At the forefront of the artificial intelligence wave, following the stunning debut of large language models (LLMs) like GPT-4, a more disruptive concept is rapidly moving from the background to the forefront—AI Agents. As highlighted in several forward-looking reports by various tech media, AI Agents are widely regarded as the “next battlefield after large models.” … Read more
ArticleOverview In this article, DigiKey introduces the method and process of deploying large models on the Raspberry Pi 5 to create a real-time translator. The project utilizes the voice recognition capabilities of the Raspberry Pi 5 to convert speech into text, which is then submitted to the large language model’s API for processing. The returned … Read more
Have you ever had a moment like this: scrolling through your social media, seeing someone mention “multimodal large models”, you give a thumbs up to pretend you understand, and the next second you rush to Baidu—“What is modality? Is it related to motorcycles?” Don’t worry, you’re not alone in this confusion. In today’s article, we … Read more
Transition AI Jim Fan: Recently, I have been somewhat silent on X. This year has been a transformative journey for me. Although emerging large language models like Grok-4 and Kimi K2 are impressive, the field of robotics still resembles a wondrous western wilderness. It reminds me of the NLP field in 2018, when GPT-1 was … Read more
In the field of artificial intelligence, especially with the rapid development of large language models (LLMs), single-agent systems have shown significant limitations. This article will delve into why we need to shift from single-agent to multi-agent systems, analyze the current technical flaws of large language models, and elaborate on how multi-agent systems can effectively address … Read more
The application of large language models in server environments during the AI era, especially in the inference phase, presents numerous opportunities. AI can be divided into two main stages: training and inference. From pre-training to fine-tuning and reinforcement, a general large model is developed, which is then used to generate application models; the inference side … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP graduate students, university professors, and corporate researchers.The vision of the community is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Paper Title: Soft Reasoning: … Read more
Source: ZHUAN ZHI This article is approximately 1000 words long and is recommended for a 5-minute read. This article presents SparseLoRA, a method to accelerate the fine-tuning of large language models through contextual sparsity. Fine-tuning large language models (LLMs) is often both computationally intensive and memory-consuming. While parameter-efficient fine-tuning methods such as QLoRA and DoRA … Read more
A professional community focused on the AIGC field, paying attention to the development and application of large language models (LLMs) such as Microsoft & OpenAI, Baidu Wenxin Yiyan, and iFlytek Spark, focusing on market research of LLMs and the AIGC developer ecosystem. Welcome to follow! With the emergence of ChatGPT, large language models have achieved … Read more
Today’s AI agents are capable of perceiving, making decisions, and taking actions independently. With the rise of large language model (LLM)-driven AI agents, we are on the brink of a new era: AI agents may form their own societies and coexist harmoniously with humans. Below, we will discuss what AI agents are, why they are … Read more