Machine learning algorithms and models
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When your bank blocks a suspicious transaction you didn't make, when a factory's AI predicts machine failure before it happens, or when a security system detects someone using a printed photo instead of their real face — that's Anomaly Detection at work. It's the AI technology that finds the needle in the haystack, the one unusual pattern among millions of normal ones.
When a customer writes "สินค้าดีมาก ส่งเร็ว ประทับใจ!" (Great product, fast delivery, impressed!) in a review, how does your business automatically know this is positive feedback? When thousands of social media comments flood in after a product launch, how can you quickly understand whether people love it or hate it? The answer is Sentiment Analysis — one of the most practical applications of AI in business today.
Every second, the world generates an unimaginable amount of data. Every Google search, every social media post, every online purchase, every sensor reading — it all adds up. In fact, we create approximately 2.5 quintillion bytes of data every single day. This massive flood of information is what we call Big Data, and learning how to harness it has become one of the most valuable skills in the modern business world.
When your phone recognizes your face in a split second, when Google Translate converts Thai to English almost perfectly, or when ChatGPT generates human-like responses — that's Deep Learning at work. It's the technology behind virtually every major AI breakthrough of the last decade, and it's transforming what computers can do.
Every time Netflix recommends a movie you actually want to watch, or your email filters out spam automatically, or your phone recognizes your face to unlock — that's Machine Learning at work. It's the technology that enables computers to learn from experience rather than being explicitly programmed for every task. And it's revolutionizing virtually every industry.
Humans naturally understand the world through multiple senses - we see images, hear sounds, read text, and watch videos simultaneously. But until recently, AI systems were limited to processing only one type of data at a time. Enter Multimodal AI - artificial intelligence that can understand and work with multiple types of data together, just like humans do.
Generative AI is revolutionizing how we work, create, and solve problems. From writing emails to generating images, this technology is now accessible to everyone. But what exactly is it, and how can you use it? This guide will explain everything you need to know in simple terms.
You've probably used ChatGPT or similar AI assistants. You ask a question, get an answer, and that's it. But what if AI could do more? What if it could plan, execute multi-step tasks, use tools, and work autonomously to achieve your goals? Welcome to Agentic AI - the next evolution of artificial intelligence.
Everyone talks about Large Language Models (LLMs) like GPT-4 and Claude. But there's a growing movement toward their smaller, more efficient cousins: Small Language Models (SLMs). These compact AI models are revolutionizing how we deploy AI in real-world applications. Let's explore what they are and why they matter.
By Dr. Kobkrit Viriyayudhakorn, CEO & Founder, iApp Technology
You've probably heard of ChatGPT, Claude, or Google's Gemini. These are all powered by something called a Large Language Model or LLM. But what exactly is an LLM? How does it work? And why should you care?
In this beginner-friendly guide, we'll explain everything you need to know about LLMs in simple terms - no PhD required!
