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Market Opportunities for AI Agents and Multi-AI Agent Systems in Vietnam

1. Executive Summary

Vietnam’s digital landscape is undergoing a rapid transformation, presenting significant opportunities for the adoption of advanced automation technologies such as AI Agents and Multi-AI Agent systems. This report provides a comprehensive analysis of the Vietnamese market, highlighting the immediate needs across key sectors including travel tourism, real estate, customer service, logistics, and manufacturing. The analysis reveals a strong government commitment to digital transformation and AI development, coupled with a high rate of technology adoption among businesses. While the market for AI Agents and Multi-AI Agent systems is still in its early stages, specific areas like customer service automation, personalized experiences in travel tourism, and efficiency improvements in real estate show immediate promise. This report recommends a phased approach to market entry, initially focusing on these high-potential areas with tailored strategies for marketing, pricing, and customer acquisition, ultimately positioning service providers to capitalize on the transformative power of AI in Vietnam.

LLM model introduction

https://allenai.org/blog/olmotrace

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Continual Learning: A Review of Variational Dropout, Mixture of Experts with Prompting, and Backdoor Attacks

1. Introduction

The field of machine learning has witnessed significant advancements in recent years, enabling models to achieve remarkable performance on a wide array of tasks. However, a fundamental challenge arises when these models are deployed in dynamic environments where new data or tasks are encountered sequentially. This paradigm, known as continual learning, necessitates the ability of a model to learn from a continuous stream of information without forgetting previously acquired knowledge.1 A major impediment to achieving this goal is catastrophic forgetting, a phenomenon where the learning of new information leads to a drastic decline in performance on previously learned tasks.4 Overcoming this challenge requires specialized techniques that can maintain a delicate balance between the model’s capacity to learn new tasks (plasticity) and its ability to retain old knowledge (stability).4

AI benchmarks assess the performance and capabilities of models in standardized tasks.

Test your prompts, agents, and RAGs. Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.

AI benchmarks assess the performance and capabilities of models in standardized tasks.