Jarvis: Uniting AI Models for Complex Tasks

🤖 In the world of AI, there are many models, each excelling at specific tasks. However, when it comes to solving complex problems across different domains and modalities, the need arises to combine the strengths of various models. This is exactly what Jarvis does, as presented in the paper "HuggingGPT: Leveraging Large Language Models to Solve Complicated AI Tasks".

Jarvis is a collaborative system consisting of a large language model (LLM) as a controller and numerous expert execution models from Hugging Face Hub.

The workflow of the system consists of four stages:
<ul>
<li>Task Planning: using ChatGPT to analyze user requests, identify their intents, and break them down into subtasks.</li>
<li>Model Selection: ChatGPT selects expert models from Hugging Face Hub based on their descriptions to solve the planned tasks.</li>
<li>Task Execution: Jarvis calls and executes each selected model, returning results to ChatGPT.</li>
<li>Response Generation: ChatGPT integrates the predictions from all models and generates responses for users.</li>
</ul>

For example, if a user asks: "Can you describe what is depicted in this image and count the number of objects in it?" GPT alone would not be able to answer this question, as it does not work with images. In this case, Jarvis connects another model specialized in image processing to solve the task. As a result, the user receives a comprehensive answer that includes a description of the image and the number of objects in it.

Thus, Jarvis allows the combination of various AI models to tackle complex tasks across different fields such as language, vision, speech, and more. This opens up new possibilities in the development of artificial intelligence.

Paper: <a href="https://arxiv.org/pdf/2303.17580v2.pdf">https://arxiv.org/pdf/2303.17580v2.pdf</a> Github: <a href="https://github.com/microsoft/JARVIS">https://github.com/microsoft/JARVIS</a>;

#ai #jarvis #huggingface #llm #gpt