Jeff Dean discusses the potential of merging Google Search with large language models (LLMs) using in-context learning, emphasizing enhanced information processing and contextual accuracy while addressing computational challenges.
This tutorial demonstrates how to perform semantic clustering of user messages using Large Language Models (LLMs) by prompting them to analyze publicly available Discord messages. It covers methods for data extraction, sentiment scoring, KNN clustering, and visualization, emphasizing faster and less effort-intensive processes compared to traditional data science approaches.
The New York Times is reportedly encouraging newsroom staff to use AI tools to suggest edits, headlines, and interview questions. The outlet has introduced a tool called Echo for summarizing articles and briefings, with editorial guidelines in place to ensure AI is used responsibly. The Times emphasizes that journalism remains under human control, with AI serving as a support tool.
> "Alongside Echo, other AI tools apparently greenlit for use by The Times include GitHub Copilot as a programming assistant, Google Vertex AI for product development, NotebookLM, the NYT’s ChatExplorer, OpenAI’s non-ChatGPT API, and some of Amazon’s AI products."
The article explores the evolution of large language model (LLM) serving, highlighting significant advancements from pre-2020 frameworks to the introduction of vLLM in 2023. It discusses the challenges of efficient memory management in LLM serving and how vLLM's PagedAttention technique revolutionizes the field by reducing memory wastage and enabling better utilization of GPU resources.
A USB stick equipped with a Raspberry Pi Zero W runs a large language model using llama.cpp. The project involves porting the model to an ARMv6 architecture and setting up the device as a composite that presents a filesystem to the host, allowing users to interact with the LLM by creating text files that are automatically filled with generated content.
The article explores the architectural changes that enable DeepSeek's models to perform well with fewer resources, focusing on Multi-Head Latent Attention (MLA). It discusses the evolution of attention mechanisms, from Bahdanau to Transformer's Multi-Head Attention (MHA), and introduces Grouped-Query Attention (GQA) as a solution to MHA's memory inefficiencies. The article highlights DeepSeek's competitive performance despite lower reported training costs.
The article introduces Huginn-3.5B, a novel AI reasoning model developed by researchers from multiple institutions. It utilizes a recurrent depth approach for efficient and scalable reasoning by refining its hidden state iteratively within a latent space, rather than relying on external token generation. This allows it to dynamically allocate computational resources and perform efficiently across various tasks without needing specialized training data.
While current large language models (LLMs) can generate syntactically correct Terraform HCL code, they often miss critical elements like permissions, event triggers, and best practices. Iterative refinement with developer input is necessary to produce deployable, functional stacks. The article suggests using tools like Nitric to provide application context and enforce security, dependencies, and best practices.
ReaderLM-v2 is a 1.5B parameter language model developed by Jina AI, designed for converting raw HTML into clean markdown and JSON with high accuracy and improved handling of longer contexts. It supports multilingual text in 29 languages and offers advanced features such as direct HTML-to-JSON extraction. The model improves upon its predecessor by addressing issues like repetition in long sequences and enhancing markdown syntax generation.
ReaderLM-v2 is a 1.5B parameter language model designed to convert raw HTML into beautifully formatted markdown or JSON. It supports multilingual input and offers improved longer context handling, stability, and advanced markdown generation capabilities.