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Projected change in population for every European country between 2025 and 2100.

🇪🇺 Europe: -152.2M (-20%)

🇺🇦 Ukraine: -23.8M
🇮🇹 Italy: -23.8M
🇵🇱 Poland: -18.8M
🇷🇺 Russia: -17.6M
🇪🇸 Spain: -14.8M
🇩🇪 Germany: -13.1M
🇷🇴 Romania: -8.1M
🇧🇾 Belarus: -4.6M
🇬🇷 Greece: -3.7M
🇧🇬 Bulgaria: -3.2M
🇷🇸 Serbia: -3.0M
🇨🇿 Czechia: -2.4M
🇭🇺 Hungary: -2.2M
🇸🇰 Slovakia: -2.1M
🇧🇦 Bosnia & Herzegovina: -1.8M
🇦🇹 Austria: -1.7M
🇭🇷 Croatia: -1.7M
🇵🇹 Portugal: -1.7M
🇱🇹 Lithuania: -1.6M
🇦🇱 Albania: -1.6M
🇲🇩 Moldova: -1.5M
🇫🇮 Finland: -1.0M
🇲🇰 North Macedonia: -950.8K
🇱🇻 Latvia: -928.2K
🇳🇱 Netherlands: -839.3K
🇧🇪 Belgium: -697.8K
🇽🇰 Kosovo: -579.4K
🇪🇪 Estonia: -518.7K
🇸🇮 Slovenia: -485.0K
🇲🇪 Montenegro: -306.7K
🇳🇴 Norway: -209.5K
🇲🇹 Malta: -185.5K
🇩🇰 Denmark: -139.3K
🇮🇸 Iceland: -35.7K
🇦🇩 Andorra: -35.7K
🇮🇪 Ireland: -21.9K
🇸🇲 San Marino: -2.4K
🇲🇨 Monaco: +9.1K
🇱🇮 Liechtenstein: +3.5K
🇱🇺 Luxembourg: +67.5K
🇨🇭 Switzerland: +158.7K
🇸🇪 Sweden: +710.3K
🇫🇷 France: +1.8M
🇬🇧 UK: +4.8M

Data is sourced from the UN World Population Prospects 2024.

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Finally Europe begins the understand that AI is more than just a trend - its a technological revolution and necessity if we want to stay on top as a nation.

The only two questions are:

- Are we too late or do we still have a chance to catch up?

- And also how exactly we want to participate. A separate frontier model will probably be unnecessary. AI infrastructure and hyperscalers in the EU, on the other hand, will be necessary

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Artificial Intelligence isn't easy!

It’s the cutting-edge field that enables machines to think, learn, and act like humans.

To truly master Artificial Intelligence, focus on these key areas:

0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.


1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.


2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.


3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.


4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).


5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.


6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.


7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.


8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.


9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms.



Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.

💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.

⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems!

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