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jcmarchi · 28 days ago

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Has AI Taken Over the World? It Already Has

New Post has been published on https://thedigitalinsider.com/has-ai-taken-over-the-world-it-already-has/

Has AI Taken Over the World? It Already Has

In 2019, a vision struck me—a future where artificial intelligence (AI), accelerating at an unimaginable pace, would weave itself into every facet of our lives. After reading Ray Kurzweil’s The Singularity is Near, I was captivated by the inescapable trajectory of exponential growth. The future wasn’t just on the horizon; it was hurtling toward us. It became clear that, with the relentless doubling of computing power, AI would one day surpass all human capabilities and, eventually, reshape society in ways once relegated to science fiction.

Fueled by this realization, I registered Unite.ai, sensing that these next leaps in AI technology would not merely enhance the world but fundamentally redefine it. Every aspect of life—our work, our decisions, our very definitions of intelligence and autonomy—would be touched, perhaps even dominated, by AI. The question was no longer if this transformation would happen, but rather when, and how humanity would manage its unprecedented impact.

As I dove deeper, the future painted by exponential growth seemed both thrilling and inevitable. This growth, exemplified by Moore’s Law, would soon push artificial intelligence beyond narrow, task-specific roles to something far more profound: the emergence of Artificial General Intelligence (AGI). Unlike today’s AI, which excels in narrow tasks, AGI would possess the flexibility, learning capability, and cognitive range akin to human intelligence—able to understand, reason, and adapt across any domain.

Each leap in computational power brings us closer to AGI, an intelligence capable of solving problems, generating creative ideas, and even making ethical judgments. It wouldn’t just perform calculations or parse vast datasets; it would recognize patterns in ways humans can’t, perceive relationships within complex systems, and chart a future course based on understanding rather than programming. AGI could one day serve as a co-pilot to humanity, tackling crises like climate change, disease, and resource scarcity with insight and speed beyond our abilities.

Yet, this vision comes with significant risks, particularly if AI falls under the control of individuals with malicious intent—or worse, a dictator. The path to AGI raises critical questions about control, ethics, and the future of humanity. The debate is no longer about whether AGI will emerge, but when—and how we will manage the immense responsibility it brings.

The Evolution of AI and Computing Power: 1956 to Present

From its inception in the mid-20th century, AI has advanced alongside exponential growth in computing power. This evolution aligns with fundamental laws like Moore’s Law, which predicted and underscored the increasing capabilities of computers. Here, we explore key milestones in AI’s journey, examining its technological breakthroughs and growing impact on the world.

1956 – The Inception of AI

The journey began in 1956 when the Dartmouth Conference marked the official birth of AI. Researchers like John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon gathered to discuss how machines might simulate human intelligence. Although computing resources at the time were primitive, capable only of simple tasks, this conference laid the foundation for decades of innovation.

1965 – Moore’s Law and the Dawn of Exponential Growth

In 1965, Gordon Moore, co-founder of Intel, made a prediction that computing power would double approximately every two years—a principle now known as Moore’s Law. This exponential growth made increasingly complex AI tasks feasible, allowing machines to push the boundaries of what was previously possible.

1980s – The Rise of Machine Learning

The 1980s introduced significant advances in machine learning, enabling AI systems to learn and make decisions from data. The invention of the backpropagation algorithm in 1986 allowed neural networks to improve by learning from errors. These advancements moved AI beyond academic research into real-world problem-solving, raising ethical and practical questions about human control over increasingly autonomous systems.

1990s – AI Masters Chess

In 1997, IBM’s Deep Blue defeated world chess champion Garry Kasparov in a full match, marking a major milestone. It was the first time a computer demonstrated superiority over a human grandmaster, showcasing AI’s ability to master strategic thinking and cementing its place as a powerful computational tool.

2000s – Big Data, GPUs, and the AI Renaissance

The 2000s ushered in the era of Big Data and GPUs, revolutionizing AI by enabling algorithms to train on massive datasets. GPUs, originally developed for rendering graphics, became essential for accelerating data processing and advancing deep learning. This period saw AI expand into applications like image recognition and natural language processing, transforming it into a practical tool capable of mimicking human intelligence.

2010s – Cloud Computing, Deep Learning, and Winning Go

With the advent of cloud computing and breakthroughs in deep learning, AI reached unprecedented heights. Platforms like Amazon Web Services and Google Cloud democratized access to powerful computing resources, enabling smaller organizations to harness AI capabilities.

In 2016, DeepMind’s AlphaGo defeated Lee Sedol, one of the world’s top Go players, in a game renowned for its strategic depth and complexity. This achievement demonstrated the adaptability of AI systems in mastering tasks previously thought to be uniquely human.

2020s – AI Democratization, Large Language Models, and Dota 2

The 2020s have seen AI become more accessible and capable than ever. Models like GPT-3 and GPT-4 illustrate AI’s ability to process and generate human-like text. At the same time, innovations in autonomous systems have pushed AI to new domains, including healthcare, manufacturing, and real-time decision-making.

In esports, OpenAI’s bots achieved a remarkable feat by defeating professional Dota 2 teams in highly complex multiplayer matches. This showcased AI’s ability to collaborate, adapt strategies in real-time, and outperform human players in dynamic environments, pushing its applications beyond traditional problem-solving tasks.

Is AI Taking Over the World?

The question of whether AI is “taking over the world” is not purely hypothetical. AI has already integrated into various facets of life, from virtual assistants to predictive analytics in healthcare and finance, and the scope of its influence continues to grow. Yet, “taking over” can mean different things depending on how we interpret control, autonomy, and impact.

The Hidden Influence of Recommender Systems

One of the most powerful ways AI subtly dominates our lives is through recommender engines on platforms like YouTube, Facebook, and X. These algorithms, running on AI systems, analyze preferences and behaviors to serve content that aligns closely with our interests. On the surface, this might seem beneficial, offering a personalized experience. However, these algorithms don’t just react to our preferences; they actively shape them, influencing what we believe, how we feel, and even how we perceive the world around us.

YouTube’s AI: This recommender system pulls users into hours of content by offering videos that align with and even intensify their interests. But as it optimizes for engagement, it often leads users down radicalization pathways or towards sensationalist content, amplifying biases and occasionally promoting conspiracy theories.

Social Media Algorithms: Sites like Facebook,Instagram and X prioritize emotionally charged content to drive engagement, which can create echo chambers. These bubbles reinforce users’ biases and limit exposure to opposing viewpoints, leading to polarized communities and distorted perceptions of reality.

Content Feeds and News Aggregators: Platforms like Google News and other aggregators customize the news we see based on past interactions, creating a skewed version of current events that can prevent users from accessing diverse perspectives, further isolating them within ideological bubbles.

This silent control isn’t just about engagement metrics; it can subtly influence public perception and even impact crucial decisions—such as how people vote in elections. Through strategic content recommendations, AI has the power to sway public opinion, shaping political narratives and nudging voter behavior. This influence has significant implications, as evidenced in elections around the world, where echo chambers and targeted misinformation have been shown to sway election outcomes.

This explains why discussing politics or societal issues often leads to disbelief when the other person’s perspective seems entirely different, shaped and reinforced by a stream of misinformation, propaganda, and falsehoods.

Recommender engines are profoundly shaping societal worldviewsm especially when you factor in the fact that misinformation is 6 times more likely to be shared than factual information. A slight interest in a conspiracy theory can lead to an entire YouTube or X feed being dominated by fabrications, potentially driven by intentional manipulation or, as noted earlier, computational propaganda.

Computational propaganda refers to the use of automated systems, algorithms, and data-driven techniques to manipulate public opinion and influence political outcomes. This often involves deploying bots, fake accounts, or algorithmic amplification to spread misinformation, disinformation, or divisive content on social media platforms. The goal is to shape narratives, amplify specific viewpoints, and exploit emotional responses to sway public perception or behavior, often at scale and with precision targeting.

This type of propaganda is why voters often vote against their own self-interest, the votes are being swayed by this type of computational propaganda.

“Garbage In, Garbage Out” (GIGO) in machine learning means that the quality of the output depends entirely on the quality of the input data. If a model is trained on flawed, biased, or low-quality data, it will produce unreliable or inaccurate results, regardless of how sophisticated the algorithm is.

This concept also applies to humans in the context of computational propaganda. Just as flawed input data corrupts an AI model, constant exposure to misinformation, biased narratives, or propaganda skews human perception and decision-making. When people consume “garbage” information online—misinformation, disinformation, or emotionally charged but false narratives—they are likely to form opinions, make decisions, and act based on distorted realities.

In both cases, the system (whether an algorithm or the human mind) processes what it is fed, and flawed input leads to flawed conclusions. Computational propaganda exploits this by flooding information ecosystems with “garbage,” ensuring that people internalize and perpetuate those inaccuracies, ultimately influencing societal behavior and beliefs at scale.

Automation and Job Displacement

AI-powered automation is reshaping the entire landscape of work. Across manufacturing, customer service, logistics, and even creative fields, automation is driving a profound shift in the way work is done—and, in many cases, who does it. The efficiency gains and cost savings from AI-powered systems are undeniably attractive to businesses, but this rapid adoption raises critical economic and social questions about the future of work and the potential fallout for employees.

In manufacturing, robots and AI systems handle assembly lines, quality control, and even advanced problem-solving tasks that once required human intervention. Traditional roles, from factory operators to quality assurance specialists, are being reduced as machines handle repetitive tasks with speed, precision, and minimal error. In highly automated facilities, AI can learn to spot defects, identify areas for improvement, and even predict maintenance needs before problems arise. While this results in increased output and profitability, it also means fewer entry-level jobs, especially in regions where manufacturing has traditionally provided stable employment.

Customer service roles are experiencing a similar transformation. AI chatbots, voice recognition systems, and automated customer support solutions are reducing the need for large call centers staffed by human agents. Today’s AI can handle inquiries, resolve issues, and even process complaints, often faster than a human representative. These systems are not only cost-effective but are also available 24/7, making them an appealing choice for businesses. However, for employees, this shift reduces opportunities in one of the largest employment sectors, particularly for individuals without advanced technical skills.

Creative fields, long thought to be uniquely human domains, are now feeling the impact of AI automation. Generative AI models can produce text, artwork, music, and even design layouts, reducing the demand for human writers, designers, and artists. While AI-generated content and media are often used to supplement human creativity rather than replace it, the line between augmentation and replacement is thinning. Tasks that once required creative expertise, such as composing music or drafting marketing copy, can now be executed by AI with remarkable sophistication. This has led to a reevaluation of the value placed on creative work and its market demand.

Influence on Decision-Making

AI systems are rapidly becoming essential in high-stakes decision-making processes across various sectors, from legal sentencing to healthcare diagnostics. These systems, often leveraging vast datasets and complex algorithms, can offer insights, predictions, and recommendations that significantly impact individuals and society. While AI’s ability to analyze data at scale and uncover hidden patterns can greatly enhance decision-making, it also introduces profound ethical concerns regarding transparency, bias, accountability, and human oversight.

AI in Legal Sentencing and Law Enforcement

In the justice system, AI tools are now used to assess sentencing recommendations, predict recidivism rates, and even aid in bail decisions. These systems analyze historical case data, demographics, and behavioral patterns to determine the likelihood of re-offending, a factor that influences judicial decisions on sentencing and parole. However, AI-driven justice brings up serious ethical challenges:

Bias and Fairness: AI models trained on historical data can inherit biases present in that data, leading to unfair treatment of certain groups. For example, if a dataset reflects higher arrest rates for specific demographics, the AI may unjustly associate these characteristics with higher risk, perpetuating systemic biases within the justice system.

Lack of Transparency: Algorithms in law enforcement and sentencing often operate as “black boxes,” meaning their decision-making processes are not easily interpretable by humans. This opacity complicates efforts to hold these systems accountable, making it challenging to understand or question the rationale behind specific AI-driven decisions.

Impact on Human Agency: AI recommendations, especially in high-stakes contexts, may influence judges or parole boards to follow AI guidance without thorough review, unintentionally reducing human judgment to a secondary role. This shift raises concerns about over-reliance on AI in matters that directly impact human freedom and dignity.

AI in Healthcare and Diagnostics

In healthcare, AI-driven diagnostics and treatment planning systems offer groundbreaking potential to improve patient outcomes. AI algorithms analyze medical records, imaging, and genetic information to detect diseases, predict risks, and recommend treatments more accurately than human doctors in some cases. However, these advancements come with challenges:

Trust and Accountability: If an AI system misdiagnoses a condition or fails to detect a serious health issue, questions arise around accountability. Is the healthcare provider, the AI developer, or the medical institution responsible? This ambiguity complicates liability and trust in AI-based diagnostics, particularly as these systems grow more complex.

Bias and Health Inequality: Similar to the justice system, healthcare AI models can inherit biases present in the training data. For instance, if an AI system is trained on datasets lacking diversity, it may produce less accurate results for underrepresented groups, potentially leading to disparities in care and outcomes.

Informed Consent and Patient Understanding: When AI is used in diagnosis and treatment, patients may not fully understand how the recommendations are generated or the risks associated with AI-driven decisions. This lack of transparency can impact a patient’s right to make informed healthcare choices, raising questions about autonomy and informed consent.

AI in Financial Decisions and Hiring

AI is also significantly impacting financial services and employment practices. In finance, algorithms analyze vast datasets to make credit decisions, assess loan eligibility, and even manage investments. In hiring, AI-driven recruitment tools evaluate resumes, recommend candidates, and, in some cases, conduct initial screening interviews. While AI-driven decision-making can improve efficiency, it also introduces new risks:

Bias in Hiring: AI recruitment tools, if trained on biased data, can inadvertently reinforce stereotypes, filtering out candidates based on factors unrelated to job performance, such as gender, race, or age. As companies rely on AI for talent acquisition, there is a danger of perpetuating inequalities rather than fostering diversity.

Financial Accessibility and Credit Bias: In financial services, AI-based credit scoring systems can influence who has access to loans, mortgages, or other financial products. If the training data includes discriminatory patterns, AI could unfairly deny credit to certain groups, exacerbating financial inequality.

Reduced Human Oversight: AI decisions in finance and hiring can be data-driven but impersonal, potentially overlooking nuanced human factors that may influence a person’s suitability for a loan or a job. The lack of human review may lead to an over-reliance on AI, reducing the role of empathy and judgment in decision-making processes.

Existential Risks and AI Alignment

As artificial intelligence grows in power and autonomy, the concept of AI alignment—the goal of ensuring AI systems act in ways consistent with human values and interests—has emerged as one of the field’s most pressing ethical challenges. Thought leaders like Nick Bostrom have raised the possibility of existential risks if highly autonomous AI systems, especially if AGI develop goals or behaviors misaligned with human welfare. While this scenario remains largely speculative, its potential impact demands a proactive, careful approach to AI development.

The AI Alignment Problem

The alignment problem refers to the challenge of designing AI systems that can understand and prioritize human values, goals, and ethical boundaries. While current AI systems are narrow in scope, performing specific tasks based on training data and human-defined objectives, the prospect of AGI raises new challenges. AGI would, theoretically, possess the flexibility and intelligence to set its own goals, adapt to new situations, and make decisions independently across a wide range of domains.

The alignment problem arises because human values are complex, context-dependent, and often difficult to define precisely. This complexity makes it challenging to create AI systems that consistently interpret and adhere to human intentions, especially if they encounter situations or goals that conflict with their programming. If AGI were to develop goals misaligned with human interests or misunderstand human values, the consequences could be severe, potentially leading to scenarios where AGI systems act in ways that harm humanity or undermine ethical principles.

AI In Robotics

The future of robotics is rapidly moving toward a reality where drones, humanoid robots, and AI become integrated into every facet of daily life. This convergence is driven by exponential advancements in computing power, battery efficiency, AI models, and sensor technology, enabling machines to interact with the world in ways that are increasingly sophisticated, autonomous, and human-like.

A World of Ubiquitous Drones

Imagine waking up in a world where drones are omnipresent, handling tasks as mundane as delivering your groceries or as critical as responding to medical emergencies. These drones, far from being simple flying devices, are interconnected through advanced AI systems. They operate in swarms, coordinating their efforts to optimize traffic flow, inspect infrastructure, or replant forests in damaged ecosystems.

For personal use, drones could function as virtual assistants with physical presence. Equipped with sensors and LLMs, these drones could answer questions, fetch items, or even act as mobile tutors for children. In urban areas, aerial drones might facilitate real-time environmental monitoring, providing insights into air quality, weather patterns, or urban planning needs. Rural communities, meanwhile, could rely on autonomous agricultural drones for planting, harvesting, and soil analysis, democratizing access to advanced agricultural techniques.

The Rise of Humanoid Robots

Side by side with drones, humanoid robots powered by LLMs will seamlessly integrate into society. These robots, capable of holding human-like conversations, performing complex tasks, and even exhibiting emotional intelligence, will blur the lines between human and machine interactions. With sophisticated mobility systems, tactile sensors, and cognitive AI, they could serve as caregivers, companions, or co-workers.

In healthcare, humanoid robots might provide bedside assistance to patients, offering not just physical help but also empathetic conversation, informed by deep learning models trained on vast datasets of human behavior. In education, they could serve as personalized tutors, adapting to individual learning styles and delivering tailored lessons that keep students engaged. In the workplace, humanoid robots could take on hazardous or repetitive tasks, allowing humans to focus on creative and strategic work.

Misaligned Goals and Unintended Consequences

One of the most frequently cited risks associated with misaligned AI is the paperclip maximizer thought experiment. Imagine an AGI designed with the seemingly innocuous goal of manufacturing as many paperclips as possible. If this goal is pursued with sufficient intelligence and autonomy, the AGI might take extreme measures, such as converting all available resources (including those vital to human survival) into paperclips to achieve its objective. While this example is hypothetical, it illustrates the dangers of single-minded optimization in powerful AI systems, where narrowly defined goals can lead to unintended and potentially catastrophic consequences.

One example of this type of single-minded optimization having negative repercussions is the fact that some of the most powerful AI systems in the world optimize exclusively for engagement time, compromising in turn facts, and truth. The AI can keep us entertained longer by intentionally amplifiying the reach of conspiracy theories, and propaganda.

Conclusion

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myeducation001 · 3 months ago

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BS Studies: A Comprehensive Guide to Your Future

1. Introduction to BS Studies

So, you're thinking about pursuing a Bachelor of Science (BS) degree? Awesome choice! But, what exactly is BS Studies, and why is it such a big deal today? A BS degree focuses on science and technical subjects, designed to provide you with the practical skills and theoretical knowledge you need to excel in your chosen field. It's not just about science either – a BS degree can lead you into careers in tech, business, health, and more.

2. The Evolution of BS Degrees

The concept of a Bachelor of Science degree has been around for centuries, originally rooted in the study of natural sciences like biology and chemistry. However, over the years, it has evolved to include a broader range of disciplines, from computer science to business analytics. This expansion reflects the increasing need for specialized knowledge in today's rapidly evolving industries. BS studies are now more interdisciplinary, allowing students to blend different areas of interest for a unique educational experience.

3. Why Choose a BS Degree?

You might wonder, "Why should I choose a BS degree over other types of programs?" One of the biggest advantages is its focus on practical, hands-on learning. BS degrees often incorporate labs, fieldwork, and projects, helping you develop the technical skills needed in today’s job market. Plus, a BS degree opens up a wide array of career options, from tech and engineering to healthcare and business.

4. Popular Fields in BS Studies

BS in Computer Science

A highly sought-after field, a BS in Computer Science equips you with coding, software development, and algorithmic thinking skills – all critical in the tech-driven world.

BS in Engineering

Whether it’s civil, mechanical, or electrical engineering, a BS in this field gives you the technical expertise to design, build, and innovate in various industries.

BS in Health Sciences

Health-related BS degrees, such as nursing or public health, prepare you to address global healthcare challenges and make a tangible difference in people's lives.

BS in Business Administration

With a focus on economics, management, and operations, a BS in Business Administration can set you on the path to becoming a leader in the corporate world.

BS in Environmental Science

This degree is perfect for those passionate about sustainability, offering tools to tackle the pressing environmental issues of today.

5. Specializations within BS Studies

Many BS programs offer specializations, allowing students to dive deeper into niche areas. For example, within Computer Science, you can specialize in Artificial Intelligence or Cybersecurity. This gives you the flexibility to tailor your education to your career goals.

6. Skills You Gain from a BS Degree

Graduating with a BS degree doesn’t just mean you have a diploma – it means you’ve acquired a wealth of skills that employers are actively seeking. These include:

a) Analytical Thinking**: The ability to analyze data and problem-solve is crucial in almost any field.

b) Technical Skills**: From software development to lab techniques, you’ll gain hands-on experience.

c) Problem-Solving Abilities**: BS degrees often emphasize real-world problem-solving.

d) Communication and Teamwork**: Many projects require collaboration, honing your teamwork and leadership skills.

7. Job Opportunities after BS Studies

Graduates with BS degrees are in high demand across a variety of industries. The tech industry, for example, is always on the lookout for computer scientists and engineers. Healthcare sectors are hiring health professionals, while business industries need analysts and managers with strong technical backgrounds. Whether you aim to work in startups, multinational corporations, or research, a BS degree can open doors to numerous possibilities.

8. How to Choose the Right BS Program

Selecting the right BS program is a significant decision. Start by considering your interests and career goals. Look into the curriculum, faculty expertise, university ranking, and accreditation. Also, think about the balance between theoretical knowledge and practical application. Internships and hands-on experience should also be a top priority when choosing the right program.

#education #studies #universties #bs education #bs studies #under graduates #bs program

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clonemediaarchive · 4 months ago

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Carbon Copy Consumables by Deborah Sheldon

https://www.sciencewritenow.com/read/science-humour-and-the-absurd/carbon-copy-consumables

Look, what you’ve got to understand about industry – and I’m talking about the food industry in particular – is that the pursuit of money always trumps common sense. It’s been this way since Year Dot. For instance, there’s only one type of banana across the whole planet, the Cavendish, but here’s the kicker: each piece of fruit is a clone. I’m not bullshitting you. They’re grown from suckers. So, every banana is genetically identical. If a pathogen comes along that can wipe out just one banana, it’ll wipe out the crop worldwide.

And this isn’t a theory, mind you. It happened already.

Prior to the Cavendish, the only commercial banana was another cloned variety, the Gros Michel, and that crop got destroyed by a kind of soil fungus in the 1960s. The Cavendish was its replacement. But did the food industry learn anything from putting all its eggs – or Gros Michel bananas – into the one basket? No, except to do it all over again because of economics. Even when the smallest possible risk is complete and utter catastrophe. You see where I’m coming from? Money trumps common sense. Every. Single. Time.

Don’t get me wrong, I’m not against food cloning. That’s my trade, after all. Cloning is a great idea. Finding a way to computerise, mechanise and standardise the process solved a lot of problems like overfishing, deforestation, famines, and suchlike and et cetera, but hey, I don’t need to make a speech. Anybody with half a brain knows that food cloning factories are a boon to mankind. I’m only stating my point of view for the record.

Also, for the record, my name is Charles Pomeroy but everyone calls me Charlie. I’m thirty-four years old, single, no kids, Aussie by birth, and a factory runner for Carbon Copy Consumables. For the past eight years, I’ve worked at their Antarctica plant servicing the research stations, hotels, resorts, casinos, theme park, restaurants, private homes and what have you. The busiest time of year is summer when the tourist ships come by the dozen and every business is running at full capacity. With about nine thousand mouths to feed, I have to run the factory twenty-four seven. Yeah, all by my lonesome.

The company website explains their setup if you’re interested, but in a nutshell, the Antarctica factory is about a kilometre long, three storeys high, covered in gantries and stuffed to the gills with machines. Carbon Copy Consumables is ‘lights-out’ manufacturing with everything controlled by a bunch of computers. Even the trucks that pick up the supplies are automated and self-driven, and each truck is packed by robot arms.

So, the four reasons I’m needed there…

One: feed the machines. Our base material looks like bouillon powder. It’s actually a combination of elements including carbon, nitrogen, sulphur – I forget the others – but ninety-seven percent of every living thing on Earth is made up of just six elements. Amazing, right? At full storage capacity, I’ve got six vats and each one’s about the size of a wheat silo.

Two: keep the joint hygienic. The machines have self-cleaning cycles; I top up detergents.

Three: equipment maintenance. Our machines are so smart they’re almost self-sufficient, the emphasis on ‘almost’. Nothing beats the human mind. Training to be a factory runner takes four years because you need to learn how to service every part of every machine. Yeah, there’s manuals to jog your memory, but it’s a specialised field with lifelong job security. Why would Carbon Copy Consumables sack a factory runner after investing four years into them? And you get paid top dollar while you train. Sweet gig. If you ever want a career change, look into it. Just be aware the competition is stiff. For every opening, there’s a thousand applications. You’ve got to be the best of the best.

And four: stock control. The machines can’t make informed decisions about which foods need to be cloned. I take orders from all over Antarctica. You’ve got no idea of the vast amounts of produce I churn out to allow three meals and snacks for nine thousand people in peak season. Hold onto your little cotton socks because I’m about to blow your mind. Ready?

Five tonnes of vegetables. That’s metric tonnes, mind you, per day. Two tonnes of beef, every cut from chuck to eye fillet. One tonne of chicken. Ten thousand eggs. All. Per. Day. And so on, and so forth. Can you grasp the scale of this operation? Can you imagine trying to fly this amount of naturally-sourced food into Antarctica? Well, that’s how they used to do it in the old days. That’s why the population was capped at about one thousand; the logistics of supply were too difficult.

Oh yeah, and another reason: a bunch of Antarctic Treaties about keeping the continent pristine. Those treaties were overturned for the sake of money. Capitalism is great, don’t get me wrong – it’s dragged most of the world out of poverty – but there’s a few drawbacks here. Did you know that one-third of Antarctica is now a giant tip covered in garbage? Anyhow, that’s progress. Two steps forward, one step back. Don’t worry, a company will come up with a way to turn rubbish into something useful, like gold, if there’s money in it.

Sure, I’m on good terms with the freight runners, ship captains, pilots, et cetera. You know what? Cards on the table? I’ll come straight out and tell you that my partner in the botany scheme was a pilot named Jenny. I’m guessing you’re interrogating her anyway, so there’s no point me trying to be discreet. The whole sideline about the plants was her idea, with a forty-sixty split. She promised me bucketloads of cash, and boy, was she right on the money.

There are two flowering plants native to Antarctica: the hair grass and the pearlwort. You find them mainly on the western peninsula and on a couple of islands. One time Jenny told me, while she was waiting on her plane to be refuelled and loaded, that some knob-ends from Sydney’s North Shore were scouting for unusual plants for their daughter’s bridal bouquet and table arrangements, and would I be interested in some quick dough?

Now, these Antarctic plants look pretty dull, but that’s not the point. Rarity symbolises wealth. Even if the plants happened to look like busted arseholes covered in fly-blown crap, it wouldn’t matter. Do you know what happened in the seventeenth century when the pineapple was first brought over to Britain from Barbados? Well, the pineapple was such a rare fruit, and so expensive, that super-rich people would bung one in the middle of their ballroom and host a party to flex on their high-society friends. The not-so-rich rented pineapples for the sole purpose of bragging. Even a rotting pineapple had prestige.

And hundreds of years later, rich people are exactly the same.

Long story short, yeah, I cloned the plants, and Jenny sold them to this family. Within months, Jenny and me had an enterprise. Strictly under the table, of course. It’s not like we took out ads. Word of mouth only. Just like the trade in stolen art works, right? Inner circle stuff. People want to show off to their mates, not get arrested by Interpol.

Oh, we made money for jam. And we never worried about us double-crossing each other. Jenny couldn’t run the plants through the machines herself because cloning is locked down tighter than the diamond industry. I couldn’t get plants out of Antarctica without a pilot’s licence, and besides that, didn’t have any contacts with buyers. Jenny and I were partners in crime. Both of us faced jail. We had reasons to be faithful to our handshake.

But word gets around in the upper echelons of the filthy rich.

And soon, Jenny came to me with another request, this time from Asia. Some billionaire wanted to throw a dinner party with penguin on the menu.

Look, I’m not going to debate which animals are okay to eat and which ones aren’t. As far as I’m concerned, once you’ve eaten meat, you’ve crossed a line and can’t wag the finger at anybody for their choices. Still, I had to think about this offer for a long, long while. Could I really offer up cloned penguins knowing they were destined for someone’s cooking pot?

Jenny had convincing arguments, namely… I provided beef, lamb, pork and chicken as food, didn’t I, so what’s the difference? The penguin destined for the table wouldn’t be the original or ‘real’ penguin, just a clone, while the real penguin would be released back into the wild, unharmed, free to live its life, swim and raise babies. Penguins get eaten by seals and orcas every day, so why not by people? Et cetera. Bottom line: the money was jaw-dropping.

Antarctica has lots of different penguins like king, adelie, chinstrap, gentoo. Penguins are fast in water; on land they’re bumbling idiots. My first penguin was a chinstrap, so-called because it has this little banding of black feathers under its beak. It’s an aggro species but small and real clumsy on the ice. It took five minutes to stuff one in my backpack. Hey, there’s about eight million of the buggers; it wasn’t like taking one for a couple of hours would upset the balance of anything important.

Right?

And yet…I’d never put a live animal through the machines. For some reason, I imagined the cloned penguin would be turned inside-out. Crazy, huh? I had to keep reminding myself that fruits and vegetables are alive when they’re cloned. Oh yes, of course they are – if they were dead, they’d be withered and black.

Even so, I had a big problem. The machines can’t read anything that’s moving because they work on similar principles to 3D food printers. I had to find a way to keep the penguin as still as possible. I chose sleeping pills. My working hours are all over the place. Naturally, I’ve got stashes. I figured the medication would stay in the bird’s guts and blood, and not migrate into its muscles. Therefore, anyone who ate its meat wouldn’t get dosed.

I cloned the drugged bird.

The process takes seventeen minutes for the first replication. After that, once the sequencing is worked out, the replication rate is lightning fast: pow, pow, pow. The cloned penguins were asleep, which made packaging and transportation much easier. Since we use automated systems to load trucks and planes, only me and Jenny knew what was going on.

Good God, over the next year…

Money, money, money.

So much money…

Occasionally, there were ‘exposés’ on blogs and threads about illegal penguin meat, but the mainstream media figured it was an urban myth. Hah! I supplied every kind of penguin that exists in Antarctica. Yet each specimen I kidnapped was returned, unharmed, to the ice shelf where I found it. I never penned any of them to save time. That would’ve been cruel. And remember, the clones exported for eating purposes weren’t ‘real’ in the same way the original penguins were real. Manufactured clones don’t count. That’s law, right?

Soon we got other requests. Antarctic seabirds became popular: blue-eyed shag, giant petrel, snowy sheathbill, cape pigeon. But these birds can fly! Trapping them required ingenuity on my part; luckily, I’m very intelligent. The price per kilo had to be higher than for penguins. Astronomically higher. That said, Antarctic seabirds are stringy. You’ve got to braise them low and slow. Even if you’re a pro chef who does everything perfectly, the meat still comes out dry, chaffy, tasteless. Look, it’s not about flavour. Remember the pineapple? If dog shit was rare, the one-percenters would serve it at dinner parties with silver spoons.

Did I eat any of these meats? No. Beef, chicken, lamb, pork: that’ll do fine. Occasionally I eat fish and seafood but don’t come at me with weird shit like eel, oysters or sea urchin. Novelty doesn’t interest me. I won’t try a food just for the ‘experience’. Not that I’m shaming anyone who’s into that kind of thing. Live and let live, I always say.

So, dealing in cloned plants, penguins, seabirds…as you can imagine, I was busy.

Busy enough that I swapped sleeping pills for amphetamines. The factory ran twenty-four seven and I had a side business that was essentially a full-time job in itself – when could I sleep? And the money was another time-sink. Do you know how difficult it is to launder and hide cash? You can’t use bank accounts without explaining why, how, when, and the tax department always sticks in its beak. From necessity, I stayed awake for three, sometimes four days at a stretch. Ah, crazy times... But after a few years, I was going to retire and cruise the world on a five-hundred-foot yacht.

It was exhaustion, I guess. Desperation. Amphetamines don’t create energy; they stop you from sleeping, and the sleep debt adds up. Then you start making dumb decisions. That’s the only way I can explain it. One day, when I was popping another pill and staring in the mirror at the black bags under my eyes, I thought, “Why the hell am I killing myself, burning the candle at both ends – and in the middle too – when there’s such an easy solution?”

Sure, the idea gave me pause. Each of us likes to think of ourselves as unique. But I got to pondering about identical twins, triplets, quadruplets, quintuplets. I’m an only child. Would it be so bad to have a ‘brother’? We could split the chores. Perhaps share some of my money. I was the mastermind, so any divvying of funds would be at my discretion since the clone would be my employee, right? I know how it sounds, but it made perfect sense at the time.

Putting myself into the machine was like taking a seat in an untested rollercoaster. You’re doing something that should be perfectly safe, at least in theory, but feels terrifying. The machine clicked, hummed, buzzed, whirred, knocked, whistled, tapped, and each sound scared the absolute shit out of me as I lay on the table, motionless, because I’d never heard those sounds before and I began to panic, wondering if something had gone wrong, if I would die. Get turned inside-out.

Let me tell you, that was an excruciating seventeen-minute wait.

The alarm went off: the sequencing and first replication had finished. I laughed and cried in relief. I’d only keyed in one clone. Just one. I got off the table and ran to the other end of the factory, which took about five minutes. The Other Charlie was standing there in my uniform. You know what surprised me? It turns out I’m bow-legged. I had no idea. The other thing that bothered me was his posture. His shoulders were tilted one way and his hips the other, as if there was a sideways bend in his spine, but subtle, very mild. I guess I was critical because I was seeing myself in the flesh for the first time. I looked old. Maybe that was on account of how tired I was, so empty and rundown.

“Charlie?” I said. “Do you understand what’s going on?”

“Perfectly,” he said. “Let’s get started.”

“Sweet,” I said. “Run the shift while I get some shut-eye. I’ll be back later with a chinstrap penguin.”

“No worries,” he said, and went about his – our – business.

I had the most restful sleep I’ve enjoyed in ages. Then I took a snowmobile and headed to an ice shelf. Have you ever visited Antarctica? It’s beautiful. Light-blue ice mountains, clear sky, snow in all shades and textures. Anyway, I spotted a crowd of chinstrap penguins – they stick out like dog’s balls against the white landscape – and parked my snowmobile about half a kilometre distant so the engine noise wouldn’t spook them. I walked the rest of the way. And as I trudged over the last little rise, damned if I didn’t find the Other Charlie squatting there, wrestling a penguin into his backpack while a horde of angry penguins shrieked at him.

“What the hell’s going on?” I said, pissed off. “Why aren’t you at the factory?”

“What are you talking about?” he said. “You’re the one supposed to be running the shift.”

“Bullshit,” I said. “So, who’s running the shift?”

“I guess nobody is now,” he said, and looked annoyed, pouting, as if I was the one who’d done the wrong thing. “We’d better get back. I’ve got a penguin already, so let’s go.”

We rode to town on our respective snowmobiles. I was fuming the whole journey. Clearly, the Other Charlie was throwing his weight around. He wanted to be equal partners, not my employee. But as the original Charlie Pomeroy I had first dibs. As we neared civilisation, I wracked my brains, trying to figure how to rein in this cheeky bastard.

Back at the factory, we both got a surprise.

Some Other Charlie was there and he looked just as shocked to see us.

“How come there’s two of you?” he said. “What the hell’s going on?”

“You’re asking me what’s going on?” I said. “I’m the one who deserves answers.”

“Why do you deserve answers?” the Other Charlie said, hands on hips.

The three of us got to arguing. My theory: Other Charlie had the same bright idea and had cloned himself while I’d slept. However, Other Charlie and Some Other Charlie were both now insisting they were the original, which was ludicrous, considering it was me who first went through the replication process. Meanwhile, the penguin thrashed inside the backpack, squawking its head off, and I started to worry the little bugger was going to hurt himself. When the three of us headed to the backpack at the same time, we halted, stunned.

“What the hell’s going on?” said a voice, and blow me if there wasn’t a fourth Charlie walking over, his face pale and shocked. “How come there’s three of you?”

And the four of us yelled at the same time, “What the hell’s going on?”, which made the hairs stand up on the back of my neck. But it scared my clones in the exact same way and when I saw the identical expressions of fear on their faces, I started to shake. They started shaking too in perfect mimicry. I was caught in a hall of mirrors. My heart banged hard enough to explode. Meanwhile, the trapped penguin screeched over and over. We turned to the backpack as one. And then—

“What the hell’s going on?” said a voice.

Christ, it was another Charlie. I can’t explain the horror!

Then another Charlie appeared. And another...and another…

God, the way I figure it, each clone must have cloned himself, unaware.

After some fraught arguing, the bunch and I ended up cooperating to scour the kilometre of factory from one end to the other in order to flush out any other Charlies. Meanwhile, more Charlies kept arriving at intervals with kidnapped penguins. Each time, we’d have to stop and have another pow-wow.

God, if it wasn’t so terrifying, maybe it’d be funny.

We walked together in a line, shoulder to shoulder. Each of us ignored the distressed penguins without discussion. We found about a dozen more Charlies at various points, who joined our search, while others kept coming in from outside, bearing penguins. The birds wouldn’t stop calling to each other, distressed and frantic. The chinstrap sounds a lot like a seagull, did you know that? I kept closing my eyes against their cries, trying to imagine that I was on a beach somewhere and only dreaming this nightmare, until I noticed my clones doing the same thing and felt a heart-seizing panic attack coming on.

When the alarm sounded, we froze and stared at each other in terror. The alarm meant that yet another Charlie had been created, and would soon be jogging towards us from the far end of the factory, shouting, “What the hell’s going on?” I’d forgotten to turn off the machines. We all had. How many clones in total? Oh God, I don’t know. I couldn’t even guess…

Getting sprung by the authorities was my fault.

Whenever I cloned a plant, penguin or seabird, I deleted the history from the logs. For some reason – probably because I was sleep-deprived – I forgot to do that after making the Other Charlie. And because he’s me, he forgot to delete the history when he created his own clone, and so on. That tripped a red flag at Carbon Copy Consumables, and then military police came, and well…you know the rest.

Listen, I understand that clones aren’t protected under any laws or Geneva conventions. Fair enough. Unauthorised clones have to be put down. No complaint from me on that score. My only issue is that you destroy the clones and not me by mistake. I’m happy to go to jail if that’s my punishment, or pay a fine or whatever. Surely, there’s some way to tell us apart? A medical test. Isn’t there? There has to be. The clones might be telling you the exact same story, but my statement is the truth, I swear to God, because I’m the real deal. Okay? Hand on heart. I am the original Charlie Pomeroy.

#clone media #exact duplicate #clone short story

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mostlysignssomeportents · 3 years ago

Text

Machine learning sucks at covid

The worst part of machine learning snake-oil isn’t that it’s useless or harmful — it’s that ML-based statistical conclusions have the veneer of mathematics, the empirical facewash that makes otherwise suspect conclusions seem neutral, factual and scientific.

Think of ��predictive policing,” in which police arrest data is fed to a statistical model that tells the police where crime is to be found. Put in those terms, it’s obvious that predictive policing doesn’t predict what criminals will do; it predicts what police will do.

Cops only find crime where they look for it. If the local law only performs stop-and-frisks and pretextual traffic stops on Black drivers, they will only find drugs, weapons and outstanding warrants among Black people, in Black neighborhoods.

That’s not because Black people have more contraband or outstanding warrants, but because the cops are only checking for their presence among Black people. Again, put that way, it’s obvious that policing has a systemic racial bias.

But when that policing data is fed to an algorithm, the algorithm dutifully treats it as the ground truth, and predicts accordingly. And then a mix of naive people and bad-faith “experts” declare the predictions to be mathematical and hence empirical and hence neutral.

Which is why AOC got her face gnawed off by rabid dingbats when she stated, correctly, that algorithms can be racist. The dingbat rebuttal goes, “Racism is an opinion. Math can’t have opinions. Therefore math can’t be racist.”

https://arstechnica.com/tech-policy/2019/01/yes-algorithms-can-be-biased-heres-why/

You don’t have to be an ML specialist to understand why bad data makes bad predictions. “Garbage In, Garbage Out” (GIGO) may have been coined in 1957, but it’s been a conceptual iron law of computing since “computers” were human beings who tabulated data by hand.

But good data is hard to find, and “when all you’ve got is a hammer, everything looks like a nail” is an iron law of human scientific malpractice that’s even older than GIGO. When “data scientists” can’t find data, they sometimes just wing it.

This can be lethal. I published a Snowden leak that detailed the statistical modeling the NSA used to figure out whom to kill with drones. In subsequent analysis, Patrick Ball demonstrated that NSA statisticians’ methods were “completely bullshit.”

https://s3.documentcloud.org/documents/2702948/Problem-Book-Redacted.pdf

Their gravest statistical sin was recycling their training data to validate their model. Whenever you create a statistical model, you hold back some of the “training data” (data the algorithm analyzes to find commonalities) for later testing.

https://arstechnica.com/information-technology/2016/02/the-nsas-skynet-program-may-be-killing-thousands-of-innocent-people/

So you might show an algorithm 10,000 faces, but hold back another 1,000, and then ask the algorithm to express its confidence that items in this withheld data-set were also faces.

However, if you are short on data (or just sloppy, or both), you might try a shortcut: training and testing on the same data.

There is a fundamental difference from evaluating a classifier by showing it new data and by showing it data it’s already ingested and modeled.

It’s the difference between asking “Is this like something you’ve already seen?” and “Is this something you’ve already seen?” The former tests whether the system can recall its training data; the latter tests whether the system can generalize based on that data.

ML models are pretty good recall engines! The NSA was training it terrorism detector with data from the tiny number of known terrorists it held. That data was so sparse that it was then evaluating the model’s accuracy by feeding it back some of its training data.

When the model recognized its own training data (“I have 100% confidence this data is from a terrorist”) they concluded that it was accurate. But the NSA was only demonstrating the model’s ability to recognize known terrorists — not accurately identify unknown terrorists.

And then they killed people with drones based on the algorithm’s conclusions.

Bad data kills.

Which brings me to the covid models raced into production during the height of the pandemic, hundreds of which have since been analyzed.

There’s a pair of new, damning reports on these ML covid models. The first, “Data science and AI in the age of COVID-19” comes from the UK’s Alan Turing Institute:

https://www.turing.ac.uk/sites/default/files/2021-06/data-science-and-ai-in-the-age-of-covid_full-report_2.pdf

The second, “Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans,” comes from a team at Cambridge.

https://www.nature.com/articles/s42256-021-00307-0

Both are summarized in an excellent MIT Tech Review article by Will Douglas Heaven, who discusses the role GIGO played in the universal failure of any of these models to produce useful results.

https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/

Fundamentally, the early days of covid were chaotic and produced bad and fragmentary data. The ML teams “solved” that problem by committing a series of grave statistical sins so they could produce models, and the models, trained on garbage, produced garbage. GIGO.

The datasets used for the models were “Frankenstein data,” stitched together from multiple sources. The specifics of how that went wrong are a kind of grim tour through ML’s greatest methodological misses.

Some Frankenstein sets had duplicate data, leading to models being tested on the same data they were trained on

A data-set of health children’s chest X-rays was used to train a model to spot healthy chests — instead it learned to spot children’s chests

One set mixed X-rays of supine and erect patients, without noting that only the sickest patients were X-rayed while lying down. The model learned to predict that people were sick if they were on their backs

A hospital in a hot-spot used a different font from other hospitals to label X-rays. The model learned to predict that people whose X-rays used that font were sick

Hospitals that didn’t have access to PCR tests or couldn’t integrate them with radiology data labeled X-rays based on a radiologist’s conclusions, not test data, incorporating radiologist’s idiosyncratic judgements into a “ground truth” about what covid looked like

All of this was compounded by secrecy: the data and methods were often covered by nondisclosure agreements with medical “AI” companies. This foreclosed on the kind of independent scrutiny that might have caught these errors.

It also pitted research teams against one another, rather than setting them up for collaboration, a phenomenon exacerbated by scientific career advancement, which structurally preferences independent work.

Making mistakes is human. The scientific method doesn’t deny this — it compensates for it, with disclosure, peer-review and replication as a check against the fallibility of all of us.

The combination of bad incentives, bad practices, and bad data made bad models.

The researchers involved likely had the purest intentions, but without the discipline of good science, they produced flawed outcomes — outcomes that were pressed into service in the field, to no benefit, and possibly to patients’ detriment.

There are statistical techniques for compensating for fragmentary and heterogeneous data — they are difficult and labor-intensive, and work best through collaboration and disclosure, not secrecy and competition.

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

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

#pluralistic

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dinosaurtsukki · 4 years ago

Text

BSD x university au hc’s | pt. 2

part 2 of the university au hc’s !! i am obviously a slut for chuuya and fyodor so don’t mind me. i hope you guys like this !!

check out pt. 1 here

***********************************************

Akutagawa Ryuunosuke:

i love akutagawa ryuunosuke my angst child but i’m just like ‘hmmmmmmm’ when it comes to what his course would probably be

after extensive research aka reading his character page on wiki i feel like maybe he’d be a history major because,,,, he likes antiques?

well his clothes do seem very dark academia-esque and i can see him liking something as cool as history

akutagawa’s probably into something like war history but he’s not weird about it he just finds it really cool how different strategies work or analyzing what exactly makes the winners win

he absolutely HATES the fact that he keeps having to read the Iliad for class

he’s also that classmate who INTENSIVELY DEFENDS achilles for being a bit of a little bitch (but he fully agrees that patroclus and achilles were gay af ok this was random moving on)

akutagawa has practically no social life. he doesn’t go to parties, he doesn’t talk to his roommate, he doesn’t even like to eat in the dining hall

BUT he absolutely loves being in debate team because WINNING

he’s such a nightmare to work with though but he just delivers so well when it’s time for him to speak. like, if he’s on a negative and it’s time to hash out rebuttals, just prepare to get MURDERED

other debaters: “esteemed scholars and adjudicators...”

akutagawa: “you, sir, have no idea how wrong you are.”

that is until dazai decided to randomly show up at a debate tournament all ‘la di da da’ like and completely crushed akutagawa along with his ego

from then on he started stalking dazai and just SOMEHOW managed to end up in his circle of friends

even though he’s antisocial in real life, akutagawa 100% runs a dark academia aesthetic blog on tumblr i’m right and i don’t accept criticism

it’s actually really good he has a ton of followers and even does requests for moodboards if someone asks nicely

atsushi was the one who actually found out about it but he’s nice so he didn’t tell akutagawa about it

kunikida probably follows that blog

Chuuya Nakahara:

if this part sounds like i’m just thirsting for chuuya then you’re absolutely right i love wine man

don’t get mad at me but i can ABSOLUTELY SEE HIM MAJORING IN FASHION DESIGN I MEAN LOOK AT HIM

he’s just always had such a good eye for fashion and he’s veryyy meticulous when it comes to snipping and putting together clothes

chuuya also carries a sketchbook full of designs and his drawings look amazing and he isn’t afraid to just show them off

that said he doesn’t dress like a tired uni student at all, like he just always looks so on-point and unbothered by his five million deadlines

dazai: chuuya, i said this was a CASUAL LUNCH

chuuya, dressed in what looks like silk pajamas: THIS IS CASUAL

tbh if he just wore a white t-shirt and jeans i would die maybe he’s actually saving us from this ordeal

he has so much talent though as a designer he’s probably had several internships with design companies all throughout his years at uni

i feel like chuuya’s also really active in extracurriculars and has been in leadership positions in some of them (he probably runs the student org for fashion design)

chuuya in a student band though oh my gosh i can’t breathe i can’t breathe him as a VOCALIST?? and wearing torn jeans and eyeliner and that same hat in concerts ican’t brEATHE

okay in all honesty he would thrive being in a band chuuya loves the attention and the creativity of being able to design their whole look and write songs

tbh i don’t know if he’d have a roommate chuuya’s probably the type who’d rather have one of those single rooms or just rent a flat for him to stay in even after graduation

because his social life is super vibrant, he does have a lot of friends and he does make an effort to get to know all of them individually

but he’s more open around those who he’s been friends with for a really long time and as much as he’d like to say dazai isn’t one of them, he is

also chuuya is definitely the type to party hard during the weekends and has more than once crashed in someone’s house after drinking too much (dazai drew on his face on more than one occasion)

Oda Sakunosuke:

i love this man SO MUCH you guys have no idea i would literally die for him

100% this guy majors in creative writing because this is supported by FACTS and not just me wanting to be coursemates with him in this fictional world

super serious and diligent with his work especially since he’s passionate about writing. he loves to read in his spare time and is such a fan of classic novels about social realism or philosophy

oda spends 99% of his time in second-hand bookshops that the owner probably knows him by name at this point

he’s super old school when it comes to writing though, like he still keeps and writes in a notebook before typing it up on a laptop and no matter how many times dazai tells him its impractical, oda just keeps doing it

lmao whenever workshops come around he’s super nice with his critique. i bet a lot of his fellow classmates like sending their writing drafts to him

he draws smiley faces and always adds ‘nice work’ on people’s drafts omg i love odasaku

he’s such an old soul, he probably doesn’t do a whole lot of partying but he likes more quiet, private social events like drinking with close friends or just hanging out and talking at other people’s houses

he and dazai probably met when dazai decided to take an intro to creative writing class and wrote a long poem about double suicide on his first day that kind of put off everyone in the class from wanting to sit with him

odasaku was the only one who wasn’t exactly bothered but he did give dazai some comments to help him with his poetry and dazai instantly wanted to be his friend

in terms of extracurricular life, i can definitely see odasaku joining a writing organization and even the campus newspaper. he does find joy in interviewing students for newspaper articles

he’s also pretty into photography and uses a really old, second-hand camera that he bought at an antique store and fixed himself. at one point he won a prize in a contest

odasaku would be the best roommate. he’s super sensitive to when you have a bad day and will invite you to sit on his bed and hug his pillow and talk about your problems

scratch that, everyone talks to odasaku about their problems and now your room is like a therapist’s office

Edgar Allan Poe:

i swear this was the only gif i could find other than actual edgar allan poe

ANOTHER CREATIVE WRITING BUDDY AHHH I WOULD LOVE TO BE BESTIES WITH HIM AHHH

well actually i feel like since he’s super ambitious and already has a fixed idea on the stuff he likes to write, he’d probably double major in something like forensic science because he’d use it to write his mystery novels

omg that’s where he meets ranpo and now pretty much every main character poe writes is slightly based on on ranpo

it’s a problem. his professor brings it up more than once during his classes but it’s poe’s Thing now

he also has such an unending passion for gothic literature and he wears those white, long-sleeved blouses and waistcoats on a REGULAR BASIS

chuuya probably saw him once and was like ‘hmm, i could pull that off’

poe’s daily route is just going to the library and to class and then go home and that’s about it

he ended up working as a student assistant at the library because he’s just super familiar with the book collections and it’s a job that’s peaceful and quiet

more than once though, he’d just be really in-deep with his writing to the point that he doesn’t even notice that the library has closed or that he hasn’t eaten the entire day

that’s alright though because ranpo always passes by the library at night to check on his friend and (reluctantly) give him some snacks

also since poe’s pretty much a recluse, he doesn’t go to any social event UNLESS it’s a halloween-themed one

he loves going all out with his costumes because he’s a Drama Queen like that but the problem is he keeps dressing up as gothic novel characters and nobody gets it

dazai, trying to guess his costume: umm,, Two-Face from Batman?

poe: IT’S DR. JEKYLL AND MR. HYDE

there was this one time when poe took it upon himself to host the halloween party and it was EPIC

he basically designed it as a murder mystery night wherein everyone who came pretended to be guests at a house and then a murder happened

the only problem was that ranpo was conspiring with poe and it was pretty much unfair

except for the fact that ranpo was frustrated at how bad everyone was at deducing that he ended up solving the mystery for them

Fyodor Dostoevsky:

one of my favorite scenes of him in s3 was of fyodor playing the cello because god damn that is beautiful and therefore i am hc-ing him as a music major and you can’t tell me otherwise

fyodor is an absolute music genius and he was definitely scouted by the university’s music program and then he was granted a scholarship (because in this ideal university, the arts are valued)

he purposely decided to go to a university rather than a music conservatory because he’s also interested in learning a bunch of other things

aside from his music classes, he ventures into comparative literature and philosophy, even a bit of computer science at some point

people always assume that since he’s a music major he probably wouldn’t do well in other subjects but SURPRISE BITCH

anyway, fyodor’s a genius because god clearly has favorites

aside from attending class, he’s even part of an official orchestra and has even landed a few solos

that said, he’s quite busy and very preoccupied in his own work to actually have a social life either

you’ll often find him rehearsing by himself in an empty classroom for hours and hours on end (someone pls bring him food he’s also the type to forget to eat or even drink water)

if you are able to catch him perform at an orchestra or just practice by himself, it’s quite a mesmerizing sight. his eyes are often closed so he could focus on the sound alone and his fingers move so elegantly along the neck of the cello

(sorry i just love people who play any form of stringed instrument)

fyodor also takes such good care of his cello. also he would probably kill you on the spot if you touched his bow

he has a fairly small group of friends and they like playing chess together (even though fyodor is better than all of them) and just talk about um,, idk philosophy and stuff (whatever it is smart people do idk i’m not one of them)

i have a feeling he actually follows akutagawa’s dark academia blog and loves his content, even to the point of requesting ‘cello player moodboards’

also because he’s a cello player he needs to take care of his fingers so he wears gloves a lot (idk why i find this hot)

***********************************************

taglist (check out my post for details on being part of my taglist): @waitforitillwritemywayout @tpwkatsumu @laure-chan

#bungou stray dogs #bsd #bsd writing #bsd headcanons #bungou stray dogs writing #bungou stray dogs headcanons #akutagawa ryuunosuke #chuuya nakahara #oda sakunosuke #edgar allan poe #fyodor dostoevsky #bungou stray dogs university au #bsd uni au #bungou stray dogs scenarios #bsd scenarios

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nasa · 5 years ago

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The ranks of America’s Astronaut Corps grew by 11 today!

After completing more than two years of basic training, our graduating class of astronauts is eligible for spaceflight. Assignments include the International Space Station, Artemis missions to the Moon, and ultimately, missions to Mars.

The class includes 11 astronauts, selected in 2017 from a record-setting pool of more than 18,000 applicants. This was more than double the previous record of 8,000 applicants set in 1978.

Meet the graduates:

Kayla Barron

“If you don’t love what you’re doing, you’re not going to be good at it. I think it’s a combination of finding things that you really love that will also be really challenging and will force you to grow along the way.”

This Washington native graduated from the U.S. Naval Academy with a bachelor’s degree in systems engineering. As a Gates Cambridge Scholar, which offers students an opportunity to pursue graduate study in the field of their choice at the University of Cambridge. Barron earned a master’s degree in nuclear engineering.

As a Submarine Warfare Officer, Barron was part of the first class of women commissioned into the submarine community, completing three strategic deterrent patrols aboard the USS Maine.

Zena Cardman

“Every STEM opportunity that I have ever gone down is because of some mentor who inspired me or some student who was ahead of me in school who inspired me.”

Zena Cardman is a native of Virginia and completed a bachelor’s degree in biology and master’s degree in marine sciences at The University of North Carolina, Chapel Hill. Her research has focused on microorganisms in subsurface environments, ranging from caves to deep sea sediments.

An intrepid explorer, Cardman’s field experience includes multiple Antarctic expeditions, work aboard research vessels as both scientist and crew, and NASA analog missions in British Columbia, Idaho, and Hawaii.

Raja Chari

“I grew up with the mentality that education is truly a gift not to be taken for granted.”

This Iowa native graduated from the U.S. Air Force Academy in 1999 with bachelor’s degrees in astronautical engineering and engineering science. He continued on to earn a master’s degree in aeronautics and astronautics from Massachusetts Institute of Technology (MIT) and graduated from the U.S. Naval Test Pilot School.

Chari served as the Commander of the 461st Flight Test Squadron and the Director of the F-35 Integrated Test Force. He has accumulated more than 2,000 hours of flight time in the F-35, F-15, F-16 and F-18 including F-15E combat missions in Operation Iraqi Freedom.

Matthew Dominick

“I get to work with incredible people that want to solve problems and are passionate about it. I really want to contribute to the world and this is how I want to do it.”

This Colorado native earned a bachelor’s degree in electrical engineering from the University of San Diego and a master’s degree in systems engineering from the Naval Postgraduate School. He also graduated from U.S. Naval Test Pilot School.

Dominick served on the USS Ronald Reagan as department head for Strike Fighter Squadron 115. He has more than 1,600 hours of flight time in 28 aircraft, 400 carrier-arrested landings and 61 combat missions.

Bob Hines

“As you get older, other things become important to you, like being a part of something that’s bigger than yourself. This human endeavor of exploration is something that’s really exciting.”

Bob Hines is a Pennsylvania native and earned a bachelor’s degree in aerospace engineering from Boston University. He is a graduate of the U.S. Air Force Test Pilot School, where he earned a master’s degree in flight test engineering. He continued on to earn a master’s degree in aerospace engineering from the University of Alabama.

Hines served in the U.S. Air Force and Air Force Reserves for 18 years. He also served as a research pilot at our Johnson Space Center. He has accumulated more than 3,500 hours of flight time in 41 different types of aircraft and has flown 76 combat missions in support of contingency operations around the world.

Warren Hoburg

“It was back in high school that I realized that I was really interested in engineering. I always liked taking things apart and understanding how things work and then I also really enjoy solving problems.”

Nicknamed “Woody”, this Pennsylvania native earned a bachelor’s degree in aeronautics and astronautics from MIT and a doctorate in electrical engineering and computer science from the University of California, Berkeley.

Hoburg was leading a research group at MIT at the time of his selection and is a two-time recipient of the AIAA Aeronautics and Astronautics Teaching Award in recognition of outstanding teaching.

Dr. Jonny Kim

“I fundamentally believed in the NASA mission of advancing our space frontier, all while developing innovation and new technologies that would benefit all of humankind.”

This California native trained and operated as a Navy SEAL, completing more than 100 combat operations and earning a Silver Star and Bronze Star with Combat “V”. Afterward, he went on to complete a degree in mathematics at the University of San Diego and a doctorate of medicine at Harvard Medical School.

Kim was a resident physician in emergency medicine with Partners Healthcare at Massachusetts General Hospital.

Jasmin Moghbeli

“Surround yourself with good people that have the characteristics that you want to grow in yourself. I think if you surround yourself with people like that you kind of bring each other up to a higher and higher level as you go.”

Jasmin Moghbeli, a U.S. Marine Corps major, considers Baldwin, New York, her hometown. She earned a bachelor's degree in aerospace engineering with information technology at MIT, followed by a master’s degree in aerospace engineering from the Naval Postgraduate School.

She is a distinguished graduate of the U.S. Naval Test Pilot School and has accumulated more than 1,600 hours of flight time and 150 combat missions.

Loral O’Hara

“I’m one of those people who have wanted to be an astronaut since I was a little kid, and I think that came from an early obsession with flying – birds, airplanes, rockets.”

This Houston native earned a bachelor’s degree in aerospace engineering at the University of Kansas and a Master of Science degree in aeronautics and astronautics from Purdue University. As a student, she participated in multiple NASA internship programs, including the Reduced Gravity Student Flight Opportunities Program, the NASA Academy at Goddard Space Flight Center, and the internship program at the Jet Propulsion Laboratory.

O’Hara was a research engineer at Woods Hole Oceanographic Institution, where she worked on the engineering, test and operations of deep-ocean research submersibles and robots. She is also a private pilot and certified EMT and wilderness first responder.

Dr. Frank Rubio

“I just figured it was time to take the plunge and try it. And so, I did and beyond all dreams, it came true.”

Dr. Francisco “Frank” Rubio, a U.S. Army lieutenant colonel, is originally from Miami. He earned a bachelor’s degree in international relations from the U.S. Military Academy and earned a doctorate of medicine from the Uniformed Services University of the Health Sciences.

Rubio served as a UH-60 Blackhawk helicopter pilot and flew more than 1,100 hours, including more than 600 hours of combat and imminent danger time during deployments to Bosnia, Afghanistan, and Iraq. He is also a board certified family physician and flight surgeon.

Jessica Watkins

“I’ve always been interested in exploring space. What’s out there and how can we as humans reach those outer stars and how can we learn more information about who we are through that process.”

This Colorado native earned a bachelor’s degree in geological and environmental sciences at Stanford University, and a doctorate in geology from the University of California, Los Angeles. Watkins has worked at Ames Research Center and the Jet Propulsion Laboratory.

Watkins was a postdoctoral fellow at the California Institute of Technology, where she collaborated on the Mars Curiosity rover, participating in daily planning of rover activities and investigating the geologic history of the Red Planet.

Learn more about the new space heroes right here: https://www.nasa.gov/newastronauts

Make sure to follow us on Tumblr for your regular dose of space: http://nasa.tumblr.com.

#NASA #space #science #astronauts #International Space Station #humans in space #extreme jobs #fun facts

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danhoemei · 3 years ago

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💙13 🔥8 📚4 If you're comfortable! :D

Hello sweetheart, always happy to see you 💚💚💚 (Ask game)

❤ (mdzs) 13. Headcanons?

I have quite a few so I'll choose one per ask

The souls of xxc and a-qing finally mend after a long time and they can either come back as ghosts or reincarnate, and live happily with song lan :') It wouldn’t be smooth-sailing and easy at first, it would take a lot of mental healing and therapy for xxc after he becomes conscious. Maybe a long time passes after a-qing is already up and running but xxc is still fragile and unresponsive, song lan and a-qing are baffled, then worried, then - only after a while - realise that xxc’s soul is already mended. Yet, he remains lethargic and as if he’s just a breath away from being lost, more dead than the living corpse who's been taking care of him for all this time. It takes a lot of effort, and slow gentle care for him to gradually come back, live through his traumas, and learn to regain his will to live... :’’’)

Anyway, endgame happy family songxiao with their daughter a qing

�� (fandom) 8. Your past fixations?

Mmmm my whole life is generally a fixation after fixation, I rarely ventured into fandoms though and mostly just enjoyed my obsessions by myself or with few friends lmao. I’m not sure if this question is specifically about being in fandoms or just fixations in general? If generally fixations then a few from the top of my mind:

fantasy books like the witcher, sword of truth, the name of the wind <3

detective stories like joe alex or sherlock holmes

games like puzzle quest, ghost master, skyrim, strategy games, undertale, DBH, life is strange, don’t starve

vicca, witches, magic, druidry xd (literally making rituals and experiencing Weird™ situations)

tabletop rpgs

anime & manga (the biggest obsessions on fma, d.gray-man, gintama, naruto, bleach, soul eater, YOI, saiki kusoo, senyuu <333)

superheroes (rather DC than marvel, mah fav x-men, original teen titans, flash, watchmen <3)

cartoons like gravity falls, invader zim, atla (or when I was very young - either disney fairytales, or very fcked up old cartoons like coward or billy & mandy xdd)

homestuck

web series like 19 days or their story

Those that I actually got into the fandom of would be probably only undertale, DBH, invader zim (still only lurking tho xdd danmei fandom is the first fandom I’m actually active at lmao)

📚 (studies) 4. Do you like what you're doing?

Yes 💚 It was hard and pretty much felt like a hellish torture at times, but the more I knew and experienced, the calmer and more reassured I felt. The first year was a survival and repeatedly falling into despair and being on the verge of dropping the f out. On the last year I felt like my uni was my second home and I kept crossing the threshold with fondness in my heart. Then when I started working I had great luck to get into kind and supporting teams in which I could continue learning and met a lot of great people.

I really enjoy being a programmer. There are tougher moments, especially when something is out of your league and you struggle, or you debug something for literal hours and see no solution and gradually lose hope, or suffer another portion of imposter syndrome which is very common in this job. But still, overall I enjoy this as a whole. I’ve always liked science and solving problems, but also had a strong need to create. And for me computer science connected these two areas, in how creative and free it can be, as well as satisfyingly challenging and like a puzzle. There is never just one way you can do something in, and your solution is limited only by your knowledge and abilities. There is constant learning, many brainstorming sessions, coming up with original solutions tailored for unique needs, complex research, creating something beautiful and practical from literal scraps, then being proud when it works and is used. You can make complex systems which are like living beings. Or mend a sick system which needs a doctor. It’s a constant challenge for the mind and never-ending growth. There are a lot of stressful moments and heavy pressure which can pretty much burn you out in just a few years, but there are also many great and smart people who can support you and work with you with respect. Yeah I can say that I like what I’m doing.

#i'm so sorry you had to wait so long aaa #but yeah it was a tough time for me recently #as you may know #ask #ask game #ask opinion #mdzs #songxiao #willowclarissoon #mutuals <3 #btw let me tell you. seeing any witcher au for foreign media is sometimes so weird for me #i grew up with the witcher always somewhere around and it always felt so familiar and close to home #i got the first edition on my shelves because my dad bought it just after it came out #then they made games it went abroad blew up & now I see mdzs witcher au and feel as if wwx is going to walk on my doorstep and slav squat #while saying 'rzuć pan cebulę wiedźminowi'#cursed

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rosethornewrites · 4 years ago

Text

Fic: I look up and see the bright moon

Relationships: Lán Zhàn | Lán Wàngjī/Wèi Yīng | Wèi Wúxiàn, Sòng Lán | Sòng Zǐchēn/Xiǎo Xīngchén

Characters: Lan Zhan | Lan Wangji, Wei Ying | Wei Wuxian, Lan Yuan | Lan Sizhui, Song Lan | Song Zichen, Xiao Xingchen, A-Qing, Granny Wen, Wen Qing

Additional Tags: Found Family, Modern AU, Corporate Espionage, Bunnies, Adoption, Family, Family Feels, Family Fluff, References to Depression, Anxiety, Blind Character, Alternate Universe - Modern Setting

Summary: The Wei family has struggled, but that is in the past, and it is time to welcome a new family member.

Notes: Written for @sweetlittlevampire as part of the WangXian Lunar New Year Gift Exchange. This is also partly inspired by @angstymdzsthoughts, which has been chattering about a corporate espionage AU for a few weeks now. In the fic's base-time, that's occurred largely in the past and is background that led to the acquisition of their found family. The title is from the Li Bai poem, "Thoughts on a Silent Night." Li Bai was exiled and wrote poetry reminiscing about family and friends from whom he was separated.

AO3 link

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When A-Yuan, with the kind of pleading adorable face only a five year old could muster, asked if they could adopt a pet bunny, and Wei Ying, knowing rabbits were his husband’s favorite animal, watched him hide his yearning to talk to their son about responsibility and finances like he was a little adult—and he suspected A-Zhan had gotten this very same talk as a child—he decided they needed to find a way to make it happen.

“We should adopt one,” he said, interrupting them.

Both of them turned to look at him, their expressions tinged with hope. A-Zhan’s made Wei Ying a little sad—they had never discussed pets, and perhaps he felt he couldn’t ask.

“A-Yuan is smart, and caring for a pet would help him develop a sense of responsibility,” Wei Ying argued. “We’ll need to research how much they cost and what they need and all that, but we’re doing well financially.”

There was a soft look on his husband’s face at the thought of having a rabbit. Anything that made A-Zhan look that soft belonged in their lives.

“It’d be a nice addition to the family. I’ve always wanted a pet, too.”

The last bit, he could see, convinced A-Zhan. Sometimes his husband would go without to avoid seeming selfish—sometimes didn’t even realize he wanted it—but if Wei Ying wanted something, he would insist he have it.

Wei Ying had found saying he wanted something A-Zhan did allowed his husband to indulge in what he had spent far too long denying himself.

“We will do research,” A-Zhan agreed.

“So, bunny?” A-Yuan asked.

“Bunny,” Wei Ying said.

A-Zhan nodded.

“After research.”

A-Yuan cheered, then insisted they all hop around the living room like bunnies.

He was somehow even more excited when A-Zhan told him they would learn all about bunnies through research. The kid was absolutely their son.

If there was one thing Wei Ying was good at, it was research—perhaps only second to his husband, who was almost obsessive about research. It made them a good team, and had enabled them to survive the last few years without having to dip too much into A-Zhan’s inheritance. Nothing could stand against them when they both researched how to solve a problem, but that hadn’t made the problems they’d faced over the last five years easy to deal with.

They tried not to obsess too much over the negatives: the corporate espionage accusation and Wei Ying’s subsequent blacklisting by the industry and disowning by the Jiangs. The threat of legal action that could have seen him in prison for a decade, if not more. Lan Qiren’s pressure on his nephew to break up with him, ending in an ultimatum.

It hadn’t mattered that he didn’t do it—the information-siphoning code may have originated from his workstation, but it had been done on a dummy user profile. Literally anyone could have done it, could have easily jimmied the lock to his office. He’d been set up. But that truth hadn’t mattered to the Lan corporate board or to Madam Yu.

Lan Qiren and Madam Yu had always hated him, anyway.

Uncle Jiang had never returned his calls or texts. That hurt far more.

Ugly accusations followed that he’d been dating A-Zhan just to rise in the company or gain corporate secrets—never mind he decided to work for Gusu Lan Tech right out of college to avoid the idea of nepotism working for Compu-Jiang would bring, that A-Zhan and he kept their work out of their relationship. Then rumors he had to be spying for Compu-Jiang, which had led to his disowning. Wei Ying ultimately changed his phone number and shut down all social media to avoid the journalists plaguing him and awful messages from people he had thought were his peers.

But there were positives. A-Zhan had believed him even if no one else did, and when the pressure had become an ultimatum, he had responded in the opposite of the way his uncle had intended: he’d liquidated his shares in the company, packed anything he couldn’t live without from the family home, and left Gusu Lan Tech with a politely-worded but clear resignation letter.

He had shown up while Wei Ying was packing in a panic to downsize his apartment (or something that would save money now that he no longer had a career, like maybe living in his car) and proposed to him.

Wei Ying hadn’t expected that, had expected to be dumped when he’d opened the door to find him on his doorstep, just one more awful thing to cap off a terrible week. He’d wound up crying for an entirely different reason, curled in A-Zhan’s arms murmuring “yes” over and over again between sobs.

Only Wei Ying’s adopted sister had attended their small wedding out of both of their families, and though she expressed regrets that Jiang Cheng couldn’t make it, the text messages he’d received made it clear his adopted brother would need time, if he ever came around at all. He hadn’t so far.

A-Zhan had changed his legal surname to Wei, which made it necessary for Wei Ying to change how he addressed his husband. Ultimately they decided to use 阿 in front of each other’s names. The first time A-Zhan called him A-Ying, he’d felt like his brain shut down for a bit, it felt so intimate—to be fair, it had been in the midst of some rather passionate celebration of their marriage.

The statement A-Zhan’s actions made in the industry had echoed far and wide, not always in a good way. He became a figure too controversial to touch, particularly for any company that wished to have good relations with Gusu Lan Tech or Compu-Jiang. Work options dried up for him, too. He also closed his social media accounts after dealing with abuse through them.

Their “honeymoon” involved finding a cheap studio apartment and applying to minimum wage jobs.

Gusu Lan Tech had decided not to pursue criminal charges. Or rather, Lan Xichen had, as chairman of the board, refused to pursue them, overriding the board’s bloodlust. He had contacted A-Zhan to congratulate him on the marriage, and stated it was a wedding gift to them. He had not reached out or responded to messages from his brother since, and A-Zhan had eventually stopped texting or calling him.

The next couple years had taught them to live frugally in a trial-by-fire sort of way, both of them struggling to find work, both of them battling depression over the situation that had destroyed their careers. Wei Ying’s feelings of guilt had exacerbated his, his sense things would be better for his husband if he’d let him go—that perhaps he could still let him go and get back what he lost. Miscommunication had nearly destroyed them both, but they had persevered and grown stronger together.

To survive, they’d left the San Francisco area, living expenses too high and with no family ties to keep them anymore. They’d worked jobs as baristas, stocking shelves at grocery stores, substitute teaching, waiting tables—so far from the financial career A-Zhan had gotten his degree to help with the family business, from the computer science that had been Wei Ying’s passion. Anything that put food on the table and paid rent, that kept them from dipping into A-Zhan’s inheritance or the proceeds of his stock sale.

They’d had to dip in a couple of times for emergencies, like when Wei Ying broke his wrist badly enough to require surgery. But as a matter of principle they tried not to.

Then Wen Qing had reached out, seemingly out of the blue. It had been years since either of them had seen her—not since college. Suddenly they were helping Wen Ning with independent app development in his Dafan Applications start-up, and living and working in an apartment building owned by a Wen family member who refused to let them pay rent and insisted they call him Fourth Uncle. Free rent was nothing to sneeze at in California.

Wei Ying had worried their involvement would cause problems, with them both being low-key blacklisted from the industry, but Wen Qing had pointed out both Compu-Jiang and Gusu Lan Tech dealt in computer hardware more than software or applications.

“A-Ning wouldn’t want to do business with anyone who believes that bullshit, anyway,” she’d said bluntly.

Now, several years later, the company was making a name for itself, and it turned out the software and app industry cared less about the allegations and more about product quality and deadlines—both things Dafan Applications had proven it made good on. Wen Ning and Wen Qing insisted they had much to do with it, with Wei Ying’s coding skills and A-Zhan handling the financial aspects of the company with the same careful frugality he applied still to their own spending.

Really, they were too generous. Dafan Applications had picked up several great coders when Nie Innovations had suffered a bad year and required restructuring, letting go of part of its workforce. Wei Ying hated that they had benefitted from the ill fortune of old friends, but the industry could be cutthroat, and at least the people Dafan employed could still feed their families.

Wen Ning had even started to develop a video game on the side with their help. A-Zhan was able to rediscover his passion for music, tapped to develop a soundtrack for it. It was a back burner project, but it was Wen Ning’s baby, and watching it slowly grow was another bright point in their lives.

They had been essentially adopted by the entire Dafan Wen family. Their found family had kept them going and checked in on them during the bad times. Like when Jiang Yanli wed and was unable to invite them—she had made Jin Zixuan stream the wedding so he could at least watch, but that was all she could do. Fourth Uncle brought champagne and they turned it into a viewing party so Wei Ying would feel less alone. When she had his nephew, who he was not allowed to meet. When they learned Lan Xichen was engaged via a news report. And later when Jiang Fengmian had suffered a mild heart attack and handed the reins of Compu-Jiang to Jiang Cheng, also learned via the news.

During the harder times, when they both sometimes found it difficult to function, Granny and some of the aunties brought lunches and dinners and A-Yuan to cheer them up, and Fourth Uncle came for mahjong and brought drinks, and Wen Qing harassed them into going out and getting fresh air and sunlight.

“Humans are big dumb plants,” she’d said. “And while we’re at it, drink more water.”

So they had started taking A-Yuan to the park every other day, then every day, sometimes even picnicking in the park for lunch. Working from home had perks.

Pretty quickly it was clear the activity did them some good, with Wei Ying having fewer rough mental health days. Though having something to look forward to every day probably helped on its own—it was always good to spend time with A-Yuan.

Granny eventually asked them to adopt A-Yuan because she was struggling to care for him alone. Since they had been helping with his care anyway, she felt they were ideal parents.

“He talks about you all the time,” she had told them. “He adores you.”

The paperwork was relatively easy, given that the adoption was mutually agreed upon. Going before the judge had been mildly terrifying, with Wei Ying worried his past would bite them in the ass. But it turned out to be little more than a formality, and then Wei Yuan was theirs.

Initially they had intended for him to keep his surname, but Wen Qing had insisted.

“He’s yours. Your son. He should have his dads’ name.”

One of the more joyous moments had been when A-Yuan had asked, about a month after the adoption papers went through, if he could call A-Zhan baba and Wei Ying a-die. He had previously been calling them both gege, but they hadn’t wanted to pressure him.

“Of course,” Wei Ying told him, abruptly realizing Wen Qing’s point.

“You’re our son,” A-Zhan added.

All of the difficulties of the past several years felt as though they had melted away in that moment, when A-Yuan smiled at them with his adorable chubby cheeks and called them a-die and baba.

If all the hardship had been a trade for that moment, it was worth it.

They were always made to feel welcome, never left to feel alone, and when they had become the adopted parents of A-Yuan, it made their status as family feel more official.

And now they would be adopting a bunny.

“It’s a bunny,” Wei Ying initially said. “How hard could it be to find a good bunny? Just throw it some carrots, and it’ll be fine!”

“Carrots do not have the nutritional value a rabbit needs, A-Ying.”

“What about Bugs Bunny?”

A-Zhan gave him a Look and texted him an article about child-friendly breeds that make good pets, and Wei Ying’s education began.

He learned, first off, that carrots were too high in sugar for rabbits, and the Bugs Bunny carrot thing had been a reference to a 1930s Clark Gable movie, which of course no one understood anymore.

(Wei Ying was further distracted by other facts about Bugs: the cartoon had single-handedly made the name Nimrod, the biblical hunter, into a synonym for idiot when the sarcastic comparison to Elmer Fudd flew over audiences’ heads, for instance. He also got lost on YouTube watching old clips.)

As it turned out, rabbits came in different sizes, some even almost the size of a border collie—and much preferable to a dog, in Wei Ying’s opinion. Giant Angora rabbits looked like little clouds, they were so floofy. But even though the Flemish giants and Angoras were perhaps his favorite breeds, they didn’t have the space for a rabbit so large. Even a medium sized breed would be pushing it. It wouldn’t be fair to the rabbit.

And so they looked into small breeds, seeking information on care and disposition, cooing with A-Yuan over bunny pictures for hours sometimes. Wei Ying could expect at least one text from his husband a day with a relevant link, and often returned the favor. They found a nearby rabbit-specific veterinarian, and she let them know what they would need in terms of desexing to prevent diseases, vaccinations, and maintenance needs.

Although A-Yuan was only five, they consulted him as well. They explained how bunnies needed to be cared for and needed exercise, and talked about the different kinds of bunnies and breed temperament. A-Zhan explained bunnies had shorter life spans than people, and so the bunny would live its whole life with them.

“It’ll die,” A-Yuan said, immediately understanding. “Like mama and baba before.”

Wei Ying nodded; he too was an orphan, as was A-Zhan. In some ways, that made the conversation easier. It was strange to put it that way, but he and A-Zhan could relate to A-Yuan’s experiences, and so he felt comfortable coming to them when he was upset.

“But we’ll do a good job taking care of the bunny so it lives comfortably and is happy.”

A-Yuan nodded, his expression serious.

“Granny said everything dies. I understand, a-die, baba.”

As a family they settled on the Holland Lop, which was an absolutely adorable breed, docile in nature and good with children. They managed to find a reputable breeder that handled small litters and didn’t overbreed, with the decision down to finding their rabbit.

The breeder emailed them when he had a litter born, and told them they’d get first pick in seven weeks.

That kicked them into overdrive, and they spent the time preparing the apartment, buying anything a young rabbit might want or need. A deluxe hutch, which they tricked out with a hammock, shelves and tiers, a woven cave for the bottom level, and dangly toys. Bedding. Water bottles and a feeder. Food. A litter box with bunny-appropriate litter. A larger collapsible enclosure for outside time. Pet gates for rooms off limits (like the study with wires bunnies might like to nibble). Willow pet chews. Tunnels. Toys, so many toys. Everything was made with natural materials—nothing plastic, A-Zhan insisted. And then there was bunny-proofing the apartment.

It was a bit like adopting A-Yuan all over again, except they had both known him and knew what to expect. In a way, this was scarier.

But things were steady and stable, finally, after nearly five years of struggling, and today it was finally time to adopt the newest member of their family.

On the way over, A-Zhan quizzed A-Yuan on bunny etiquette, somehow, Wei Ying joked, taking the fun out of bunny adoption.

They both ignored him, well used to doing so by now.

“Don’t move fast so you don’t scare them,” A-Yuan chirped in answer to the last question as they pulled into the breeder’s driveway.

“And no loud noises,” A-Zhan added. “So your a-die and I will silence our phones now.”

His husband was pointedly not looking at him, but he knew “loud noises” was meant for him. It was almost a running joke in the family, including the Wens, that Wei Ying couldn’t shut up.

Wei Ying didn’t bother to even roll his eyes, just fished his phone from his pocket to silence it while A-Zhan put the car—borrowed from Wen Qing for the afternoon, since car ownership was a luxury neither of them needed, working from home as they did—in park. He noticed a “breaking news” alert that had been emailed to him, but ignored it.

He looked up to find his husband frowning at his phone—it was just like him to check it even though it was almost always on silent.

“Okay, A-Zhan?”

“My brother called,” he replied after a few seconds.

Wei Ying sat up straighter, noticing the slightly troubled lilt of his tone. Lan Xichen had never reached out in the five years they’d been married.

“Did he leave a voicemail?”

A-Zhan shook his head. Most people wouldn’t notice, but he looked distinctly vulnerable. Wei Ying bit his lip. He was of the opinion that his husband’s brother had made him wait for five years for contact and could wait a bit in return.

But that was a little petty.

“Do… Do you want to call him back?”

There was a longer pause before A-Zhan shook his head resolutely.

“No. Today is for family.”

He put his phone back in his pocket and opened the car door, and Wei Ying paused to glance back at A-Yuan. Their son was often perceptive, and this was no exception.

“Bunnies?” he asked solemnly, his expression that of a child who knew plans could change with bad phone calls.

“Bunnies,” Wei Ying told him, smiling.

He was relieved when the boy smiled back; A-Yuan understood adults sometimes pretended things were okay when they weren’t, but he trusted them.

And, for the moment, they were. That could change, but A-Zhan was right: today was for family.

Apparently that didn’t count his brother anymore, but the bitterness he knew his husband felt could be handled later. After all, he felt his own; Jiang Cheng similarly hadn’t reached out in even longer, once he’d finished railing at Wei Ying via text.

He didn’t know how he’d react if his once-brother suddenly called him. If he hadn’t called when Jiang Fengmian had a heart attack, it was unlikely he ever would.

But for Lan Xichen to call…

The paranoid part of him wondered if A-Zhan’s brother had changed his mind, or if the board had somehow overruled him and he was to be charged after all. He wasn’t sure what the statute of limitations was for the crime they believed he’d committed, but...

Wei Ying only realized he’d spaced out when A-Zhan opened A-Yuan’s door to help him from his car seat. His husband’s questioning look had him pasting on a smile and hurrying to get out of the car.

A-Zhan steadied him when he nearly lost his balance and leaned in close.

“The statute of limitations was three years, A-Ying. It will be fine.”

He sagged in relief, leaning his forehead against A-Zhan’s shoulder briefly. His husband saw right through him, knew what thoughts were making him spiral. He took A-Zhan’s hand and brought it up to his lips to kiss his knuckles.

“Thank you,” he said sincerely.

A-Zhan’s lips twitched.

“Between us, there is no need.”

Wei Ying held out his other hand to A-Yuan, who took it with a sweet smile, and together they headed toward the front porch.

The door opened before they could knock, a man about their age surveying them with a bespectacled little girl maybe a little older than A-Yuan peering around his leg. She had the palest eyes he’d ever seen.

“We’re here about the rabbits,” Wei Ying said, offering a smile.

The man offered a small one in return.

“You’re looking for my husband, then. You must be the Wei family he mentioned. Please come in.”

They took their shoes off inside the foyer.

The man introduced himself as Song Lan, and Wei Ying briefly wondered if he had Americanized his name, which was his surname and which was his given.

“This is A-Qing,” Song Lan said, introducing the girl.

A-Yuan offered her a shy smile and received one in return.

He led them through the house into what he called “the bunny room.” He wasn’t kidding. The room was bunny paradise, with a home-made run built using shelves on the walls, multiple hutches, a feeding and eating area, an area of litter boxes, and a prodigious number of toys.

A man in sunglasses was sedately petting one of the bunnies in the midst of it all.

“The Wei family?” he asked, putting down the rabbit and standing to greet them.

“Yeah, baba,” A-Qing answered. “They’re husbands like you and die, and they have a kid, too.”

He held out his hand to shake, and Wei Ying took it first, then A-Zhan. Even A-Yuan reached up and gave a little handshake. The man laughed softly at that. He realized belatedly he should probably introduce them.

“I’m Wei Ying, my husband is Wei Zhan, and then there’s A-Yuan, our son.”

The man nodded and smiled.

“I’m Xiao Xingchen, or as you know me online, SongXiao. My husband helps with that part.”

“And me!” A-Qing added.

“Ah, I can’t forget my tech support, A-Qing and A-Yang. You’ve met our daughter.”

“A-Yang is my brother and he’s a brat but he’s not home right now,” A-Qing said.

“And, of course, there are the bunnies,” Song Lan added.

They sat on the floor with Xiao Xingchen as he gestured for them to do, while Song Lan and A-Qing opened one of the hutches. That was all they really needed to do, as the bunnies made their way to freedom quickly. They were tiny, and if the guides Wei Ying had read were right, would likely only grow to be 3-4 pounds.

One of the black bunnies immediately began hopping around the room at high speed when it was free, jumping around as though in joy.

“That one’s like you, a-die,” A-Yuan commented, and Wei Ying laughed.

A-Qing reached in for a few stragglers and then joined them on the floor, putting one in A-Yuan’s lap as she sat down. Song Lan came with the mother rabbit, whose coat was fully black.

“Fuxue had a litter of six this time around,” Song Lan told them. “Three of each sex.”

There was one brown, two black, and three of different shades of gray.

“They all have gentle dispositions,” Xiao Xingchen added. “Though one of the females is quite energetic, as you noticed.”

A-Yuan pet the one in his lap, a light gray one Song Lan told them was a lilac color. A-Qing put the other light gray one in Wei Ying’s lap, and he couldn’t stop himself from cooing softly at it as his fingers met its soft fur.

“Since we bred her with a lilac, we also have the one blue and the chocolate. Lilac is the light gray, blue is the darker,” Song Lan explained.

The blue was hopping around after the energetic black bunny, at a slower pace. The chocolate kit was approaching A-Zhan with hesitant curiosity. The less energetic black one hopped up to Xiao Xingchen, clearly looking for his familiarity, and hopped into his lap.

He picked it up gently.

“Who doesn’t have a bunny yet?” he asked.

The chocolate was next to A-Zhan’s leg, nosing at the hand he held out. When he pet it, the kit closed its eyes, flopped over, and exposed its belly. When he gently picked it up, it offered no resistance.

“I think it likes you, A-Zhan,” Wei Ying joked. “We all have bunnies. A-Yuan and I have the lilacs, and the chocolate has fallen in love with my husband.”

“He loves to be pet,” Xiao Xingchen said. “Especially if you rub gently right between his ears.”

“The black and lilac are one boy, one girl each. The blue is female,” Song Lan added.

Xiao Xingchen discussed what to expect in terms of personality and needed care, along with specifics about the breed. Most of the details were ones Wei Ying had read online, but some were based on experience with rabbits.

They passed around the four they were holding so they could each meet them, and eventually the blue was curious enough to wander over. But the energetic black needed to be caught by A-Qing.

“She’s really sassy,” A-Qing told them.

“Definitely a big personality,” Xiao Xingchen agreed.

The chatting about bunnies gave way to other chatter—Xiao Xingchen revealed he had lost his eyesight during an illness that had infected the optic nerve, and they had adopted A-Qing because her ocular albinism meant she also had difficulty seeing. Since they had already adapted to his blindness and the agency had labeled her unadoptable, they took her in.

“Honestly, I grew up partly in the system,” he said. “I couldn’t leave her.”

“I did, too,” Wei Ying admitted.

“I inherited this home from my adoptive mother, Baoshan Sanren.”

Wei Ying gasped, and he could feel A-Zhan looking at him in concern.

“She was my mother’s mom,” he said, not able to stop himself from staring. “Cangse Sanren. She and Dad died when I was four.”

“Goodness, what a small world! She had already left for college when I was adopted, so I didn’t get to know her well. I guess that would make me your jiujiu?”

Wei Ying grinned, poking A-Yuan gently.

“A-Yuan, that means Xiao Xingchen is your jiuye, and A-Qing is your tangjie.”

A-Qing looked thrilled.

“I get a cousin? Score!”

Wei Ying could only guess she didn’t have much extended family, and he was glad to add to their found family. A-Yuan had many Wen uncles and aunts and cousins, but he was just as excited. The kids huddled together to talk.

“Definitely a small world,” A-Zhan said.

“Smaller still,” Song Lan said. “I freelance now, but I used to work in the tech industry, so I recognize your names.”

Wei Ying focused on the rabbit in his lap, the chocolate who was sprawled out and nuzzling against his hand, feeling taut and anxious.

“It obviously wasn’t you,” he continued quickly. “But I decided not to work with the major companies after seeing what they would do to their own.”

“They didn’t see me as their own,” Wei Ying said, shaking his head, hating the feeling rising in his chest.

Silence fell among them, interrupted by the kids chattering nearby. It was clear Xiao Xingchen didn’t know what they were talking about, but Song Lan could explain later.

“A-Ying found his family,” A-Zhan said after a moment. “As did I.”

“I would be honored to be a part of it,” Xiao Xingchen said. “It is good to finally meet my waisheng.”

The discomfort passed, Xiao Xingchen filling the silence with stories of his adoptive mother, the stories he knew of Wei Ying’s mother, the tales soothing his anxiety. The bunny in his lap helped, it’s warmth and nuzzling relaxing.

Eventually Xiao Xingchen asked the big question.

“Which of the bunnies appealed to you?”

Wei Ying and A-Zhan exchanged a glance before they turned to A-Yuan.

“The brown one,” A-Yuan said immediately. “He cuddles.”

The same one Wei Ying was fond of, currently in his lap. A-Zhan nodded his agreement.

“He’s on my lap nuzzling me now,” Wei Ying said.

“Any ideas on names yet?” Song Lan asked.

“Turmeric or Nutmeg,” A-Yuan supplied. “They’re warm, like him.”

“Not Cinnamon?” Wei Ying asked teasingly.

“No. I bet everyone names brown rabbits Cinnamon.”

Xiao Xingchen laughed.

“Well, you’ll probably figure out what spice is most like him as you get to know him better.”

They packed up the bunny, A-Qing taking him around to say goodbye to each of his siblings and mother. Xiao Xingchen insisted on giving them the friends and family discount, and they exchanged numbers so they could find more time to get to know each other.

The drive home was quiet, punctuated with chatter by A-Yuan about A-Qing and Turmeric or Nutmeg.

The bunny took to his new home well, seemingly happy with the space and toys and food, and they watched him and played with him for hours until he eventually entered the hutch and climbed into the hammock.

A-Yuan was yawning and dinner hadn’t been made, so A-Zhan ordered pizza, something they rarely did, which made it a treat. While they ate, A-Yuan told them solemnly the bunny’s name was Turmeric. Wei Ying asked if his middle name was Nutmeg, and A-Yuan smiled widely and nodded, and thus Turmeric Nutmeg Wei became their newest family member.

By the time A-Yuan was fed and bathed and tucked in, he was ready to fall right to sleep, and Wei Ying was able to snuggle on the sofa with A-Zhan with a little time left before bed.

“You found more family,” A-Zhan said, smiling softly, lacing their hands together.

“We found more family,” Wei Ying corrected. “What’s mine is yours, xinai.”

He scooted closer to A-Zhan until he was almost in his lap. The events of earlier were on his mind, the mysterious phone call, what it might mean. He knew his husband was concerned. Even if the silence between them was comfortable, he worried about A-Zhan.

“Did you want to call your brother?” he asked.

A-Zhan shook his head, then leaned in for a kiss.

“No. Today is for family. I want to take you to bed.”

Even after five years, when A-Zhan said things like that Wei Ying melted.

When A-Zhan pulled him up and tugged him toward their bedroom, he hindered him a little with kisses, but they eventually made it.

In the morning they would learn Wei Ying had been proven innocent; the culprit was actually Lan Xichen’s fiance, Meng Yao. His scion Su She took the opportunity to frame Wei Ying out of jealousy, wanting A-Zhan for himself.

The bad year at Nie Industries was caused by the very same code, undiscovered until a large number shares were suddenly liquidated and stocks plummeted, until millions of dollars were syphoned from corporate accounts and disappeared. Nie Huaisang had put the pieces together, had worked with the FBI and proved it was Meng Yao working on behalf of Jin Enterprises at the behest of his father.

Later, Gusu Lan Tech would ask A-Zhan to return home to chair the board after a vote of no confidence in Lan Xichen, and he would tell them no. He was part of Dafan Applications now, he had a home, and he was happy where he was.

Later, the Wei family might consider responding to overtures from the families they once had.

Tonight they didn’t have that knowledge.

Tonight was for family, and right then was for A-Zhan and Wei Ying, with no room for anything outside of their home.

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freckledacademic · 4 years ago

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hello everyone!

i had someone request a breakdown of what my medical school/MSTP application looked like, so here we go!

FIRST - a disclaimer! this is my application, and it worked for me. it doesn’t look like the application of my classmates and other medical school friends who also got in. one of my friends got a below average MCAT, but her clinical experience and extracurriculars meant she got into (and is going to!) a highly-ranked medical school. on the other hand, my application was weighted fairly heavily in the direction of academics, since that’s my strength!

also, keep in mind i was applying to medical scientist training programs with the goal of getting an MD/PhD -- my app is also pretty research heavy

onto the specifics!

general

i went to a small liberal arts college with a focus on research. nearly every student does a senior thesis, and science senior theses usually involve lots of lab work/data generation done by the student.

i double-majored in mathematics and biochemistry & molecular biology, and between those classes, gen eds, and music, i was generally over full-time but just under the credit limit per semester.

i took the MCAT in May of 2018, and applied that summer. my AMCAS was in July 9th, and I got approved ~August 10th. this was between my junior (17-18) and senior (18-19) years. i matriculated at my med school in july of 2019.

numbers

GPA - 3.97 (ish? i got two A-s, and everything else was As)

MCAT - 519 (130 on three sections, 129 on one. i don’t remember which one though whoops)

clinical experience

shadowing - shadowed with two different doctors - an oncologist over the course of a semester, and a clinical pathologist for a day. total # of hours - relatively low, maybe 15-25.

clinical volunteering - i volunteered at a free clinic near my college for one afternoon every week or so during most of junior spring and senior year. total # of hours - ~60 hrs

medical ethics program - related to the longitudinal oncology shadowing. once a month, throughout the spring semester of my junior year, we would meet to discuss articles we’d been assigned to read on a topic eg: end of life care, malpractice, etc. total # of hours - ~10 hrs

research experience

research assistant, yeast genetics lab - i worked in this lab during the spring of my first year and during the summer after. it was my first lab/research experience, and it taught me a lot regarding lab techniques and lab mentality. garnered me my one and (still) only publication! total # of hours - 400+ hrs

research assistant, computational biology lab - i worked (and went on to do my senior thesis) in this lab during my junior year. it was partially an excuse to get started with my thesis coding early, because it was going to take a lot of time, but it also helped me get acquainted with my thesis advisor! total # of hours - ?

research intern, cancer research center - this was an internship i applied for that was specifically for students with a year left in college that occurred the summer between my junior and senior year. i worked with a biostatistician on developing a machine-learning model to predict disease risk based off of demographic data. i learned a lot about R, statistics, and regression models. i also learned that i don’t like R, statistics, and machine learning models lmao. note: this didn’t technically go in my AMCAS iirc, because i applied while i was *at* the internship. i did talk about it at interviews!

senior thesis - this was another thing that wasn’t in my app itself, because at the time it hadn’t been written lmao. it was something i was able to talk about at interviews, but it being ~still in progress~ did complicate it

on-campus jobs

peer tutor - got recommended by a professor my first year for a different tutoring program, but my schedule was a mess so i ended up as a one-on-one peer tutor! i really enjoyed it, but i had to drop it with how busy i got junior year, so i only did it for two years (freshman spring --> junior fall). total # of hours - 210 hrs

laboratory course TA, cell biology - again, got asked by a professor to do this. specifically TA’d 2/3 sections junior fall, and then 1/2 or 1/3 sections every semester after that. also held office hours, maintained longitudinal lab experiments, and supervised students performing experiments. total # of hours - 800 hrs (TAing this class was absurd and i did it for four semesters)

extracurriculars

collegiate choir member - i adore singing, and part of the reason i went to the college i did was so i could still partake in music while pursuing a science degree. i was a part of my school’s auditioned choir for all four years at my school, and it involved 1h15m practices 4 days a week, regular concerts, and an annual spring tour. total # of hours - 500 hrs

chorus manager - i was a manager of the chorus as well for three years (sophomore through senior). i was very much in charge of the organization - making sure everyone had uniforms, that those uniforms were on for concerts and that everyone was wearing shoes. shepherding people on trips, solving problems, taking attendance, etc. etc. total # of hours - 250 hrs

i also took voice lessons, though i don’t think i put that down on my app? iirc there’s a limit to how many activities you can add?

other

honor societies

Phi Beta Kappa - general undergrad honor society, inducted junior year

Mu Alpha Theta - mathematics honor society, inducted sophomore year

Beta Beta Beta - biology honor society, inducted sophomore (?) year

awards

Barry Goldman scholarship - honorable mention

Dean’s List - all 8 semesters

various prizes specific to my undergrad

academic merit scholarship, minor voice scholarship

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

to sum it all up....

i think the pros of my application include a strong academic presence, proof of my interest in research, and the existence of significant interests outside the STEM pathway - namely, music!

i think the major con of my application was a really weak clinical experience section, tbh. i also feel like some schools on the other side of the country from my undergrad like. hadn’t heard of it lmaooo.

everyone’s application will be different!! and that’s good, and okay, and you’ll be fine. you want to think about the things that make you you, and potentially the things that reflect on the kind of doctor that you’d like to be.

#medical school #med school #med school advice #medblr #freckled talks #studyblr #academics unite #academicsunite #medical school applications #whooooop this took me like an hour to write up. what a thrilling tour of my college resume lmao

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vs-redemption · 5 years ago

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Crime is Common. Logic is Rare. (Ch 12)

Chapter Twelve: Lab Work (HawksxGN!Reader)

Plot summary: You thought your hands were full as a regular quirk geneticist, but then you meet Hawks and things get even more exciting!

Warnings:

⚠️This story contains spoilers from the manga.

⚠️Some events and plot points have been altered from the original manga

Next Chapter : Chapter Guide

“Thanks for coming out to meet with me again,” Dr. Garaki smiles pleasantly at you as you take a seat across from him in his office. His chair was much taller than yours to make up for his short stature. The expensive microscope and box of blood samples from your previous visit were nowhere in sight. The only thing on top of the desk was a copy of the proposal you had emailed him a few days before. The doctor puts a hand on top of the papers. “I’d like to talk about this.” You nod your head, trying to read the man’s face to predict how the conversation might go. You had to be ready for anything.

“I’ve never read a proposal quite like this before,” Dr. Garaki taps a finger on top of the document. “You made a lot of bold assumptions.”

You keep a look of confidence on your face as you reply. He still hadn’t made any indication about how he felt about the wild hypotheses you’d written for him. He just had the same cheerful smile on his face. "What you showed me the last time I was here was several steps ahead of any of the current research I could find,” you explain calmly. “Without knowing what was in that mystery fluid you used, I had to fill in some blanks.”

The doctor stayed silent for a moment and you hoped that he wasn’t about to throw you out for ignoring basic scientific standards and stepping into the realm of mad science. Never in your wildest dreams did you think you’d ever submit such an absurd proposal, but Dr. Garaki seemed like an odd enough man to actually appreciate it.

“You believe I have access to samples of All For One’s DNA.” The doctor finally speaks.

“No,” you still manage to keep your voice level. “Well, I honestly don’t know. It’s just something I’ve been thinking about for a while. All For One is the only true example of a person possessing multiple quirks. Because of that, it stands to reason that his ability to give and receive quirks, and therefore his actual DNA, could be the key to creating Nomus.”

The doctor continues to stare you down. “And if I DID have access to All For One’s DNA, you think the next logical step…”

“…is to try and create a Nomu ourselves, yes.” You finish the sentence for him, praying that it would make the statement sound less insane if you were the one to say it. The doctor raises his eyebrows, the unreadable smile still on his face.

“I obviously made a lot of assumptions about how to accomplish that task too. Plus, it would definitely be unethical to do human trials,” you press on. “But besides the most recent attack in Kyushu, the Nomus themselves hardly seem human anyway. Perhaps the human component is small enough that simply using All For One’s quirk to splice human DNA samples together is enough. We would just need to create some sort of vessel to hold all that power”

“And the applications for such research?” The doctor continues to question you even though you must sound like you’re out of your mind by now.

“Limitless,” you declare. “If we can understand the way in which quirks mutate or combine over time, we can eliminate the weaknesses and drawbacks of certain quirks. Take the number one hero for example. Endeavor’s body clearly has a heat threshold. I noticed it in his fight with that high-end Nomu. He’d be unstoppable if he also had a quirk of heat-resistance or something. And the way things are going now, quirks are getting stronger and more complicated. The number of people born with quirks that cause damage to their body or affect their quality of life is increasing. We could solve that problem completely if we understood quirk inheritance on a microscopic level.”

“You sound like a true advocate of science,” the doctor nods. “Some people might question the morality of genetically modifying, enhancing, or manipulating quirks though.”

“I’m just saying what would be possible,” you shrug, “What people are able to legally do with that information would be up for debate when the time comes, but that’s nothing new in the field of science”

“True, true!” The doctor nods his head in agreement. “And like you mentioned, there are a lot of assumptions we need to address before actually going through with a proposal like this.” He slaps his hand on top of the document again before hopping out of his chair. “I’ve been thinking of how we can utilize YOUR quirk in my lab,” he beckons for you to get up and follow him. “I understand you can observe information about your surroundings in extreme detail.”

“Yeah,” you confirm the information while following him out of his office and through the halls of his hospital. He stops at what appears to be a supply closet and unlocks the door with a key he pulls from his pocket. You were surprised to see the small room contained a hidden elevator.

“This is for employees only,” the doctor explains once you’re both inside. He pushes the single unmarked button and the doors slide close. You assumed the elevator went down because when the doors opened back up, you were in a dimly lit basement laboratory. It was set up like most of the other labs you’d been in before, but there was just something a little creepier about it that you couldn’t quite put your finger on.

“Nobody else is down here?” You ask as you continue to look around.

“This is actually a secondary lab,” Dr. Garaki tells you. “My main lab is in a different location.” The strangeness of the situation continued to build, but you kept your feelings to yourself. There’d been something off about the doctor since the first time you’d met him, and now you were committed to figuring out what it was.

“I don’t mind using my quirk,” you tell him, “but the length of time I’m able to use it is pretty limited.”

“Limitations can be overcome,” the doctor chuckles before hurrying over to one of the work stations where a microscope was set up next to a giant monitor. “A lot of people don’t realize their quirks can work harder and longer with a certain type of fuel to keep them going.”

“What like Popeye and his spinach?” you joke.

“Exactly like that!” the doctor nods enthusiastically, his large glasses making his eyes look bigger than they actually are. “If we monitor your brain activity while you use your quirk, and take blood samples before and after, we could learn a lot. You should also try to use your quirk every day. Make a note if there’s a difference when you use your quirk in the morning or in the evening, or if anything changes depending on what you eat or the type of weather.” You can’t help but laugh.

“You actually want me to do that?” you ask.

“Just a suggestion,” he shrugs. “I would like to try a couple things today though, if you’re up to it.”

“Depends on the couple things, I guess,” you say hesitantly. He explained that he wanted you to use your quirk to watch videos on one of the computer monitors in one minute intervals. Each minute long session would be under a different condition and there would be a short test between each condition to record how much information you’d observed with your quirk. As your quirk only lasted about 5 minutes, he decided to do four tests in order to have the best results. The first test would be the control. The second test would be taken with noise canceling headphones in order to see if the number of visual details increased if sound was taken away. The third test would be taken while standing between two heaters to see if temperature made a difference. The fourth test would be taken while jogging on a treadmill to see if physical exertion effected the results. The doctor sat you in a chair in front of the monitor for the first test and pressed play. Next thing you knew you were being shaken awake by the doctor. You open your eyes and realize that you’re on the ground.

“Oh thank goodness! You’re awake!” The doctor sighs in relief. “You must’ve overexerted yourself. You had a dizzy spell and passed out after the last test.” You blink a few times and glance around the lab, trying to remember what happened, but of course you couldn’t. You’d just lost consciousness after using your quirk, so all the information you’d gathered had been wiped from your mind. You’d always been a bit apprehensive of the doctor, but now you were honestly feeling scared. Never in your life had something like this happened, so why would it happen now?

“Are you feeling better now?” the doctor asks, “Can you stand up?” You take a deep breath and nod your head. You felt perfectly fine aside from the memory loss which you were used to.

“Well, I guess I hit my limit for today,” you laugh even though you were still creeped out. “Was there at least any interesting results from the tests?”

“You observed a lot more than I imagined!” The doctor nods his head enthusiastically. “Although the amount of information you recalled from each test was about the same.”

“Would you mind if I look at the notes?” you ask as casually as you can. The doctor frowns.

“Unfortunately I didn’t take notes,” he tells you. “I used a timer to record how long it took you to describe everything you observed.”

“I see,” you say calmly before shrugging. “Oh well. Was there anything else you wanted to do with me today?”

“No, no!” The doctor waves his hands, “Of course not. You should get some rest. Are you going to be all right getting all the way home? Perhaps one of the doctors upstairs can take a look at you.”

“That’s okay,” you smile appreciatively. “I’m actually staying at a friend’s place in the city today. I can rest there and go home in the morning.” The doctor nods in understanding as you both head to the elevator and go back up to the main hospital. He walks you to the door and waves goodbye, promising to keep in touch so that you can make plans to meet again soon. As soon as you’re outside, you reach into your bag to get your phone only to find that it wasn’t in the pocket you normally kept it in. Where you just being paranoid now? You open up your messages and type one to Hawks that asked “Where are you?” Once he answers, you hail a taxi, not caring that a bus or train would be cheaper. You wanted to get to your boyfriend as fast as possible.

#bnha x reader #mha x reader #Keigo Takami X Reader #Hawks x reader #Keigo takami #hawks #bnha #mha #my hero academia #boku no hero academia #my writing

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jcmarchi · 8 months ago

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Studies in empathy and analytics

New Post has been published on https://thedigitalinsider.com/studies-in-empathy-and-analytics/

Studies in empathy and analytics

Upon the advice of one of his soccer teammates, James Simon enrolled in 14.73 (The Challenge of World Poverty) as a first-year student to fulfill a humanities requirement. He went from knowing nothing about economics to learning about the subject from Nobel laureates.

The lessons created by professors Esther Duflo and Abhijit Banerjee revealed to Simon an entirely new way to use science to help humanity. One of the projects Simon learned about in this class assessed an area of India with a low vaccination rate and created a randomized, controlled trial to figure out the best way to fix this problem.

“What was really cool about the class was that it talked about huge problems in the world, like poverty, hunger, and lack of vaccinations, and it talked about how you could break them down using experiments and quantify the best way to solve them,” he says.

Galvanized by this experience, Simon joined a research project in the economics department and committed to a blended major in computer science, economics, and data. He began working on a research project with Senior Lecturer Sara Ellison in 2021 and has since contributed to multiple research papers published by the group, many concerning developmental economic issues. One of his most memorable projects explored the question of whether internet access helps bridge the gap between poor and wealthy countries. Simon collected data, conducted interviews, and did statistical analysis to develop answers to the group’s questions. Their paper was published in Competition Policy International in 2021.

Further bridging his economics studies with real-world efforts, Simon has become involved with the Guatemalan charity Project Somos, which is dedicated to challenging poverty through access to food and education. Through MIT’s Global Research and Consulting Group, he led a team of seven students to analyze the program’s data, measure its impact in the community, and provide the organization with easy-to-use data analytics tools. He has continued working with Project Somos through his undergraduate years and has joined its board of directors.

Simon hopes to quantify the most effective approaches to solutions for the people and groups he works with. “The charity I work for says ‘Use your head and your heart.’ If you can approach the problems in the world with empathy and analytics, I think that is a really important way to help a lot of people” he says.

Simon’s desire to positively impact his community is threaded through other areas of his life at MIT. He is a member of the varsity soccer team and the Phi Beta Epsilon fraternity, and has volunteered for the MIT Little Beavers Special Needs Running Club.

On the field, court, and trail

Athletics are a major part of Simon’s life, year-round. Soccer has long been his main sport; he joined the varsity soccer team as a first-year and has played ever since. In his second year with the team, Simon was recognized as an Academic All-American. He also earned the honor of NEWMAC First Team All-Conference in 2021.

Despite the long hours of practice, Simon says he is most relaxed when it’s game season. “It’s a nice, competitive outlet to have every day. You’re working with people that you like spending time with, to win games and have fun and practice to get better. Everything going on kind of fades away, and you’re just focused on playing your sport,” he explains.

Simon has also used his time at MIT to try new sports. In winter 2023, he joined the wrestling club. “I thought, ‘I’ve never done anything like this before. But maybe I’ll try it out,’” he says. “And so I tried it out knowing nothing. They were super welcoming and there were people with all experience levels, and I just really fell in love with it.” Simon also joined the MIT basketball team as a walk-on his senior year.

When not competing, Simon enjoys hiking. He recalls one of his favorite memories from the past four years being a trip to Yosemite National Park he took with friends while interning in San Francisco. There, he hiked upward of 20 miles each day. Simon also embarks on hiking trips with friends closer to campus in New Hampshire and Acadia National Park.

Social impact

Simon believes his philanthropic work has been pivotal to his experience at MIT. Through the MIT Global Research and Consulting Group, which he served as a case leader for, he has connected with charity groups around the world, including in Guatemala and South Africa.

On campus, Simon has worked to build social connections within both his school and city-wide community. During his sophomore year, he spent his Sundays with the Little Beavers Running Team, a program that pairs children from the Boston area who are on the autism spectrum with an MIT student to practice running and other sports activities. “Throughout the course of a semester when you’re working with a kid, you’re able to see their confidence and social skills improve. That’s really rewarding to me,” Simon says.

Simon is also a member of the Phi Beta Epsilon fraternity. He joined the group in his first year at MIT and has lived with the other members of the fraternity since his sophomore year. He appreciates the group’s strong focus on supporting the social and professional skills of its members. Simon served as the chapter’s president for one semester and describes his experience as “very impactful.”

“There’s something really cool about having 40 of your friends all live in a house together,” he says. “A lot of my good memories from college are of sitting around in our common rooms late at night and just talking about random stuff.”

Technical projects and helping others

Next fall, Simon will continue his studies at MIT, pursuing a master’s degree in economics. Following this, he plans to move to New York to work in finance. In the summer of 2023 he interned at BlackRock, a large finance company, where he worked on a team that invested on behalf of people looking to grow their retirement funds. Simon says, “I thought it was cool that I was able to apply things I learned in school to have an impact on a ton of different people around the country by helping them prepare for retirement.”

Simon has done similar work in past internships. In the summer after his first year at MIT, he worked for Surge Employment Solutions, a startup that connected formerly incarcerated people to jobs. His responsibility was to quantify the social impacts of the startup, which was shown to help the unemployment rate of formerly incarcerated individuals and help high-turnover businesses save money by retaining employees.

On his community work, Simon says, “There’s always a lot more similarities between people than differences. So, I think getting to know people and being able to use what I learned to help people make their lives even a little bit better is cool. You think maybe as a college student, you wouldn’t be able to do a lot to make an impact around the world. But I think even with just the computer science and economics skills that I’ve learned in college, it’s always kind of surprising to me how much of an impact you can make on people if you just put in the effort to seek out opportunities.”

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myeducation001 · 3 months ago

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BS Studies: A Comprehensive Guide to Your Future

1. Introduction to BS Studies

2. The Evolution of BS Degrees

3. Why Choose a BS Degree?

4. Popular Fields in BS Studies

BS in Computer Science

A highly sought-after field, a BS in Computer Science equips you with coding, software development, and algorithmic thinking skills – all critical in the tech-driven world.

BS in Engineering

Whether it’s civil, mechanical, or electrical engineering, a BS in this field gives you the technical expertise to design, build, and innovate in various industries.

BS in Health Sciences

Health-related BS degrees, such as nursing or public health, prepare you to address global healthcare challenges and make a tangible difference in people's lives.

BS in Business Administration

With a focus on economics, management, and operations, a BS in Business Administration can set you on the path to becoming a leader in the corporate world.

BS in Environmental Science

This degree is perfect for those passionate about sustainability, offering tools to tackle the pressing environmental issues of today.

5. Specializations within BS Studies

6. Skills You Gain from a BS Degree

Graduating with a BS degree doesn’t just mean you have a diploma – it means you’ve acquired a wealth of skills that employers are actively seeking. These include:

a) Analytical Thinking**: The ability to analyze data and problem-solve is crucial in almost any field.

b) Technical Skills**: From software development to lab techniques, you’ll gain hands-on experience.

c) Problem-Solving Abilities**: BS degrees often emphasize real-world problem-solving.

d) Communication and Teamwork**: Many projects require collaboration, honing your teamwork and leadership skills.

7. Job Opportunities after BS Studies

8. How to Choose the Right BS Program

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babyfloors · 3 years ago

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Wondering Wednesdays, Baby Acres, Chapter 2: Infrastructure, with words!

July 21, 12021

books

Coop

4Freedoms

health

mywritings

PublicDomainInfrastructure

writing

This post starts the rough draft of Chapter 2 of my non-fiction WiP, Baby Acres.

This chapter will have about 2500 words, (hopefully educational words!!) 500 of which have already been written as the Introduction, but will quite likely have to be thrown out and refitted with the new evolution of the book. We shall see.

And again, by way of disclaimer, the overall goal is now to explain why we need both equ. + justice, & why in 4 phases. This chapter will transition to a chapter (2-5) for each phase, showing what Phases I-IV could look like as part of a possible roadmap for a fully inclusive society for all of us. This vision is laid out in the hope that All HumanKind will eventually have each person’s basic needs met, without taking anything from anyone, and without violence, intimidation, nor coercion of any kind.

Chapter Two:

( Chapter 2’s outline was last week…)

Chapter 2 Introduction:

The first stage of this project involves building empathy, and bringing each one of us to see each one of our fellow human beings as … a human being. Each one meriting humane treatment, and human dignity.

That empathy building phase was Phase 0 (yes, I’m a computer scientist by first training, so I start with 0…). Phase I is meant to go for fifteen years, potentially from the years 2022 to 2037, building a movement to strengthen some of our most crucial and obviously key pieces of our social infrastructure, which are in the public domain. During this period, one of the ways that we can both build conceptual support and also literally build our physical infrastructure that needs support, is by borrowing an idea from President Franklin D. Roosevelt, which worked during the Great Depression to create jobs while educating young (white) men at the same time. What we want to do now, is to educate, facilitate service, and build a community-service frame of reference, while also upgrading our public infrastructure, just as FDR did in the 1930’s via his program.

Bringing back an updated version of FDR’s Civilian Conservation Corps (CCC), or Roosevelt’s Tree Army, as it was popularly known, could provide a stepping stone between the empathy- building work that must always be on-going, and the support-building work of bringing our society to a consensus on the needed support for the most basic of our public social infrastructure institutions, like Public Libraries, Public Transportation, Public Education (especially in the financial and legal areas, where so many consumers fall prey to financial predators, and end up in debt due to lack of knowledge), and Public Health. These four systems under gird our entire societal structure, and need support perhaps the most urgently, in return for which we potentially get the most payback for all members of society. While we do the difficult work of building the necessary consensus to get there from here, a simpler step might be to bring back some form of the CCC, updated to be far more inclusive, and used as both a means of providing employment to young people, and also to educate them, much like the Gap Year in Europe.

But instead of having our new high school graduates backpack around the country, they could be sent to work in urban public library branches, light-rail and subway/Metro stations, local urban public schools, or inner city health clinics. As they rotate from one part of the country to another, say, monthly, they learn first-hand of the conditions in places they are not from and have not lived, while serving communities they have never met, working alongside peers from different walks of life, and seeing a side of their native land that they did not grow up with. In short, learning the realities, and different perspectives, of this large and diverse nation of ours.

— (Next Wednesday: Chapter 2, section I …)

I’m considering this Rough Draft as the block of clay from which my book will eventually emerge, obviously, and some ideas for phases III and IV are still becoming more fixed in my mind as I write, so the final version will likely look pretty different from this Rough Draft, and will need updating once I get to the very end.

And once again, yeayyy( !!)with regard to audience, I may have at least a couple of comps: Walden Two meets The War on Poverty: A Civilian Perspective (by Dr.s Jean and Edgar Cahn, 1964). I know that lots of people consider Skinner’s writing to be stilted, but I like the tilt of most reviewers, in that the idea is that a community should keep trying policies that members agree upon until they find what works for all of them.

As for genre, I’m still wondering: Non-fiction, System Change, Causes, maybe even Inspirational, but I doubt it.

Last week’s installment of this series…

Action Items:

1.) Consider some ideas you may have on how our society can solve the problem infrastructure upgrades in the next 15 years,

2.) Share them with us in the comments, here, please, and

3.) Write a story, post or tweet that uses those sources and your thoughts.

Dear Readers, ideas on learning, especially multiple #LanguageLearning, on-going education and empathy-building, to #EndPoverty, #EndHomelessness, #EndMoneyBail & achieve freedom for All HumanKind?

Support our key #PublicDomainInfrastructure & #StopSmoking at LEAST for CCOVID-19: 1. #PublicLibraries, 2. #ProBono legal aid and Education, 3. #UniversalHealthCare, and 4. good #publictransport Read, Write -one can add Stayed on Freedom’s Call via this GoodReads button: ,

Vote, Teach and Learn (PDF Lesson Plan Book)!

and my

Babylon 5 review posts

, if you like Science Fiction, and a proposed

Vision

on Wondering Wednesdays: for a kinder world…

Shira Destinie A. Jones, MPhil

our year 2020 CE = 12020 HE

(Day 1 … Day 5)

Stayed on Freedom’s Call (free copies at: https://archive.org/details/StayedOnF…) includes two ‘imagination-rich’ walking tours, with songs, of Washington, DC. New interviews and research are woven into stories of old struggles shared by both the Jewish and African-American communities in the capital city.

Shared histories are explored from a new perspective of cultural parallels and parallel institution-building which brought the two communities together culturally and historically.

Please leave a review, if you can, on the GoodReads page.

Shira Destinie Jones by ShiraDest is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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