#Which language is used in data science?

[datasciencecoursetip]

Which language is used in data science?

The Future of AI and Data Science: A Collaborative Evolution

Artificial Intelligence (AI) and Data Science are two of the most transformative forces in today’s digital world. Rather than existing in competition, these fields complement one another—working together to solve complex problems, unlock new opportunities, and drive innovation across nearly every industry.

Both AI and data science are rapidly evolving, and their future looks exceptionally bright. As technology advances, their roles will become even more critical—not only in building smarter systems but also in shaping how humans interact with machines and data.

Let’s take a closer look at what the future holds for these interconnected fields.

The Future of Artificial Intelligence

AI is no longer a distant concept from science fiction—it’s here, and it’s changing the world in real time. From recommendation algorithms on streaming platforms to sophisticated fraud detection in banking, AI is deeply woven into the fabric of modern life. And its influence is only growing.

1. Continued Advancements in Intelligence

AI systems are becoming smarter and more capable. With ongoing research in machine learning, deep learning, and reinforcement learning, future AI models will be able to tackle more complex tasks, adapt faster to new information, and make decisions with higher accuracy. These improvements will allow AI to go beyond narrow applications and start exhibiting broader problem-solving capabilities, even if true general intelligence remains a distant goal.

2. Transforming Industries

Nearly every sector stands to benefit from AI. In healthcare, AI is improving diagnostics and personalizing treatments. In finance, it’s automating trading strategies and assessing credit risks more efficiently. In transportation, we’re seeing the rise of autonomous vehicles that rely on real-time data and decision-making to navigate safely. Even agriculture, retail, and manufacturing are harnessing AI to enhance productivity, reduce costs, and deliver better services.

AI's ability to analyze massive datasets, learn from them, and act accordingly will lead to smarter business operations, predictive maintenance in industrial systems, more efficient supply chains, and customer experiences that feel truly personalized.

3. The Rise of Autonomous Systems

Autonomous systems—like self-driving cars, drones, and service robots—are perhaps the most visible and exciting frontier for AI. These machines rely on AI not just for "seeing" the world through sensors and cameras, but also for interpreting what they observe, predicting outcomes, and making decisions on the fly. As technology progresses, we’ll see wider adoption of autonomous vehicles, robotic assistants in homes and hospitals, and even autonomous delivery services.

4. Better Human-AI Communication Through NLP

Natural Language Processing (NLP), the branch of AI that deals with understanding and generating human language, has seen tremendous progress. Virtual assistants like Siri and Alexa are just the beginning. Future NLP systems will be able to understand context better, detect emotion, and even engage in complex reasoning. This means smoother customer service through AI chatbots, more accurate translation tools, and content generation that feels human-like and creative.

5. Ethics and Responsible AI

As AI becomes more deeply integrated into society, ethical considerations become more urgent. There is growing awareness of the potential for bias in AI algorithms, particularly when those algorithms influence decisions in hiring, lending, policing, or healthcare. Governments, organizations, and researchers are working together to develop frameworks for responsible AI—focusing on transparency, accountability, and fairness. Expect regulation to increase, especially in high-stakes areas, as society grapples with the social and moral implications of AI.

The Future of Data Science

While AI might get most of the headlines, data science is the engine that powers intelligent decision-making. It’s the field responsible for collecting, cleaning, analyzing, and interpreting data in ways that lead to actionable insights. Without high-quality data and thoughtful analysis, AI would be flying blind.

1. The Age of Data-Driven Decision Making

We live in a data-rich era. Businesses, governments, and even individuals are generating more data than ever before—from online purchases and social media posts to sensors in smart homes and factories. Data science enables organizations to make sense of this ocean of information. In the future, every strategic decision—from marketing campaigns to public policy—will increasingly be based on data-driven insights, rather than intuition alone.

2. Evolving Analytical Techniques

Data science itself is evolving. Traditional statistical methods are being combined with cutting-edge techniques like predictive analytics (which forecasts future trends) and prescriptive analytics (which recommends actions to achieve specific outcomes). These advanced tools help companies not only understand what is happening, but also why it’s happening and what they should do next.

3. Closer Integration with AI

Data science and AI are becoming deeply intertwined. AI models rely on the work of data scientists to gather and prepare data, select the right features, and evaluate performance. Conversely, AI enhances data science by automating many aspects of the data pipeline—from detecting anomalies to suggesting patterns. This symbiotic relationship means professionals in both fields will increasingly need to understand the other’s tools, concepts, and challenges.

4. Collaboration Across Disciplines

Data science is no longer confined to IT departments or research labs. Today’s complex problems require interdisciplinary collaboration. Data scientists must work closely with domain experts, engineers, business analysts, and AI researchers to understand context, formulate the right questions, and design solutions that are both technically sound and practically useful. This collaborative spirit will only grow as the field matures.

5. Demystifying AI Through Explainability

One of the challenges of modern AI systems—especially deep learning models—is their lack of transparency. These "black box" models can make accurate predictions, but it’s often difficult to understand why they made a specific decision. This is where data scientists play a critical role: developing tools and techniques that make AI models more explainable and interpretable. As trust becomes a key factor in AI adoption, explainability will be essential in areas like healthcare, finance, and criminal justice.

A Shared Future

Rather than existing in silos, AI and data science are becoming parts of a single ecosystem—where data fuels intelligent systems, and intelligent systems make data more useful. As both fields grow, professionals will need to be more versatile, blending technical expertise with communication skills, ethical awareness, and a deep understanding of real-world problems.

Organizations that embrace this convergence—investing in talent, infrastructure, and responsible innovation—will be best positioned to thrive in a rapidly changing world.

Ultimately, the future isn’t about choosing between AI and data science. It’s about recognizing how they work together to create smarter, fairer, and more impactful solutions for society.

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

[rin-may-1103]

Delilah's Language (part four)

Previous | Master Post | Next (to be written)

The nice female scientist (whose name Danny can't remember) turned and started leading them through the crowd. Dr. Trynul huffed but stuck close, probably to try and find a way to discredit Danny's ability. (The two brothers followed but stayed silent, just watching with, for some reason, confusion AND excitement.)

Damian turned and looked up (not by much, mind you) at Danny, curiosity oozing off him in purple streaks. "You said they used their whole bodies, could you clarify?"

Danny hummed, tilting his head as he thought about how to, well, not dumb down the explanation, but make it more digestible. The kid was smart, but he didn't need a whole history lesson topped off with social science and cultural themes. That would just be a waste of time, especially during a birthday party.

"The gorilla language, specifically the purple-backed gorilla dialect I know, uses a mixture of gestures and sounds. Somewhere between, like, 75/25 and 85/15. The vocal aspect is used to emphasize." Danny began, nodding his head as he thought it out.

Damian frowned, but green fog floated around his head, showing that he was concentrating on what he was being told and not upset.

"So, a grunt after a gesture could mean it's a statement or fact. Like someone saying they ARE going to do something. A chirp after a gesture could mean a question, like COULD I do this? Unlike human languages, gorillas focus more on straightforward and simple communication. They don't really have any reason to stretch out what they want or need; they just need to make sure the other understands quickly and clearly."

"What, they don't talk about pretty flowers they saw?" Dr. Trynul cut in, rolling his eyes.

"They could," Danny hummed, ignoring the condescending aspect of the question, "they like talking to each other when they have nothing else to do, and they're smart and opininated creatures. they like pretty things, I'm sure they do talk about pretty flowers or leaves they saw."

"Sure, and I bet they also tell each other about how they keep their fur clean and what mud makes them look bad."

Damian was glaring at the man, obviously getting fed up with the interruption. Danny would usually just deal with the man and slowly drive him crazy to the point he leaves Danny alone, but Damian looked like he was ready to stab the guy. (Not like Danny would stop him if he did, but like, Danny should do something about it before that happens.)

Danny glanced at the woman leading them; she was too focused on her conversation with another scientist to be paying attention. which was good, because what Danny was about to do and say was true, but he still would prefer to gather more evidence for an air-tight case. Can't do that if other people wanted to look into it, legally.

"You know," Danny started, clasping his hands behind his back while keeping a straight face. "I wonder if your colleagues would like to know that you've been manipulating your research data."

Dr. Trynul whipped around and glared at him while Damian and his brothers slowed down in confusion and surprise. Danny kept walking.

"How dare you accuse me of such scandalous actions? I should report-" he started, quickly speeding up to match Danny's pace.

"Three papers, released to the public and scientific community. Published under a well-known science journal and written by the one and only Dr. Jake M. Trynul." Danny started, glancing at the large glass tank to the right, where a few otters swam by, gleefully splashing around and having fun.

No one but the four people walking with him was paying attention.

"The connection between environmental factors and animal behavior, Gorillas and the effect humans have on them, and finally, your newest paper, the effects of human and gorilla relationships," Danny listed, ticking them off on his hand.

"I might not be a scientist, Dr. Trynul," Danny smiled, stopping and turning to look at the man, "but I do know how to read data and do the math myself. You have blatantly manipulated scientific data gathered by yourself and your team and falsified finds all so you can trick others and, more specifically, your superiors into investing more money and resources into your research."

Tilting his head, Danny studied the man in front of him, who was flushed red in anger and clammy with nerves. Danny hadn't given any evidence that what he was saying was true yet, but the man still glanced around like someone was going to strip his license right then and there. (Which was evidence enough if you asked Danny, no one got that nervous over baseless claims.)

"You might happen to remember that I had been given the opportunity to help your team with researching and studying Dalilah and her family. An opportunity that allowed access to the team's whole process. Which meant I had access to the unedited and raw data that had been collected. Data, I might add, that I had been required to read through and help collect."

"i don't know how you've managed to do this with so many bright minds on your team, let alone get it past so many others, but i'd like to remind you Dr. Trynul, that if this did get out, with all the evidence I do have, mind you, you'd be in some serious trouble. Not only would your license be revoked but you'd face possible imprisonment. fraud, especially on a federal level, is taken very seriously."

The man gaped at him, his mouth opening and closing for a few seconds before he settled on growling at Danny, "You're lying, you don't have anything. This is libel! I should get you arrested for defamation of character!"

"Oh, bless your heart," Danny held a hand over his chest and batted his eyes, watching as the man grew even more furious. One of the brothers, Dick maybe, choked and started caughing.

"First of all," Danny started, holding up a finger, "libel is written defamation. Slander is oral defamation. Second of all, you can't get me arrested for defamation. You'd have to provide evidence that I had intended you or the public harm. And file the case in a state that deals with criminal libel. which I just said doesn't apply here."

"Third of all," Danny crossed his arms, lifting an eyebrow, "I've been collecting evidence for months now. The only reason you're not being interrogated by the authorities and your superiors is that I've been busy with other things. So, I suggest you pack your stuff, go home, and evaluate your life. because I'm definitely going to be submitting my evidence after today."

Well, not right away. Like he said earlier, Danny wanted to collect more evidence. Like, sure, what he had now would definitely get the man in trouble, but Danny wanted air-tight.

Turning away, Danny started walking in the direction their temporary guide had disappeared. Damian and his brothers took a moment but quickly started following.

"holy shit," Tim breathed, glancing back at the seething man. "Do you actually have the evidence, or were you making that up to scare him?"

"I actually have the evidence, but it's back home, so it'll take 'while before I can actually submit it." Danny admitted. now that that was taken care of, he could get back to what he was actually here for.

"Alright, 'nough about him. Y'all wanted to hear about Dalilah and the language." Danny clapped his hands, turning his head to look at the three. The two older brothers looked like they'd rather continue questioning him, but Damian practically lit up in yellow light, all confusion and glee (?) from before disappearing.

"You said they liked talking when they have nothing else to do, do they not typically like to converse?" Damian asked, an almost unnoticeable skip now in his step.

"That's the thing, they talk all the time. They use a more elaborate and obviouse dialect when bored and a more straightforward and instinctual one when busy. It's fascinating." Danny smiled, shoving his hands into his pockets.

"Oh, there you guys are!" their temporary guide cut in, "I thought I lost you guys. Come on, Delilah is just up ahead. She's going to be so excited to see you, Danny."

Danny smiled, picking up his pace when Damian (not rushed, because the kid seemed way too formal to do something as 'childish' as running) caught up to her side.

Glancing back, the two brothers were nowhere in sight.

Next (to be written)

[probablyasocialecologist]

In January 2019, world-renowned food and nutrition experts published a groundbreaking study. The culmination of two years’ work by 37 authors, the EAT-Lancet report set out to answer the question: how can we feed the world’s growing population without causing catastrophic climate breakdown? The publication was high profile. Launched in the prestigious peer-reviewed Lancet medical journal, the report came out in 12 languages, and a flagship event at the World Health Organisation (WHO) in Geneva, Switzerland was planned for March. But in the days leading up to the launch, the WHO pulled out. The health agency’s withdrawal followed a massive online backlash, which had concentrated on one of the report’s recommendations: to cut global red meat consumption by 50 percent.  New evidence seen by DeSmog suggests this surge of outrage against the report was stoked by a PR firm that represents the meat and dairy sector.  A document seen by DeSmog appears to show the results of a campaign by the consultancy Red Flag, which catalogues the scale of the backlash to the report.  The document indicates that Red Flag briefed journalists, think tanks, and social media influencers to frame the peer-reviewed research as “radical”, “out of touch” and “hypocritical”. It highlights that negative coverage outnumbered neutral or positive stories, with thousands of critical posts shared on X about the research, alongside more than 500 negative articles. “Red Flag turned EAT-Lancet into a culture war issue,” Jennifer Jacquet, professor of environmental science and policy at the University of Miami, and expert in lobbying, told DeSmog. “Instead of having nuanced conversations about the data, Red Flag takes us back to mud slinging.” “This document is a portrait of what we’re up against – as people who care about the truth, about climate change, and about the future,” she said.

10 April 2025

[envyi5envious]

RESEARCH.. JUST RESEARCH.

࿐ — 𝙋𝘼𝙄𝙍𝙄𝙉𝙂 : YANDERE (Red Robin) Tim Drake x GN Reader. 𝙎𝙔𝙉𝙊𝙋𝙎𝙄𝙎 : He was scribbling in a notebook, and you wondered what he was writing. 𝙒𝙊𝙍𝘿𝘾𝙊𝙐𝙉𝙏 : 1.7k. 𝙒𝘼𝙍𝙉𝙄𝙉𝙂𝙎 : Dark. Obsessive tendencies and stalking. 𝙉𝙊𝙏𝙀𝙎 : English isn’t my first language. I don't know why this took so long. Enjoy ♡

Class had just begun, and the familiar sound of shuffling papers and low murmurs filled the air. You had recently been transferred to AP Computer Science by your mother’s request. The teacher was discussing data analysis. They turned to the whiteboard, where they had written several bullet points. “First, we need to understand data collection.”

“This is where we gather information from various sources. It’s essential to choose reliable methods. Can anyone provide an example?” A young man raised his hand, mainly focused on the notebook on his desk.

“Yes, Drake.” The teacher replied as they leaned their backside against their desk. “We could use sensors or databases.”, “Correct. Well done.” After a few minutes, you tuned out the sound of their voice. Mainly focused on taking down the notes written on the board. Your ears perked up at the mention of an assignment. The teacher’s gaze swept across the room, lingering on a few students. “Next week, you’ll begin to work on a project analyzing a dataset of your choice. You will be required to pick your own partners this week so you have the weekend to prepare.”

The students responded with a few quiet hums and the teacher ended the class like that. The room was mainly silent besides the few people speaking to ask other students to be their partners. Assuming since you were new you wouldn’t get picked, you stood up to talk to one of your random classmates only to be met by a chest slamming into your nose.

“Shit-”

You heard a familiar voice say, their hands reaching out to secure you before you fell. “Are you alright?” They asked. Once your vision cleared, you realized why it was familiar. It was the same guy that answered the teacher. “Drake?” Your mutter came out before you could stop it, he let out a dry chuckle. “Tim, actually. Drake’s my family name.” He corrected. “Sorry about that. I was just coming to ask you if you wanted to be partners since I noticed you were new.” What a coincidence, you were about to do the same thing. “Oh, well I’m lucky then. We can meet at the Gotham library later, like 5PM-ish?” You weren’t sure if he’d be okay with giving his number off to a complete stranger.

He hummed for a second, thinking if he was busy around that time. Then he nodded his head as confirmation. “It’s a date. Talk to you later, (L/N).” He said before leaving the class, phone in his hands as he typed away like crazy. You could literally hear the sound of his thumbs touching the screen from that far away. Sighing, you sat back into your desk. You decide to try finishing your homework early today so you could focus on planning for the project. You even texted your mom not to pick you up since you would be meeting with Tim later. When you were done, you stood up to go for a walk to the cafeteria. Maybe you could get some coffee to stay awake. All AP classes were no joke, you were a little annoyed at your mom for forcing you to go to them so suddenly. While you were smart, you weren’t exactly a fan of school. You just did what you had to do to pass and that’s all. So when you found out you would have to be learning more because of your ‘potential’ you got rightfully pissed. It didn’t matter though. Once you were in AP, you can’t get out of it unless your parents signed for it (which your mother clearly isn’t budging on) or you flunk. And you weren’t about to fail Senior year just to get out of harder classes. Once you reached it, the room was mainly empty as most people went home. But the worker was still there until school closing time. There were groups still there, most likely waiting for their rides. You decided to order a croissant with ice coffee, making your way to an empty table to eat. You pulled out one of your notebooks to get to planning ideas.

—

The Sun had already set in Gotham due to the amount of buildings surrounding the city causing the car Tim was in to be fully dark, the only source of light was that of the laptop on his lap. The image broadcasted was that of the cafeteria’s cameras directed at you. You were writing notes with one hand and eating a pastry with the other. He couldn’t take his eyes off you. He had one of his notebooks beside him, taking notes when he noticed any quirks of yours. Like how you would subconsciously bite your nails or pick at your skin when you were stressed and the food you ordered. Then he took a look at what you were writing. At first he thought you were still working on ideas for the project. But as he kept reading, he realized that it seemed to be more of a fantasy novel. “Hm.. If I can just.. There we go.” He mutters to himself as he managed to zoom close enough to the book’s cover to see that it was a novel. ‘The Whispers of the Assassin.’ Quite the title. He searches the book online to have it delivered to the manor as soon as possible. “The Whispers of the Assassin follows Elara, a skilled assassin haunted by her past. Tasked with eliminating a crime lord responsible for her family's down.. Okay, I’ll read it later.” Tim thought to himself that he could suggest using this novel as a dataset, might help you be more interested to work with him on the project.

He’ll decide once he reads the book himself, for now, it’s best not to bring it up. When he realized the time was close to 5PM, Tim moved to the driver’s seat of his car to reach the library before you did. He would be a cover story that he was there the whole time.

—

When you finally reached the library, you found Tim scribbling notes in the same notebook he was using during class.When he heard your footsteps, he closed the book before you could get too close. Placing it back into his bag, he pulled out a tablet. “Hey.” He gave you a small smile. “Hey back.” You sat on the other side of the table, pulling out your own notes. “I wrote a few ideas on what we could use as a dataset and the methods. You can tell me which ones you find interesting.” You slid the papers to him, letting him read everything. “Hmm.. Good. The ideas, I mean. Here, we could use a novel. What novels do you like?”

“Well, I was reading a novel recently about a book called ‘The Whispers of the Assassin.’ It’s really good, you should read it. But I thought maybe we could use that.” Great minds think alike. You saw him typing away at his comically large tablet, he skimmed through the summary. He didn’t answer right away, almost like he was absorbed in the story.

But eventually he directed his face back to you. “Interesting. I’ll buy it later.” He tapped his index finger, eyes slightly unfocused. Before he stopped abruptly. “Since we’re basically done planning, there’s not much to do here.” He chuckles, turning to face his attention to one of the windows. “What do you like about the book?” His gaze wasn’t on you but he was still talking to you. “Well.. I like the main character, Elara. She’s a total badass. Her family died because of this mob boss and she goes after him to avenge her family. She honestly reminds me of Batman.” You could see him try to stop himself from cracking a smile from that. “Yeah, now I have to read it. I’ve had an obsession with Batman since I was a kid.” That explains the huge bat logo on his shirt. “Oh, so you’re a superhero nerd?” He nodded his head, smiling.

“Oh, shit. I completely forgot to tell you my name. It’s (Y/N).” You instinctively reached your hand out for him to shake and he surprisingly shook it as soon as you held it out. “That’s a pretty name.” He mused on it for a second before freeing your hand from his grip. “What else do you like to do?” The single sentence led to a conversation for a few hours before you left for your respective homes.

—

“Young master Tim, a delivery has arrived in your name.” Alfred’s voice could be heard through the door as he insisted on repeatedly knocking till Tim answered. “Thank you, Alfred.” He was about to close the door but the older man blocked the way with the tip of his foot. “I’m sorry to be a bother but Master Bruce has been concerned with your amount of screen time.”

Tim sighed slightly, he couldn’t help but be annoyed at the fact that they were taking time out of his busy schedule just to worry over nothing. “I can guarantee you both that I am fine. Just been busy with projects. AP classes are kind of kicking my ass right now. Thanks again.” He took the package from him without another word, pushing the man’s foot with his own. He quickly closed the door before he could be berated with even more of their concerns.

His room was clean but definitely not organized. Wires and computers were everywhere, books filled to the brim with the most minute of details about you. He made his way back to his bed, closing his laptop and pulling out his phone and earphones. He put the small buds in his ears, playing ‘8 HOURS OF BROWN NOISE’ as he began reading the novel. Four hours later, he had already finished it. Though, he had trained his mind to be able to handle large amounts of information in short periods. While the book most definitely had its flaws, it wasn’t bad. Now, just to finish the project so he can spend more time with you.

☆ 𝙢𝙖𝙨𝙩𝙚𝙧𝙡𝙞𝙨𝙩. ©◞✶ envyi5envious

[lactoseintolerentswag]

Rise Characterizations Pt. 3!!!

Now that Leo and Raph are done, it's Donnie's turn for character analysis as a writing reference. So without further ado,

Donnie Character Notes

Language Habits:

Straight up talks like a redditor who hasn't touched enough grass (affectionate)

Oscillates between very scientific paper polished, sometimes adding a dazzle of shakespearean for dramatics, or abbreviations/a shorter version of a word with a more fun connotation (i.e. "brekkie" instead of breakfast)

Uses food as surprised exclamations or curses, "oh my peaches and cream", "banana pancakes!"

Emphasizes each syllable of a long word when he's excited or trying to make a point. Conquered becomes con-qu-ered

Either exaggerates his speech or speaks in deadpan

The science terms he uses as battle cries aren't chosen at random, but rather are related to the action/subject at hand, i.e. yelling "fibonacci" when throwing his spinning tech-bo

Will overly describe an item or a situation, and often gets caught up in these observations before processing what just happened

Will repeatedly yell "help!" when he's distressed and/or outnumbered

Refers to Mikey as "Michael"

Refers to his brothers as "brethren" or "gentlemen"

Refers to splinter as either "father", "papa", or "dad" depending on the weight of the situation

Refers to his tech as his "babies"

Answers the phone with, "You're conversing with Donatello"

Uses "gesundheit" instead of bless you

Personality:

The fixer, he supplies the family with tech and resources. He always has a trinket made for the situation at hand and/or offers his knowledge/data collected. He's always prepared to help. Even with outside resources, he likes to feel useful in solving their problems (i.e., building Todd that dog park)

The theater kid, in a similar vein to leo, Donnie has his own style of dramatics. He often uses shakespeare-like language, is mentioned to regularly recite the jupiter jim musical soundtrack, and has a music mode for his battle shell. He belongs on a stage, or at least thinks he does

Not good at lying, despite the glamour he can put on in the spotlight. This may be due to the side of himself that over explains his thoughts

An over-thinker, who really tends to over-complicate things. His first theory or idea will always be the most extreme buck-wild concept. After some filtering, he still word vomits

A dreamer/big idea guy. He does have big ideas and goals. A lot of these he's able to put into place, although some go a little haywire (see Albearto). He doesn't do things in halves, and puts everything into a project

Meticulous, someone who's very detail oriented. As mentioned before he tends to over-complicates things. This may be impacted by his love for data and collecting information (he does record Everything for a reason)

Always on the edge of violence, which is surprising. Donnie's not known as being the angry archetype of tmnt, but he can get a little violent in his fighting style and does often cite his desire to use lethal force

Low empathy, which is mainly due to his issues processing and recognizing emotions. He's been pegged as unemotional, but in canon he's rather emotional and expressionate, just lacking the skills to process such emotion (he's just like me fr)

Praise motivated, as seen with his interactions with Splinter. Also desires the praise of his brothers, who he doesn't feel understand him with all the teasing that's sent towards his direction. This also pushes him to seek validation and acceptance in other groups (i.e. the purple dragons), to feel a sense of security or belonging

Ignores his own mistakes, and will often pretend like they didn't exist or ever happen. This most likely has to do with his desire for praise, so he feels bad when he fails. If he never made a mistake, he never has to feel bad

Miscellaneous:

Fourth to unlock mystic powers

Uses "Bootyyyshaker9000" as most of his usernames and passwords, with his alt. username being "Alpha-Bootyyyshaker9000"

Has a fear of bees, spiders, and of course beach balls

Breaks the fourth wall the most

Loves the smell of pineapple, hates the texture

Has a hobby of rooting around in the junkyard and dumpster diving

Uses cheat codes in video games

Mikey's next of course :)

[mariacallous]

For much of living memory, the United States has been a global leader of scientific research and innovation. From the polio vaccine, to decoding the first human chromosome, to the first heart bypass surgery, American research has originated a seemingly endless list of health care advances that are taken for granted.

But when the Trump administration issued a memorandum Monday that paused all federal grants and loans—with the aim of ensuring that funding recipients are complying with the president’s raft of recent executive orders—US academia ground to a halt. Since then, the freeze has been partially rescinded for some sectors, but it largely remains in place for universities and research institutions across the country, with no certainty of what comes next.

“This has immediate impact on people’s lives,” says J9 Austin, professor of psychiatry and medical genetics at the University of British Columbia. “And it’s terrifying.”

The funding freeze requires agencies to submit reviews of their funded programs to the Office of Management and Budget by February 10. The freeze follows separate orders issued last week to US health agencies—including to the National Institutes of Health, which leads the country’s medical research—to pause all communications until February 1 and stop almost all travel indefinitely.

The confusion is consummate. If the funding freeze continues through February, and even beyond, how will graduate students be paid? Should grant applications—years long in the writing—still be submitted by the triannual grant submission deadline on February 5? What does this mean for clinical trials if participants and lab techs can’t be paid? Will all that research have to be scrapped thanks to incomplete data?

Even if Trump fully reverses the freeze on research funding, the damage, multiple sources say, has been done. Although for now the funding freeze is temporary, the administration has shown how it might wield the levers of government. The implication is that withdrawing funding could be done more permanently, and could be done to individual institutions, individual organizations, both private and public. This won’t just set a precedent for the large East Coast or West Coast universities, but those located in both red and blue states alike.

While always an imperfect arrangement, science in the US is largely funded by a complex system of grant applications, reviews by peers in the field (both of which have had to be halted as part of the communications pause), and the competitive distribution of NIH funds, says Gerald Keusch, emeritus professor of medicine at Boston University and former associate director of international research for the NIH. According to its website, the NIH disburses nearly $48 billion in grants per year.

When it comes to medical research, America truly is first, and if it abdicates that position, the void left behind has global ramifications. “In Canada, we have always looked to NIH as an exemplar of what we should be trying to do,” says Austin, speaking to me independently of any roles and affiliations. “Now, that’s collapsed.”

Science is, in its very nature, collaborative. Many consortiums and alliances within scientific fields cross borders and language barriers. Some labs may be able to find additional funding from alternative sources such as the European Union. But it is unlikely that a continued withdrawal of NIH funding could be plugged by overseas support. And Big Pharma, with its seemingly endless funds, is unlikely to step up either, according to sources WIRED spoke with.

“This can’t be handed off to drug companies or biotech, because they’re not interested in things that are as preclinical as a lot of the work we’re discussing here,” says a professor of genetics who agreed to speak anonymously out of fear of retribution. “Essentially, there’s a whole legion of university-based scientists who work super damn hard to try to figure out some basic stuff that eventually becomes something that a drug company can drop $100 million on.”

The millions of dollars awarded to high-achieving labs is used to fund graduate students, lab techs, and analysts. If the principal investigator on a research team is unsuccessful in obtaining a grant through the process Keusch describes, often that lab is closed, and those ancillary team members lose their jobs.

One of the potential downstream effects of an NIH funding loss, even if only temporary, is a mass domestic brain drain. “Many of those people are going to go out to find something else to do,” the professor of genetics says. “These are just like jobs for anything else—we can’t not pay people for a month. What would the food service industry be like, for example, or grocery stores, if they don’t pay somebody for a month? Their workers will leave, and pharma can only hire so many people.”

WIRED heard over and over, from scientists too fearful for their teams and their jobs to speak on the record, that it won’t take long for the impact to reach the general population. With a loss of research funding comes the closure of hospitals and universities. And gains in medical advancement will likely falter too.

Conditions being studied with NIH funding are not only rare diseases affecting 1 or 2 percent of the population. They’re problems such as cancer, diabetes, Alzheimer’s—issues that affect your grandmother, your friends, and so many people who will one day fall out of perfect health. It’s thanks to this research system, and the scientists working within it, that doctors know how to save someone from a heart attack, regulate diabetes, lower cholesterol, and reduce the risk of stroke. It’s how the world knows that smoking isn’t a good idea. “All of that is knowledge that scientists funded by the NIH have generated, and if you throw this big of a wrench in it, it’s going to disrupt absolutely everything,” says the genetics professor.

While some are hopeful that the funding freeze for academia could end on February 1, when the pause on communications and therefore grant reviews is slated to lift, the individuals WIRED spoke with are largely skeptical that work will simply resume as before.

“When the wheels of government stop, it’s not like they turn on a dime and they just start up again,” says Julie Scofield, a former executive director of NASTAD, a US-based health nonprofit. She adds that she has colleagues in Washington, DC, who have had funding returned to their fields, and yet remain unable to access payment through the management system.

Austin says that already the international scientific community is holding hastily arranged online support groups. Topics covered range from the banal—what the most recent communication from the White House implies—to how best to protect trainees and the many students on international visas. But mostly they’re there to provide support.

“I’ve had a lot of messages from people just expressing gratitude that we could actually get together,” Austin says. “There’s just so much unaddressable need. None of us has the answers.”

Scientists, perhaps more than any other profession, are trained to “learn and validate conclusions drawn from observation and experimentation,” says Keutsch. That applies to the current situation. And what they observe during this pause of chaos does not portend well for the future of the United States as a pinnacle of scientific excellence.

“If people want the United States to head toward being a second-class nation, this is exactly what to do. If the goal is, in fact, to make America great, this is not a way to do it,” says the genetics professor. “This is not a rational, thoughtful, effective thing to do. It will merely destroy.”

This story has been written under a pseudonym, as the reporter has specific and credible concerns about potential retaliation.

[1920secretsociety]

Let's talk about the prison scene:

This has always been my favourite scene in Ghostbusters. I know it may seem a bit weird to find this scene amazing but bare with me.

This scene is their most vulnerable moment, they are in a state of limbo at whether they are going to jail or walk free. No one is in control here, they have no idea what's going to happen, meaning we see the true and hidden characteristics of each Ghostbuster.

Egon

Egon mostly proves to be everything we thought. He is logical and intelligent, he seems to be the only one who has done any form of in-depth research on Dana's case. He found the architect's name (Ivo Shandor) and all his history while working full time as a Ghostbuster, inventing their equipment and keeping an eye on the storage facility's data, reinforcing the preconception that Egon is a workaholic and incredibly smart. However, it's also a moment you see a new characteristic - confidence. When Egon tells Venkman, Ray and Winston what is happening, Egon is only expecting them to listen, when everyone else in the cell decides to listen you see that Egon is shocked. It's obvious that he doesn't expect anyone to listen but when he comes to terms with the fact the other prisoners are listening, Egon gains confidence. When stating facts about Shandor, Egon becomes increasingly confident and he even stands up at the end to prove the severity of the situation. This is never seen before as Egon is usually subdued, quiet and rarely ever uses body language, and in this whole scene he is completely different. This shows that Egon has the characteristic of confident and that he can command a presence, which is shown by the fact that everyone starts listening to him. He is a confident individual when it comes to his science and facts, especially when explaining them and this is completely shown here. Egon is not always the reserved scientist we thought, he has confidence but just expresses it differently.

Ray

Ray is shown to be intelligent in multiple different ways. Ray is completely involved in the investigation of Dana's apartment as he does his part of the research and learns the foundation that the architecture is weird, it's a conductor and these are all facts Egon builds on. This shows that Ray is a intelligent individual who gives facts that are as valuable as Egon's, and along with this a different type of intelligence emerges. In this scene, Ray is the only one who realises that Venkman has been lying the whole time about studying. This took a lot of intelligence to work out as you usually trust people, especially ones you have known for a long time and come from a credential background. The fact Ray works it out shows that he has a deep knowledge of his friends, proving that he has amazing intelligence when it comes to understanding people. However, it also shows Ray's bravey. Making an accusation that big could have backfired as Venkman may have become deeply offended, if he actually had studied. Therefore, it shows that Ray is an individual who is intelligent and incredibly brave as questioning people's credentials, could result in a massive argument or a falling out. This whole scene shows that Ray has an amazing perception and intelligence of people as he deduces Venkman's lies immediately and that he has confidence in each of his analysis, as he said that Venkman was lying with full confidence, despite the only legitimate evidence being a feeling.

Winston

Winston is showed to be intelligent and logical. In this scene he seems to be the only one whose genuinely concerned about going to jail, as he's the only one who shouts at the guard, trying to get out. The rest are not really bothered, they're more concerned about sharing information on Gozer, but as much as Winston believes in it he understands that being in jail is the more important problem, as they can't stop Gozer if they're locked up. He is also the only one who realises that the reality of Gozer coming is just going to look insane in court, as to the outside world ghosts are not really considered real, never mind a ghost attack. All of this reveals that Winston is intelligent and logical, he is a realist and has an amazing perception of the outside world and social situations. He's as intelligent as Egon but in a completely different way, his intelligence lies in his knowledge of the real world, and this gives him a vital role within Ghostbusters as without Winston's intelligence of the outside world they would have no real idea of how to navigate it.

Venkman

Venkman is shown to be helpful and caring. After Egon is finished explaining that Gozer is coming back all the prisoners are still around him. This would make Egon uneasy as he hates social situations and he has no more science or facts to make him feel confident as he's finished explaining. Venkman knows how uneasy Egon is and helps as he starts singing. He does this to draw attention away from Egon and while singing he forces everyone back, and he keeps going they are all far away from Egon. It's a small thing but it shows how much he really does care about Egon and all of the Ghostbusters. Venkman obviously understands them all on a deep level as it took him about a second to realise that Egon was uncomfortable. This all shows that despite all his faults Venkman is helpful and more importantly caring, he will do anything to insure that the rest of the his colleagues are happy and secure, even if it means embarrassing himself.

This is why I absolutely love this scene. We see glimpses of their personalities that we never see in any other situation. It's the most honest they all are, it shows their character development and more importantly gives us all a million more reasons to love them.

[crystal-wingeddragon-spikes]

My fan made Animation vs Coding part 2

Do you think stick figure AI would "assume" data type of all number to be float, double, or decimal?

...What? This is not a well-known fun fact outside STEM community?

So many people have this problem, someone made a whole webpage explaining it.

More organic explanation here; Defining a right data type is a big deal in programming. At least the programmer who manually assign it float/double would know why it went wrong.

JavaScript, however, will automatically assign an appropriate data type, and is advertised to be more beginner-friendly... Can you see why this became a meme?

0.1 and 0.2 will be considered double data type, which can't be accurate expressed in base 2.

There is only (1/2), (1/4), (1/8), ... ,(1/(2 power n)) in base 2.

It can't accurately express (1/10 and 2/10), but it still makes a very good approximation. That is why it is only 0.00000000000000004 off.

This is why in most statistic analysis and calculator use decimal data type. Or banking uses fixed-point numbers data. They both have their limitation; Decimal requires more computing power, which mean more specialized device. While fixed-point works fine with money because it's transferring money, not doing maths. It would never have to deal with 0.3333333... dollar.

Do you know what language is from the same family as JavaScript? That's right, it's Flash's programming language, ActionScript.

I told you my Computer Science grade was horrid, but this is very basic, so I am more confident explaining it.

[kimyoonmiauthor]

Why Spell Check (and some grammar check) isn't AI

So I've seen in the wake of Nanowrimo some people claim that spell check is AI and thus is like Gen AI, and I saw the claim originator on Twitter, but when I pressed them, they basically tried to say they had a degree in computer science, so when I pressed into them if they knew what they were talking about, they couldn't answer because obviously don't know about AI.

For some background I've done some light programming (If you look at the Korean name generator, that's all me). And I also have relatives that did programming.

Here, I can lay out how spell check works without AI or a fancy algorithm.

The oldest spellchecks didn't use AI or Gen AI, they used what is your basic corresponding tables.

If you use something like google sheets (database), you can do this pretty quickly yourself though with a lot of manpower.

Here is a list of commonly misspelled words.

Add that with another table with how they are commonly misspelled.

Then you need a table with "common typos"

Then you need one more table for "Words the user adds."

The algorithm is basically this: Set up a loop. A loop is a mechanism that has an algorithm (or set of instructions in it) which repeats until a certain instruction is met. This loop with this algorithm will check for words. In this case, anything with letters, usually encompassing ' and - (though some programs ignore dashes).

So[,][ ]it[ ]will[ ]look[ ]at[ ]letters[ ]in[ ]this[ ]sentence[ ]and[ ]figure[ ]out[ ]if[ ]it[ ]is[ ]spelled[ ]correctly.

The first loop in the previous sentence will look at the word "so" by selecting everything it knows to be a letter in English. Tada "S, o" Then correspond that to the dictionary. So shows up in the dictionary listing it has of English words. Thanks Webster. (If you're British, the OED)

The Algorithm concludes the word is spelled correctly. No more work needs to be done on So. The next word is it "i, t" correspond that to the dictionary and so on.

If you have a "bad word" for example "alot" then the work is, word is spelled incorrectly. Next "work to be done" is to find out if this word is in the "commonly misspelled" words list. If yes, then underline the word in red to get it corrected.

AKA run Algorithm to underline word (usually a few lines of code if you're doing it the old way).

Then the algorithm moves on. The function of right click/Cntrl click is saying, OK, this word, "alot" is it commonly misspelled? Here are a list of corrections according to this other table. This is the work that needs to be done: We need a popup table. We need to pull from the database this misspelling, and then we need to pull from this other database and pull corresponding correct spellings based on this. Then you set up an if-then If the user clicks on this word, change highlighted word.

This is your basic spelling algorithm. You do not need gen AI for this or AI.

Grammar works similarly. You need a table, the type of speech it is (n, v, adv, adj) and then to load in "rules" one should use. You do not need AI. You need some basic programming skills. On the table of somewhere between "Hello, world" (1) and "OMG, I created artificial intelligence like Data " (10) My "Korean name generator" is like 2.5? in difficulty (minus all of the language and cultural knowledge). Haha. Still mocking myself. But a Spellcheck is not far from that. it is like 3. You could build one fairly easily with PHP and database access to a dictionary and misspelled words with corrections.

But Google pulled from the Enron Emails.

In this case, you can sorta fuzzy logic it and create bigger algorithms, mostly to sort out the *grammar* and *New words* that were used that aren't already in the database, which basically is another loop, but with an add to database function. (i.e. table). Then you would correspond this with another loop to look at "odd grammar" and flag it.

You can use AI to sort it faster than a basic algorithm, but nope, you do not need AI to correspond it. A basic algorithm would do. You can also use AI for "words that look similar to this one" and "Words commonly used in place of this one"

But overall, You do not need AI for a grammar check. You only need a dictionary, a set of commonly held rules of English and exceptions (maybe some Noam Chomsky, though he's controversial), and then some programming skill to get past the hurdle.

But Grammar check could use AI

AI as it stands is basically a large algorithm to match large datasets to the words you use. But the problem is that the datasets are taken from users who did not volunteer to put in that information.

It is not Data on Enterprise have novel experiences of every day and learning how to function in the human world by processing it through a matrix of quantum computing.

So WHEN grammar check does use AI, the AI is mostly doing the crunching of the corresponding the information into a more neat table option, as I understand it. It is not the same thing as Gen AI or your average spell check and Microsoft algorithm from say 2000.

Those are not equal things. Instead, adding Gen AI to say, Microsoft Word, is more like stealing your words for the machine (which BTW, Microsoft absolutely did and you need to transfer out to Anti-AI programs/Apps.) and corresponding them for Gen AI future use for people who can't write worth a damn, and then "averaging" it out. Elew. Who wants to write to the average? That's anti-Creative.

And just because it uses an Algorithm, doesn't automatically use AI.

Look, I can write a algorithm now:

Loop: If you want to be strong...

Go outside.

Do cardio.

Go lift weights.

Make sure you eat a healthy diet and balanced which includes reducing refined sugars and do not eat bad fats.

That equally is a set of instructions, but that's not automatically AI.

I programmed my calculator to spit out the quadratic formula. And this isn't even officially programming, this is a script. Dudes, if you're going to call that AI, then you need help with learning computer programming.

The threshold for making AI v spellcheck is a lot, lot higher programming than a set of simple tables and a loop that looks for letters and spaces corresponding it to an existing dictionary. If that's you're threshold for AI, then when you type words, you are caught in an algorithm. Ooooooo... OMG, when you pull up a dictionary to spellcheck yourself, that's AI. C'mon. The threshold is a might higher to make AI or "victim of algorithm" as in Twitter.

So anytime someone says, "All Spellcheck uses genAI/AI" Laugh in their faces and say no. 'cause like, I'm a terrible programmer, and even I'm like, Meh, not that hard to set up spell check, give me a solid dictionary database and I'll do ya.

That said, A human will beat AI on grammar anytime and will be able to sort weird spellings faster and A-OK, or not.

[aziraphales-library]

Hi! I was wondering if you could recommend any good fics with some sort of language barrier/difference in language between Az and Crowley. I just recently read Buried Treasure by InfeffableToreshi and I really liked it, would love those kinda vibes if you can find it in other fics. I also read an Omegaverse royalty fic where Az pretends he can’t speak Crowleys language, not a huge fan of omegaverse but smth like that would be good too. Longer works would be preferred but anything works. Thanks!

Hello! I haven't read the fic you mentioned, so am unsure of the vibes, but here are some fics featuring language/communication differences...

Food is Love by Sodium_Azide (T)

A chance childhood meeting leads to a lifetime sharing languages, hijinks, and meals together. Aziraphale and Anthony reach across cultures and nourish each others’ hearts and souls.

Scientific Discovery by SeasNStars (T)

Aziraphale had just departed on a two-month-long research study to a secluded island. He ends up encountering a creature of only myth and legend, a marvel to science to be sure.

Across Tides and Currents by doorwaytoparadise, Sodium_Azide (T)

A modern fantasy AU, featuring an inauspicious first meeting, inter-species romance, and meeting in the middle. Aziraphale lives a quiet life on the New England coastline, enduring the bad weather and gathering marine data for the local universities. During the worst storm of the worst hurricane season he can remember, something washes up on shore. "He looked back towards the ocean as the lightning flashed again. There. On the shoreline, rocking in the storm swell. Oh God no."

A Quiet Place by NightValeian (T)

Once upon a time, a silent angel and an outspoken demon met on a wall. Over time, they manage to find some common ground.

Dismantled by Asking_for_a_Fiend (M)

"But calling you by the SKU is really derogatory.” Disgust once again settles on the man’s expressive face. “A-zero-F-L. Eizero-F-L. Eizirofel. Aziraphale?” The handler’s face lights up. “Hey, d’you know there’s an angel called Aziraphale? Aziraphale. What do you think?” The griffin hums, testing the name in his mind. “Not that bad. I’ll think about it.” *** Aziraphale is a griffin raised in a human world, without any contact with others of his own specie, but not really craving it, either. But there's this one handler at the sanctuary, determined to teach him how to be a griffin. Aziraphale is not sure if he shares the ambition, still, the handler himself may be worth giving this a try.

Pitch Black, Pale Blue by enbeeemcee (M)

Crowley's about at the end of his rope. His internship is nothing more than washing all the equipment used by the interns who actually do real scientific research and his boss hates him. His friends either urge him to keep at it or encourage him to drink and forget. Neither of which are particularly helpful. Then, one night, he finds something on the beach.

And the one you mentioned...

Buried Treasure by IneffableToreshi (E)

Aziraphale Fell and his small team are working hard to uncover a mystery that has been discovered deep in the desert in a region with no known history of habitation... ...and they are having very little luck coming up with anything other than stone brick upon stone brick. But as their sponsorships are dwindling and Aziraphale's hopes for the project are beginning to well and truly die, he literally falls right into the site's secret, and discovers something much, much more important than he could have ever imagined.

- Mod D

[ayeforscotland]

What is Dataflow? Part 2: Diagrams

This is the second part of a couple of posts about Dataflow, particularly why it's important for the world going forward and relating to the Crowd Strike IT disaster.

Read the first part here.

Before I get into this one today, I wanted to address a couple of things.

Firstly, Dataflow is something that nearly every single person can understand. You do NOT:

Need to have a degree in Computing Science

Need to work in IT

Need to be a data analyst / Spreadsheet master

If any of you see the word 'Data' and feel your eyes glazing over, try and snap out of it because, if you're anything like me, Dataflow is much more approachable as a concept.

Secondly, what do I mean by IT?

Traditionally in most of our media the all-encompassing 'IT department' handles everything to do with technology. But every business works differently and there are many job titles with lots of crossover.

For example, you can be an infrastructure engineer where your focus is on building and maintaining the IT infrastructure that connects your organisation internally and externally. This is a completely different role from an Application Portfolio Manager who is tasked with looking after the Applications used in business processes.

Both are technical people and come under the banner of 'IT' - but their roles are focused in different areas. So just bear that in mind!

Now that's out of the way, let's begin! This one will be a little bit deeper, and questions welcome!

An Intro to Diagrams

You probably do not need a history of why pictures are important to the human race but to cover our bases, ever since we put traced our hands on a cave wall we have been using pictures to communicate.

Jump forward in time and you have engineers like Leonardo Da Vinci drafting engineering schematics.

You get the idea, humans have been creating diagrams (Pictures) for thousands of years. Centuries of refinement and we have much more modern variations.

And there's one main reason why diagrams are important: They are a Common Language.

In this context, a Common Language helps bridge a language gap between disciplines as well as a linguistic gap. A Spanish electrician and a German electrician should be able to refer to the same diagram and understand each other, even if they don't know each other's language.

The reason they can do this is because they're are international standards which govern how electrical diagrams are created.

A Common Language for Digital?

Here's an image I've shown to clients from governments and institutions to global organisations.

Everything around us, from the products we use to the bridges we drive over and the buildings we live, work, enjoy and shop in had diagrams backing them.

You would not build a skyscraper without a structural engineering diagram, you would not build an extension on your house if an architect couldn't produce a blueprint.

Why is there not an equivalent for the Digital World and for Dataflow?

Where is the Digital Common Language?

This is the bit where the lightbulb goes on in a lot of people's heads. Because, as I mentioned in Part 1, the flow of data is the flow of information and knowledge. And the common mistake is that people think of dataflow, and only ever think about the technology.

Dataflow is the flow of information between People, Business Processes *and* Technology Assets.

It is not reserved to Technology specialists. When you look at the flow of data, you need to understand the People (Stakeholders) at the top, the processes that they perform (and the processes which use the data) and the technology assets that support that data.

The reason why this is important is because it puts the entire organisation in context.

It is something that modern businesses fail to do. They might have flow charts and network diagrams, and these are 'alright' in specific contexts, but they fall to pieces when they lack the context of the full organisation.

For example, here is a Network Diagram. It is probably of *some* value to technical personnel who work in infrastructure. Worth bearing in mind, some organisations don't even have something like this.

To be absolutely clear, this diagram will hold some value for some people within the organisation. I'm not saying it's completely useless. But for almost everyone else, it is entirely out of context, especially for any non-technical people.

So it doesn't help non-technical people understand why all of these assets are important, and it doesn't help infrastructure teams articulate the importance of any of these assets.

What happens if one of those switches or routers fails? What's the impact on the organisation? Who is affected? The diagram above does not answer those questions.

On the other side of the business we have process diagrams (aka workflow diagrams) which look like this.

Again we run into the same problem - This is maybe useful for some people working up at the process layer, but even then it doesn't provide context for the stakeholders involved (Are there multiple people/departments involved throughout) and it doesn't provide any context for technical personnel who are responsible for maintaining the technology that supports this process.

In short, nobody has the big picture because there is not a common language between Business & IT.

Conclusion

So what do we do? Well we need to have a Common Language between Business & IT. While we need people with cross-functional knowledge, we also need a common language (or common framework) for both sides of the organisation to actually understand each other.

Otherwise you get massively siloed departments completely winging their disaster recovery strategies when things like Crowd Strike goes down.

Senior Management will be asked questions about what needs to be prioritised and they won't have answers because they aren't thinking in terms of Dataflow.

It's not just 'We need to turn on everything again' - It's a question of priorities.

Thing is, there's a relatively simple way to do it, in a way that looking at any engineering diagram feels simple but actually has had decades/centuries of thought behind it. It almost feels like complete common sense.

I'll save it for Part 3 if you're interested in me continuing and I'll make a diagram of my blog.

The important thing is mapping out all the connections and dependencies, and there's not some magic button you press that does it all.

But rigorous engineering work is exactly that, you can't fudge it with a half-arsed attempt. You need to be proactive, instead of reacting whenever disaster strikes.

[enbycrip]

On TDOV, a historian’s perspective on the transphobic nonsense some people spout about bones, burials and gender.

We have been finding burials of gender divergent folk and folk outside the binary since we started any form of organised study of archaeology. The 18th and 19th century European and European-derived archaeologists who didn’t recognise that were the ignorant ones, assuming their definitions of gender were “universal”. Including within the binary; their definitions were deeply reductionist and deeply inaccurate, failing to recognise that even societies they actively claimed some form of cultural descent from had very different definitions of what a “man” or “woman” was from theirs.

Notably, they came from a *very* colonialist society that was doing its best to destroy living cultures with fuller definitions of gender than it allowed. We are still living in a framework of repairing the damage they did in how we understand past cultures, and modern media is frequently very unhelpful on how it reports on discoveries and interpretations, erasing or flattening the huge element of interpretation and uncertainty involved even with the exciting level of data that new techniques can give us on any given site.

Anyone who takes even a cursory look into bioarchaeology will find out that “sexing” bones - which every modern archaeologist knows is at best one element in gendering the person they belonged to - is *incredibly* fucking difficult. A lot of the techniques for it involve measuring proportions of anatomical features that may well not be present - it’s rare for a specimen to be intact. And, frankly - humans are a spectrum in every physical feature. Height, weight, bone density, width of shoulders and pelvis etc etc. Cheaper and more widespread DNA testing is slowly making more people realise that chromosomes aren’t the final word they were told in first year high school biology either. For the remains of people from some cultures, we have some written history to cross-reference archaeology with about the meanings of artefacts. In others, we don’t - and even when we do, half the time what we have is the interpretation of two lines from some dude from a different culture altogether, or from an aside in a religious tract, or a recipe book.

Almost every older specimen we have has been reinterpreted multiple times. Probably the oldest remains as yet found in the UK, from circa 31,000 BCE based on current best estimates, were known as “the Red Lady of Paviland” because they were dyed red with ochre, and the person who found them in 1832 decided from that fact and fact that there was jewellery found with them that they belonged to a female Roman-era sex worker. From around 200 years more work, and considerably more data, we now interpret them as having belonged to a male hunter-gatherer, likely from a nomadic or semi-nomadic people who frequently lived much closer to the coast than the place in Wales where the remains were found. We do not have the information about this person’s culture, how their culture viewed gender, the pronouns their language used and if they related to gender; this was minimum 20,000 years before and on the other side of the world from the oldest writing we have as yet found. We are putting data together and interpreting as best we can, with the awareness that there could be a discovery tomorrow that could utterly change that.

I’m sorry if this seems an obscure topic to address for TDOV, but I do know how this idea of “the bones don’t lie” has got into plenty of folks’ heads when they are having a bad time. So I wanted to address the fact that not only is it bawbins, the entire series of assumptions it posits is bawbins of the type that TERFs and transphobes, like racists, misogynists, and fascists in general, like to spew out there - that gender, or indeed race, is biological, fixed, and essential, that their understandings of it are “fact-based”, and that science is a series of fixed, immutable facts that are just lying there like stones on a beach.

And all of that is complete and utter bawbins.

Gender, and race, like all human frameworks of knowledge and understanding, are constructs - tools we use to understand a ridiculously complex and difficult world. They may *reference* objective facts, but are not in *themselves* facts. None of which means they aren’t incredibly meaningful or don’t have massive impacts on our experiences as humans. But they are mutable, ever-evolving, and incredibly open to constant interpretation and re-definition, and while your individual experience of them will be mediated by your culture, it’s also unique to you too.

No one else’s definition of your gender can *ever* be more accurate than yours. Because it is *yours*.

Happy Trans Day of Visibility, everyone 🏳️‍⚧️💛🤍🖤💜🩷🤍💙

[the-cat-ara]

fanchild Metamy

Name: Iris Rose Parents: Amy Rose and Metal Sonic Age: 17 Height: 100 cm Gender: Female

Personality: She is quiet, observant. She is not very patient and prefers to take actions on her own, despite that she is obedient. She is very loyal and cares a lot about her family (the whole egg family).

Likes: sweet things and chocolate ice cream. She likes writing and reading, boxing and also knitting a bit (but only when she feels she has a lot of thoughts on her mind). Loves helping her dad and playing with or watching her uncles Cubot and Orbot. He enjoys talking to her Aunt Sage about complex things (although she mostly just listens to her and asks questions). He also enjoys learning robotics and science from her grandfather. Wants to learn baking because she was told her mother really liked it.

Dislikes: Sonic (that bad imitator of her father who also annoys her grandfather). Failing and having her failures thrown in her face. All her grandfather's enemies.

Data: She is the first organic android created on her world. Her skeleton is metallic, but her skin is organic, able to feel like any normal organic creature. She is able to regulate her temperature, to some extent. She inherited her mother's super strength, enhanced with her robotic part. She is very resistant to blows, fast and agile, she can also adapt and learn quickly. Although she fights melee, she can also use long or short distance weapons, like her mother she can make her personal weapon (kusarigama) appear and disappear, which she calls Kichi Kichi Kama. She is not mute, but prefers not to speak. Very few people have heard her speak (only her family circle) she prefers to communicate with sign language or simply not speak, especially with her enemies and strangers. Created from a piece of Metal Sonic's nucleus which was fused with Amy's DNA (all this required a great energy from the chaos emeralds, causing them to enter a state of hibernation), later it was implanted in Amy's womb where it developed successfully, but bringing side effects over the months, until Iris was born. Iris does not have many memories of her mother, she was only with her for the first year after she was born, Amy died after being hospitalized all that time (the process was too much for her body).

[signedaiko]

Hey Aiko! Thanks for the HCs from the last request, I really enjoyed reading every word of them.

You know those math equations where they spell “I Love You” as the final answers?

Could I request HCs of [MTMTE/LL] Perceptor where Cybertronian![Reader] [Gender Neutral] has a huge crush on him and pretends to ask for his help with some equations when they’re actually confessing their love to him through the complicated numbers and symbols?

Happy Ending: He likes them back.

Perceptor X Reader [MTMTE]

In which Perceptor's lab assistant confesses in the numeric language they both work with.

Reader is: Gender Neutral | Cybertronian | Autobot. Romantic.

The science wing aboard the Lost Light had become your home the last couple of months

More specifically, Perceptor's lab, where you'd been handpicked by the scientist to assist him with research

It was a dream come true! I mean, whatever the quest the ship was on was, it was fun and all, but having read every last one of Perceptor's research papers and lab reports, it felt wrong to be the one picked to help

He was Cybertron's genius, the best of the best, and he wasn't weird about it either

Most days you'd stand in the lab, comparing and accumulating data relevant to his research, taking care of any specimen, and cleaning

Most commonly, you were tasked with chemical waste disposal; each chemical was different, and each process was longer than the last

Today, however, Perceptor tasked you with sorting through the 'other' portfolio of research data

It was a list of all kinds of extras that were never finished, tasks galore.

It was also the perfect opportunity for you to work closely with him, since he was helping you comb through it

"What about the anti-anti-matter gun?"

"Anything with the word 'gun' is just Brainstorm trying to get me to do his projects for him. Dispose of it."

While he handled the physical box of 'other' projects, you were sifting through the online database

Thankfully your job was easier because you'd been so distracted watching him across from you and mulling over your plan that you wouldn't get anywhere otherwise

Truthfully, you'd long since sorted through everything using a quick sorting algorithm, but you'd been pretending to keep busy as you contemplated the pros and cons of confessing to Perceptor

He was your boss; if he didn't feel the same, it'd be awkward working with him every day

But your work performance was dropping with all the time you spent staring at him and daydreaming of your lives together

"Sorry, Perceptor, one last question. What is this?"

It was an entry with one equation: 9x - 7i > 3(3x - 7u)

"Don't know; you can note what it solves and delete the main file."

"But what are we solving for?"

Your bait worked, and the scientist stood up to walk behind you, leaning over your chair to get a better glimpse

"Nothing, you're supposed to fill in for 'i' and 'u,' but you can simplify it."

He leaned further in to point at the brackets

"Multiply everything in there by three. Yes, just like that. So now we have 9x - 7i > 9x - 21u."

As funny as it was that he thought you couldn't calculate it on your own, you let him continue

"9x is then cancelled out on both sides, leaving you with -7i > -21u. just divide by three and then..."

"i <3 u"

"Yes, exactly, that's as simplified as you can get it until you identify 'i' and 'u'"

Your smile faltered, realizing he may have still not understood what it said

God, how could you have expected this to work?

"Thanks, Perc—"

"For example, you could substitute 'i' for 'Perceptor' and 'u' for you. Come on now, don't act like I couldn't figure out your game. You think so little of my intellect?"

When you turned to look at him, you realized he was looking at you rather than the screen, a cocky smile sprawled across his face

"I swear I don't; I was just—"

"Just what? Thinking I thought you couldn't solve simple operations? Thinking I would have 'forgotten' such a small equation in my data banks?"

You hid your face behind your hands to try and hide the blush, but Perceptor was already chuckling and pulling you up from where you sat

"Well, if it means anything..."

He reached to the keyboard, adding an extra character

'i <3 u 2'

Author's Note - I love this guy. What a fella, what an enjoyable cocky fella!!!! Thank you for requesting!

[max1461]

Linguists deal with two kinds of theories or models.

First, you have grammars. A grammar, in this sense, is a model of an individual natural language: what sorts of utterances occur in that language? When are they used and what do they mean? Even assembling this sort of model in full is a Herculean task, but we are fairly successful at modeling sub-systems of individual languages: what sounds occur in the language, and how may they be ordered and combined?—this is phonology. What strings of words occur in the language, and what strings don't, irrespective of what they mean?—this is syntax. Characterizing these things, for a particular language, is largely tractable. A grammar (a model of the utterances of a single language) is falsified if it predicts utterances that do not occur, or fails to predict utterances that do occur. These situations are called "overgeneration" and "undergeneration", respectively. One of the advantages linguistics has as a science is that we have both massive corpora of observational data (text that people have written, databases of recorded phone calls), and access to cheap and easy experimental data (you can ask people to say things in the target language—you have to be a bit careful about how you do this—and see if what they say accords with your model). We have to make some spherical cow type assumptions, we have to "ignore friction" sometimes (friction is most often what the Chomskyans call "performance error", which you do not have to be a Chomskyan to believe in, but I digress). In any case, this lets us build robust, useful, highly predictive, and falsifiable, although necessarily incomplete, models of individual natural languages. These are called descriptive grammars.

Descriptive grammars often have a strong formal component—Chomsky, for all his faults, recognized that both phonology and syntax could be well described by formal grammars in the sense of mathematics and computer science, and these tools have been tremendously productive since the 60s in producing good models of natural language. I believe Chomsky's program sensu stricto is a dead end, but the basic insight that human language can be thought about formally in this way has been extremely useful and has transformed the field for the better. Read any descriptive grammar, of a language from Europe or Papua or the Amazon, and you will see (in linguists' own idiosyncratic notation) a flurry regexes and syntax trees (this is a bit unfair—the computer scientists stole syntax trees from us, also via Chomsky) and string rewrite rules and so on and so forth. Some of this preceded Chomsky but more than anyone else he gave it legs.

Anyway, linguists are also interested in another kind of model, which confusingly enough we call simply a "theory". So you have "grammars", which are theories of individual natural languages, and you have "theories", which are theories of grammars. A linguistic theory is a model which predicts what sorts of grammar are possible for a human language to have. This generally comes in the form of making claims about

the structure of the cognitive faculty for language, and its limitations

the pathways by which language evolves over time, and the grammars that are therefore attractors and repellers in this dynamical system.

Both of these avenues of research have seen some limited success, but linguistics as a field is far worse at producing theories of this sort than it is at producing grammars.

Capital-G Generativism, Chomsky's program, is one such attempt to produce a theory of human language, and it has not worked very well at all. Chomsky's adherents will say it has worked very well—they are wrong and everybody else thinks they are very wrong, but Chomsky has more clout in linguistics than anyone else so they get to publish in serious journals and whatnot. For an analogy that will be familiar to physics people: Chomskyans are string theorists. And they have discovered some stuff! We know about wh-islands thanks to Generativism, and we probably would not have discovered them otherwise. Wh-islands are weird! It's a good thing the Chomskyans found wh-islands, and a few other bits and pieces like that. But Generativism as a program has, I believe, hit a dead end and will not be recovering.

Right, Generativism is sort of, kind of attempting to do (1), poorly. There are other people attempting to do (1) more robustly, but I don't know much about it. It's probably important. For my own part I think (2) has a lot of promise, because we already have a fairly detailed understanding of how language changes over time, at least as regards phonology. Some people are already working on this sort of program, and there's a lot of work left to be done, but I do think it's promising.

Someone said to me, recently-ish, that the success of LLMs spells doom for descriptive linguistics. "Look, that model does better than any of your grammars of English at producing English sentences! You've been thoroughly outclassed!". But I don't think this is true at all. Linguists aren't confused about which English sentences are valid—many of us are native English speakers, and could simply tell you ourselves without the help of an LLM. We're confused about why. We're trying to distill the patterns of English grammar, known implicitly to every English speaker, into explicit rules that tell us something explanatory about how English works. An LLM is basically just another English speaker we can query for data, except worse, because instead of a human mind speaking a human language (our object of study) it's a simulacrum of such.

Uh, for another physics analogy: suppose someone came along with a black box, and this black box had within it (by magic) a database of every possible history of the universe. You input a world-state, and it returns a list of all the future histories that could follow on from this world state. If the universe is deterministic, there should only be one of them; if not maybe there are multiple. If the universe is probabilistic, suppose the machine also gives you a probability for each future history. If you input the state of a local patch of spacetime, the machine gives you all histories in which that local patch exists and how they evolve.

Now, given this machine, I've got a theory of everything for you. My theory is: whatever the machine says is going to happen at time t is what will happen at time t. Now, I don't doubt that that's a very useful thing! Most physicists would probably love to have this machine! But I do not think my theory of everything, despite being extremely predictive, is a very good one. Why? Because it doesn't tell you anything, it doesn't identify any patterns in the way the natural world works, it just says "ask the black box and then believe it". Well, sure. But then you might get curious and want to ask: are there patterns in the black box's answers? Are there human-comprehensible rules which seem to characterize its output? Can I figure out what those are? And then, presto, you're doing good old regular physics again, as if you didn't even have the black box. The black box is just a way to run experiments faster and cheaper, to get at what you really want to know.

General Relativity, even though it has singularities, and it's incompatible with Quantum Mechanics, is better as a theory of physics than my black box theory of everything, because it actually identifies patterns, it gives you some insight into how the natural world behaves, in a way that you, a human, can understand.

In linguistics, we're in a similar situation with LLMs, only LLMs are a lot worse than the black box I've described—they still mess up and give weird answers from time to time. And more importantly, we already have a linguistic black box, we have billions of them: they're called human native speakers, and you can find one in your local corner store or dry cleaner. Querying the black box and trying to find patterns is what linguistics already is, that's what linguists do, and having another, less accurate black box does very little for us.

Now, there is one advantage that LLMs have. You can do interpretability research on LLMs, and figure out how they are doing what they are doing. Linguists and ML researchers are kind of in a similar boat here. In linguistics, well, we already all know how to talk, we just don't know how we know how to talk. In ML, you have these models that are very successful, buy you don't know why they work so well, how they're doing it. We have our own version of interpretability research, which is neuroscience and neurolinguistics. And ML researchers have interpretability research for LLMs, and it's very possible theirs progresses faster than ours! Now with the caveat that we can't expect LLMs to work just like the human brain, and we can't expect the internal grammar of a language inside an LLM to be identical to the one used implicitly by the human mind to produce native-speaker utterances, we still might get useful insights out of proper scrutiny of the innards of an LLM that speaks English very well. That's certainly possible!

But just having the LLM, does that make the work of descriptive linguistics obsolete? No, obviously not. To say so completely misunderstands what we are trying to do.