Mohit Goyal

When I first encountered Transformer-Squared (Transformer²) technology, I was skeptical. Could yet another iteration of language models change the game? After diving deep into the research papers and speaking with AI engineers implementing these systems, I’m convinced we’re witnessing a genuine paradigm shift in how AI adapts to specific tasks. Beyond Static Models: AI That Evolves in Real-Time Imagine having a conversation with an AI that subtly shifts its entire thinking pattern when you move from casu...

Introduction Titans represent a significant leap forward in artificial intelligence, particularly in addressing the challenge of long-term memory. Unlike traditional models like Transformers, which struggle with extensive historical contexts, Titans integrate short-term attention mechanisms with robust, trainable long-term neural memory modules. This hybrid architecture improves complex tasks' efficiency, scalability, and accuracy. Titans achieve this by mimicking human-like memory systems, incorporating...

Introduction Reinforcement learning (RL) has emerged as a powerful paradigm for training agents to make decisions in complex environments, achieving remarkable success in game-playing and robotics. However, the journey towards more general and capable AI requires that we tackle the challenge of scaling RL algorithms. The ability to effectively scale both search and learning processes is crucial for unlocking the full potential of RL, enabling it to address increasingly complex, real-world problems. This ...