In this article, I am going to review and implement the deep learning paper titled “CBAM: Convolutional Block Attention Module” by Woo et al. [1]. As the name suggests, this is essentially a block we can attach to a CNN-based model to enhance feature quality by performing an attention mechanism. Despite the name attention, it…

Retrieval-Augmented Generation (RAG) is the most widely used LLM use case across organizations. By vectorizing documents and retrieving semantically similar chunks at query time, RAG mitigates hallucinations, grounds responses and bypasses static knowledge cutoffs imposed by model pretraining. However, in a real-life scenario, standard vector-based RAG runs into limitations for complex queries, such as those…

This week, I moderated a panel on world models at the All In conference (no relation to the podcast), and it gave me a chance to dig into one of the most mysterious corners of the AI world. The big players in the space are Yann LeCun’s AMI Labs and Fei-Fei Li’s World Labs —…

We’re seeing a big debate over AI safety and a potential slowdown, as Anthropic CEO Dario Amodei recently published a plan to “pace the frontier,” while Nvidia CEO Jensen Huang has publicly echoed President Donald Trump’s claims that the AI backlash is a hoax and regulation is unnecessary.