Artificial Intelligence is the ability of a computer, or a robot controlled by a computer to mimic human intelligence, and it is rapidly changing in the world.

Subfields of artificial intelligence (AI)

→ Machine learning (ML)
→ Deep learning (DL)
→ Natural language processing (NLP)
→ Computer vision

Nowadays, AI is very familiar to most people. It is in everything we use today. for example, automated tasks, AI chatbots, image searching, generate content. AI is still in its early stages of development, but it has the potential to revolutionize the industry. As AI technology continues to improve, we can expect to see more innovative ways to use AI in industry.

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An Artificial Neural Network is a computational model miming the process of biological networks of neurons in the human brain. Also, we can say Artificial Neural Network is a learning algorithm that models the input and output relationships.

An Artificial Neural Network primarily consists of three layers,

Input layer -Accepts inputs in several different formats. Each node in the input layer represents a feature or attribute of the data.

Hidden layer -This is an intermediate layer between the input and output layers. The network typically has one or more hidden layers. It performs all the calculations to find hidden features and patterns.

Output layer -This layer produces the output after the input undergoes a series of transformations using the hidden layer.

Advantages of ANNs,

> Parallel processing
> Work with incomplete knowledge
> Memory distribution

Disadvantages of ANNs,

> Ensuring proper network structure
> Hardware dependence
> Difficulty in showing the issue to the network

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Agentic AI vs AI Agents

What is Agentic AI? 🔎

Agentic AI refers to AI systems that act autonomously like an agent. This means they can make decisions, plan actions, and carry them out with minimal human intervention to achieve a specific goal. These systems don’t just react to inputs. they:

• Set goals
• Adapt dynamically
• Use tools, APIs, or even other agents
• Reflect and improve over time

Agentic AI combines elements from;

•Reinforcement Learning
•Planning
•Multi-modal models
•Memory & reflection loops

Think of them as mini digital workers that can think, act, learn, and sometimes even collaborate. 🧑💻

What Are AI Agents? 🔎

Put simply, AI agents are the building blocks of Agentic AI. An AI agent is any autonomous or semi-autonomous system that:

• Perceives the environment (via input)
• Decides what to do (via reasoning, rules, or learning)
• Takes action (via APIs, automation, or output)

Examples include:

• A shopping bot that books flights & hotels
• Customer support agents
• A research agent that finds, summarises and organises information

Real-world frameworks;

• LangChain Agents
• AutoGPT
• Meta’s CICERO
• OpenDevin

The hidden risk🔥

Agentic AI is pushing AI from being just a tool to becoming a true collaborator. We’re moving toward systems that can handle multi-step tasks, manage uncertainty, and even work in multi-agent ecosystems. This has huge potential for;

• Autonomous software engineering
• Self-healing infrastructure
• Automated research assistants
• Personalised digital workers

#AgenticAI #AIAgents #AutonomousAI #FutureOfAI #AIInnovation #ArtificialIntelligence #MultiAgentSystems #ReinforcementLearning #AIAutomation #TechTrends #MachineLearning #AIRevolution #GenerativeAI #DigitalWorkers #InfinityAI

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Natural Language Processing (NLP)

Natural Language Processing is a field of Artificial Intelligence (AI). It allows computers to understand, interpret, and generate human language. NLP combines computer science, linguistics, and machine learning. It helps machines work with text, speech, and other forms of natural language. NLP is a technology. It enables computers to read, understand, and respond to human language. For example, when you use a voice assistant like Siri or Google Assistant, NLP works behind the scenes. It processes your words and gives a meaningful response.

Importance of NLP

• Text translation (e.g., Google Translate)
• Speech recognition (e.g., voice typing)
• Sentiment analysis (e.g., detecting feelings in reviews)
• Chatbots and virtual assistants

How NLP Works

• Text preprocessing
• Tokenization
• Part-of-Speech (POS) tagging
• Named Entity Recognition (NER)
• Parsing
• Semantic Analysis

NLP Techniques

• Bag of Words (BoW)
• Word Embeddings
• Deep Learning Models

Applications of NLP

• Machine Translation
• Speech Recognition
• Sentiment Analysis
• Text Summarization

#NLP #NaturalLanguageProcessing #AI #MachineLearning #DeepLearning #LanguageAI #NLPBasics #AIEducation #TechLearning #ArtificialIntelligence #MLCommunity #NLPApplications #FutureOfAI #TextProcessing #InfinityAI

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Result - Token දෙකක් දුරින් තිබුණත් attention Mechanism එකට ඒවා සෘජුවම එකිනෙක සම්බන්ධ කරන්න පුලුවන්. නිසා Transformer architecture එක long-range dependencies හොඳින් හසුරුවනවා. මේක තමා කලින් Transformer layers with attention එකේදී පැහැදිලි කරේ.

5) Training & Understanding: where “understanding” comes from

Pretraining objective ⛈ - ප්‍රධානවම Tokenization & Next word prediction. LLM එක බිලියන ගණනක් Text කියවලා ඒවා අතර තියෙන statistical pattern ඉගෙන ගන්නවා.

Why it seems like “understanding” 🧠 - LLM එක දත්ත අතර ඇති සහසම්බන්ධතා (correlations) ඉගෙන ගන්නවා ( වාක්‍ය ඛණ්ඩ, කරුණු, තර්කන රටා වගේ හැම දෙයක්ම). ඊට පස්සේ මේ ඉගන ගත්ත ‍රටා පුහුණු දත්තවල පුනරාවර්තනය වෙනවා. (knowledge reasoning)

Limitations of that understanding ⏱️ - මේ statistical pattern හඳුනාගැනීම සවිඥානික හෝ පදනම් වූ තර්කනයක් වෙන්නේ නෑ. මේක නිසා මේවත් එක්ක ගනුදෙනු කරද්දි නිවැරදිව සන්නිවේදනය නොකරද්දී අපිට ව්‍යාජ හෝ විකාර තොරතුරු ලැබෙනවා. IT terminology වලින් කියනවා නම්, අපි ඒකට කියන්නේ “hallucination” කියලා. 😅 "Give respect, get respect." වගේ තමයි.

Example for LLMs - OpenAI ChatGPT, Google Gemini, Anthropic Claude, Meta LLaMA (Large Language Model Meta AI), Google T5.

#LLM #LargeLanguageModels #AIExplained #NLP #DeepLearning #MachineLearning #Transformers #NeuralNetworks #AICommunity #TechEducation #ArtificialIntelligence #MLBasics #LanguageModels #FutureOfAI #InfinityAI

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🔥 Big news for multilingual NLP! Check out this new paper on MMBERT

🌐🤖 Summary: The PDF describes how a modern encoder model (MMBERT) was trained using massive multilingual data. It shows how it achieves better results in language understanding and translation tasks across many languages.

#MMBERT #MultilingualNLP #NLPResearch #AIModels #DeepLearning #BERT #LanguageAI #MachineLearning #ArtificialIntelligence #NLPCommunity #MultilingualAI #TechNews #InfinityAI