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Articles by Arthi Rajendran
A 1979 Murder Finally Solved using AI
Seventeen years old, Esther was shy, funny, and mild-mannered, the fourth of seven children in a close-knit family. She was inseparable from her sister, Elizabeth, who was only a year older. On the cold night of February 9, 1979, Esther left her parents’ home in Beaumont, California, to visit Elizabeth in nearby Banning. It was a familiar walk. But this night, she never arrived. The following day, searchers made a horrifying discovery: Esther’s body, dumped in a snowpack off Highway 243.
What is Machine Learning? (Part 2): Types, Uses, and Your First Model
“Sit. Good. Here’s a cookie.” That’s how I began my video on machine learning. Odd way to start? Maybe. But it’s actually the perfect analogy for one of the most important concepts in ML: reinforcement learning. In Part 1 of this series, we broke down the basics of what machine learning is, how it works, and the six key ingredients you need to “cook” the perfect Machine Learning Biryani. If you haven’t read that yet, I recommend checking it out first.
Machine Learning Explained (With Biryani 🍛)
You use it every day. It’s in your face unlock, it’s behind your Netflix recommendations, it’s how Amazon knows what you want before you do. You already know what we’re talking about: Machine Learning (ML). Behind every “Recommended for You” list, every search suggestion, and even your voice assistant lies this simple yet complex technology. Today, I’m breaking it down in the easiest way possible: with food. Yes, Biryani. I’m Arthi — a data scientist for over six years now.
Steal These Prompts From OpenAI’s Official Guide (Before Everyone Else Does)
OpenAI has already told us exactly how to unlock that missing 90%, but they buried it deep inside developer documentation that most people will never read. And that’s where I went digging. What I found are not “tips” or “cool hacks.” They are battle-tested frameworks used by OpenAI’s own developers, ready for you to steal. Today, I’m giving you the five prompt strategies that will make your AI act less like a friendly intern and more like a top-tier teammate. Not a medium member?
AI Builders, Read This: What the U.S. AI Action Plan Means for Your Stack
AI Builders, Read This: What the U.S. AI Action Plan Means for Your Stack “Breakthroughs in these fields have the potential to reshape the global balance of power, spark entirely new industries, and revolutionize the way we live and work.” — President Donald J. Trump, foreword to the Plan On 23rd July, the White House released a slim, 28-page PDF titled America’s AI Action Plan. On its face, it reads like a spending bill with footnotes.
What Trump’s Ankles Taught Me About AI & Chronic Venous Insufficiency?
What Trump’s Ankles Taught Me About AI & Chronic Venous Insufficiency? A free dashboard turned an obscure vascular term into headline fodder overnight. What if we aimed that same data firehose at real public‑health problems instead of clickbait? Last Monday night, “chronic venous insufficiency” vaulted from near‑zero to 1 million U.S. searches in six hours, briefly outranking Taylor Swift’s latest Easter‑egg theories.
I Pasted Context 6 Times into LLMs Before Realizing There’s a Smarter Way
Welcome to Code Like A Girl, a space that celebrates redefining society's perceptions of women in technology. Share your story with us! Follow publication How I hacked around context limits with a simple retrieval layer that just works! I’ll admit it. I pasted the same block of context into my prompt six times. Not because I didn’t know better, but because the model kept forgetting the critical details halfway through. And when your project depends on consistent answers, you do what you must.
When ChatGPT Spat 42 Wrong “Facts”
Welcome to Code Like A Girl, a space that celebrates redefining society's perceptions of women in technology. Share your story with us! Follow publication Guardrails 101: How to Unit-Test Your Language Model for Hallucinations It started with a simple prompt. “Hey ChatGPT, can you pull the latest trial data on Zoltarizumab?” I asked it to summarize findings from PubMed. Within seconds, it gave me glowing results: a 2023 Phase III success, FDA approval, and patient outcomes. One small problem.
What No One Tells You About Switching Careers to Work in AI
Women in Tech is a publication to highlight women in STEM, their accomplishments, career lessons, and stories. Follow publication When I first walked into a room of AI engineers, I carried a laptop, a notepad, and years of clinical trial data in my head. Not exactly the typical AI origin story. I had spent most of my early career parsing adverse event reports, teaching biostatistics, and helping pharma teams make sense of patient outcomes.
Python Diagnosed My Marketing Bottleneck
We'd spent three weeks planning a product email funnel. The pitch was strong. The list was clean. The visuals looked like they belonged in an Apple keynote. Still, open rates were low, and conversion was worse. The team started tossing around guesses: Maybe the subject line was too vague. Maybe the CTA button was in the wrong spot. Maybe Tuesdays just weren't our day. That's when I opened up my Python notebook. Not because I knew the answer, but because I didn't. I was tired of hunches.
Context Window: AI’s Working Memory
Let’s imagine that you are trying to write an essay while only seeing the last few sentences you wrote. That’s how AI models operate, with a limited memory span called the context window. It’s not the model’s fault. It’s just how they’re designed. Welcome to the 26th/100 article in the “AI Explained Like I’m 10” series. A context window is the maximum number of tokens an LLM can read at once.
Tokenization: Why AI Doesn’t Read Words Like We Do
AI doesn’t read text like humans. It breaks things into chunks called tokens before doing anything else. That’s how LLMs read faster, smaller, and smarter. Welcome to the 25th/100 article in the “AI Explained Like I’m 10” series. A token is a piece of text, not always a full word. Examples: “cat” = 1 token “categorical” = 3 tokens “🏖️” = 1 token “antidisestablishmentarianism” = 7+ tokens Tokens are the puzzle pieces AI uses to “understand” and generate language. Think of a sentence like a Lego castle.
Embedding Models: Turning Words Into Math š§®
How does AI know that āburgerā and āfriesā go together, but āburgerā and ātelescopeā donāt? Itās because of embedding models, the math brains behind the magic. Welcome to the 24/100 article in the āAI Explained Like Iām 10ā series. An embedding is a way to turn words into numbers, specifically, into vectors (just like we saw in the last blog).
LangChain Explained Like It’s a Lunchbox 🍱
LLMs are smart. But to get real work done, they need help with memory, tools, and coordination. That’s where LangChain comes in. Welcome to the 22/100 article in the “AI Explained Like I’m 10” series. LangChain is a framework that helps connect an LLM to: Tools (like web search or calculators) Memory (like customer history) Logic (like multi-step plans) It’s not another AI. It’s the lunchbox that holds all the useful stuff the LLM needs to do its job. Imagine you’re sending a kid to school.
Agents: When LLMs Act Like Little Employees
LLMs are great at answering questions. But what if you want them to take action? Open a tool? Search the web? Email a client? You need an AI agent. Welcome to the 21/100 article in the “AI Explained Like I’m 10” series. An AI agent is like giving your LLM a to-do list and a toolbox. Instead of just generating text, it can: Make decisions Interact with software Plan steps and try again if something fails Think of it as turning a talker into a doer.
Redefining White-Collar Work: The Rise of AI Agents in the Modern Office
If someone told you five years ago that AI would be conducting performance reviews, approving refunds, and maybe even interviewing job candidates, you’d probably smile and get back to your inbox. Today, that inbox is shrinking, and it is not because the work is getting lighter. But because AI agents are doing it for you. Let’s talk about what’s really happening in offices right now. Not in some sci-fi future. Today.
Why GPT Sometimes Lies Like a 5-Year-Old đźŽ
We all know that one kid. You ask, “Did you break the vase?” They pause… and say, “No, a unicorn did.” GPT isn’t much different. It doesn’t lie out of malice. It lies like a kid making up a story — confidently, creatively, and completely convinced. That’s called a hallucination. Welcome to the 5/100 article in the “AI Explained Like I’m 10” series. It’s when GPT says something wrong, but with full confidence. It might invent a research paper. Cite a fake quote.
Two Sides of the Same Coin‐Baclofen‐Induced Intoxication in a Chronic Hemodialysis Patient
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AI Agents 101: Your First Step into Autonomous Systems
Arthi Rajendran · Follow 5 min read · -- Photo by YAROSLAV SAPRYKIN on Unsplash Hey there! 👋 Ready to dive into the world of AI agents? Don’t worry if terms like “autonomous systems” or “agent architectures” sound intimidating — we’re going to break everything down into bite-sized pieces that actually make sense.
AI in Space Exploration: Navigating the Cosmos
Arthi Rajendran · Follow 7 min read · -- Photo by Joel Filipe on Unsplash Space exploration has long captured the human imagination, but the vastness of the universe presents unparalleled challenges. Missions to distant planets, moons, and beyond require highly complex planning, precision, and adaptability to survive the harsh environments of space.
The Role of Generative AI in Journalism: Crafting Headlines and Articles
Arthi Rajendran · Follow 6 min read · -- Generative AI is transforming industries across the board, and journalism is no exception. Traditionally, writing news articles, generating headlines, and editing content have been entirely human-driven processes, reliant on the expertise, intuition, and editorial judgment of seasoned journalists.
Using Python and AI to Model Complex Biological Systems
And how the Nobel Prize in Chemistry was given to AI Arthi Rajendran · Follow 4 min read · -- Biological systems, ranging from molecular interactions within cells to entire ecosystems, are intricate networks that present significant modeling challenges. These systems involve a staggering number of variables, often interconnected in ways that are difficult to predict.
AI-Driven Creativity: How Generative Models are Redefining Intellectual Property
Arthi Rajendran · Follow 8 min read · -- Photo by Bermix Studio on Unsplash The rise of generative AI — artificial intelligence that can produce original content ranging from artwork and music to literature and designs — has opened up new possibilities in the creative fields. These models can write poems, compose music, generate visual art, and even design fashion, all based on complex algorithms that process existing data and synthesize it into something new.
The Intersection of AI and Mental Health: AI Models for Diagnosing and Treating Disorders
Arthi Rajendran · Follow 9 min read · -- The relationship between artificial intelligence (AI) and mental health is growing closer as researchers, clinicians, and developers recognize the potential for AI to revolutionize how mental health disorders are diagnosed and treated. Mental health disorders, which range from depression and anxiety to more complex conditions like schizophrenia and bipolar disorder, are often difficult to diagnose accurately.
Generative AI in Film: Script Writing and Scene Design Powered by AI
Arthi Rajendran · Follow 8 min read · -- Photo by Chris Murray on Unsplash The art of filmmaking has always been a blend of creativity and technical precision. Writers and directors craft intricate stories, while production teams bring them to life through set design, lighting, and cinematography. As the film industry evolves, generative AI is emerging as a tool that is reshaping these creative processes, particularly in script writing and scene design.
Python’s Role in Democratizing AI: Empowering Non-Programmers to Build AI Systems
Arthi Rajendran · Follow 8 min read · -- Photo by Arnaud Jaegers on Unsplash Artificial intelligence, once reserved for data scientists and engineers with specialized expertise, is now becoming accessible to a broader audience. At the heart of this shift is Python, a programming language that has simplified AI development to the point where even non-programmers can build and deploy AI systems.
The Role of AI in Solving Global Water Scarcity: Generative Models for Resource Management
Arthi Rajendran · Follow 7 min read · -- Photo by Salah Ait Mokhtar on Unsplash Water scarcity is one of the most critical challenges facing the world today. With increasing demand, climate change, and growing populations, access to clean, sustainable water resources is becoming increasingly limited. Traditional approaches to water management, which often rely on historical data and manual interventions, are proving inadequate in the face of rapidly changing environmental conditions.
Generative AI and the Future of Music: Python Models Composing the Next Hit Song
Arthi Rajendran · Follow 8 min read · -- Photo by Providence Doucet on Unsplash Music, long seen as the domain of human creativity, is undergoing a transformation. The rise of generative AI is pushing the boundaries of what’s possible in composition, allowing machines to create music that ranges from classical symphonies to the latest chart-topping hits.
From Text to Video: Python Tools for Creating AI-Generated Visual Narratives
Arthi Rajendran · Follow 6 min read · -- Photo by Vladislav Klapin on Unsplash Video content has rapidly emerged as a dominant form of storytelling, engaging audiences across various platforms. Now, with the rise of Generative AI, the process of turning written text into fully realized video narratives is undergoing a significant transformation.
AI for Sustainability: How Generative AI is Paving the Way for Zero Waste Solutions
Arthi Rajendran · Follow 7 min read · -- Photo by Milena Trifonova on Unsplash Sustainability has become one of the most pressing issues of our time. From climate change to resource depletion, the need for more sustainable practices is no longer just an option — it’s a necessity. Fortunately, technology is stepping up to the challenge.
Python and Quantum AI: Building Hybrid Models for Supercharged Learning
Arthi Rajendran · Follow 7 min read · -- Artificial intelligence is moving faster than ever, and the rise of quantum computing is poised to take it even further. Enter the era of Quantum AI — the merging of quantum computing and AI, which promises to supercharge machine learning models and revolutionize how we process data.
Generative AI and Fashion: Designing the Future of Wearable Technology
Arthi Rajendran · Follow 7 min read · -- Photo by Dom Hill on Unsplash Fashion has always been about pushing boundaries, from bold new silhouettes to experimental textiles. But today, the intersection of Generative AI and fashion is taking the industry to uncharted territory. As technology blends seamlessly with design, the future of fashion isn’t just about what we wear — it’s about how our clothes interact with us.
AI in Gaming: Python Libraries Powering the Future of Game Development
Arthi Rajendran · Follow 7 min read · -- The gaming industry is in the midst of a massive transformation, and at the heart of this revolution lies artificial intelligence (AI). From creating more dynamic in-game environments to enhancing player interactions with non-player characters (NPCs), AI is now an essential component of modern game development.
The Future of Chatbots: From Transactional to Conversational AI with Python and Generative AI
Arthi Rajendran · Follow 8 min read · -- Photo by BoliviaInteligente on Unsplash Chatbots have come a long way from their humble beginnings as simple text-based programs designed to handle basic customer queries. In 2024, they are evolving into fully-fledged conversational agents capable of holding meaningful, nuanced dialogues with users.
Leveraging Python in Building Explainable AI: The Future of Transparent Algorithms
Arthi Rajendran · Follow 6 min read · -- As artificial intelligence (AI) becomes an integral part of decision-making in industries ranging from healthcare to finance, one critical challenge has emerged: explainability. AI models, particularly deep learning systems, are often seen as “black boxes” — they generate predictions without revealing how they arrived at those conclusions. This opacity has led to concerns about trust, fairness, and accountability, especially in high-stakes applications.
The Science of Curiosity: Building Curiosity-Driven AI Systems in Python
Arthi Rajendran · Follow 6 min read · -- Curiosity has always been a driving force behind human innovation. It’s that innate desire to explore the unknown, to ask “what if?” that has led us to uncover some of the greatest scientific and technological breakthroughs. But what happens when we try to instill that same sense of curiosity in machines? Can we build AI systems that don’t just respond to instructions, but actively seek out new knowledge and solutions?
How Python Transformed from a Simple Tool to a Creative Powerhouse
Arthi Rajendran · Follow 5 min read · -- Photo by Veronica on Unsplash Python, once an unsung hero in the realm of automation, has transformed into a powerhouse, fueling today’s most creative breakthroughs in generative AI. This journey, from basic task automation to breathing life into creative digital systems, has been one marked by steady evolution and an unwavering commitment to simplicity. But how did we get here?
Cracking the Code of Future Climate Shifts: How Cutting-Edge Tech is Revolutionizing Environmental Predictions
Arthi Rajendran · Follow 4 min read · -- Photo by Li-An Lim on Unsplash Artificial Intelligence (AI) has emerged as a pivotal tool in the fight against climate change. While many focus on its applications in other sectors, AI’s transformative impact on climate science is redefining how we understand, predict, and adapt to environmental shifts.
Fine-Tuning LLMs in 2025: Is Retrieval-Augmented Generation the Better Bet?
The world of large language models (LLMs) is evolving at a blistering pace, and 2025 promises to be a transformative year for both fine-tuning and retrieval-augmented generation (RAG). Arthi Rajendran · Follow 7 min read · -- The world of large language models (LLMs) is evolving at a blistering pace, and 2025 promises to be a transformative year for both fine-tuning and retrieval-augmented generation (RAG).
Your 5-step Guide to implementing MLOps
A large number of ML models are being developed each day by individuals & Industries worldwide. But what percentage of them become usable applications? Many data scientists at the beginning of their careers struggle with the concept of "productionizing" one's work. This article will serve as a simple guideline for taking that first step into the world of MLOps! Follow these 5 steps to get started with MLOps & push those piled-up models into production.
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