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What Is NLP and How Does It Affect Your Daily Life (Without You Noticing)?

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What Is NLP and How Does It Affect Your Daily Life Introduction Natural Language Processing, or NLP for short, sounds like something that only computer scientists or AI researchers deal with. But the truth is, NLP is quietly working behind the scenes in your everyday life—often without you even realizing it. From the moment you unlock your phone to the time you scroll through your emails or ask a voice assistant to play your favorite song, NLP is there, helping machines understand and respond to human language. In this blog, we’ll break down what NLP really is, in simple terms, and show you some everyday situations where it’s making your life easier, faster, and sometimes even smarter—without needing you to be a tech expert. What is NLP in simple words Natural Language Processing is a branch of artificial intelligence that helps computers understand, interpret, and respond to human language. Unlike programming languages, which follow strict rules, human language is messy, emotional...
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Ethics in AI: Simple Scenarios You Can Discuss With Friends

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Ethics in AI Introduction As artificial intelligence becomes part of our daily lives, from recommendation systems to smart assistants, a quiet but important conversation is growing louder: how should AI behave, and who decides? Ethics in AI isn't just a topic for researchers or policy makers. It’s a real-life issue that affects anyone using or interacting with modern technology. The best way to understand and explore AI ethics is through relatable situations that make you think. In this blog post, we’ll walk through simple, real-world scenarios you can easily bring up with friends or coworkers. These conversations not only help build awareness but also prepare us for decisions we might face sooner than expected. Should an AI assistant lie to protect someone’s feelings? Imagine someone asks their AI, “Do I look good in this outfit?” and the outfit clearly doesn’t fit well. Should the AI tell the truth, or should it be polite and say, “Yes, you look great,” even if it’s not t...
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Avoiding Hallucinations: How to Verify AI Output Like a Pro

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AI Hallucinations Introduction As more people begin using AI tools like ChatGPT, one common problem that pops up is hallucination. In simple terms, a hallucination is when the AI gives you an answer that sounds right but is completely made up. It might cite a fake article, invent a quote, or present false facts in a confident tone. This can lead to wrong decisions, misinformation, and confusion—especially if the user fully trusts the response without checking it. In this post, we’ll walk through practical steps you can take to verify the information generated by AI tools. Whether you’re using AI to write blogs, summarize articles, or explain technical concepts, these tips will help you become more aware and responsible when using AI-generated content. 1. Double-Check with Trusted Sources The first and most important step is to always verify the information with a trusted source. If AI says something that sounds new or surprising, don’t just assume it’s true. Google it. Cross-check ...
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AI in Real Life: 8 Surprising Use Cases You Can Try Today

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AI in Real Life Introduction When most people hear the term "artificial intelligence," they imagine futuristic robots or highly technical tools reserved for experts. But the reality is that AI is already part of our everyday lives in ways that are both useful and surprisingly simple. In this blog post, we’ll explore five practical and often overlooked use cases of AI that you can try today without needing any advanced technical knowledge. These examples are accessible, realistic, and designed to help you understand how AI is not just for scientists and engineers, but for everyday users like you. Automating Email Responses One of the most immediate and useful applications of AI is in managing your email inbox. Many AI tools, including features built into Gmail or Microsoft Outlook, now offer intelligent suggestions for replies based on the content of incoming messages. Some tools even go further by automatically drafting full responses or sorting your emails by urgency ...
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What are some ethical implications of Large Language models?

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Ethical consideration in LLMs In this article, we're going to address the ethical challenges surrounding the usage of large language models, including potential biases and privacy issues.  Bias in AI system: AI systems, particularly, will deliver biased results. There's no other way to get around it. Search engine technology, for example. It's not neutral, processes a large amount of data, and prioritizes results based on clicks and user preferences. So, a search engine can become essentially an echo chamber. And you can see that it will uphold certain biases of the real world, potentially because looking at the clicks and the user preferences of a majority of users using that search engine. And this is how we can come up with prejudices and stereotypes that are furthered online because of these systems that are in place. So, when we talk about bias in AI, it could be something like as simple as a gender bias, right? So, gender biases should be avoided, but it can't sho...
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Introduction to the fine tuning in Large Language Models

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Fine tuning in LLM In this article, we're going to delve into the techniques used to adapt a generic pre-trained model for a specific task or application. Now, pre-trained language models can do really impressive stuff just immediately off the shelf, including things like text generation, summarization, and even providing levels of code. But LLMs are not one-size-fits-all as you can imagine and maybe I want to specialize it even further after the model has been trained. I'd like to make it really good at answering questions based on certain topics such as medicine or travel or code or something. This is what the idea of fine-tuning comes in.  Methods used in Fine tuning: Fine-tuning a large language model refers to the process of taking a pre-trained language model or a model that's already been trained on a large dataset and further training it on a smaller task-specific dataset. Now this process will allow the model to adapt and specialize its capability. And if we take t...
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What are the impacts of data size and its quality on LLMs?

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Data on LLMs In this article, I'm going to explore the impact of data size and quality and how it can affect a large language model. So, we understand that these large language models are really good at text generation and they're great at being just deep learning models, they're transformers, right? At their very core, they're just like any other AI or machine learning model and they just happen to be good at natural language generation. Now these models, since they're able to learn the complexity of human language, their potential is really immense. You can almost tell that these applications are going to be developed for all sorts of things.  Understanding Data Quality: Now, even depending on let's say if we're trying to do something like text generation or text image generation or image captioning or all sorts of sectors from finance to telecom and healthcare, they're all going to be using these tools. Then the question is, is, well what data should ...
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