How history’s biggest tech bubble explains where AI is headed next The post The AI Bubble Will Pop — And Why That Doesn’t Matter appeared first on Towards Data Science.
Google has launched Gemini 3 and claims it to be the most intelligent model yet, with the best reasoning, indicating significant progress in the use of AI in different modes. While previously, Gemini 3 had only restricted itself to mere language interactions, it has now entered the new era where AI not only comprehends commands but completes the entire task. This new feature is nothing short of a miracle for the developers who have been waiting for such […]
Author(s): AI Rabbit Originally published on Towards AI. Agentic Era If your architecture still looks like “User Query Vector DB LLM,” you aren’t building an AI application; you’re building a hallucination engine. The “naive” RAG era where we just dumped PDFs into Pinecone and prayed for the best is officially over. Here is the technical reality of RAG in 2025.The article discusses the evolution of Retrieval-Augmented Generation (RAG) pipelines, emphasizing the transition from outdated linear architectures to more […]
For decades, it’s been known that subtle chemical patterns exist in metal alloys, but researchers thought they were too minor to matter — or that they got erased during manufacturing. However, recent studies have shown that in laboratory settings, these patterns can change a metal’s properties, including its mechanical strength, durability, heat capacity, radiation tolerance, and more. Now, researchers at MIT have found that these chemical patterns also exist in conventionally manufactured metals. The surprising finding revealed a […]
Google Beam, our first true-to-life 3D video communication platform, made great progress in 2025.
Understanding AI in 2026 — from machine learning to generative models The post Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained appeared first on Towards Data Science.
Home Table of Contents KV Cache Optimization via Tensor Product Attention Challenges with Grouped Query and Multi-Head Latent Attention Multi-Head Attention (MHA) Grouped Query Attention (GQA) Multi-Head Latent Attention (MLA) Tensor Product Attention (TPA) TPA: Tensor Decomposition of Q, K, V Latent Factor Maps and Efficient Implementation Attention Computation and RoPE Integration KV Caching and Memory Reduction with TPA PyTorch Implementation of Tensor Product Attention (TPA) Tensor Product Attention with KV Caching Transformer Block Inferencing Code Experimentation Summary […]
Every year, global health experts are faced with a high-stakes decision: Which influenza strains should go into the next seasonal vaccine? The choice must be made months in advance, long before flu season even begins, and it can often feel like a race against the clock. If the selected strains match those that circulate, the vaccine will likely be highly effective. But if the prediction is off, protection can drop significantly, leading to (potentially preventable) illness and strain […]
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