The long long journey...The long long journey...
Dive into the architecture and working principles of Mixture of Experts (MoE) models, exploring popular frameworks like Mixtral 8X7B, DBRX, and Deepseek-v2. Learn their applications and advantages, implement an MoE model using Python, and evaluate its performance on tasks like logical reasoning, summarization, and entity extraction.
Support Vector Machine (SVM) is a classic algorithm in machine learning. This article focuses on the formula derivation in SVM, such as detailed reasoning of the margin distance formula, and the formulation of the primal and dual problems. It delves into optimization problems, including constructing the Lagrangian function to handle constrained optimization problems and using KKT conditions to find optimal solutions. It also covers the characteristics of polynomial and Gaussian kernel functions.
Experience the exciting moments of the VLDB 2024 academic conference, savor the culinary feast of Guangzhou, and appreciate the unique charm of Lingnan culture. From a modern metropolis to corners filled with historical charm, explore an inclusive and open city.
The development of AI relies on large amounts of data, and high-quality text data on the internet is gradually depleting, posing a 'data wall' challenge to the AI industry. This article explores current strategies to address this issue—from improving data quality and using synthetic data to model fine-tuning and reinforcement learning. Learn how to overcome the data bottleneck to ensure the continuous development of AI.
This article details the application scenarios of fine-tuning, prompting, and chain of thought techniques, providing specific steps for preparing data, fine-tuning models, and evaluating results using the example of generating blog articles. The GPT-4o-mini model, recently released by OpenAI, is fine-tuned via an online interface, offering performance close to GPT-4 at half the price of GPT-3.5.
In the process of achieving Artificial General Intelligence (AGI), the design patterns of AI Agents play an important role. Unlike traditional AI methods, Agents allow AI to make multiple modifications during task execution and leverage external tools and partners. This flexibility makes Agents an important path to achieving AGI in 2024. Combining Andrew Ng’s speech at the Red Shirt AI Summit and research papers from the past year, this article organizes 16 AI Agent design patterns and categorizes them into four major design paradigms: reflection, tool use, planning, and multi-agent collaboration.
The reflection paradigm includes patterns such as basic reflection, Reflexion Actor, and LATS, which improve the reasoning and decision-making capabilities of agents through self-reflection and external feedback. The tool use paradigm emphasizes enhancing the functionality of agents by invoking external tools. In the planning paradigm, methods like ReAct and Plan and Execute improve the flexibility and adaptability of agents by combining reasoning and action and developing multi-step plans. The multi-agent collaboration paradigm coordinates multiple agents to complete complex tasks through supervision and hierarchical teams.
These design patterns provide a theoretical foundation and practical guidance for the development of AI Agents, helping developers better utilize foundational models to achieve task automation and intelligence. Although Agents are a promising way to achieve AGI, they are not the only method. Agents can be combined with other technologies, such as RAG and user involvement, to achieve more complex task solutions.