#data analysis and visualization courses

[dancesingwithdata]

Become a Certified Tableau Specialist – Your Path to Success

Master Tableau with expert-led training designed for beginners and pros. Prepare for Tableau Desktop Specialist certification with hands-on projects and real-world skills. Enroll now!

[cambtech]

Explore Cambtech’s premium online courses crafted to upskill professionals in high-demand fields. From logistics, chartering, port operations, and international trade to full stack development, data analysis, AWS cloud, and cybersecurity—our expert-led programs are designed for real-world success. Get exclusive certifications including IITM Pravartak. With flexible, self-paced learning, Cambtech helps you stay ahead in your career. Start your premium learning journey today and unlock your future.

[jcmarchi]

AI literacy: Essential for today's workforce and businesses

New Post has been published on https://thedigitalinsider.com/ai-literacy-essential-for-todays-workforce-and-businesses/

AI literacy: Essential for today's workforce and businesses

Bringing generative AI into a business or even public sector organization is consuming the minds of leaders around the world, some approaching the topic with a positive outlook and some with caution.

According to BCG, a mere 10% of organisations have managed to successfully integrate gen AI into their workflows at scale, gaining a significant advantage over their competitors who are in danger of falling behind in this rapidly evolving landscape. 

But what does AI literacy mean? 

AI literacy is having the knowledge and practical understanding of AI, its use cases, power, and limitations. This could include prompting mastery, the ability to identify when and how to use it, to critically evaluate its outputs, and being able to adapt in a workplace empowered with AI.  

AI literacy is a key skill set the whole workforce must master and employers must endorse. 

Why AI literacy matters

Future-proofing your business

I briefly mentioned competitiveness above, AI driven companies have a strong competitive advantage in their market, according to HBR.

Those AI-first companies are also way ahead of the market in scaling AI predictive solutions and, hence, future-proofing their business. AI literacy enables this innovation across the whole workforce.

More importantly, the future workforce is already relying on AI in their education, and it is critical that businesses and public sector organisations remain top of mind for the job market of the present and the future.

Your guide to agentic AI

Agentic AI refers to artificial intelligence systems that act autonomously, make decisions, set goals, and adapt to their environment with minimal human intervention.

Boosting productivity across all teams 

In a study done by Harvard Business School, AI can help your employees do work 25% faster and 40% better.

AI can help employees across multiple departments across marketing, sales, human resources, customer service, and even frontline operations boost their productivity by helping automate tasks and provide business intelligence, data analysis, and more.

Boosting AI literacy would liberate the workforce from admin tasks, allowing more time for strategic and deep work. 

Empower your decision-makers with insights

AI literacy is key in ensuring your workforce is mastering the art of prompting to ensure they are attaining the right insights they need to make more informed decisions.

AI tools can help reduce research time by up to 70-80%, which enables fast action by your teams when needed. More importantly, AI literacy is critical to ensuring your teams are able to evaluate AI outputs and insights, as well as understand potential biases or limitations of AI, especially in sensitive industries like legal and healthcare.

Enhance collaboration

AI features are now being offered by most productivity vendors we use and love, introducing the technology in meeting solutions, chat, and emails.

Avail these solutions, and then train and empower your workforce to make the most out of the improved collaboration benefits that AI brings to the workplace. AI literacy across the organization can help bridge gaps between the different teams, for example, technical and non-technical roles, as well as international teams working across languages. 

Ensuring responsible use of this emerging technology

It is critical that beyond learning how to maximize the benefits of this technology, your workforce is aware of the ethical implications of AI, evaluating and selecting the most secure vendor, and training your teams on important topics like bias, responsibility, and fairness in AI. This helps your organization avoid the risks associated with its increased usage. 

How to develop AI literacy

Accessible resources and continuous learning

The development of AI literacy begins with access to appropriate tools and resources. Fortunately, knowledge of AI does not require an advanced technical background.

Excellent starting points include online courses and books such as Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, which provide approachable explanations of AI’s business applications and implications. 

Engaging with community forums like Reddit’s r/MachineLearning or attending free webinars by institutions such as Stanford University is an often overlooked way to gain some practical insights. These resources cater to varying levels of familiarity, making AI literacy accessible to everyone.

AI is an ever-evolving field. Staying updated is essential. You need to learn continuously to adapt effectively. On the industry scale, this approach builds a workforce that is proactive, well-informed, and resilient in the face of change.

Free Artificial Intelligence Membership – Become an Insider

Join thousands of other artificial intelligence professionals and test drive you AIAI membership without spending a dime.

Hands-on experience

AI learning can be accelerated by applying it practically. When you work with AI, you gain an understanding of the ways in which it can be applied and of the ways in which it cannot yet be applied or might not ever be applied.

Hands-on experience with AI leads to a more profound comprehension of the technology and its potential. Staying with the technology, in whatever form it currently takes, is vital to maintaining a necessary foundation.

We can develop our expertise in AI by competing in contests, working in interactive learning spaces, and by all sorts of meaningful event organizing that serves to introduce ASU to the kinds of things we’re up to with AI. How is this happening?

Work is happening at the level of individual students who are experimenting with the kinds of platforms that integrate AI into productivity tasks such as data visualization or customer support.

Direct engagement with AI technologies and nurturing a culture of continuous learning around these technologies enable individuals and organizations to gain a much clearer understanding of the capabilities and limitations of AI. And this is not just preparation for today; it is quite literally preparation for the future.

[sizzlingcreatorcycle]

Master Data Analytics courses with Takeoff Upskill – Learn Data Analysis Easily

Data Analytics is the process of collecting, organizing, and analyzing data to make better decisions. In today's world, businesses generate a huge amount of data every day. This data, when properly analyzed, helps businesses improve their operations, understand customer behavior, and increase profits. Takeoff Upskill provides training in Data Analytics courses to help individuals and businesses make sense of their data and use it effectively.

At Takeoff Upskill, we focus on teaching the key skills needed for Data Analytics. Our courses cover topics like data collection, data cleaning, data visualization, and data interpretation. We use tools such as Excel, SQL, Python, and Power BI to help learners understand data analysis easily. Whether you are a beginner or someone looking to upgrade your skills, our training programs are designed to meet your needs.

One of the main benefits of learning Data Analytics courses is that it opens up many career opportunities. Companies in various industries, such as healthcare, finance, marketing, and retail, need data analysts to make informed business decisions. With the right training from Takeoff Upskill, you can build a successful career in this field.

[powerbisolutions-blog]

The Future of Data Analytics: Emerging Trends in Power BI

In the ever-evolving landscape of data analytics, Power BI continues to lead the charge with cutting-edge innovations that empower businesses to make data-driven decisions. As organizations increasingly rely on analytics to stay competitive, understanding the emerging trends in Power BI becomes essential for leveraging its full potential.

Here’s a look at what the future holds for Power BI and how these trends can revolutionize data analytics.

AI-Driven Insights

Artificial Intelligence (AI) is reshaping data analytics, and Power BI is at the forefront of this transformation. Features like AI-powered visualizations and natural language processing (NLP) allow users to interact with their data intuitively.

With tools like Quick Insights and AI Builder, businesses can uncover hidden patterns and predict trends, enabling smarter decision-making. The integration of AI makes it easier than ever for users to derive actionable insights without extensive technical expertise.

Real-Time Data Analytics

The demand for real-time insights is surging as businesses seek to make quicker decisions. Power BI’s real-time dashboards and streaming data capabilities provide up-to-the-second updates, enabling organizations to monitor key metrics and respond proactively.

This trend is especially crucial for industries like retail, finance, and healthcare, where timely decisions can have a significant impact.

Enhanced Integration with Third-Party Tools

Power BI’s seamless integration with tools like Microsoft Excel, Azure, and third-party applications is becoming even more robust. As businesses work with diverse data sources, enhanced integration ensures a unified view of their data ecosystem. This capability streamlines workflows, reduces silos, and fosters better collaboration across teams.

Custom Visualizations and Reports

Tailored insights are becoming a necessity for organizations aiming to address specific business needs. Power BI’s customization capabilities allow users to create bespoke dashboards and reports, ensuring that analytics align with unique goals. This trend reflects the growing importance of personalized solutions in today’s data-driven world.

Growing Adoption of Mobile Analytics

With remote work and mobile operations on the rise, Power BI’s mobile app is gaining traction. Users can access and interact with dashboards from anywhere, ensuring that decision-makers remain informed and agile, regardless of location.

Power BI continues to evolve, offering businesses unparalleled capabilities in data visualization, real-time monitoring, and AI-driven analytics. By staying ahead of these emerging trends, organizations can harness the full potential of Power BI to drive growth and innovation. For more details reach out power bi solutions.

[cavillionlearning]

Industry-Leading Visualization with Tableau Data Visualization

1. User-Friendly Interface: The intuitive design allows anyone to easily start visualizing data.

2. Seamless Integration: Connects easily with various data sources for efficient analysis.

3. Quick Insights: Generate actionable insights within minutes.

4. Integrated Collaboration Tools: Share dashboards and insights with your team effortlessly.

5. Regular Updates: Tableau continuously evolves with new features and improvements.

Want to take your skills to the next level? Join our 1-day Tableau Bootcamp on 2nd November (Online)!

[fabbuilder]

Top Analytics Tools for Tracking Website Performance and User Engagement

In today’s digital age, understanding your website’s performance and user engagement is essential for any business. Whether you’re running an e-commerce site, a blog, or a corporate webpage, having the right analytics tools can help you gather valuable insights about your audience, their behaviors, and how they interact with your content. This blog will explore some of the top analytics tools available that can help you track website performance and improve user engagement.

Why Website Performance and User Engagement Matter

Before diving into the tools, it’s important to understand why monitoring website performance and user engagement is crucial. A well-performing website ensures that visitors can navigate easily and find the information they need without frustration. Good user engagement means visitors are interacting with your content, leading to longer session durations, lower bounce rates, and ultimately higher conversion rates.

1. Google Analytics

Overview: Google Analytics is perhaps the most popular web analytics tool available. It’s free and offers a wide range of features that help you track various metrics on your website.

Key Features:

Real-Time Tracking: See how many visitors are currently on your site and which pages they are viewing.

Audience Insights: Understand demographics, interests, and behavior patterns of your visitors.

Conversion Tracking: Set up goals to monitor how well your site converts visitors into customers.

Use Cases: Google Analytics is excellent for businesses of all sizes. Whether you’re tracking a small blog or a large e-commerce site, the insights gained can help you refine your marketing strategies and improve user experience.

2. Adobe Analytics

Overview: Adobe Analytics is a powerful tool primarily used by larger enterprises. It offers advanced analytics capabilities and is part of the Adobe Experience Cloud.

Key Features:

Advanced Segmentation: Divide your audience into specific groups for targeted analysis.

Predictive Analytics: Use machine learning to forecast future trends and user behavior.

Cross-Channel Tracking: Monitor user interactions across various platforms and devices.

Use Cases: Companies that run large-scale marketing campaigns benefit from Adobe Analytics, as it provides in-depth insights that help optimize campaigns and improve ROI.

3. Hotjar

Overview: Hotjar focuses on understanding user behavior through visual data. It offers unique tools like heatmaps and session recordings.

Key Features:

Heatmaps: Visual representations of where users click, scroll, and move on your site.

Session Recordings: Watch recordings of real user sessions to see how they interact with your site.

Feedback Polls: Gather direct feedback from users about their experience.

Use Cases: Hotjar is ideal for UX/UI designers and marketers looking to enhance website usability. By understanding where users struggle, you can make necessary adjustments to improve the overall experience.

4. SEMrush

Overview: SEMrush is an all-in-one marketing toolkit that also offers robust analytics features, especially for SEO.

Key Features:

Traffic Analysis: Understand where your website traffic comes from and how users behave once they arrive.

Keyword Tracking: Monitor your keyword rankings and discover new opportunities for optimization.

Competitor Analysis: Analyze your competitors’ performance and strategies to identify areas for improvement.

Use Cases: SEMrush is beneficial for digital marketers focused on SEO. By understanding your own performance as well as your competitors’, you can develop effective strategies to boost your online presence.

5. Mixpanel

Overview: Mixpanel is a user analytics platform focused on tracking user interactions with web and mobile applications.

Key Features:

Event Tracking: Monitor specific actions users take on your site, such as clicks and sign-ups.

Cohort Analysis: Segment users based on their behavior and track their engagement over time.

A/B Testing: Experiment with different versions of your website to see what resonates more with your audience.

Use Cases: Mixpanel is particularly useful for SaaS companies looking to improve user retention and engagement through data-driven insights.

6. Crazy Egg

Overview: Crazy Egg provides simple yet effective tools to visualize user behavior on your website.

Key Features:

Heatmaps and Scrollmaps: Understand how users navigate your site and which sections attract the most attention.

A/B Testing Tools: Easily test different layouts and content to see what performs best.

User-Friendly Interface: Crazy Egg is straightforward to set up and use, making it accessible for beginners.

Use Cases: Ideal for startups and small businesses looking to optimize their landing pages and improve user experience without a steep learning curve.

7. Piwik PRO

Overview: Piwik PRO offers a privacy-focused analytics solution that is great for businesses concerned about data compliance.

Key Features:

On-Premise Hosting: Gives you complete control over your data and compliance with regulations like GDPR.

Customizable Dashboards: Tailor your analytics reports to meet specific business needs.

User-Centric Tracking: Focuses on providing insights while ensuring user privacy.

Use Cases: Organizations in regulated industries, such as healthcare and finance, can benefit from Piwik PRO to ensure compliance while gaining valuable insights.

8. HubSpot Analytics

Overview: HubSpot is known for its CRM and marketing automation tools, and it also offers robust analytics features.

Key Features:

Detailed Reporting: Track website performance, user engagement, and marketing campaign effectiveness.

Traffic Sources Analysis: Understand how users are finding your site and which channels are most effective.

Integration with Marketing Tools: Easily connect your analytics with HubSpot’s CRM for a comprehensive view of customer interactions.

Use Cases: Businesses already using HubSpot for marketing will find its analytics tools seamlessly integrated, making it easy to track performance across all efforts.

9. Fab Builder Analytics

Overview: Fab Builder Analytics is a powerful data analytics and visualization tool that helps you turn your data into informative reports.

Key Features:

Custom Dashboards: Create interactive reports that pull data from multiple sources, including Google Analytics.

Real-Time Collaboration: Share dashboards and collaborate with your team in real-time.

Easy Data Integration: Connect with various data sources for comprehensive reporting.

Use Cases: Ideal for teams that need to present data insights to stakeholders in a clear and visually appealing manner.

Conclusion

Choosing the right analytics tool for tracking website performance and user engagement is crucial for driving business success. Each tool offers unique features that cater to different needs, from basic tracking to advanced predictive analytics. By leveraging these tools, you can gain valuable insights into your audience, refine your strategies, and ultimately enhance your website’s performance.

Investing in analytics tools ensures you can refine strategies, engage users effectively, and drive long-term growth. Fab Builder Analytics stands out with its customizable dashboards and easy collaboration, making it a great choice for teams presenting data insights to stakeholders.

In today’s competitive landscape, investing in the right analytics tools is not just a choice — it’s a necessity. Start exploring these options today, and see how they can transform your online presence!

[shivanshi770]

Unlocking the Power of R: A Comprehensive Guide to Data Analytics

This article introduces R, a powerful programming language for statistical computing and data visualization in data analytics. It highlights R's key features and its role in the analytics workflow. Additionally, it offers practical techniques to help beginners and those looking to enhance their skills. Read more to know how to effectively harness R for data analysis.

[hob28]

Your Career with a Google Data Studio Course in Vasai-Virar

Introduction: Mastering Data Visualization

In today's data-driven world, the ability to analyze and visualize data is crucial for making informed decisions. Google Data Studio is a powerful tool that allows users to create interactive and dynamic reports and dashboards. Whether you're a student, a marketing professional, or a data analyst, learning Google Data Studio can significantly enhance your data presentation skills. Enrolling in a Google Data Studio Course in Vasai-Virar will equip you with these essential skills. This article will explore the benefits of learning Google Data Studio, the demand for data visualization expertise, career opportunities, and why HrishiComputer is the best place to learn.

Why Learn Google Data Studio?

Learning Google Data Studio offers numerous benefits that can enhance various aspects of your professional life:

Enhanced Data Reporting: Google Data Studio enables you to turn raw data into insightful visualizations. By enrolling in a Google Data Studio Course in Vasai-Virar, you can learn how to create compelling reports that clearly communicate key metrics and trends.

Improved Decision-Making: Effective data visualization helps identify patterns and insights quickly, leading to better decision-making. Mastering Google Data Studio can help you provide valuable data-driven recommendations.

Career Advancement: Proficiency in Google Data Studio is a highly sought-after skill in many industries, including marketing, finance, and healthcare. Completing a Google Data Studio Course in Vasai-Virar can make you a more competitive candidate and open up new career opportunities.

Growing Demand for Data Visualization Skills

The ability to visualize data effectively is increasingly important as more businesses rely on data to drive their strategies. Companies need professionals who can transform complex data sets into clear, actionable insights. Enrolling in a Google Data Studio Course in Vasai-Virar will prepare you for roles such as data analyst, business intelligence specialist, and marketing analyst, where data visualization is a critical skill.

Career Opportunities and Earnings with Google Data Studio Skills

Career opportunities for those skilled in Google Data Studio are diverse and often come with attractive salary packages:

Data Analyst: Analyzing data sets and creating visual reports to support business decisions. Data analysts typically earn between INR 4,00,000 and INR 7,00,000 annually.

Business Intelligence Specialist: Developing and maintaining business intelligence tools and dashboards. Salaries for these roles range from INR 5,00,000 to INR 9,00,000 per year.

Marketing Analyst: Using data to analyze market trends and measure the effectiveness of marketing campaigns. Marketing analysts can earn between INR 3,50,000 and INR 6,00,000 annually.

Google Data Studio Course Syllabus at HrishiComputer

HrishiComputer in Vasai-Virar offers a comprehensive Google Data Studio Course designed to equip you with essential data visualization skills. The syllabus includes:

Introduction to Google Data Studio: Learn the basics of data visualization and navigate the Google Data Studio interface.

Connecting Data Sources: Understand how to connect various data sources, including Google Analytics, Google Sheets, and SQL databases, to Google Data Studio.

Building Reports and Dashboards: Gain hands-on experience in creating, customizing, and managing reports and dashboards.

Using Visualization Tools: Explore different types of charts and visualizations, such as bar charts, pie charts, and geo maps, to effectively represent your data.

Advanced Features: Learn advanced techniques such as creating calculated fields, blending data, and using filters to create dynamic reports.

Collaboration and Sharing: Discover how to share your reports and collaborate with team members in real-time.

Why Choose HrishiComputer for Google Data Studio Training?

HrishiComputer in Vasai-Virar is a top choice for learning Google Data Studio due to its comprehensive curriculum and expert instruction. Here’s why our Google Data Studio Course stands out:

Experienced Instructors: Our trainers are certified professionals with extensive experience in data visualization and Google Data Studio. They provide practical insights and hands-on guidance.

Interactive Learning: The course includes real-world projects and practical exercises, ensuring that you gain the skills needed to create impactful data visualizations.

Recognized Certification: Upon completing the course, you will receive a certification from HrishiComputer, which is highly valued by employers and enhances your resume.

Affordable Fees: We offer competitive pricing to make our courses accessible to everyone. Our goal is to provide quality education at an affordable cost.

How to Enroll

Ready to enhance your data visualization skills and boost your career prospects? Enroll in our Google Data Studio Course in Vasai-Virar at HrishiComputer. Our comprehensive training program is designed to equip you with the skills needed to excel in today’s data-driven world.

Sign Up for Google Data Studio Training at HrishiComputer and start transforming your data skills today!

Frequently Asked Questions (FAQ)

Q1: Do I need prior experience with data visualization to join the course?A: No prior experience is necessary. The course is suitable for both beginners and those looking to enhance their data visualization skills.

Q2: What type of certification will I receive upon completion?A: Upon successful completion of the course, you will receive a certification from HrishiComputer, recognized by many employers.

Q3: Are classes conducted online or offline?A: The primary mode of instruction is offline, providing a hands-on learning experience. However, we also offer supplementary online resources.

Q4: What is the duration of the course?A: The course typically spans 8 weeks, with flexible scheduling options to accommodate students and working professionals.

Q5: How can data visualization skills benefit my career?A: Data visualization skills are highly valuable across various job roles, enhancing your ability to interpret and communicate data effectively. These skills make you a more attractive candidate to employers and can lead to better job opportunities and higher earnings.

By completing this course, you will gain a comprehensive understanding of Google Data Studio, from basic functionalities to advanced features. This will empower you to create impactful data visualizations and make informed, data-driven decisions. Join HrishiComputer today and master the skills essential for success in the modern workplace.

[sandy1674686]

How to Become a Data Scientist: Essential Skills and Training Programs

In today's data-driven world, the role of a data scientist has become increasingly valuable across industries. With the ability to analyze complex data sets and derive meaningful insights, data scientists play a crucial role in decision-making processes for businesses. If you're interested in pursuing a career in this field, it's important to understand the essential skills and training programs that can help you become a successful data scientist.

Quantitative and Analytical Skills: First and foremost, data scientists must possess strong quantitative and analytical skills. They should have a solid foundation in mathematics, statistics, and computational methods. Proficiency in programming languages such as Python or R is also essential for data manipulation and analysis. These skills form the building blocks for data scientists to effectively understand, interpret, and work with data.

Data Visualization and Communication: Data scientists need to not only analyze data but also present their findings in a clear and concise manner. data visualization using Power Bi or Tableau allow them to create compelling visual representations of complex data sets. Effective communication skills, both written and verbal, are also required to explain their findings to non-technical stakeholders within the organization.

Machine Learning and Artificial Intelligence: Machine learning and artificial intelligence (AI) are fundamental tools in the data science toolkit. Data scientists must have a good understanding of various machine learning algorithms and techniques. Knowledge of frameworks like TensorFlow, PyTorch, or scikit-learn is essential for building predictive models, clustering, and classification tasks. Staying updated with the latest advancements in AI is crucial for harnessing the true potential of data science.

Domain Expertise: While technical skills are important, having domain expertise is equally vital for a data scientist. Understanding the industry or field in which you work allows you to ask the right questions, identify relevant data sources, and generate valuable insights. Whether it’s finance, healthcare, marketing, or any other sector, acquiring domain knowledge will set you apart as a data scientist.

Training Programs:

Academic Degrees: Pursuing a Bachelor's, Master's, or Ph.D. degree in a field like computer science, statistics, mathematics, or data science itself provides a solid foundation for a career in data science. These programs cover theoretical concepts, statistical methods, programming skills, and practical applications.

Online Courses and Bootcamps: There has been a rise in online platforms offering data science courses and bootcamps. Platforms like Coursera, edX, and Udacity provide comprehensive courses covering various aspects of data science, including programming, statistics, and machine learning with python. These courses are flexible and allow you to learn at your own pace.

Industry-Specific Training: Some organizations offer specialized training programs tailored to the specific needs of their industry. These programs often provide hands-on experience with real-world data sets and practical problem-solving scenarios. Attending industry conferences, webinars, and workshops can also supplement your learning journey.

Conclusion:

Becoming a successful data scientist requires a combination of technical expertise, analytical skills, and domain knowledge. By honing your skills in mathematics, programming, machine learning, and data visualization, you can navigate the world of data science more effectively. Additionally, pursuing academic degrees or enrolling in online courses and industry-specific training programs can provide you with a well-rounded education. With a consistent effort to upgrade your skills and stay updated with the latest trends, you can establish yourself as a valuable data scientist in today's data-driven landscape.

[jcmarchi]

From refugee to MIT graduate student

New Post has been published on https://thedigitalinsider.com/from-refugee-to-mit-graduate-student/

From refugee to MIT graduate student

Mlen-Too Wesley has faded memories of his early childhood in Liberia, but the sharpest one has shaped his life.

Wesley was 4 years old when he and his family boarded a military airplane to flee the West African nation. At the time, the country was embroiled in a 14-year civil war that killed approximately 200,000 people, displaced about 750,000, and starved countless more. When Wesley’s grandmother told him he would enjoy a meal during his flight, Wesley knew his fortune had changed. Yet, his first instinct was to offer his food to the people he left behind.

“I made a decision right then to come back,” Wesley says. “Even as I grew older and spent more time in the United States, I knew I wanted to contribute to Liberia’s future.”

Today, the 38-year-old is committed to empowering Liberians through economic growth. Wesley looked to the MITx MicroMasters program in Data, Economics, and Design of Policy (DEDP) to achieve that goal. He examined issues such as micro-lending, state capture, and investment in health care in courses such as Foundations of Development Policy, Good Economics for Hard Times, and The Challenges of Global Poverty. Through case studies and research, Wesley discovered that economic incentives can encourage desired behaviors, curb corruption, and empower people.

“I couldn’t connect the dots”

Liberia is marred by corruption. According to Transparency International’s Corruptions Perception Index for 2023, Liberia scored 25 out of 100, with zero signifying the highest level of corruption. Yet, Wesley grew tired of textbooks and undergraduate professors saying that the status of Liberia and other African nations could be blamed entirely on corruption. Even worse, these sources gave Wesley the impression that nothing could be done to improve his native country. The sentiment frustrated him, he says.

“It struck me as flippant to attribute the challenges faced by billions of people to backward behaviors,” says Wesley. “There are several forces, internal and external, that have contributed to Liberia’s condition. If we really examine them, explore why things happened, and define the change we want, we can plot a way forward to a more prosperous future.”  

Driven to examine the economic, political, and social dynamics shaping his homeland and to fulfill his childhood promise, Wesley moved back to Africa in 2013. Over the next 10 years, he merged his interests in entrepreneurship, software development, and economics to better Liberia. He designed a forestry management platform that preserves Liberia’s natural resources, built an online queue for government hospitals to triage patients more effectively, and engineered data visualization tools to support renewable energy initiatives. Yet, to create the impact Wesley wanted, he needed to do more than collect data. He had to analyze and act on it in meaningful ways.

“I couldn’t connect the dots on why things are the way they are,” Wesley says.

“It wasn’t just an academic experience for me”

Wesley knew he needed to dive deeper into data science, and looked to the MicroMasters in DEDP program to help him connect the dots. Established in 2017 by the Abdul Latif Jameel Poverty Action Lab (J-PAL) and MIT Open Learning, the MicroMasters in DEDP program is based on the Nobel Prize-winning work of MIT faculty members Esther Duflo, the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics, and Abhijit Banerjee, the Ford Foundation International Professor of Economics. Duflo and Banerjee’s research provided an entirely new approach to designing, implementing, and evaluating antipoverty initiatives throughout the world.

The MicroMasters in DEDP program provided the framework Wesley had sought nearly 20 years ago as an undergraduate student. He learned about novel economic incentives that stymied corruption and promoted education.

“It wasn’t just an academic experience for me,” Wesley says. “The classes gave me the tools and the frameworks to analyze my own personal experiences.”

Wesley initially stumbled with the quantitative coursework. Having a demanding career, taking extension courses at another university, and being several years removed from college calculus courses took a toll on him. He had to retake some classes, especially Data Analysis for Social Scientists, several times before he could pass the proctored exam. His persistence paid off. Wesley earned his MicroMasters in DEDP credential in June 2023 and was also admitted into the MIT DEDP master’s program.

“The class twisted my brain in so many different ways,” Wesley says. “The fourth time taking Data Analysis, I began to understand it. I appreciate that MIT did not care that I did poorly on my first try. They cared that over time I understood the material.”

The program’s rigorous mathematics and statistics classes sparked in Wesley a passion for artificial intelligence, especially machine learning and natural language processing. Both provide more powerful ways to extract and interpret data, and Wesley has a special interest in mining qualitative sources for information. He plans to use these tools to compare national development plans over time and among different countries to determine if policymakers are recycling the same words and goals.

Once Wesley earns his master’s degree, he plans to return to Liberia and focus on international development. In the future, he hopes to lead a data-focused organization committed to improving the lives of people in Liberia and the United States.

“Thanks to MIT, I have the knowledge and tools to tackle real-world challenges that traditional economic models often overlook,” Wesley says.

[datavalleyai]

Becoming a Data Storyteller: Power BI Course Insights

In an increasingly data-driven world, the ability to transform raw numbers and statistics into compelling narratives is a valuable skill. Data storytelling is not only about presenting facts; it’s about making data relatable, understandable, and actionable. Power BI, a powerful data visualization and business intelligence tool by Microsoft, is at the forefront of enabling individuals to become proficient data storytellers. This blog post will provide you with insights into how a Power BI course can help you become a proficient data storyteller and elevate your analytics skills.

The Rise of Data Storytelling

Data storytelling is the art of using data to convey a message or make a persuasive argument. It involves crafting a narrative that allows data to resonate with your audience, whether they are business stakeholders, colleagues, or the general public. In today’s data-driven world, organizations are increasingly looking for professionals who can bridge the gap between data analysis and effective communication.

Why Power BI?

Power BI has emerged as a leading tool in the field of data visualization and business intelligence. Its user-friendly interface, robust data modeling capabilities, and integration with various data sources make it a popular choice among data professionals. Here’s why Power BI is a great choice for aspiring data storytellers:

Intuitive Interface: Power BI’s drag-and-drop interface allows users to create visually appealing reports and dashboards without the need for extensive coding or technical expertise. This accessibility is particularly valuable for those new to data visualization.

Data Connectivity: Power BI can connect to a wide range of data sources, including Excel, databases, cloud services, and web data connectors. This versatility ensures that you can work with the data sources most relevant to your industry or organization.

Advanced Analytics: Power BI offers a suite of powerful analytics tools, including DAX (Data Analysis Expressions) for custom calculations and measures. These features enable you to dive deep into your data and uncover meaningful insights.

Sharing and Collaboration: Power BI allows you to share reports and dashboards securely with colleagues and stakeholders. This collaborative feature ensures that your data stories reach the right audience.

Integration with Microsoft Ecosystem: If your organization uses Microsoft products like Excel, SharePoint, or Azure, Power BI seamlessly integrates with these tools, enhancing your workflow and efficiency.

The Power BI Course Journey

Embarking on a Power BI course is a transformative experience. It equips you with the knowledge and skills needed to harness the full potential of this tool for data storytelling. Here’s what you can expect from your Power BI course journey:

1. Fundamentals of Power BI:

The course typically starts with an introduction to Power BI’s interface and basic functionalities. You’ll learn how to connect to data sources, import data, and create your first visualizations. Understanding the building blocks of Power BI is crucial as it forms the foundation for more advanced techniques.

2. Data Transformation and Modeling:

Data often needs to be cleaned and transformed before it can be used effectively in Power BI. In this phase of the course, you’ll discover how to reshape data, create calculated columns and measures using DAX, and build relationships between different data tables. This step is essential for creating accurate and meaningful visualizations.

3. Creating Compelling Visualizations:

Power BI’s strength lies in its ability to create stunning visualizations. Your course will guide you through the process of creating various chart types, maps, tables, and custom visuals. You’ll learn best practices for designing visualizations that convey your message effectively.

4. Advanced Analytics and Insights:

Once you’ve mastered the basics, your course will delve into more advanced topics. You’ll explore advanced DAX functions, time intelligence, and other analytical techniques that allow you to extract deeper insights from your data. This is where you’ll begin to uncover the stories within your data.

5. Dashboard and Report Creation:

In this phase, you’ll learn how to combine individual visualizations into cohesive dashboards and reports. You’ll discover techniques for creating interactive dashboards that enable your audience to explore the data themselves. Effective dashboard design is crucial for engaging storytelling.

6. Data Storytelling and Communication:

The final and perhaps most important aspect of your Power BI course is learning how to tell compelling data stories. You’ll gain insights into structuring your reports and dashboards to convey clear narratives. This includes using titles, descriptions, and annotations to guide your audience through the data.

Real-World Applications:

Throughout your Power BI course, you’ll have opportunities to work on real-world projects and case studies. This hands-on experience is invaluable for applying what you’ve learned and building a portfolio of data storytelling projects. It’s also a chance to tackle data challenges similar to those you’ll encounter in your future career.

Certification and Career Opportunities:

Many Power BI courses offer certification upon completion. A Power BI certification can enhance your resume and demonstrate your proficiency to potential employers. Whether you’re looking to advance in your current role or pursue a new career in data analytics, a Power BI certification can open doors to exciting opportunities.

Conclusion

In a world where data is abundant but insights are scarce, becoming a data storyteller is a valuable skill. A Power BI course can be your gateway to mastering the art of data storytelling. By learning how to leverage Power BI’s capabilities, you can transform raw data into compelling narratives that drive informed decisions and meaningful change. So, if you’re ready to embark on a journey of data discovery and storytelling, consider enrolling in a Power BI course, and unleash your potential as a data storyteller.

[technicalfika]

Unlocking Your Future: How to Become an SQL Engineer at Top Tech Companies

As a college student with aspirations of landing your dream job at a top tech giant company, becoming an SQL Engineer can open the doors to a data-driven and fulfilling career. SQL (Structured Query Language) is an essential skill in the tech industry, enabling professionals to manage and analyze vast amounts of data efficiently. In this comprehensive guide, we will explore the key steps to…

[mecachrome]

📊 LANDOSCAR AO3 STATS (may 2025)

notes

sorry this literally took 2 weeks to write... unfortunately the data was retrieved april 28 and it is now may 12.

other work: i previously wrote a stats overview that covered landoscar's fic growth and breakout in 2023 :) i've kept some of the formatting and graphs that i showed there, while other things have been removed or refined because i felt they'd become redundant or unnecessary (aka they were basically just a reflection of fandom growth in general, and not unique or interesting to landoscar as a ship specifically).

methodology: i simply scraped the metadata for every fic in the landoscar tag (until april 28, 2025) and then imported it into google sheets to clean, with most visualizations done in tableau. again, all temporal data is by date updated (not posted) unless noted otherwise. this is because the date that appears on the parent view of the ao3 archives is the updated one, so it's the only feasible datapoint to collect for 3000+ fics.

content: this post does not mention any individual authors or concern itself with kudos, hits, comments, etc. i purely describe archive growth and overall analysis of metadata like word count and tagging metrics.

cleaning: after importing my data, i standardized ship spelling, removed extra "814" or "landoscar" tags, and merged all versions of one-sided, background, implied, past, mentioned etc. into a single "(side)" modifier. i also removed one fic entirely from the dataset because the "loscar" tag was being mistakenly wrangled as landoscar, but otherwise was not actually tagged as landoscar. i also removed extra commentary tags in the ships sets that did not pertain to any ships.

overall stats

before we get into any detailed distributions, let's first look at an overview of the archive as of 2025! in their 2-and-change years as teammates, landoscar have had over 3,409 fics written for them, good enough for 3rd overall in the f1 archives (behind lestappen and maxiel).

most landoscar fics are completed one-shots (although note that a one-shot could easily be 80k words—in fact they have about 30 single-chapter fics that are at least 50k words long), and they also benefit from a lot of first-tagged fic, which is to say 82.3% of landoscar-tagged fics have them as the first ship, implying that they aren't often used as a fleeting side pairing and artificially skewing perception of their popularity. in fact, over half of landoscar fics are PURELY tagged as landoscar (aka otp: true), with no other side pairings tagged at all.

this percentage has actually gone down a bit since 2023 (65.5%), which makes sense since more lando and oscar ships have become established and grown in popularity over the years, but it's also not a very big difference yet...

ship growth

of course, landoscar have grown at a frankly terrifying rate since 2023. remember this annotated graph i posted comparing their growth during the 2023 season to that of carlando and loscar, respectively their other biggest ship at the time? THIS IS HER NOW:

yes... that tiny squished down little rectangle... (wipes away stray tear) they grow up so fast. i also tried to annotate this graph to show other "big" landoscar moments in the timeline since, but i honestly struggled with this because they've just grown SO exponentially and consistently that i don't even feel like i can point to anything as a proper catalyst of production anymore. that is to say, i think landoscar are popular enough now that they have a large amount of dedicated fans/writers who will continuously work on certain drafts and stories regardless of what happens irl, so it's hard to point at certain events as inspiring a meaningful amount of work.

note also that this is all going by date updated, so it's not a true reflection of ~growth~ as a ficdom. thankfully ao3 does have a date_created filter that you can manually enter into the search, but because of this limitation i can't create graphs with the granularity and complexity that scraping an entire archive allows me. nevertheless, i picked a few big ships that landoscar have overtaken over the last 2 years and created this graph using actual date created metrics!!!

this is pretty self-explanatory of course but i think it's fun to look at... :) it's especially satisfying to see how many ships they casually crossed over before the end of 2024.

distributions

some quick graphs this time. rating distribution remains extremely similar to the 2023 graph, with explicit fic coming out on top at 28%:

last time i noted a skew in ratings between the overall f1 rpf tag and the landoscar tag (i.e. landoscar had a higher prevalence of e fic), but looking at it a second time i honestly believe this is more of a cultural shift in (f1? sports rpf? who knows) fandom at large and not specific to landoscar as a ship — filtering the f1 rpf tag to works updated from 2023 onward shows that explicit has since become the most popular rating in general, even when excluding landoscar-tagged fics. is it because fandom is getting more horny in general, or because the etiquette surrounding what constitutes t / m / e has changed, or because people are less afraid to post e fic publicly and no longer quarantine it to locked livejournal posts? or something else altogether? Well i don't know and this is a landoscar stats post so it doesn't matter but that could be something for another thought experiment. regardless because of that i feel like further graphs aren't really necessary 🤷‍♀️

onto word distribution:

still similar to last time, although i will note that there's a higher representation of longfic now!!! it might not seem like much, but i noted last year that 85% of landoscar fics were under 10k & 97% under 25k — these numbers are now 78% and 92% respectively, which adds up in the grand scheme of a much larger archive. you'll also notice that the prevalence of <1k fic has gone down as well.

for the fun of it here's the wc distribution but with a further rating breakdown; as previously discussed you're more likely to get G ratings in flashfic because there's less wordspace to Make The Porn Happen. of course there are nuances to this but that's just a broad overview

side ships

what other ships are landoscar shippers shipping these days??? a lot of these ships are familiar from last time, but there are two new entries in ham/ros and pia/sai overtaking nor/ric and gas/lec to enter the top 10. ships that include at least one of lando or oscar are highlighted in orange:

of course, i pulled other 814-adjacent ships, but unfortunately i've realized that a lot of them simply aren't that popular/prevalent (context: within the 814 tag specifically) so they didn't make the top 10... because of that, here's a graph with only ships that include lando or oscar and have a minimum of 10 works within the landoscar tag:

eta: other primarily includes oscar & lily and maxf & lando. lando doesn't really have that many popular pairings within landoscar shippers otherwise...

i had wanted to explore these ships further and look at their growth/do some more in depth breakdowns of their popularity, but atm they're simply not popular enough for me to really do anything here. maybe next year?!

that being said, i did make a table comparing the prevalence of side ships within the 814 tag to the global f1 archives, so as to contextualize the popularity of each ship (see 2023). as usually, maxiel is very underrepresented in the landoscar tag, with galex actually receiving quite a boost compared to before!

additional tags

so last time i only had about 400 fics to work with and i did some analysis on additional tags / essentially au tagging. however, the problem is that there are now 3000 fics in my set, and the limitations of web scraping means that i'm not privy to the tag wrangling that happens in Da Backend of ao3. basically i'm being given all the raw versions of these au tags, whereas on ao3 "a/b/o" and "alpha/beta/omega dynamics" and "au - alpha/beta/omega" and "alternate universe - a/b/o" are all being wrangled together. because it would take way too long for me to do all of this manually and i frankly just don't want to clean that many fics after already going through all the ship tags, i've decided to not do any au analysis because i don't think it would be an accurate reflection of the data...

that being said, i had one new little experiment! as landoscar get more and more competitive, i wanted to chart how ~angsty~ they've gotten as a ship on ao3. i wanted to make a cumulative graph that shows how the overall fluff % - angst % difference has shifted over time, but ummmm... tableau and i had a disagreement. so instead here is a graph of the MoM change in angst % (so basically what percentage of the fics updated in that month specifically were tagged angst?):

the overall number is still not very drastic at all and fluff still prevails over angst in the landoscar archive. to be clear, there are 33.2% fics tagged some variation of fluff and 21.4% fics tagged some variation of angst overall, so there's a fluff surplus of 11.8%. but there has definitely been a slight growth in angst metrics over the past few months!

—

i will leave this here for now... if there's anything specific that you're interested in lmk and i can whip it up!!! hehe ty for reading 🧡

[the-necromancer-wife]

"I could explain more about the Laplace Demon concept if you, dear readers, are interested but that would be for another occasion. Another essay hehe."

Pleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleasepleaseplease 🛐

Explaining Laplace's Demon tehory in the context of 14dwy.

Thank you so much for giving me an excuse to talk about this dear anon!

In this post i'll elaborate further about something i said in the end of this super long post. As always TW for 14dwy spoilers!

(Joke's on you, sir, i DID come here for the tales of old)

And this tale in specific is really old. Around two hundred years old to be specific. In 1814 a man known by the name Pierre-Simon de Laplace wrote an essay about a deterministic concept known later as the "Laplace's demon" (although he already seemed to be exploring this concept since 1773)

We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past could be present before its eyes.

(Pierre Simon Laplace, Philosophical Essay on Probabilities.)

But what does this all mean? Well basically this man spoke about a hypothetical "intelligence" (he didn't precisely used the word "demon") that, knowing the precise location of every particle in the universe and where they were headed, they would be able to guess the past and future values for any given time. In other words, we would be referring to an almighty hypothetical "thing" able to see the past and future of every single thing in the universe.

Of course this is all a model, a theory, an exploration on what would happen if something like this existed in the first place. After all, it was all a philosophical essay in the first place, wasn't it?

"Une intelligence ... Rien ne serait incertain pour elle, et l'avenir, comme le passé, serait présent à ses yeux."

Of course, Mr. Laplace wasn't the only one to explore the idea of an almighty intelligence since other philosophers like Condorcet, Holbach and Diderot also wrote about it.

Now there are several theories (some more modern than others) to explain why an intelligence of this nature will never be possible to exist but there is one that might ring a bell for some people "The Chaos Theory"

Or as some might know it: The butterfly effect.

This theory basically poses that minor variations between the starting conditions of two systems can result in major differences. That's why you say that "A butterfly flying in certain direction today can lead to huge catastrophes tomorrow" it's not something (so) literal but it is useful to illustrate how small can be the variation and how huge can be the result. Of course the change doesn't have to be huge. It can be a minor change, but a change nonetheless.

Btw chaos theory is applicable when knowledge of the system is imperfect, whereas Laplace's demon assumes perfect knowledge of the system, therefore the variability leading to chaos in chaos theory and non-variability in the knowledge of the world Laplace's demon holds are noncomparable.

But, What does this all has to do with 14dwy?

Glad you ask. Actually a lot.

Starting off with the idea of how perfect is this game integrating even its genre (Visual Novel) to the theory. I believe there is no bigger example of the butterfly efect than a Visual Novel, where choosing (or not) certain options can lead to certain results (big or small). Very much like the butterfly effect. And funnily enough, it's us, the player, the embodiment of this umpredictability, since we are the ones that make the choices in the game. We are the antagonists of a hypothetical Laplace's Demon. We are it's antonym. We are an angel. The idea of a change that the system cannot predict. Of course this is questionable. Because as much as we have certain "freedom" we still need to abide by the choices that the very game gives to us.

This takes us back to the question of who is the entity that speaks to us in this cryptic messages on day four? Who is this (allegedly) Laplace's Demon? and what are my personal arguments on why i don't think it's [REDACTED].

Let's start by breaking down the messages. I have them all decoded in my previous post along with a really easy (i hope so) explanation on how the cipher works.

"...ATTEMPTING TO REWRITE WHAT HAS ALREADY BEEN ORDAINED SINCE THE BEGINNING"

Here, the entity mentions the certainty of the past, but not the certainty of the future.

"...ATTEMPTING TO DIG UP THE ROOTS OF FATE AS THOUGH IT WERE A WEED AND PLANTING YOUR OWN CORRUPT SEEDLING IN ITS PLACE"

Here, the entity presumably refers to the idea of our (very limited) free will and how we're pushing the limits of the system (in this case by attempting to keep advancing on a route that supposedly leads nowhere else). To make this more clear, this entity speaks to us when we load the save file multiple times trying to keep advancing down that path when the course of action contemplated is that we should just stop and load another save file. We're persistent creatures, after all.

"PERHAPS WE ARE THE SAME, THEN"

Of course fucking ✨not✨

"I TOO FIND ENJOYMENT IN DISRUPTING THE VINES OF KISMET AND WATCHING HIM STRUGGLE"

Now i swear i never heard the word "kismet" before this day. But it seems to be some sort of archaic synonim of the word "fate". Although according to Cambridge Dictionary it is actually "A force that (some people) think controls what happens in the future, and is outside human control"

Also, this entity seems to be having its fun with Ren/REDACTED's struggles. It doesn't really see our unpredictability (yet) as a threat.

"TWAS I WHO GAVE HIM HIS GIFT, AS I DID WITH OTHERS..."

AND THIS

This right here boy oh boy.

I gave him his gift: A rant about human deities through the holy act of programming.

(As pretentious as this title is, please hear me out)

There's a really interesting article named Embracing Λόγος: Programming as Imitation of the Divine that basically says:

The programmer must begin by defining things – material or conceptual. “We are unable to reason or communicate effectively if we do not first make the effort to know what each thing is.” (Rayside, Campbell) By considering the ontological questions of the things in our world, in order to represent them accurately (and therefore ethically) in our programs, the programmer enters into the philosophical praxis. Next, the programmer adds layers of identity and logic on top of their ontological discovery, continuing in the praxis.

But the programmer takes it a step further – the outcome of their investigation is not only their immaterial thought but, in executing the program, the manifestation of their philosophical endeavor into material reality. The program choreographs trillions of elementary charges through a crystalline maze, harnessing the virtually infinite charge of the Earth, incinerating the remains of starlight-fueled ancient beings in order to realize the reasoning of its programmer. Here the affair enters into the realm of Ethics.

“The programmer is attempting to solve a practical problem by instructing a computer to act in a particular fashion. This requires moving from the indicative to the imperative: from can or may to should. For a philosopher in the tradition, this move from the indicative to the imperative is the domain of moral science.” (Rayside, Campbell) Any actions taken by the program are the direct ethical responsibility of the programmer.

Furthermore, the programmer, as the source of reason and will driving a program, manifesting it into existence, becomes in that instant the λόγος σπερματικός (“logos spermatikos”) incarnate. The programmer’s reason, tapped into the divine Reason (λόγος), is generated into existence in the Universe and commands reasonable actions of inanimate matter.

Basically the programmer goes through each and every stage a deity would go through when creating the universe.

AND GUESS WHO IS A PROGRAMMER IN 14DWY???

(Ren/REDACTED in case you don't know hehe)

When the entity says "I gave him his gift" i believe this is exactly what he is referring to. While a Laplace's Demon knows every particle in the physical systems (and assumes it's knowledge of said system is perfect), a programmer works with Operative Systems (Windows, Linux, Ubuntu).

As for us, the angel, the antagonist of the demon of Laplace, we are the chaos theory, the one that conceives the knowledge of the system as imperfect.

Btw the person manipulating some choices in certain moments? Totally Ren/REDACTED. As they have the power of messing with the game and are totally self-aware of this being a visual novel.

But who gave Ren/REDACTED this power? Was Ren/REDACTED so skillful that they were able to defy the laws of worldbuilding? Picture this: the equivalent would be a programmer in the real world so skillful that they become able to defy the reality itself.

I believe the responsible is this "all knowing entity" since it just said it itself "It was I who gave him his gift". After all he is, and i quote, "THE PAST, PRESENT, AND FUTURE, COMBINED INTO ONE" really ominous shit.

There are obvious gaps in this theory but it's the best i can do with the limited knowledge i have. I am not a physicist so i can't really dwell in formulas and numbers as much as i would. Maybe i'll interview a professor in college in the future but for now i hope this is enough.

[alpaca-clouds]

We need to talk about AI

Okay, several people asked me to post about this, so I guess I am going to post about this. Or to say it differently: Hey, for once I am posting about the stuff I am actually doing for university. Woohoo!

Because here is the issue. We are kinda suffering a death of nuance right now, when it comes to the topic of AI.

I understand why this happening (basically everyone wanting to market anything is calling it AI even though it is often a thousand different things) but it is a problem.

So, let's talk about "AI", that isn't actually intelligent, what the term means right now, what it is, what it isn't, and why it is not always bad. I am trying to be short, alright?

So, right now when anyone says they are using AI they mean, that they are using a program that functions based on what computer nerds call "a neural network" through a process called "deep learning" or "machine learning" (yes, those terms mean slightly different things, but frankly, you really do not need to know the details).

Now, the theory for this has been around since the 1940s! The idea had always been to create calculation nodes that mirror the way neurons in the human brain work. That looks kinda like this:

Basically, there are input nodes, in which you put some data, those do some transformations that kinda depend on the kind of thing you want to train it for and in the end a number comes out, that the program than "remembers". I could explain the details, but your eyes would glaze over the same way everyone's eyes glaze over in this class I have on this on every Friday afternoon.

All you need to know: You put in some sort of data (that can be text, math, pictures, audio, whatever), the computer does magic math, and then it gets a number that has a meaning to it.

And we actually have been using this sinde the 80s in some way. If any Digimon fans are here: there is a reason the digital world in Digimon Tamers was created in Stanford in the 80s. This was studied there.

But if it was around so long, why am I hearing so much about it now?

This is a good question hypothetical reader. The very short answer is: some super-nerds found a way to make this work way, way better in 2012, and from that work (which was then called Deep Learning in Artifical Neural Networks, short ANN) we got basically everything that TechBros will not shut up about for the last like ten years. Including "AI".

Now, most things you think about when you hear "AI" is some form of generative AI. Usually it will use some form of a LLM, a Large Language Model to process text, and a method called Stable Diffusion to create visuals. (Tbh, I have no clue what method audio generation uses, as the only audio AI I have so far looked into was based on wolf howls.)

LLMs were like this big, big break through, because they actually appear to comprehend natural language. They don't, of coruse, as to them words and phrases are just stastical variables. Scientists call them also "stochastic parrots". But of course our dumb human brains love to anthropogice shit. So they go: "It makes human words. It gotta be human!"

It is a whole thing.

It does not understand or grasp language. But the mathematics behind it will basically create a statistical analysis of all the words and then create a likely answer.

What you have to understand however is, that LLMs and Stable Diffusion are just a a tiny, minority type of use cases for ANNs. Because research right now is starting to use ANNs for EVERYTHING. Some also partially using Stable Diffusion and LLMs, but not to take away people'S jobs.

Which is probably the place where I will share what I have been doing recently with AI.

The stuff I am doing with Neural Networks

The neat thing: if a Neural Network is Open Source, it is surprisingly easy to work with it. Last year when I started with this I was so intimidated, but frankly, I will confidently say now: As someone who has been working with computers for like more than 10 years, this is easier programming than most shit I did to organize data bases. So, during this last year I did three things with AI. One for a university research project, one for my work, and one because I find it interesting.

The university research project trained an AI to watch video live streams of our biology department's fish tanks, analyse the behavior of the fish and notify someone if a fish showed signs of being sick. We used an AI named "YOLO" for this, that is very good at analyzing pictures, though the base framework did not know anything about stuff that lived not on land. So we needed to teach it what a fish was, how to analyze videos (as the base framework only can look at single pictures) and then we needed to teach it how fish were supposed to behave. We still managed to get that whole thing working in about 5 months. So... Yeah. But nobody can watch hundreds of fish all the time, so without this, those fish will just die if something is wrong.

The second is for my work. For this I used a really old Neural Network Framework called tesseract. This was developed by Google ages ago. And I mean ages. This is one of those neural network based on 1980s research, simply doing OCR. OCR being "optical character recognition". Aka: if you give it a picture of writing, it can read that writing. My work has the issue, that we have tons and tons of old paper work that has been scanned and needs to be digitized into a database. But everyone who was hired to do this manually found this mindnumbing. Just imagine doing this all day: take a contract, look up certain data, fill it into a table, put the contract away, take the next contract and do the same. Thousands of contracts, 8 hours a day. Nobody wants to do that. Our company has been using another OCR software for this. But that one was super expensive. So I was asked if I could built something to do that. So I did. And this was so ridiculously easy, it took me three weeks. And it actually has a higher successrate than the expensive software before.

Lastly there is the one I am doing right now, and this one is a bit more complex. See: we have tons and tons of historical shit, that never has been translated. Be it papyri, stone tablets, letters, manuscripts, whatever. And right now I used tesseract which by now is open source to develop it further to allow it to read handwritten stuff and completely different letters than what it knows so far. I plan to hook it up, once it can reliably do the OCR, to a LLM to then translate those texts. Because here is the thing: these things have not been translated because there is just not enough people speaking those old languages. Which leads to people going like: "GASP! We found this super important document that actually shows things from the anceint world we wanted to know forever, and it was lying in our collection collecting dust for 90 years!" I am not the only person who has this idea, and yeah, I just hope maybe we can in the next few years get something going to help historians and archeologists to do their work.

Make no mistake: ANNs are saving lives right now

Here is the thing: ANNs are Deep Learning are saving lives right now. I really cannot stress enough how quickly this technology has become incredibly important in fields like biology and medicine to analyze data and predict outcomes in a way that a human just never would be capable of.

I saw a post yesterday saying "AI" can never be a part of Solarpunk. I heavily will disagree on that. Solarpunk for example would need the help of AI for a lot of stuff, as it can help us deal with ecological things, might be able to predict weather in ways we are not capable of, will help with medicine, with plants and so many other things.

ANNs are a good thing in general. And yes, they might also be used for some just fun things in general.

And for things that we may not need to know, but that would be fun to know. Like, I mentioned above: the only audio research I read through was based on wolf howls. Basically there is a group of researchers trying to understand wolves and they are using AI to analyze the howling and grunting and find patterns in there which humans are not capable of due ot human bias. So maybe AI will hlep us understand some animals at some point.

Heck, we saw so far, that some LLMs have been capable of on their on extrapolating from being taught one version of a language to just automatically understand another version of it. Like going from modern English to old English and such. Which is why some researchers wonder, if it might actually be able to understand languages that were never deciphered.

All of that is interesting and fascinating.

Again, the generative stuff is a very, very minute part of what AI is being used for.

Yeah, but WHAT ABOUT the generative stuff?

So, let's talk about the generative stuff. Because I kinda hate it, but I also understand that there is a big issue.

If you know me, you know how much I freaking love the creative industry. If I had more money, I would just throw it all at all those amazing creative people online. I mean, fuck! I adore y'all!

And I do think that basically art fully created by AI is lacking the human "heart" - or to phrase it more artistically: it is lacking the chemical inbalances that make a human human lol. Same goes for writing. After all, an AI is actually incapable of actually creating a complex plot and all of that. And even if we managed to train it to do it, I don't think it should.

AI saving lives = good.

AI doing the shit humans actually evolved to do = bad.

And I also think that people who just do the "AI Art/Writing" shit are lazy and need to just put in work to learn the skill. Meh.

However...

I do think that these forms of AI can have a place in the creative process. There are people creating works of art that use some assets created with genAI but still putting in hours and hours of work on their own. And given that collages are legal to create - I do not see how this is meaningfully different. If you can take someone else's artwork as part of a collage legally, you can also take some art created by AI trained on someone else's art legally for the collage.

And then there is also the thing... Look, right now there is a lot of crunch in a lot of creative industries, and a lot of the work is not the fun creative kind, but the annoying creative kind that nobody actually enjoys and still eats hours and hours before deadlines. Swen the Man (the Larian boss) spoke about that recently: how mocapping often created some artifacts where the computer stuff used to record it (which already is done partially by an algorithm) gets janky. So far this was cleaned up by humans, and it is shitty brain numbing work most people hate. You can train AI to do this.

And I am going to assume that in normal 2D animation there is also more than enough clean up steps and such that nobody actually likes to do and that can just help to prevent crunch. Same goes for like those overworked souls doing movie VFX, who have worked 80 hour weeks for the last 5 years. In movie VFX we just do not have enough workers. This is a fact. So, yeah, if we can help those people out: great.

If this is all directed by a human vision and just helping out to make certain processes easier? It is fine.

However, something that is just 100% AI? That is dumb and sucks. And it sucks even more that people's fanart, fanfics, and also commercial work online got stolen for it.

And yet... Yeah, I am sorry, I am afraid I have to join the camp of: "I am afraid criminalizing taking the training data is a really bad idea." Because yeah... It is fucking shitty how Facebook, Microsoft, Google, OpenAI and whatever are using this stolen data to create programs to make themselves richer and what not, while not even making their models open source. BUT... If we outlawed it, the only people being capable of even creating such algorithms that absolutely can help in some processes would be big media corporations that already own a ton of data for training (so basically Disney, Warner and Universal) who would then get a monopoly. And that would actually be a bad thing. So, like... both variations suck. There is no good solution, I am afraid.

And mind you, Disney, Warner, and Universal would still not pay their artists for it. lol

However, that does not mean, you should not bully the companies who are using this stolen data right now without making their models open source! And also please, please bully Hasbro and Riot and whoever for using AI Art in their merchandise. Bully them hard. They have a lot of money and they deserve to be bullied!

But yeah. Generally speaking: Please, please, as I will always say... inform yourself on these topics. Do not hate on stuff without understanding what it actually is. Most topics in life are nuanced. Not all. But many.