Browsing: AI Guides

Use Claude for scientific research by giving it a bounded question, identifiablehe files it must return. Check then

This week, world leaders descended on Manhattan for the UN General Assembly. It’s also New York Climate Week—investors, policymakers, advocates, and journalists are colliding at panels, talks, and fancy dinners.

Benjamin Nweke described a failure that took him three weeks to notice. He had turned on Structured Outputs for a pipeline that parsed payment confirmation messages into transaction records, and his reconciliation job started flagging a small, steady stream of mismatches — a couple of percent of a week’s volume. Not crashes. Not malformed rows.…

Retrieval-augmented generation (RAG) answers questions using information retrievedfrom a set of documents. That document collection is called a corpus. A typical evaluationsends a well-written question to a tidy corpusand checks whether the retriever returns the correctpassage.

Shubnam was packing boxes for a move into a new home when their friend sent them an Instagram video. The footage had only been up for a few hours, but it was days old, recorded at a Delhi protest this spring against a bill that would have narrowed the legal recognition for transgender people in…

When José Morales Bernal crossed the border into the United States on April 8, 2024, the day before his 32nd birthday, it should have triggered a chain of technological alerts and human responses. 

MIT Technology Review today published our investigation into how many people have died near the “virtual wall” of surveillance towers that the US government has installed along the US-Mexico border.

Spirit Airlines collapsed. It started auctioning its assets. What would you normally expect to see in an airline’s bankruptcy auction? Aircraft, equipment, software, maybe even airport slots too. But what made the headlines was their data. Not any data- their operational data, such as emails and Teams messages.

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…