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Course Outline
Introduction to Multimodal Interfaces
- What are multimodal interfaces?
- Benefits and challenges of multimodal interactions
- Real-world applications in various industries
Multimodal AI and Human-Computer Interaction
- Understanding human-centered AI design
- Key AI technologies powering multimodal interfaces
- Psychological and cognitive considerations in human-AI collaboration
Speech Recognition and Natural Language Processing (NLP)
- Speech-to-text and text-to-speech technologies
- Using OpenAI's Whisper or Mozilla DeepSpeech
- Improving AI-driven voice interactions
Gesture Recognition and Motion Tracking
- Understanding hand tracking and body gestures
- Implementing gesture control in UI design
- Hands-on with open-source gesture recognition libraries
Eye Tracking and Gaze-Based Interaction
- Introduction to eye-tracking technology
- Use cases in accessibility and adaptive interfaces
- Developing gaze-based input systems
Multimodal Fusion: Integrating Multiple Input Methods
- How AI combines speech, gestures, and vision
- Building adaptive and personalized AI interactions
- Best practices for seamless multimodal experiences
Prototyping and Implementing Multimodal Interfaces
- Designing user-friendly AI-powered interfaces
- Prototyping multimodal interactions with Figma and AI tools
- Developing real-world applications using Python and AI frameworks
Testing and Evaluating Multimodal Interfaces
- Usability testing methodologies for multimodal AI
- Measuring user experience and satisfaction
- Refining and optimizing AI-driven interactions
Future Trends in Human-AI Collaboration
- Advancements in multimodal AI and deep learning
- Emerging trends in human-computer interaction
- The role of AI in the future of user experience
Summary and Next Steps
Requirements
- Basic understanding of AI and machine learning concepts
- Familiarity with UI/UX design principles
- Some experience with programming (Python preferred)
Audience
- UI/UX designers
- Product managers
- AI researchers
14 Hours