Latest Stories

Most recently published stories in Chapters.

Latest Technologies Adopted by a Modern Manifolds Valve Manufacturer
The need for accurate and effective fluid control systems has caused a drastic change in the manufacturing sector, particularly in the field of Manifolds Valve Manufacturer services. Such manufacturers have a vital role to play in producing valves and parts utilized in pneumatic and hydraulic systems, and the adaptation of sophisticated technologies has become imperative in order to achieve the standards of industries. An up-to-date Manifolds Valve Manufacturer needs to adopt innovative solutions in order to be reliable, long-lasting, and assure perfect performance.
By GM Hitech9 months ago in Chapters
How I Made My First $10 by Sharing Something I Thought Was Useless
It all started with a weird question I got on Reddit... A random user DM’d me after reading one of my comments in a niche tech thread:
By 马石宝9 months ago in Chapters
How Managers Keep Your Holiday Rental Guest-Ready
Keeping a holiday rental property in pristine condition for guests requires more than just occasional cleaning—it demands proactive care, industry expertise, and constant attention to detail. That’s where a professional holiday property management company steps in, ensuring your rental always meets guest expectations and maintains five-star standards.
By best real estate9 months ago in Chapters
Journal of Napoleon Bonaparte
Germany, France, Elba — 1813–1814 May 2, 1813 – Lützen They say I am back. The Allies believed Russia had broken me, that France would collapse beneath the weight of corpses and snow. But I have raised another army — boys and old men, yes, but enough. At Lützen, they learned the Emperor still strikes fast and hard. The Prussians fell back, astonished.
By Alain SUPPINI9 months ago in Chapters
The Library That Ate Time
Birthdays. The annual forced march towards the grave, punctuated by awkward greetings and questionable cake choices. Mine, however, come with a unique little flavor: a yearly seventeen-minute vacation from consciousness. My brain's way of saying, "Nope, not dealing with another year of this." While the normies get balloons and heartfelt (read: generic) cards, I get a temporary flirtation with the void and the persistent suspicion that my existence is some cosmic sitcom I didn't audition for.
By Gugulethu Msomi9 months ago in Chapters
The Rise of Machine Learning. AI-Generated.
Introduction: A Shift Toward Learning from Data ‎As the 1990s began, Artificial Intelligence once again stood at a crossroads. The expert systems of the 1980s had shown promise but were limited by their dependence on manually coded rules. These systems could not easily adapt to new situations or learn from experience. That’s when a paradigm shift occurred: instead of telling machines exactly what to do, researchers began developing methods that allowed machines to learn from data. This approach, called Machine Learning, would become the foundation of modern AI. ‎ ‎ ‎--- ‎ ‎1. What Is Machine Learning? ‎ ‎Machine Learning (ML) is a branch of AI that focuses on building systems that can improve their performance over time without being explicitly programmed. Instead of writing rules for every scenario, programmers train algorithms on data, allowing machines to recognize patterns, make predictions, and even make decisions based on experience. ‎ ‎For example, instead of teaching a machine all the rules of grammar, an ML model could be trained on thousands of sentences and learn to predict the structure of a new one. This marked a fundamental change in how AI systems were built, moving from logic and rules to probability, statistics, and pattern recognition. ‎ ‎ ‎--- ‎ ‎2. The Role of Data and Algorithms ‎ ‎In the 1990s, the amount of digital data began to grow rapidly, thanks to the internet and advances in computing. At the same time, powerful algorithms such as decision trees, support vector machines (SVMs), and Bayesian networks were being developed. These tools allowed machines to analyze complex datasets, find hidden relationships, and make accurate predictions. ‎ ‎For example, researchers started building systems that could sort emails into spam or not-spam by learning from thousands of labeled messages. This kind of learning — called supervised learning — became central to many real-world applications. ‎ ‎ ‎--- ‎ ‎3. AI in the Real World: ‎ ‎Games, Search, and More ‎One of the most iconic AI achievements of the 1990s was IBM’s Deep Blue, a chess-playing computer that famously defeated world champion Garry Kasparov in 1997. Deep Blue combined traditional AI planning with brute-force search and evaluation — but it symbolized how powerful data and computing had become. ‎ ‎Meanwhile, machine learning techniques began making their way into everyday tools. Search engines like Google used learning algorithms to rank webpages. Recommendation systems for books, movies, and music became increasingly accurate. AI was becoming more practical, embedded quietly into technologies people used every day. ‎ ‎ ‎--- ‎ ‎4. Limitations and Challenges ‎ ‎Despite its growing power, machine learning in the 1990s still faced several challenges. Computers were faster than before but still limited in memory and processing power. Training large models took time and required significant effort in data cleaning and feature engineering — the process of manually selecting which pieces of data the model should focus on. ‎ ‎Moreover, early ML systems were often opaque: they could make predictions, but it was hard to understand why. This “black box” nature raised concerns, especially in fields like medicine or finance, where transparency and trust were critical. ‎ ‎ ‎--- ‎ ‎5. Laying the Groundwork for the Future ‎Despite its limitations,
By Tech Ai 9 months ago in Chapters
The Rise of Expert Systems
Introduction: ‎ ‎A Fresh Start After the AI Winter ‎After the challenges of the 1970s, Artificial Intelligence faced a crossroads. The field had experienced disillusionment, with high expectations followed by a significant drop in funding and interest. But as the 1980s approached, AI began to bounce back. A new wave of optimism emerged, fueled by Expert Systems — a technology that brought AI back into the spotlight. Expert systems, which mimicked human expertise in specific domains, marked a fresh direction for AI, proving that intelligent machines could succeed in solving real-world problems, even with limited resources. ‎ ‎ ‎--- ‎ ‎1. What are Expert Systems? ‎ ‎Expert systems were designed to solve problems by mimicking the decision-making abilities of human experts. Unlike earlier AI programs, which focused on symbolic reasoning alone, expert systems combined large knowledge bases with sophisticated reasoning algorithms. These systems could “reason” through problems and offer solutions by applying rules and knowledge accumulated over time. ‎ ‎For example, an expert system in medicine could diagnose illnesses based on a set of symptoms, while one in engineering might provide solutions for mechanical problems. These systems were developed to perform specific tasks that required human-like judgment and expertise, making them useful in fields like medicine, finance, engineering, and law. ‎ ‎ ‎--- ‎ ‎2. The Birth of Expert Systems: ‎A New Era in AI ‎ ‎The rise of expert systems can be traced back to the early 1980s, with systems like MYCIN — a medical expert system designed to diagnose bacterial infections and recommend treatments. MYCIN, developed at Stanford University, was one of the first examples of an AI system that could make decisions in a specialized field. While it wasn’t perfect, it demonstrated the power of AI in real-world applications. ‎ ‎Another influential system, DENDRAL, was created to help chemists identify molecular structures. Both MYCIN and DENDRAL were successful because they used large, structured sets of rules and facts — knowledge bases that mimicked the reasoning of expert professionals. ‎ ‎ ‎--- ‎ ‎3. The Rise of AI in Industry ‎ ‎In the 1980s, expert systems quickly gained popularity in industry. Corporate and government agencies saw the potential of AI to assist with decision-making processes, and the demand for expert systems grew rapidly. Companies began investing in developing these systems for a wide variety of applications. In the field of finance, for example, expert systems helped analysts evaluate investment opportunities. In engineering, they were used to troubleshoot machines and optimize production processes. ‎ ‎The idea was that these systems could act as “consultants,” providing expertise and guidance to users without requiring them to be experts themselves. This was a key factor in the resurgence of AI — expert systems were no longer just academic experiments; they had real-world value. ‎ ‎ ‎--- ‎ ‎4 The AI Winter Ends: New Funding and Renewed Interest ‎. ‎The success of expert systems marked the end of the AI Winter. The promising results led to renewed funding from both private sector companies and government agencies. AI research labs were revived, and researchers began to focus on improving and expanding expert systems. ‎ ‎By the mid-1980s, the global market for expert systems was growing, and these systems were being used in a variety of industries, from healthcare to telecommunications. This resurgence brought AI back into the public eye, and the media began to focus more on AI's potential, reigniting the optimism that had initially fueled the field. ‎ ‎ ‎--- ‎ ‎5. Challenges and Limitations of Expert Systems ‎ ‎Despite their early success, expert systems had limitations. While they could perform specific tasks with impressive accuracy, they lacked the flexibility and adaptability of human experts. Expert systems could only work within the narrow knowledge base they were given, and they often required constant updates to reflect new information. Furthermore, they struggled with uncertainty and incomplete information — challenges that humans could often overcome with intuition and experience. ‎ ‎While expert systems were effective in their specific domains, they lacked the general intelligence needed to handle broader problems. Despite these limitations, expert systems paved the way for future developments in AI by demonstrating that specialized, rule-based systems could be a useful tool. ‎ ‎ ‎--- ‎ ‎Conclusion: ‎The Expert System Boom and Beyond. ‎ ‎The 1980s were a pivotal decade in AI history. Expert systems gave AI a second chance, restoring the field's reputation and proving its value in solving real-world problems. The success of these systems led to renewed investments, research, and a sense of hope for the future of AI. Although expert systems were not the ultimate answer to artificial general intelligence, they laid the groundwork for future AI innovations and brought AI out of the academic realm and into the industrial world.
By Tech Ai 9 months ago in Chapters
Message from the Future – The Bizarre Adventures of the Quirkston Family
Dinner at the Quirkston house was about to become a full-blown nutritional disaster. Paula Quirkston had invited guests over — a big deal in their part of the suburbs — and boldly promised them a traditional delicacy: boneless fish stew. Never mind that she didn’t actually have any fish, or money to buy fish, or the slightest clue how to cook said stew without setting off the smoke alarm.
By Angela David9 months ago in Chapters
Message from the Future – The Bizarre Adventures of the Quirkston Family
It was a calm Wednesday evening in the Quirkston household — at least, as calm as things ever got around there. After another one of Paula Quirkston’s semi-edible dinner experiments (potato casserole that somehow tasted like pencil shavings and regret), the family was preparing for their usual nightly ritual: television.
By Angela David9 months ago in Chapters
The Order of the Silver Dragons
The archipelago of Agarthia had long seen conflict. If the western island of Chordia didn't have two nations fighting for land, then the eastern island of Bolchia did.
By Britain Modean9 months ago in Chapters
The Success Story of a Failed Man
Once, in a crowded city that moved too fast for anyone to notice a man stumbling, there lived someone known as Rajan. He was once a dreamer, like most people in their twenties—full of ideas, ambition, and plans. He started a business at 26. It failed. Tried again at 30. It failed harder. Married at 32. Divorced by 34. Every time he stood up, life swept his legs out from under him.
By Sumon Ahmed9 months ago in Chapters
Part 37: The Anomaly’s Awakening
In the void between timelines — a realm without time, without color, where even echoes feared to linger — something stirred.
By William9 months ago in Chapters