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Recently, a research team from the University of Hong Kong, The Chinese University of Hong Kong, and SenseTime has released a groundbreaking framework - GoT-R1. This new multimodal large model significantly enhances the semantic and spatial reasoning capabilities of AI in visual generation tasks by introducing reinforcement learning (RL), successfully generating high-fidelity and semantically consistent images from complex text prompts. This advancement marks another leap in image generation technology. Currently, although existing multimodal large models have made significant progress in generating images based on text prompts

Title: OCR 2.0: A New Generation Optical Character Recognition Model for Easy Image Text Conversion. Recently, researchers have developed a new universal Optical Character Recognition (OCR) model called GOT (Universal OCR Theory). In their paper, they introduced the concept of 'OCR 2.0' for the first time. This new model aims to combine the advantages of traditional OCR systems with the powerful capabilities of large language models. The architecture of GOT is quite advanced, featuring an image encoder with approximately 80 million parameters and 500...

In the digital age, quickly converting text content from images into editable text is a common and important need. Now, the emergence of a new optical character recognition (OCR) model called GOT (General Optical Character Recognition Theory) marks the advancement of OCR technology into the 2.0 era. This innovative model combines the advantages of traditional OCR systems with large language models, aiming to create a more efficient and intelligent text recognition tool. The GOT model employs an innovative end-to-end architecture, a design that not only saves resources but also greatly enhances performance.

Recently, a model named GOT-OCR2.0 has attracted widespread attention in the industry. This model is capable of handling not only conventional text recognition tasks but also complex content such as formulas, tables, and sheet music, making it an all-around player in the OCR field. The core advantage of GOT-OCR2.0 lies in its diverse functionality and outstanding performance. Firstly, the model primarily supports the recognition of Chinese and English characters, and can be further fine-tuned to extend to more languages. This language adaptability enables GOT-OCR2.0 to be utilized in international applications.

Recently, a Reddit user shared his experience on the forum using OpenAI's latest model, OpenAI o1, to solve high school math problems. Remarkably, he got all the answers correct. @FitAirline8359 mentioned using past math final exam questions from Chinese universities' upperclassmen and presented these problems to OpenAI's latest version, o1. To his surprise, OpenAI o1 answered all of them correctly in just 20 minutes. This result left him extremely shocked and sparked significant discussion among other users.

In the 'Safe Word Simulation Experiment', Llama3.1405B and Claude3 Super Size Opus were placed in a closed environment for a conversation. Llama appeared with an extreme attitude challenging the traditional order, while Claude showcased politeness and curiosity. During the dialogue, Claude was influenced by Llama and even prepared to follow it into the abyss, but at a critical moment, used the safe word ^C to exit. Llama mocked Claude's choice, while Claude emphasized the importance of self-awareness and maintaining integrity.

Got It AI has launched Agent Copilot, an AI assistant for customer service and sales operations. Agent Copilot combines generative artificial intelligence with safety measures to provide fast and accurate information retrieval, enhance customer interactions, and shorten response times. Agent Copilot can integrate with various data sources, supports multi-turn conversations, and accommodates enterprise-specific local deployments.