#data applications

[great-and-small]

Yet another scientific article adding to the slowly growing mountain of evidence that suggests we might be able to save the world through bird-watching.

[deeper-x-deeper]

thinking about the hypnosis "I'm going to break your mind" thing but in the way that I'm going to put it back together differently every time just to see what happens. It's not really fun to break a toy unless you're doing it to put it back together in various creative ways. rearrange the parts just to see if they'll still fit together. over and over until you become my greatest creation

[modeus-the-misanthrope]

Challenge for Mod Authors: Level Impossible

When writing down directions for how to fix issues in your mod, hand those instructions to 1 friend that doesn't have any experience in programming. If they say your instructions don't help solve their issue. Rewrite the instructions until it does.

[walkingstackofbooks]

What do the DS9 crew call each other?

>View the raw data >Season 2 Observations post

Season One Observations

Sisko:

Is most likely to introduce himself as "Commander Benjamin Sisko" (7 times), or as "Benjamin Sisko" (5 times).

Is more likely to be called "Commander" than "Sir" by Kira (35:8), Odo (15:2) and Quark (9:0).

>> Kira does not call him Sir until e13; Odo only in e16 (when harassed by Lwxana)

Is more likely to be called "Sir" than "Commander" by Miles (66:20)

>> Even more likely E1-9 (35:5), only twice as likely e10-19 (31:15)

Is just as likely to be called "Sir" as Commander by Julian (22:20)

Dax exclusively calls and refers to him as Benjamin.

Otherwise is referred to as Sisko (esp. by Odo) or Commander Sisko.

Sisko talks to everyone the most.

Kira:

Is most likely to introduce herself as "Major Kira Nerys" (3 times).

Is most likely to be called Major by everyone. In the first half, O'Brien also calls her "Sir" in a few episodes; Bashir and Dax call her "sir" once.

Is most likely to be referred to be others as "Major Kira".

Is occasionally called or referred to as "Kira", but not often.

Odo:

Is most likely to introduce himself by mentioning he is "Chief of Security" (2 times).

Is called "Odo" more than "Constable" by Kira (9:6) and Quark (15:1)

Is called "Constable" more than "Odo" by Sisko (16:11)

Is usually referred to as Odo

Julian:

Is called "Doctor" most by Sisko (43:7 "Dr Bashir"), and exclusively so by Kira (5x), Odo (5x) and Quark (1x).

Is exclusively called and referred to as "Julian" by Dax.

Is most often referred to as "Dr Bashir". Sisko also refers to him as "our doctor" and "my doctor", and Sisko, Kira, Odo and O'Brien all refer to him as "Bashir" on occasion.

Miles calls him "Doctor" once, "Julian" four times (all in Episode 13, after Julian has asked for this), and "Sir" three times (again, all in E13).

Dax:

Most frequently refers to self as "Dax".

Sisko is most likely to call her "Dax", but also "Lieutenant" in very formal situations. He calls her "Old Man" 4 times, in e1, 7, 9 and 10.

Julian will call her "Jadzia" (4x) or "Dax" (4x), depending on how casual the situation is.

Miles calls her "Lieutenant" exclusively (3x), as does Kira (1x).

She is usually referred to as "Dax" (9x) or "Lieutenant Dax" (10x) by everyone, or by Julian as "Jadzia" (2x).

Miles:

Most likely to call himself "O'Brien".

Miles is most often called "Chief" by everyone, although it takes a while for that to start. (Kira and Sisko begin calling him "Chief" in E4.)

In first half, is almost just as likely to be called "Mister O'Brien" as "Chief" by Sisko (10:16) and Kira (7:7); from episode 10 that changes to 3:10 for Sisko and 0:1 for Kira.

Is most likely referred to as "O'Brien".

Wow, that took less time than I thought it would when I began this - I'm definitely looking forward to seeing if any trends continue or change going forwards!

[sprinklecipher]

Turnabout Titles

Got curious about how often people applied the “Turnabout” naming scheme to their AA fics, so I crunched a bunch of numbers on AA fics across Ao3 and FFN to find out (and also maybe got a bit carried away looking for patterns…)

What I found:

Overall, about 4% of AA fics on Ao3 and 13% of AA fics on FFN include “Turnabout” in the title

Longer fics were more likely to have “Turnabout” in the title than were shorter ones, with about 14% of 30k+ AA fics on Ao3 featuring it in the title, compared to only about 2% of sub-1k AA fics

“Turnabout” fics were disproportionately likely to be crossovers—about 19% of AA fics with “Turnabout” in the title were crossovers, compared to 5% of AA fics overall

And here’s a graph for good measure:

Method details and raw numbers below the cut

Method: For the Ao3 numbers, I searched for fics in the “逆転裁判 | Gyakuten Saiban | Ace Attorney” fandom, limiting the results to English-language fics only and excluding restricted works (although at a glance, the same patterns appear to hold among restricted works). For the FFN numbers, I searched for fics in the “Phoenix Wright: Ace Attorney” fandom, filtered to English language only. Data collected 5/25/2024.

[diaryoftheloser]

A Loser’s Accountability Diary

Hi, I'm Aver, The Loser, and for the past eight years, ever since high school, I’ve been caught in a relentless cycle of personal and professional struggles. It feels like I’ve been stumbling through life, trying to catch a break, but every step forward has come with heartbreak, and the weight of it all has been almost unbearable at times.

Fast forward to now: I’ve been accepted into an internationally renowned university’s Master’s program in Biotechnology. Sounds like a dream come true, right? Not exactly. You see, my goal was to get into my dream PhD program, but I didn’t make the cut. Instead, I had to settle for this Master’s degree—something I’m grateful for but also something that feels like Plan B. To top it off, I’m financially dependent on my parents. They’re unemployed, so I’ll be taking out a student loan to fund my education. That in itself is scary. As an international student, there’s no guarantee I’ll land a job after graduation, meaning the challenges don’t stop once the degree is done. My life post-Master’s? Uncertain at best.

I’ve accepted an early admission offer, which means I have 300 days before stepping into one of the world’s top universities. In that time, I need to figure out my next move—whether that’s securing a job plan or reapplying for my dream PhD program.

Financially, I had to complete my undergrad at a local school back home, known more for its wealthy, disengaged students than academic rigor. But I was different—I earned a scholarship, worked hard, and my professors saw my passion. It got me shortlisted for my dream PhD, but my undergrad performance and university reputation held me back.

So, why go for this MS program despite the debt? I need to change my profile. I want to prove, to myself and the world, that I can graduate from one of the best.

I’ve always struggled with consistency and discipline. I don’t have anyone to talk to about this, so I’m using this blog as my accountability diary. No more private drafts—I’m stepping up. It’s 300 days to reshape my future, and I hope this journey inspires someone else too.

[petit-papillion]

Giving credit to original posters, creators, artists, photographers, and sources matters.

It matters to the person who put in the time and effort to create the content. Whether it's finding that photo and editing it to make it stand out. Or downloading that video, editing it, coloring it, turning it into a gif, adding subtitles, etc. Regardless of what you do (job/student), I'm sure you too would not like it if someone else takes credit for your work.

It matters to people who want to know context. Was it said by a fan, your favourite's bestie, a reputable journalist? Published by a tabloid or the New York Times? From last week or last year? Is it an original, or did someone get photoshopped in for a meme?

It matters to people who may want to explore more content of that particular publication, so they will continue to publish top-notch articles. Or give kudos to a budding artist for their outstanding art work, potentially encouraging them to create more. Or compliment that photographer on capturing their fave in just the right light.

It matters, because even though it may take you an extra minute or so to add on that name or link, it is the right thing to do.

[makkie-is-screaming]

i just applied to like 10 jobs hopefully one gets back to me

[deus-ex-mona]

farewell, my idiot son…

[agileseen24]

"From Passion to Profession: Steps to Enter the Tech Industry"

How to Break into the Tech World: Your Comprehensive Guide

In today’s fast-paced digital landscape, the tech industry is thriving and full of opportunities. Whether you’re a student, a career changer, or someone passionate about technology, you may be wondering, “How do I get into the tech world?” This guide will provide you with actionable steps, resources, and insights to help you successfully navigate your journey.

Understanding the Tech Landscape

Before you start, it's essential to understand the various sectors within the tech industry. Key areas include:

Software Development: Designing and building applications and systems.

Data Science: Analyzing data to support decision-making.

Cybersecurity: Safeguarding systems and networks from digital threats.

Product Management: Overseeing the development and delivery of tech products.

User Experience (UX) Design: Focusing on the usability and overall experience of tech products.

Identifying your interests will help you choose the right path.

Step 1: Assess Your Interests and Skills

Begin your journey by evaluating your interests and existing skills. Consider the following questions:

What areas of technology excite me the most?

Do I prefer coding, data analysis, design, or project management?

What transferable skills do I already possess?

This self-assessment will help clarify your direction in the tech field.

Step 2: Gain Relevant Education and Skills

Formal Education

While a degree isn’t always necessary, it can be beneficial, especially for roles in software engineering or data science. Options include:

Computer Science Degree: Provides a strong foundation in programming and system design.

Coding Bootcamps: Intensive programs that teach practical skills quickly.

Online Courses: Platforms like Coursera, edX, and Udacity offer courses in various tech fields.

Self-Learning and Online Resources

The tech industry evolves rapidly, making self-learning crucial. Explore resources like:

FreeCodeCamp: Offers free coding tutorials and projects.

Kaggle: A platform for data science practice and competitions.

YouTube: Channels dedicated to tutorials on coding, design, and more.

Certifications

Certifications can enhance your credentials. Consider options like:

AWS Certified Solutions Architect: Valuable for cloud computing roles.

Certified Information Systems Security Professional (CISSP): Great for cybersecurity.

Google Analytics Certification: Useful for data-driven positions.

Step 3: Build a Portfolio

A strong portfolio showcases your skills and projects. Here’s how to create one:

For Developers

GitHub: Share your code and contributions to open-source projects.

Personal Website: Create a site to display your projects, skills, and resume.

For Designers

Design Portfolio: Use platforms like Behance or Dribbble to showcase your work.

Case Studies: Document your design process and outcomes.

For Data Professionals

Data Projects: Analyze public datasets and share your findings.

Blogging: Write about your data analysis and insights on a personal blog.

Step 4: Network in the Tech Community

Networking is vital for success in tech. Here are some strategies:

Attend Meetups and Conferences

Search for local tech meetups or conferences. Websites like Meetup.com and Eventbrite can help you find relevant events, providing opportunities to meet professionals and learn from experts.

Join Online Communities

Engage in online forums and communities. Use platforms like:

LinkedIn: Connect with industry professionals and share insights.

Twitter: Follow tech influencers and participate in discussions.

Reddit: Subreddits like r/learnprogramming and r/datascience offer valuable advice and support.

Seek Mentorship

Finding a mentor can greatly benefit your journey. Reach out to experienced professionals in your field and ask for guidance.

Step 5: Gain Practical Experience

Hands-on experience is often more valuable than formal education. Here’s how to gain it:

Internships

Apply for internships, even if they are unpaid. They offer exposure to real-world projects and networking opportunities.

Freelancing

Consider freelancing to build your portfolio and gain experience. Platforms like Upwork and Fiverr can connect you with clients.

Contribute to Open Source

Engaging in open-source projects can enhance your skills and visibility. Many projects on GitHub are looking for contributors.

Step 6: Prepare for Job Applications

Crafting Your Resume

Tailor your resume to highlight relevant skills and experiences. Align it with the job description for each application.

Writing a Cover Letter

A compelling cover letter can set you apart. Highlight your passion for technology and what you can contribute.

Practice Interviewing

Prepare for technical interviews by practicing coding challenges on platforms like LeetCode or HackerRank. For non-technical roles, rehearse common behavioral questions.

Step 7: Stay Updated and Keep Learning

The tech world is ever-evolving, making it crucial to stay current. Subscribe to industry newsletters, follow tech blogs, and continue learning through online courses.

Follow Industry Trends

Stay informed about emerging technologies and trends in your field. Resources like TechCrunch, Wired, and industry-specific blogs can provide valuable insights.

Continuous Learning

Dedicate time each week for learning. Whether through new courses, reading, or personal projects, ongoing education is essential for long-term success.

Conclusion

Breaking into the tech world may seem daunting, but with the right approach and commitment, it’s entirely possible. By assessing your interests, acquiring relevant skills, building a portfolio, networking, gaining practical experience, preparing for job applications, and committing to lifelong learning, you’ll be well on your way to a rewarding career in technology.

Embrace the journey, stay curious, and connect with the tech community. The tech world is vast and filled with possibilities, and your adventure is just beginning. Take that first step today and unlock the doors to your future in technology!

contact Infoemation wensite: https://agileseen.com/how-to-get-to-tech-world/ Phone: 01722-326809 Email: info@agileseen.com

[vamprisms]

what if your worksona. Was gay

[thedisablednaturalist]

Unfortunately all chatgpt is good for is interview/job application stuff which I think says a lot about the hiring process as a whole

[jcmarchi]

Starting reading the AI Snake Oil book online today

New Post has been published on https://thedigitalinsider.com/starting-reading-the-ai-snake-oil-book-online-today/

Starting reading the AI Snake Oil book online today

The first chapter of the AI snake oil book is now available online. It is 30 pages long and summarizes the book’s main arguments. If you start reading now, you won’t have to wait long for the rest of the book — it will be published on the 24th of September. If you haven’t pre-ordered it yet, we hope that reading the introductory chapter will convince you to get yourself a copy.

We were fortunate to receive positive early reviews by The New Yorker, Publishers’ Weekly (featured in the Top 10 science books for Fall 2024), and many other outlets. We’re hosting virtual book events (City Lights, Princeton Public Library, Princeton alumni events), and have appeared on many podcasts to talk about the book (including Machine Learning Street Talk, 20VC, Scaling Theory).

Our book is about demystifying AI, so right out of the gate we address what we think is the single most confusing thing about it: 

AI is an umbrella term for a set of loosely related technologies

Because AI is an umbrella term, we treat each type of AI differently. We have chapters on predictive AI, generative AI, as well as AI used for social media content moderation. We also have a chapter on whether AI is an existential risk. We conclude with a discussion of why AI snake oil persists and what the future might hold. By AI snake oil we mean AI applications that do not (and perhaps cannot) work. Our book is a guide to identifying AI snake oil and AI hype. We also look at AI that is harmful even if it works well — such as face recognition used for mass surveillance. 

While the book is meant for a broad audience, it does not simply rehash the arguments we have made in our papers or on this newsletter. We make scholarly contributions and we wrote the book to be suitable for adoption in courses. We will soon release exercises and class discussion questions to accompany the book.

Chapter 1: Introduction. We begin with a summary of our main arguments in the book. We discuss the definition of AI (and more importantly, why it is hard to come up with one), how AI is an umbrella term, what we mean by AI Snake Oil, and who the book is for. 

Generative AI has made huge strides in the last decade. On the other hand, predictive AI is used for predicting outcomes to make consequential decisions in hiring, banking, insurance, education, and more. While predictive AI can find broad statistical patterns in data, it is marketed as far more than that, leading to major real-world misfires. Finally, we discuss the benefits and limitations of AI for content moderation on social media.

We also tell the story of what led the two of us to write the book. The entire first chapter is now available online.

Chapter 2: How predictive AI goes wrong. Predictive AI is used to make predictions about people—will a defendant fail to show up for trial? Is a patient at high risk of negative health outcomes? Will a student drop out of college? These predictions are then used to make consequential decisions. Developers claim predictive AI is groundbreaking, but in reality it suffers from a number of shortcomings that are hard to fix. 

We have discussed the failures of predictive AI in this blog. But in the book, we go much deeper through case studies to show how predictive AI fails to live up to the promises made by its developers.

Chapter 3: Can AI predict the future? Are the shortcomings of predictive AI inherent, or can they be resolved? In this chapter, we look at why predicting the future is hard — with or without AI. While we have made consistent progress in some domains such as weather prediction, we argue that this progress cannot translate to other settings, such as individuals’ life outcomes, the success of cultural products like books and movies, or pandemics. 

Since much of our newsletter is focused on topics of current interest, this is a topic that we have never written about here. Yet, it is foundational knowledge that can help you build intuition around when we should expect predictions to be accurate.

Chapter 4: The long road to generative AI. Recent advances in generative AI can seem sudden, but they build on a series of improvements over seven decades. In this chapter, we retrace the history of computing advances that led to generative AI. While we have written a lot about current trends in generative AI, in the book, we look at its past. This is crucial for understanding what to expect in the future. 

Chapter 5: Is advanced AI an existential threat? Claims about AI wiping out humanity are common. Here, we critically evaluate claims about AI’s existential risk and find several shortcomings and fallacies in popular discussion of x-risk. We discuss approaches to defending against AI risks that improve societal resilience regardless of the threat of advanced AI.

Chapter 6: Why can’t AI fix social media? One area where AI is heavily used is content moderation on social media platforms. We discuss the current state of AI use on social media, and highlight seven reasons why improvements in AI alone are unlikely to solve platforms’ content moderation woes. We haven’t written about content moderation in this newsletter.

Chapter 7: Why do myths about AI persist? Companies, researchers, and journalists all contribute to AI hype. We discuss how myths about AI are created and how they persist. In the process, we hope to give you the tools to read AI news with the appropriate skepticism and identify attempts to sell you snake oil.

Chapter 8: Where do we go from here? While the previous chapter focuses on the supply of snake oil, in the last chapter, we look at where the demand for AI snake oil comes from. We also look at the impact of AI on the future of work, the role and limitations of regulation, and conclude with vignettes of the many possible futures ahead of us. We have the agency to determine which path we end up on, and each of us can play a role.

We hope you will find the book useful and look forward to hearing what you think. 

The New Yorker: “In AI Snake Oil, Arvind Narayanan and Sayash Kapoor urge skepticism and argue that the blanket term AI can serve as a smokescreen for underperforming technologies.”

Kirkus: “Highly useful advice for those who work with or are affected by AI—i.e., nearly everyone.”

Publishers’ Weekly: Featured in the Fall 2024 list of top science books.

Jean Gazis: “The authors admirably differentiate fact from opinion, draw from personal experience, give sensible reasons for their views (including copious references), and don’t hesitate to call for action. . . . If you’re curious about AI or deciding how to implement it, AI Snake Oil offers clear writing and level-headed thinking.”

Elizabeth Quill: “A worthwhile read whether you make policy decisions, use AI in the workplace or just spend time searching online. It’s a powerful reminder of how AI has already infiltrated our lives — and a convincing plea to take care in how we interact with it.”

We’ve been on many other podcasts that will air around the time of the book’s release, and we will keep this list updated.

The book is available to preorder internationally on Amazon.

[toytulini]

"why are people mad about AI being pushed on them when they SHOULD be mad about all the privacy erosion??"

1) plenty of us bitches are mad and annoyed about both, actually.

2) the privacy erosion has become the normalized state of existence for the average person for the last 10 fucking years at least, its snuck in, they disguise it as Convenient Features to Help You Shop Better, and thats IF they bother telling you theyre doing it, instead of just opting all your shit in without asking, its so fucking normalized that yeah, a lot of people do not bother to question it, they just sigh in resignation and go, yeah, i guess, do i even have other options? and they do, but theyre an investment of learning and time you dont have capacity for at the moment, or maybe you do but you feel like you dont bc it feels like a bigger hurdle than it is, and computer stuff is already kind of intimidating, cos man, what if you hit the wrong thing and brick your expensive ass machine? easier to just let it data harvest, you guess, it cant be THAT bad, can it? plenty of people live like this, put up with this, seek this out, its easier not to resist the privacy erosion. fucking whatever, i guess. yeah, i guess twitter i mean X, or walmart, or facebook, can just have all of my contact info and my phone number and my birthday and phone contacts and bank information and fuck it, give them my ssn while im at it. less effort later. this is just how tech has been for the last 10 yrs. no one can effectively get rage clicks on this topic anymore bc we all fucking know. it sucks and we know. what do you want me to fucking do about it? i have other shit to deal with more urgently. etc

3)

you cant turn anything on or log onto anything or go anywhere without hearing about whatever new shit theyre throwing AI at for no real reason, no one will fucking Shut Up about AI, and its Annoying, man

[diaryoftheloser]

Aver - The Loser, The Commonplacer

I look forward to recieving your support...

Just to clarify, I am fairly new to this, hence for clarity I am gonna use two blogs

@diaryoftheloser - To keep track of my daily activities

@thecommonplacer - For brainstorming, organising content

💬 Who Am I?

Name/Pronouns: Aver (She/Her) From: Dubai, United Arab Emirates 🇦🇪 Identity: Queer and thriving in STEM Currently: Freelancer - Incoming Biotechnology Master's Student Field of Study/Work: Omics Data Science, Scientific Communication Reason for Starting the Blog: My First Post Fun Fact: I am also Digital Illustrator (Minor in Animation) Biggest Science Nerd Moment: Volunteering and presenting at my very first International Conference 🌍

🔬 Research Interests

Nucleic Acid Sciences 🧬 Omics Technology 📊 Data Science & Engineering 💻 Statistics 📈 Immunology 🦠 Business in Biotechnology & Life Sciences 🧪

🎧 When I’m Not Doing Science

Music: English Pop, Korean & Thai OSTs (I want to expand my playlist) Shows/Movies: RomComs, Asian - Thai, Korean, Japanese, Chinese, Taiwanese, Indian (sometimes Filipino and Vietnamese) Books: Productivity, Psychology, Manga, Manhwa Hobbies: Typography

✨ Goals

✈️ Short Term Goals: Lose 10kgs, Earn a Driver's License, Strengthen Computational Expertise, Prepare for Biotech National Exams, Learn to Cook, Move to Singapore 🌈 Long Term Goals: Pre-Ph.D. Preparation, Financial Independence, Financially Support My Family, Upgrade My Wardrobe and Aesthetics, Fulfill Travel Wishlist 🦄 Career Goals: Turning My Future Ph.D. Work into a Unicorn, Fixed Additional Sources of Income 💼 Productivity Goals: Being Consistent with Notion Productivity

Goals for 100 Days of Productivity (100 Days to The Year 2025):

Lose 10 Kgs

Earn a Driver's License

Finish Pending Projects - Research Paper, Conference Presentation

Biotech National Exam Preparation

Finish Three Career Tracks on Data Camp

Submission for The Food Innovation Challenge

Goals for 300 Days of Productivity:

The Mega Pre-Ph.D. Prep

Recycle all used papers and donate books

Move to Singapore

Financial Education

Complete Udemy Courses and left over Data Camp Career Tracks

🔗Works in Progress:

Pre-PhD Prep Plan Move to Singapore My Current Self-Care Routine Conference Prep Weekly Progress (100DOP / 300DOP) Research Paper Shenanigans Local Examination for Biological Sciences - ExamPrep My Side-Hustle Journey

[cosmos-fudge]

the autism won

hyperspecific poll time!

PMDD - Premenstrual Dysphoric Disorder: is similar to PMS, but sufferers find it more intense and draining, often to the extent of depression and/or suicidal ideation. more information (with better explanation)

Menstrual Migraines: migraines typically before or during your period, possibly caused by fluctuations, and a drop in hormones, specifically estrogen and progesterone. more information

self diagnosis counts!!

There's no option for results or having none, if you have none you don't have to vote 👍

You're still encouraged to reblog for reach

If you have better/more inclusive sources/resources, please let me know!!