OpenAI Updates ChatGPT 4! New GPT-4 Turbo with Vision API Generates Responses Based on Images
TLDROpenAI has introduced a significant update with the release of GPT-4 Turbo, an enhanced API endpoint capable of interpreting visual elements. This development allows for the integration of image recognition within software applications, enabling features such as calorie tracking from food images and visual UI design without coding. The update underscores the progression of software development, where AI and vision capabilities are becoming increasingly accessible, though costs remain a consideration for widespread adoption.
Takeaways
- 🚀 OpenAI has released an update for ChatGPT-4, introducing a new GPT-4 Turbo with Vision API that can generate responses based on images.
- 🌟 The updated API endpoint enhances the model's capabilities, allowing visual elements to be processed within software applications.
- 📸 Examples of applications leveraging this technology include a fitness app that can analyze food images for nutritional information and a no-code tool for designing and prototyping website UIs.
- 🔄 Over time, OpenAI updates its endpoints to provide improved versions of the AI model, deprecating older versions for software development purposes.
- 💡 The new GPT-4 endpoint enables the integration of visual AI in production-level environments, which was not possible with previous versions.
- 📈 Cost considerations are important when implementing vision AI; OpenAI's pricing is competitive but can become expensive depending on usage scale.
- 🔎 Comparisons with industry competitors like Anthropic's Opus model show differences in pricing and feature capabilities, with OpenAI offering visual element processing.
- 🛠️ Understanding both no-code and code methods for software development is valuable, as it provides an advantage in creating more effective and customizable applications.
- 📊 The potential for AI with vision capabilities is vast, enabling the creation of innovative software applications that can process and generate content based on visual input.
- 🎯 For businesses, striking a balance between the cost of AI services and the value provided to consumers is crucial for achieving profitability.
Q & A
What is the recent update to ChatGPT 4 by OpenAI?
-The recent update by OpenAI is the release of an upgraded API endpoint for the ChatGPT 4 model, which now includes the capability to understand and process visual elements from images.
How does the new GPT-4 Turbo with Vision API enhance software development?
-The GPT-4 Turbo with Vision API allows developers to integrate visual recognition capabilities into their software applications. This means that the software can now process and understand images, such as identifying objects within the images, which can lead to the creation of more interactive and sophisticated applications.
What kind of examples are mentioned in the script that showcase the capabilities of the new API?
-Two examples are mentioned: Healthify Snap, an app that can analyze images of food and provide nutritional information such as calories, fats, and proteins; and TLD Draw, a no-code tool that allows users to draw a website UI and then generate a code version of it.
How does the cost of using the new GPT-4 Turbo API compare to an industry competitor like Anthropic?
-The cost of using the GPT-4 Turbo API is around $10 per 1 million inputs. In comparison, Anthropic's Opus model costs $15 for 1 million tokens. However, it's noted that the Opus model may not have the same visual element processing capabilities as GPT-4 Turbo.
What is the significance of the ability to process images at a production level environment through the API?
-The ability to process images at a production level through the API means that developers can now create software that can analyze and react to visual data on a larger scale, which was not possible with previous versions of the API that were more limited to user interfaces.
How might the cost of visual processing affect the end consumer?
-While the cost of visual processing might be relatively affordable for developers (around $0.69 per user based on the example given), it could still add up and potentially affect the end consumer through subscription costs or limitations on the number of images they can process.
What is the importance of understanding both code and no-code methods in software development?
-Understanding both code and no-code methods provides developers with a versatile skill set. While no-code solutions can be faster and easier to implement, knowing how to code gives developers a distinct advantage, especially when it comes to creating more complex and customized software applications.
What does the future hold for software applications with AI and vision capabilities?
-The future of software applications with AI and vision capabilities looks promising, as it opens up new possibilities for creating interactive, sophisticated, and user-friendly applications that can understand and react to visual data, leading to enhanced user experiences and innovative solutions.
How does the script suggest the industry is progressing in terms of software development?
-The script suggests that the industry is progressing towards making software development more accessible and easier through the use of no-code solutions and AI integration. This trend is reducing the barrier to entry for creating software applications and encouraging innovation.
What is the role of the new endpoint in facilitating no-code ways of leveraging AI technology?
-The new endpoint plays a crucial role in facilitating no-code ways of leveraging AI technology by allowing developers to integrate advanced AI capabilities, such as visual element processing, directly into their applications without the need for extensive coding, thus making AI more accessible and easier to incorporate into various projects.
Outlines
🚀 Introduction to GPT-4 Model Update and Its API Endpoints
This paragraph introduces a recent significant update released by OpenAI regarding the GPT-4 model. The update includes an upgraded API endpoint that enables the integration of visual elements into software applications. The speaker aims to provide a comprehensive understanding of this new feature, including its implications for software development and no-code approaches. The update is described as exciting because it allows developers to access visual elements within GPT-4, which was not possible with previous versions. The speaker also mentions the deprecation of older endpoints and the continuous improvement of AI models over time.
📈 Cost-Effectiveness and Examples of GPT-4 Model in Action
In this paragraph, the speaker discusses the cost implications of using the GPT-4 model, comparing it to an industry competitor, Anthropic. The cost is mentioned as $10 per 1 million inputs for OpenAI's GPT-4, whereas Anthropic's Opus model is priced at $15 for 1 million tokens. The speaker also notes that the Opus model may not have the capability to handle visual elements as GPT-4 does. The paragraph includes an example of a fitness tracking app that can analyze images of food to provide nutritional information, and a no-code software called TL Draw, which allows users to design website UIs and generate code for them. The speaker emphasizes the importance of understanding traditional coding methods to leverage these technologies effectively and mentions the potential high costs associated with using AI for visual processing at a large scale.
Mindmap
Keywords
💡OpenAI
💡ChatGPT 4
💡Vision API
💡API Endpoint
💡Software Development
💡No-Code
💡Healthify Snap
💡TL Draw
💡Anthropic
💡Cost Analysis
💡AI and Vision Capabilities
Highlights
OpenAI has released a significant update with the GPT-4 model, introducing GPT-4 Turbo with Vision API.
The new GPT-4 Turbo allows for the integration of visual elements into the AI's responses.
This update enables developers to leverage visual recognition capabilities within their software applications.
The previous GPT-3.5 model was updated on January 25th, 2024, and older versions are being phased out.
The new endpoint for GPT-4 is exciting as it brings vision capabilities to production-level environments.
Healthify Snap is an example of an app that uses the new API to analyze food images and provide nutritional information.
TLD Draw is a no-code tool that allows users to design website UIs and generate code for them.
The cost of using the new GPT-4 Vision API is $10 per 1 million inputs, which is competitive in the industry.
Anthropic's Opus model costs $15 for 1 million tokens but does not offer the same visual element integration.
The vision pricing calculator shows that processing a 1024x1024 image costs around $0.765.
For a software application processing 10,000 images of this size, the cost would be approximately $765.
The potential for creating software applications with AI and vision capabilities is a significant advancement.
Understanding both code and no-code methods provides a distinct advantage in software development.
The integration of vision capabilities with AI is expected to lead to the creation of more sophisticated software applications.
The new GPT-4 Turbo update is a step towards the next level of AI integration in various industries.
Creator Corbin discusses the implications of this technology for software development and no-code solutions.
The video provides insights into the future of AI and its role in making software development more accessible and efficient.
