An article discussing the concept of monosemanticity in LLMs (Language Learning Models) and how Anthropic is working on making them more controllable and safer through prompt and activation engineering.
A deep dive into the theory and applications of diffusion models, focusing on image generation and other tasks, with examples and PyTorch code.
An article discussing the use of Deep Q-Networks (DQNs) in reinforcement learning, which combines the principles of Q-Learning with function approximation capabilities of neural networks to address limitations of traditional Q-learning such as scalability issues and inability to handle continuous state and action spaces.
This article explains the concept of abstraction in neural networks and its connection to generalization. It also discusses how different components in neural networks contribute to abstraction and reveals an interesting duality between abstraction and generalization.
Stay informed about the latest artificial intelligence (AI) terminology with this comprehensive glossary. From algorithm and AI ethics to generative AI and overfitting, learn the essential AI terms that will help you sound smart over drinks or impress in a job interview.
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