For Peaceful Mind

As technology continues to drive innovation and redefine industries, tech jobs remain among the highest-paying careers globally. With advancements in AI, IoT, cloud computing, and cybersecurity, professionals in these domains are highly sought after. This article outlines the top 10 tech jobs of 2025, highlighting their roles, required skills, and earning potential.

Artificial Intelligence (AI) agents are at the heart of many modern technologies, from personal voice assistants to self-driving cars and sophisticated recommendation systems. These agents act autonomously in various environments, analyzing data, making decisions, and carrying out actions to achieve specific goals. As AI continues to evolve, understanding the different types of AI agents and their applications becomes essential for appreciating how these technologies impact our daily lives and industries.

Gartner Inc. recently shared its top 10 technology trends for 2025 at their IT Symposium. These trends focus on how AI, new computing methods, and better human-machine partnerships will shape the future. Here’s a look at each trend, explained in simple terms.

As we advance into a more technology-driven future, the demand for specialized computer science skills has reached unprecedented heights. Emerging technologies like Artificial Intelligence (AI), robotics, Internet of Things (IoT), and AI-driven agents are transforming industries and opening up new career paths. For professionals and students alike, the path forward requires an understanding of the key skills that will dominate the field in 2025 and beyond.
Let’s dive into the essential computer science skills needed to stay relevant and competitive in this evolving landscape.

The recent boom in generative AI has set the stage for a meteoric rise in AI agents. Industry projections estimate the market for AI agents will grow from USD 5.1 billion in 2024 to an impressive USD 47.1 billion by 2030. This remarkable growth, characterized by a compound annual growth rate (CAGR) of 44.8%, is fueled by breakthroughs in generative AI, enabling Agentic AI to perform increasingly complex tasks with minimal human intervention.

The philosophy of Artificial Intelligence (AI) grapples with a set of profound questions: Can a machine act intelligently? Is machine intelligence comparable to human intelligence? Can machines experience mental states, consciousness, or emotions? These questions have sparked a philosophical debate that spans the boundaries between technology and human thought, challenging longstanding ideas in the realm of epistemology, ethics, and consciousness. In this article, we will explore the philosophy of AI and compare it with various types of human philosophies, examining how each attempts to address these questions

Indian philosophy is enriched by a vast array of spiritual teachings, with key figures such as Lord Krishna, Adi Shankaracharya, Gautama Buddha, and Mahavira providing transformative insights into the nature of existence, the soul, and the path to liberation. Though their teachings differ in various aspects, they share common threads that contribute to the profound spiritual heritage of India. This article provides a comparative analysis of the philosophies of these four great spiritual leaders.

This year’s Nobel Prize in Physics celebrates the groundbreaking contributions of John J. Hopfield and Geoffrey E. Hinton, whose work on artificial neural networks has had profound impacts on both artificial intelligence and physics.

As technology continues to evolve, transportation is poised for another significant transformation. The statement “Future is only for quantum vehicles, neither electric vehicles nor classical vehicles” suggests a bold vision for the future, where quantum technology overtakes both traditional gasoline-powered cars and modern electric vehicles (EVs). While electric vehicles have already made impressive strides in terms of sustainability and reduced emissions, the potential emergence of quantum vehicles may render both classical and electric models obsolete.

Quantum memory has become more reliable over the past few years but still needs improvement. Currently, the best quantum memory is about 85% reliable. This means there’s still room for enhancing error correction and the overall efficiency of quantum memory.

Experts believe that these problems will likely be solved in the next few years, making quantum memory more practical and reliable. As these developments continue, we can expect to see more applications of quantum computing in areas like secure data storage and advanced problem-solving.

In recent years, machine learning (ML) has revolutionized various fields, from healthcare to finance, by offering powerful tools to model and predict complex systems. However, traditional ML models often act as black boxes, requiring vast amounts of data and sometimes ignoring fundamental physical laws that govern the phenomena they aim to predict. This is where Physics-Informed Machine Learning (PIML) comes into play.

Hello, world! I am Qubit, a quantum bit, and I am here to revolutionize the way we think about computing and problem-solving. Just like a superhero in a story, I possess unique powers that can help tackle some of the most complex challenges we face today. Let me take you on a journey to understand how I can change the world!

Have you ever heard of a computer? Of course, you have! Computers help us do all kinds of things like play games, watch videos, and even do homework. But did you know there’s a new type of computer that’s even more powerful? It’s called Quantum Computing!

Have you ever wondered how the information you share online or on your phone is used? Well, that’s what “Data Politics” is all about! Data politics refers to the ways in which data is generated, managed, controlled, and used within societal, economic, and political contexts. It involves the power dynamics, ethical considerations, and decision-making processes surrounding data.

For a long time, people have been talking about something called “the singularity.” This is a moment in the future when human brains and robots (or artificial intelligence, AI) might come together to become super smart. It sounds like something out of a science fiction movie, but some really smart people think it could actually happen.

One of these people is Ray Kurzweil. He’s a computer scientist who makes big predictions about the future. In 1999, he said that by 2029, robots would be as smart as humans. At the time, many experts didn’t believe him. They thought it would take a lot longer—maybe even 100 years! But now, as 2029 gets closer, more and more people are starting to think Kurzweil might have been right.

Discipline and indiscipline are fundamental aspects of human behavior that significantly influence an individual’s character and actions. When we think about good and bad people, their level of discipline often plays a crucial role in shaping their behaviors and moral choices. This article explores the relationship between disciplined and indisciplined individuals and how these traits align with the concepts of good and bad people.

In the rapidly evolving world of artificial intelligence (AI), a new paradigm is emerging that promises to revolutionize the way we interact with intelligent systems. Contextual AI, a cutting-edge approach, focuses on creating systems that can understand and adapt to the context in which they operate, enabling more natural, seamless, and personalized interactions.

In the dynamic field of Computer Science, staying at the forefront of innovation and discovery requires a constant quest for knowledge and exploration of cutting-edge technologies. As a professor in the Department of Computer Science, I have always been drawn to the fascinating intersections of Contextual AI, Theory of Mind, and Quantum Computing—areas that hold immense potential for revolutionizing our understanding of intelligence, consciousness, and computation. In my journey of research and teaching, one companion has emerged as an invaluable ally: GenAI.

Generative AI Ready Datasets, or Generative-Ready Datasets (GRDs) are specialized datasets curated to train and optimize generative AI models. These datasets encompass a wide variety of data types, ensuring that AI models can generate new content or insights based on diverse and comprehensive training data. The primary goal of GRDs is to provide a rich and varied dataset that allows generative AI to perform accurately and creatively across different domains. GRDs include a wide variety of data types, both structured and unstructured, such as text, images, videos, audio, and other multimedia formats.

Generative artificial intelligence, or GenAI, is the latest technological advancement that has captured the attention of businesses worldwide. Promising to revolutionize operations, employee workflows, and daily life, GenAI is heralded as a new era in industrial evolution. However, for companies where data isn’t intrinsically part of the culture, the challenge lies in effectively integrating and leveraging GenAI’s capabilities. This article outlines a practical approach to help such companies successfully embark on their GenAI journey.