#CausalAI | Aiwa-AI

This post explores the groundbreaking AI research areas emerging from the shadow of deep learning, from AI that understands cause and effect to systems that combine the best of human-like logic and machine-like pattern matching. In this post, we explore: 🤔 The limitations of our current deep learning models. 🔗 Causal AI: The quest to teach machines the concept of "why." 🤝 Neuro-Symbolic AI: The powerful hybrid of pattern recognition and logical reasoning. 💧 Liquid Neural Networks & Other Novel Architectures: AI that adapts and evolves in real-time. ✍️ How these new paradigms are essential for building the trustworthy and capable AI of the future

Beyond Deep Learning: What Groundbreaking AI Paradigms Will Shape Humanity's Next Chapter?

🧠 From 'Why' to 'What If': Exploring the Next Wave of Artificial Intelligence For the past decade, one paradigm has reigned supreme in the world of AI: Deep Learning . This powerful technique, which uses vast neural networks to recognize patterns in data, has given us everything from language models like ChatGPT to image generators and medical diagnostics. But as powerful as deep learning is, it has fundamental limitations. It's a master of correlation but a novice at causa

⚛️ AI's Future Frontiers