This article is authorized for reprint by AI New Media Quantum Bit (Public Account ID: qbitai). For reprinting, please contact the source. This article is about 3000 words long and is recommended to be read in 6 minutes. The gap between large models will continue to widen! Li Yanhong’s latest internal speech has been exposed, … Read more
Lu Yu, Yu Jinglei, Chen Penghe Abstract: With the rapid development of generative artificial intelligence, intelligent agents based on large models have gradually acquired capabilities such as multimodal perception, retrieval-augmented generation, reasoning and planning, interaction, and evolution. This research proposes the basic concept and framework of teaching intelligent agents based on large models, with the … Read more
Author:DeanSacoransky and Deniz Askin The Impact of DeepSeek-r1 DeepSeek publicly released the r1 model less than a month ago. Suddenly, everyone can publicly use a powerful reasoning model for application and model development. In this article, we propose a simple intelligent agent workflow that empowers DeepSeek-r1 to perform information retrieval in an intelligent agent manner! … Read more
1. The Dilemma of Traditional RAG2. Innovative Breakthroughs of Agentic RAG3. Advantages and Application Prospects of Agentic RAG In the context of rapid development in artificial intelligence, significant progress has been made in large language model (LLM) technology, but it also faces many challenges. Retrieval-Augmented Generation (RAG) technology has emerged to provide new avenues for … Read more
1. Introduction With the rapid evolution of generative artificial intelligence, multimodal large models are increasingly demonstrating their advantages in multimodal content understanding and generation. The multimodal large model (hereinafter referred to as “large model”) refers to artificial intelligence models capable of processing and understanding various modal data inputs such as text, images, audio, and video. … Read more
Depending on the goals we wish to achieve and the criteria by which we measure success, there are various methods to create artificial intelligence. The scope is extremely broad, ranging from highly complex systems like autonomous driving and robotics to common components of our daily lives, such as facial recognition, machine translation, and email classification, … Read more
Source: Machine Heart This article is about 2000 words, suggested reading time is 5 minutes. Recently, a joint team from Tsinghua University, the University of Hong Kong, Renmin University, and Wallface Intelligence proposed a new self-evolution strategy for intelligent agents: Investigate – Consolidate – Exploit (ICE). “Learning from history can help us understand the rise … Read more
Star marks can often see us~ Reply in the background「Join the group」 to join the modern advertising circle | Abstract | Human digital twin (HDT) technology can present human physiological characteristics in a digital form, but in the field of social communication and marketing, due to its lack of consciousness and autonomous linguistic and behavioral … Read more
The capabilities of intelligent agents can be classified based on their level of intelligence and the complexity of their functions. Different levels of intelligent agents exhibit significant differences in handling task complexity, learning ability, decision-making ability, and adaptability. These classifications help researchers and engineers understand and design intelligent systems, and choose appropriate technologies and methods … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering audiences including NLP graduate students, university teachers, and corporate researchers. The community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for the advancement of beginners. … Read more