Artificial Intelligence
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Сеть:📈 Аналитический обзор Telegram-канала Artificial Intelligence
Канал Artificial Intelligence (@artificial_intelligence_com) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 72 419 подписчиков, занимая 1 719 место в категории Технологии и приложения и 4 348 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 72 419 подписчиков.
Согласно последним данным от 30 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 405, а за последние 24 часа — -37, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 6.63%. В первые 24 часа после публикации контент обычно набирает 1.94% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 4 801 просмотров. В течение первых суток публикация набирает 1 407 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 11.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, linkedin, linux, udemy, 040k|.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“🔒 Welcome Artificial Intelligence Channel
Buy ads: https://telega.io/c/Artificial_Intelligence_COM”
Благодаря высокой частоте обновлений (последние данные получены 31 августа, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
72 419
Подписчики
-3724 часа
-1227 дней
+40530 день
Архив постов
72 410
📱Artificial Intelligence and Machine Learning
📱Machine Learning and AI Foundations: Clustering and Association
72 410
🔅 Machine Learning and AI Foundations: Clustering and Association
📝 Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.
🌐 Author: Keith McCormick
🔰 Level: Intermediate
⏰ Duration: 3h 33m
📋 Topics: Machine Learning, Artificial Intelligence
🔗 Join Artificial Intelligence and Machine Learning for more courses
72 410
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022k| 🔰 The Coding Space
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🔰 2hrs on top & 8hrs in channel!
72 410
📱Artificial Intelligence and Machine Learning
📱Choosing the Right ML Approach for Your Business Case
72 410
🔅 Choosing the Right ML Approach for Your Business Case
📝 Learn the system components of machine learning (ML), their function in the AI ecosystem, and how to choose the best approach for your business pipeline.
🌐 Author: Lyron Andrews
🔰 Level: Intermediate
⏰ Duration: 1h 42m
📋 Topics: Machine Learning, Artificial Intelligence
🔗 Join Artificial Intelligence and Machine Learning for more courses
72 410
🔅 PREMIUM CHANNELS
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🔰 Web Development
-◦-◦--◦--◦-◦--◦--◦-◦--
221k| 🔰 Linkedin Learning
138k| 🔰 Udemy Premium
133k| 🔰 Web Development
-◦-◦--◦-
117k| 🔰 Python 3
100k| 🔰 JavaScript Training
088k| 🔰 Machine Learning
-◦-◦--◦-
067k| 🔰 Artificial Intelligence
067k| 🔰 Data Analysis and Databases
064k| 🔰 React and NextJs
-◦-◦--◦-
061k| 🔰 Linux and DevOps
049k| 🔰 100 Days of Python
047k| 🔰 OpenAI Mastery
-◦-◦--◦-
047k| 🔰 Business and Finance
044k| 🔰 Best Telegram Channels
040k| 🔰 Udemy Learning
-◦-◦--◦-
040k| 🔰 Zero to Mastery
040k| 🔰 Mobile Apps
035k| 🔰 Linkedin Learning Courses
-◦-◦--◦-
035k| 🔰 Codedamn Courses
034k| 🔰 React 101
031k| 🔰 Crypto Tutorials
-◦-◦--◦-
030k| 🔰 Coding Interview
025k| 🔰 Telegram's Shorts
022k| 🔰 Linux Training
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022k| 🔰 The Coding Space
-◦-◦--◦--◦-◦--◦--◦-◦--
🔰 Add Your Channel
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🔰 2hrs on top & 8hrs in channel!
72 410
🔗 Machine Learning, Simplified
Ever wondered what Machine Learning really means and how it impacts your everyday life?
ML is not just about fancy algorithms—it's about how machines learn like humans to make decisions, automate tasks, and personalize your digital experiences. 🔍✨
Here are real-world use cases you interact with daily:
🔹 Generative AI → ChatGPT, Midjourney
🔹 Speech Recognition → Siri, Alexa
🔹 Computer Vision → Face ID, Self-driving cars
🔹 RPA & Stock Trading Bots → Automating workflows & finance!
72 410
🔅 GraphRAG Essential Training
📝 Learn how to build robust AI applications by creating knowledge graphs for retrieval-augmented generation (RAG) in Python using LangChain and Neo4j.
🌐 Author: Dr. Clair Sullivan
🔰 Level: Intermediate
⏰ Duration: 1h 39m
📋 Topics: Retrieval-Augmented Generation, Knowledge Graph Augmentation, Knowledge Graphs
🔗 Join Artificial Intelligence and Machine Learning for more courses
72 410
🔰 Why Python is a Must-Have Skill?
If you're diving into programming or data science, mastering Python is essential! Its versatility and simplicity make it the go-to language across industries.◆ Powerful and Versatile From web development to data analysis, Python’s broad libraries and frameworks adapt to almost any project. ◆ Data-Driven Python, combined with libraries like Pandas and NumPy, allows you to analyze and manipulate datasets efficiently. ◆ Automate the Boring Stuff Automate repetitive tasks, streamline workflows, and boost productivity with Python’s easy-to-use scripts. ◆ AI and Machine Learning With frameworks like TensorFlow and Scikit-learn, Python is at the forefront of AI, enabling you to build predictive models and explore deep learning. ◆ Readable and Beginner-Friendly Python’s simple syntax makes it easy to learn, even for beginners, without sacrificing power and functionality. ◆ Community Support Backed by a massive global community, Python is constantly evolving, with new libraries and resources available at your fingertips.
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+1
⚡️ Agentic Reward Modeling is a fresh project from THU-KEG, the goal of which is to rethink the approach to training agent systems.
This tool aims to develop reward methods where the agent does not simply follow commands, but learns to understand its actions in the context of more complex tasks and long-term goals.
Key Features:
- Instead of standard RL methods, where rewards often depend on pre-set criteria, the emphasis here is on developing more complex strategies that adapt to changing environments and goals.
- The tool helps model rewards in such a way that the agent can independently adjust its actions, learn from mistakes and, ultimately, demonstrate more “human” decision making.
- Developers can use this approach in multi-agent systems and complex tasks where dynamic assessment of the effectiveness of actions is important.
This tool is interesting not only for its theoretical potential, but also for its practical applications in the field of creating more autonomous and intelligent systems. Agentic Reward Modeling opens up new possibilities for studying agents that can learn in real time, which makes it promising for further research and integration into real applications.
▪️Paper: https://arxiv.org/abs/2502.19328
▪️Code: https://github.com/THU-KEG/Agentic-Reward-Modeling
