia, respectively. This mass extinction is not mainly caused by rising temperatures, but by the devastation of natural habitats.

Suppose we managed to invent clean and unlimited energy. This technological feat would be feted by the vast majority of scientists, synonymous in their eyes with a drastic reduction in CO2 emissions. In my opinion, it would lead to an even worse disaster. I am deeply convinced that, given the current state of our appetites and values, this energy would be used to intensify our gigantic project of systemic destruction of planetary life. Isn't that what we've set out to do—replace forests with supermarket parking lots, turn the planet into a landfill? What if, to cap it all, energy was free?

[...C]limate change has emerged as our most important ecological battle [...] because it is one that can perpetuate the delusional idea that we are faced with an engineering problem, in need of technological solutions. At the heart of current political and economic thought lies the idea that an ideal world would be a world in which we could continue to live in the same way, with fewer negative externalities. This is insane on several levels. Firstly because it is impossible. We can't have infinite growth in a finite world. We won't. But also, and more importantly, it is not desirable. Even if it were sustainable, the reality we construct is hell. [...]

It is often said that our Western world is desacralised. In reality, our civilisation treats the technosphere with almost devout reverence. And that's worse. We perceive the totality of reality through the prism of a hegemonic science, convinced that it “says” the only truth.

The problem is that technology is based on a very strange principle, so deeply ingrained in us that it remains unexpressed: no brakes are acceptable, what can be done must be done. We don't even bother to seriously and collectively debate the advisability of such "advances". We are under a spell. And we are avoiding the essential question: is this world in the making, standardised and computed, overbuilt and predictable, stripped of stars and birds, desirable?

To confine science to the search for "solutions" so we can continue down the same path is to lack both imagination and ambition. Because the “problem” we face doesn't seem to me, at this point, to be understood. No hope is possible if we don't start by questioning our assumptions, our values, our appetites, our symbols... [...] Let's stop pretending that the numerous and diverse human societies that have populated this planet did not exist. Certainly, some of them have taken the wrong route. But ours is the first to forge ahead towards guaranteed failure. »

— Aurélien Barrau, particle physicist and philosopher, in an interview in Télérama about his book L'Hypothèse K

[alexanderwales]

Talking about AI with people who don't know about AI is always fun.

Guy: Yeah, an AI can write "apple", but it's never seen an apple.

Me: I mean, we have multi-modal models now, but I get what you mean.

Guy: What's that?

Me: Er, we have multi-modal models that are trained on text and pictures and video and audio. So they've "seen" an apple.

Guy: Wow, that's wild. But I guess they've never tasted or held an apple.

Me: I mean ... there is not, in principle, any reason you couldn't hook it up to sensors. There are artificial "tongues" used in food science and research that can "taste" things. Which is not the same thing as a human tongue, but you could, in theory, train a huge multi-modal neural net on a wide variety of taste inputs that were combined with auditory and visual inputs. They're not doing that, so far as I know.

Guy: A computer can hold and taste an apple?

Me: Yeah. I mean, the model could be trained on data, and then use tool hook-ins to control a robot arm with sensors, and then all the collected data could be used to train another model, which would, when writing about an apple, have associations between all its "senses" and so in some way would be able to describe an apple using different data streams. But I don't think that's what you meant when you said that.

Guy: No, it was. A computer can eat an apple. Huh.

[quasi-normalcy]

So here’s a question that’s recently troubled me: When did average people stop knowing how their technology works?

I don’t even mean at the level of engineers or even electronic hobbyists; I mean like…at the level of general physical principles. Like “touch screens work by deforming a charged conductor layer, bringing it closer to a second layer and altering the local current to register a touch.” Did average people ever know this sort of stuff? Because it seems like they did. Like, maybe it’s just bias in the type of late-19th/early-20th century fiction I read, but it seems like people knew, at least in general principles, how, say, a victrola worked; they were interested in radio; they knew the basics of electricity.

So when did they stop? Like, how many people actually know how their computers work? How many people know about the humongous backend of physical infrastructure that’s necessary to support cloud computing or LLMs?

I mean, there’s an entire subgenre of horror stories that’s just about personal electronics doing spooky things…and why shouldn’t there be? As far as most people are concerned, they’re surrounded at all times by unfathomable nonhuman entities that mostly do what they’re supposed to, but sometimes don’t for unfathomable reasons. Honestly, I’m surprised people aren’t as superstitious about it as 17th century sailors were about the sea.

And I mean, part of it is just increasing disciplinary specialisation meaning you can’t know things fully; and part of it is just that computers and software tend to be black boxes (and to hide the backend). But also, to a large extent, we don’t even try to explain it.

Like, I assume that kids in the early 20th century studied how electricity works and how mechanics and such work in their science classes. But I grew up in the 1990s “Age of Computers” and I can’t recall anyone ever sitting me or my class down in public school to explain how logic circuits work. Did they do it for other kids in the 1990s? Are they doing it now?

I don't know; I just keep thinking that there's benefit to knowing that the world is rationally explicable, but it just seems to be getting more and more opaque to most people. I think we might be reaping the consequences of this.

[echoes-of-hee]

— Hundred Broken Hearts LHS

PAIRING ; heeseung x fem!reader

GENRE ;  enemies to lovers (one sided), angst warning ahead, fluff

SUMMARY ; If there is one constant in Y/n's life, it would be the hate she harbours for most men. Things worsened when her younger sister, Lia, kept on falling for the wrong guys.

wc; 5k for now

notes. exam is almost finished and I need to write something to release my stress asap.

Reply or send an ask to be added to the taglist !!

                   PROLOGUE BELOW

THROUGHOUT Y/n's twenty-one years of living, she had never once dated. Not once.

She did have crushes on guys and had even involved herself with a few situationships before, but it was nothing serious. To her, getting a boyfriend-a real one was a different ball altogether.

She had always viewed being in a relationship with men as "3C: creepy, claustrophobic and cringy!" for as long as she could remember. That was how y/n had always described it. Interacting or maintaining close relationships with the opposite sex was already too much for her, let alone having a real boyfriend. y/n could already picture how stressful it would be.

Sure, she talked and made a few connections here and there with guys she deemed tolerable, but that was simply the base of it: connections. They drifted apart, and she'd ring them once or twice to ask them particular questions and recommendations for the best university in town and what courses they'd recommend to major in. Everything was just for the sake of getting diverse and objective opinions.

Truth be told, y/n could care less about their mouly misogynistic opinions. she'd never forgotten their flustered faces and the changes in their voices every time she told them she was planning to apply for a Computer Science bachelor's degree at Whitsburg University, one of the top universities in Cardiff.

But for someone who has never been genuinely in love, that's a very courageous thing to say the '3C principle in general. Well, not so much if you're from Kim's household.

Growing up, she and her younger sister, Lia, had witnessed their supposed-to-be-loving mother and father fight through their teenage years. Until they got divorced (with the cons of having to eat together with her father and the new wife twice a month) to maintain the non-existent perfect father-and- daughter relationship.

The absurdity of it appalled y/n so much that she once questioned her mom. Still, after being scrambled out of the house, y/n learned the hard way never to defy her mom's orders anymore, as ludicrous as it seemed.

That's how you get a glimpse of why y/n would do this despite never dating anyone else. The only man who had ever broken her heart was her father.

That is until she stumbled upon a handsome and cute psychology major hottie who kept hitting on her after almost hooking up at karina's party.