Recently, Alibaba algorithm expert Kun Cheng participated in the ICASSP 2017 conference with the paper titled Improving Latency-Controlled BLSTM Acoustic Models for Online Speech Recognition. Author Kun Cheng communicating with attendees The research of this paper is based on the premise that to achieve better speech recognition accuracy, the Latency-controlled BLSTM model was used in … Read more
Excerpt from Awni Translation by Machine Heart Contributors:Nurhachu Null,Lu Xue Since the application of deep learning in the field of speech recognition, the word error rate has significantly decreased. However, speech recognition has not yet reached human-level performance and still faces multiple unresolved issues. This article discusses various aspects of the unresolved problems in speech … Read more
HuggingGPT is a recent representative in the hot direction of Agents, enabling LLMs like ChatGPT to utilize various models from the HuggingFace community (including but not limited to text-to-image, image-to-text, speech-to-text, and text-to-speech), allowing LLMs to drive other intelligent agents for multimodal capabilities. The original paper and Chinese introduction are as follows: Original Paper HuggingGPT：https://arxiv.org/abs/2303.17580 … Read more
ChatGPT has become the manager of hundreds of models. In recent months, the surge in popularity of ChatGPT and GPT-4 has showcased the extraordinary capabilities of large language models (LLMs) in language understanding, generation, interaction, and reasoning. This has drawn significant attention from both academia and industry, revealing the potential of LLMs in constructing general … Read more
HuggingGPT, developed by Zhejiang University and Microsoft Research Asia, also known as JARVIS, can automatically analyze the required AI models based on the user’s natural language description and directly call the corresponding models on Huggingface to provide a solution for the user. 1. Workflow of HuggingGPT The workflow consists of four stages: Task Planning:ChatGPT parses … Read more
Machine Heart reports Machine Heart Editorial Team Is the potential of agents underestimated? AI agents were a hot topic last year, but many may not have a clear concept of how much potential AI agents really have. Recently, Stanford University professor Andrew Ng mentioned in a speech that they found workflows built on GPT-3.5 performed … Read more
Professor Andrew Ng recently shared his views on Agents at the Sequoia AI Summit. Although some media outlets have reported on this, they sacrificed accuracy for the sake of timeliness by using machine translation, which increased unnecessary reading barriers. The Agent Universe has reorganized and translated a version that retains Professor Ng’s original intent while … Read more
Solving complex artificial intelligence tasks is a key step towards achieving Artificial General Intelligence (AGI). Despite the abundance of AI models targeting different domains and modalities, they struggle to handle complex AI tasks. Given the remarkable capabilities of large language models (LLMs) in language understanding, generation, interaction, and reasoning, the authors advocate that LLMs can … Read more
Since the advent of ChatGPT, various GPTs have emerged. Recently, Microsoft launched HuggingGPT and open-sourced the corresponding project on GitHub – Jarvis. Just these two points are enough to pique the public’s interest. Today’s article will simply interpret HuggingGPT, specifically the paper – HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face[1]. … Read more
PanChuang AI Sharing Source | GPT Reprinted from | Machine Heart 【Introduction】ChatGPT has become the manager of hundreds of models this time. In recent months, the successive popularity of ChatGPT and GPT-4 has showcased the extraordinary capabilities of large language models (LLMs) in language understanding, generation, interaction, and reasoning, which has garnered significant attention from … Read more