Multimodal Integration with OpenAI

This lesson introduces students to the concept of multimodal AI, focusing on applications that can process and integrate multiple types of data, such as images and audio. Students will explore the advantages of multimodal AI and get a general picture of OpenAI’s offerings in image and audio processing. By the end of the lessons, students will be able to design a basic architecture for a multimodal AI application.

This lesson introduces students to the powerful capabilities of GPT-4 Vision in image analysis. Student will explore how this advanced AI model can interpret and describe complex visual content, bridging the gap between computer vision and natural language processing. The lesson covers the fundamentals of how GPT-4 Vision works, its applications in various fields, its limitations and hands-on exercises to demonstrate its image analysis capabilities.

This lesson introduces students to the powerful capabilities of DALL-E in image generation and editing. DALL-E is an advanced AI model that can create and modify images based on natural language descriptions. Students will explore the intersection of natural language processing and computer vision, learning how to use DALL-E’s capabilities to bring textual descriptions to life visually.

This lesson explores the fascinating world of speech technologies, focusing on two key areas: speech recognition and speech synthesis. The lesson begins with an introduction to speech recognition using OpenAI’s Whisper model, enabling students to transcribe audio files into text and even translate spoken content into English. Next, students will jump into text-to-speech capabilities, learning how to generate lifelike spoken audio from written text using OpenAI’s TTS (text-to-speech) model. Finally, students will create a simple demonstration that combines both speech recognition and synthesis into a single application.

This lesson guides students in creating a sophisticated multimodal AI application that integrates text, image, and audio processing. Focusing on a language tutor app, students will simulate real-world scenarios to practice language skills. The lesson covers generating situational prompts, creating corresponding images, implementing speech recognition, and producing AI responses in text and audio. It also includes building an intuitive user interface using the Gradio library. By the end, students will have practical experience in synthesizing various AI technologies into a cohesive application.