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Agents AI

What Are the Types of AI Agents? Overview and Examples

Reviews, Technology /

Introduction: The Diversity of AI Agents in Modern Technology AI agents are programs or systems that can independently make decisions and perform tasks based on data from their environment. Modern artificial intelligence uses various types of agents that differ in complexity, scope, and decision-making methods. This makes it possible to create solutions tailored to specific […]

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AI Agent

What are AI Agents? A Simple Explanation for Beginners

Uncategorized /

Introduction: Why is it worth learning about AI Agents? IArtificial intelligence (AI) is increasingly present in our daily lives—from voice assistants in smartphones, through movie recommendations on streaming platforms, to smart home management systems. One of the key concepts in the world of AI is so-called AI agents. Understanding what AI agents are and how

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AI (XAI)

Explainable AI: How to Understand Decisions Made by Artificial Intelligence Models

Technology /

Introduction to Explainable AI (XAI) What is Explainable AI and Why is it Important? Explainable AI (XAI) is a field focused on developing methods and tools that make it possible to understand how and why AI models make certain decisions. As complex models such as deep neural networks or ensemble learning algorithms become more popular,

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AI Futures

Unveiling AI’s Future: Transformers vs. RNNs in Machine Learning

Reviews, Technology /

Introduction to AI and Machine Learning Landscape The realm of Artificial Intelligence (AI) and Machine Learning (ML) is a rapidly evolving universe, characterized by the continuous emergence of revolutionary paradigms, algorithms, and methodologies aimed at solving complex problems and enhancing the capabilities of machines to learn from and interpret data. At the heart of this

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AI Model

Reinforcement Learning in Industrial Applications: Case Studies and Implementations

Uncategorized /

Introduction What is Reinforcement Learning? Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment. Unlike supervised learning, which relies on labeled data, RL is based on a system of rewards and penalties. The agent takes actions in the environment, receives feedback in the

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