Omni Prompting with gpt-4o-mini | A Staple In The Future of AI Software?
TLDRThis video discusses the concept of 'omn' prompting, utilizing the capabilities of the GPT-40 Mini model for multimodal AI interactions, including text, vision, and voice. The presenter highlights the benefits of using visual prompts alongside text, demonstrates a screen-sharing app that integrates with GPT-40 Mini for real-time analysis and questions, and emphasizes the potential of low-cost, high-output AI models for creating personalized assistants and innovative applications.
Takeaways
- 🌟 The concept of 'omn' prompting is introduced, which combines text, vision, and voice inputs for AI models like gpt-4o-mini.
- 🚀 The release of gpt-4o-mini is significant, offering multi-modal capabilities and a lower cost that makes it viable for various applications.
- 💡 The speaker emphasizes the importance of context and the potential of using visual prompts alongside text in AI interactions.
- 🛠️ API calls can be made with multiple modalities using gpt-4o-mini, including text and image inputs, and potentially voice in the future.
- 💰 The price of using gpt-4o-mini is highlighted as being very low, making it accessible for integration into various projects and applications.
- 🖼️ The speaker demonstrates a screen share app called 'cognicast' that uses gpt-4o-mini to analyze images and provide descriptions.
- 🔍 The app 'cognicast' can be used to monitor and analyze any selected window or the full screen, offering a personalized assistant experience.
- 📈 The potential of gpt-4o-mini is discussed in terms of creating a hyper-personal assistant that could integrate voice, text, and images.
- 🔗 The integration of gpt-4o-mini into websites is suggested as a way to provide users with immediate analysis and insights from images and text.
- 🎨 The speaker shares a website where users can test out 'omn' prompting with gpt-4o-mini, offering a hands-on experience of the technology.
- 🔍 The future possibilities of integrating voice with text and images in AI models are discussed, indicating an exciting direction for AI development.
Q & A
What is the concept of 'Omni Prompting' discussed in the video?
-Omni Prompting is a concept that leverages multimodal inputs such as text, vision, and voice to interact with AI models like GPT-4 Mini, allowing for a more comprehensive and contextual interaction with the AI.
Why did the presenter choose to discuss GPT-4 Mini in the video?
-The presenter chose to discuss GPT-4 Mini because of its capabilities to handle multimodal inputs and its significantly lower cost, which makes it viable for a wide range of applications.
How can GPT-4 Mini take advantage of both text and visual inputs?
-GPT-4 Mini can take advantage of both text and visual inputs by making an API call that includes text type and an image URL, allowing for a more integrated response based on both types of input.
What is the significance of the low cost of GPT-4 Mini for its applications?
-The low cost of GPT-4 Mini makes it feasible to use for a variety of applications, including screen share apps and image analysis, without worrying about the financial implications of extensive API calls.
What is the 'Cognic Cast' app mentioned in the video?
-Cognic Cast is a screen share app created by the presenter that uses GPT-4 Mini to analyze and respond to what is happening on the computer screen, enhancing the user's interactive experience with AI.
How does the presenter plan to integrate voice input into the Omni Prompting concept?
-The presenter plans to integrate voice input by expecting future updates to the GPT-4 Mini model that will allow voice to be included in the same API call as text and image inputs, creating a more seamless interaction.
What is the price structure for using GPT-4 Mini as mentioned in the video?
-The price for using GPT-4 Mini is 15 cents per million input tokens and 60 cents per 1 million output tokens, with a provision for low-resolution image inputs costing 0.42 USD each.
What is the role of the 'detail love' setting in the presenter's code?
-The 'detail love' setting in the presenter's code is used to select low resolution for the images being sent to GPT-4 Mini, which helps in reducing the cost of each image input.
How does the presenter's screen share app work with GPT-4 Mini?
-The screen share app works by capturing screenshots of the selected window or screen, sending it along with a text prompt to GPT-4 Mini, and then receiving a response that analyzes or describes the content of the image.
What is the potential impact of the low cost of GPT-4 Mini on future AI applications?
-The low cost of GPT-4 Mini could lead to a surge in the development of AI applications that utilize multimodal inputs, making AI more accessible and integrated into various aspects of daily life and work.
What are the presenter's thoughts on the future of open-source models in relation to GPT-4 Mini?
-The presenter is excited about the potential of open-source models, such as those from Anthropic, to offer similar capabilities to GPT-4 Mini but at a lower cost, despite the potential hassle of hosting them.
Outlines
🤖 Introduction to Omni-Prompting with GPT 4.0 Mini
The speaker introduces the concept of 'omni-prompting,' which integrates text, vision, and voice inputs for AI models. They express excitement about the GPT 4.0 Mini's capabilities, especially its ability to handle multiple modalities in a single API call. The speaker also mentions the model's affordability, suggesting it makes advanced AI features more accessible. They demonstrate an application called 'Cognic Cast,' which uses GPT 4.0 Mini to analyze screenshots and provide responses to user queries.
🖼️ Utilizing Visual and Textual Inputs for Enhanced AI Interaction
This paragraph delves into the practical application of using both visual and textual prompts with AI, as exemplified by the speaker's experience with CLA 3.5. They discuss creating a React component and using images to expedite the development process, highlighting the efficiency gains from the integration of visual inputs. The speaker also anticipates the future integration of voice, which could further streamline interactions with AI models.
💡 Exploring the Potential of Low-Cost AI Models for Innovative Applications
The speaker explores the implications of the reduced cost of running AI models like GPT 4.0 Mini, suggesting it opens up new possibilities for app development. They showcase a screen-sharing app that can analyze and describe content on the user's screen, emphasizing the ease of customization and the potential for integrating voice commands. The speaker also mentions plans to share the app on GitHub and encourages viewers to join their community for access.
🌐 Sharing Resources for Omni-Prompting and Anticipating Future Developments
In the final paragraph, the speaker shares resources for those interested in trying out omni-prompting, directing viewers to their website for a demo. They also discuss the potential for integrating security measures and the flexibility of using low-cost AI models for various applications. The speaker expresses excitement about the future of AI, including the upcoming release of CLA 3.5 and open-source models, and thanks the audience for their support.
Mindmap
Keywords
💡Omni Prompting
💡GPT-4.0 Mini
💡API Call
💡Multimodality
💡Screen Share App
💡Cognicast
💡Price of GPT-4.0 Mini
💡Output Token Window
💡Hyper Personal Assistant
💡Open Model Versions
💡Anthropic
Highlights
Introduction of the concept of Omni Prompting with gpt-4o-mini, a new approach to AI software.
The release of gpt-4o-mini and its potential to revolutionize AI with its multimodal capabilities.
The importance of context in smaller AI models and the benefits of combining text, vision, and voice inputs.
Demonstration of creating an API call with text and image inputs using gpt-4o-mini.
The anticipation of voice integration into the model for a fully multimodal AI experience.
The economic feasibility of using gpt-4o-mini due to its low cost.
Introduction of the cognic cast app, a screen share application that integrates with gpt-4o-mini.
How the app can analyze screenshots and provide descriptions based on the image content.
The pricing structure of gpt-4o-mini and its affordability for developers.
The potential of gpt-4o-mini to serve as a hyper-personal assistant with screen share capabilities.
The integration of gpt-4o-mini with other open-source models for broader AI applications.
The creator's plan to share the screen share app on GitHub for community use.
The demonstration of the app's ability to monitor and analyze any selected window or full screen.
The future enhancement of the app with voice integration for hands-free operation.
The website AIS sv. Tech where users can test Omni prompting without access to gpt-4o-mini.
The ease of integrating Omni prompting into websites and the potential for custom security measures.
The lowering cost of large language model APIs and the opportunities it presents for innovation.
The anticipation of the release of CLA 3.5 and its potential impact on AI development.
The creator's excitement for the future of AI and the possibilities opened by the decreasing costs and advancements in technology.