In November 2022, OpenAI launched ChatGPT, which quickly became the fastest-growing web app. Microsoft and Google also revealed plans to integrate chatbots with search, despite early hiccups. The tech now promises to revolutionize daily internet interactions, from office software to photo editing. The rapid development of AI has left us grappling with its impact.

Researchers from multiple universities have developed Gemini, a comprehensive framework for optimizing performance, energy efficiency, and monetary cost (MC) in DNN chiplet accelerators. Gemini employs innovative encoding and mapping strategies, a dynamic programming-based graph partition algorithm, and a Simulated-Annealing-based approach for optimization. Experimentation demonstrates Gemini’s superiority over existing state-of-the-art designs.

Rust Burn is a new deep learning framework developed in Rust, prioritizing flexibility, performance, and ease of use. It leverages hardware-specific features, such as Nvidia’s Tensor Cores, for fast performance. With a broad feature set and a growing developer community, it shows potential to address existing framework limitations and become a versatile deep learning solution.

The review explores the evolution and challenges of Large Language Models (LLMs) such as ChatGPT, highlighting their transition from traditional statistical models to neural network-based ones like the Transformer architecture. It delves into the training, fine-tuning, evaluation, utilization, and future advancements of LLMs, emphasizing ethical considerations and societal impact. For more details, refer to the…

The increasing use of cloud-hosted large language models raises privacy concerns. Secure Multi-Party Computing (SMPC) is a solution, but applying it to Privacy-Preserving Inference (PPI) for Transformer models causes performance issues. SecFormer is introduced to balance performance and efficiency in PPI, demonstrating improvements in privacy and performance for large language models.

Language models are crucial in natural language processing, trending towards larger, intricate models to process human-like text. A challenge is balancing computational demand and performance. The introduction of TinyLlama, a compact language model with 1.1 billion parameters, addresses this by efficiently using resources while maintaining high performance. It sets a new precedent for inclusive NLP…

Stanford University researchers unveiled Mobile ALOHA, a low-cost, bimanual mobile robot capable of performing household tasks. The robot, an improved version of static ALOHA, uses an imitation learning process and Action Chunk with Transformers algorithm to learn new skills. Mobile ALOHA is affordable, open-source, and run by off-the-shelf hardware, making it a promising advancement in…

The article emphasizes the challenges and benefits of adopting generative AI in enterprises. It warns about the inaccuracies and potential risks associated with large language models (LLMs) due to hallucinations, but also highlights the necessity and transformative potential of leveraging generative AI for productivity and strategic advantage. The recommendations include prioritizing data foundation, building an…

The text discusses different methods of merging large language models using mergekit and how to use them to create new combined models without requiring a GPU. It provides examples of configurations for four merging methods: SLERP, TIES, DARE, and Passthrough, and details the steps for implementing each method. The tutorial also explains how to use…

The author highlights key aspects of Applied Machine Learning often overlooked in formal Data Science education. These include thoughtful target selection, dealing with imbalanced data, using real-life testing, meaningful performance metrics, and reconsidering the importance of scores. The insights are aimed at helping junior and mid-level data scientists enhance their career. [50 words]