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Course Outline
Introduction to Multimodal AI for Smart Assistants
- What is multimodal AI?
- Applications of multimodal AI in virtual assistants
- Overview of AI-powered assistants (ChatGPT, Google Assistant, Alexa, etc.)
Understanding Speech Recognition and NLP
- Speech-to-text and text-to-speech conversion
- Natural Language Processing (NLP) for conversational AI
- Sentiment analysis and intent recognition
Integrating Computer Vision for Smart Assistants
- Image recognition and object detection
- Facial recognition and sentiment detection
- Use cases: Virtual agents with visual capabilities
Multimodal Fusion: Combining Voice, Text, and Vision
- How multimodal AI processes multiple inputs
- Designing seamless interactions across modalities
- Case studies: AI-powered virtual agents with multimodal interfaces
Building a Multimodal Virtual Assistant
- Setting up a conversational AI framework
- Connecting speech recognition, NLP, and vision APIs
- Developing a prototype smart assistant
Deploying AI-Powered Assistants in Real-World Applications
- Integrating virtual agents into websites and mobile apps
- AI-driven automation for customer support and user experience
- Monitoring and improving AI assistant performance
Challenges and Ethical Considerations
- Privacy and data security in AI-driven assistants
- Bias and fairness in AI interactions
- Regulatory compliance for AI-powered assistants
Future Trends in Multimodal AI for Smart Assistants
- Advancements in AI-driven conversation models
- Personalization and adaptive learning in virtual agents
- AI’s evolving role in human-computer interaction
Summary and Next Steps
Requirements
- Basic understanding of AI and machine learning
- Experience with Python programming
- Familiarity with APIs and cloud-based AI services
Audience
- Product designers
- Software engineers
- Customer support professionals
14 Hours