NEW HuggingGPT 🤗 - One Model to Rule Them All (Is this AGI?)

Introduction to Hugging GPT

In this section, the speaker introduces Hugging GPT, a new large language model by the company Hugging Face. The speaker explains that Hugging GPT brings together different large language models and machine learning models in one toolset controlled by AI.

What is Hugging GPT?

• Hugging GPT is a new large language model by Hugging Face.

• It promises to bring all disparate large language models and machine learning models together in one giant toolset controlled by AI.

• Large language models today are primarily for text, but we're going to need video, audio, and other formats brought into one place.

• Hugging GPT is like a central brain with an enormous toolset of large language models and artificial intelligence tools at its disposal.

How Does it Work?

• When given a prompt, hugging GPT does task planning, model selection, executes the task using different models as needed, and then summarizes the data into a response for the user.

• Large Language Models (LLMs) demonstrate excellent results in zero-shot or few-shot settings but are still weaker than some experts. Specialized models are better at specialized things.

• LLMs should be able to coordinate with external models to handle complicated AI tasks.

Conclusion

In this section, the speaker concludes that hugging GPT is an exciting development that brings together different large language models and machine learning tools in one place. The speaker emphasizes that hugging GPT represents a step closer towards AGI (Artificial General Intelligence).

Final Thoughts

• Hugging GPT is a step closer to AGI (Artificial General Intelligence).

• Hugging GPT brings together different large language models and machine learning tools in one place.

• Hugging GPT represents an exciting development in the field of artificial intelligence.

Hugging Face's ChatGPT: An Open Approach to AI Orchestration

This video discusses how Hugging Face's ChatGPT uses a large language model as an interface to route user requests to expert models. It covers 24 tasks such as text classification, object detection, semantic segmentation, image generation, question answering, text-to-speech and text of video.

How it Works

• A single generalized model orchestrating a bunch of fine-tuned models for each individual subtask would do it really well.

• Hugging GPT uses large language model as an interface to Route user request to expert models.

• Hooking GPT is not limited to visual exception task but can address tasks in any modality or domain.

• Hugging GPT adopts a more open approach by assigning and organizing tasks based on model descriptions.

Advantages

• This architecture allows models to do what they do best whether you're a fine-tuned model or a generalized model.

• The paper gives many examples of how it works and can be used.

Conclusion

• This open and continuous manner brings us one step closer to realizing artificial general intelligence.