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29 articles
This explainer explores how AI-driven smartphone optimization works and why specific settings significantly impact system performance and user experience. It covers machine learning architectures, data collection, and privacy implications.
Jeff Bezos-backed Flourish is pursuing a revolutionary approach to AI development by studying real neurons to decode the brain's core computational principles, with $500 million in funding and a $2.5 billion valuation.
This article explains the technical and ethical challenges behind Tesla's FSD system, focusing on training data quality, human trust, and the implications for autonomous driving development.
Learn to implement and experiment with fundamental AI concepts including neural networks, transformers, and attention mechanisms through hands-on coding exercises.
Learn how DiffusionBlocks, a new AI training method, breaks neural networks into independent modules for faster and more efficient training. This breakthrough could revolutionize how we build and improve AI systems.
Learn to build neural networks for object detection similar to Tesla's Full Self-Driving software using TensorFlow and OpenCV.
This article explains the mathematical foundations and mechanisms of neural networks, the core architecture underlying modern AI systems. You'll learn how neural networks process information through layers of interconnected nodes and how they learn from data.
This article explains how transfer learning—a key AI concept—enables robots to rapidly adapt to new tasks by reusing pre-trained models, as demonstrated by the Honor D1 humanoid robot.
This explainer explains how superposition helps large AI models work better by storing and connecting information in overlapping ways, making them more powerful and creative.
This article explains the concept of inference optimization in AI, why it's critical for modern AI deployment, and how companies like Nebius are investing heavily in this area.
Learn to build and analyze a simple AI system using PyTorch, understanding the foundational technologies behind AI safety and alignment that were central to Musk's testimony.
A detailed tutorial explores Equinox, a JAX-native neural network library, highlighting its use of PyTrees, filtered transforms, and stateful layers for efficient model development.
