Multimodal AI: Integrating Senses for Intelligent Systems

• Basic understanding of artificial intelligence and machine learning
• Proficiency in Python programming
• Familiarity with data handling and preprocessing

Audience

• AI researchers
• Data scientists
• Machine learning engineers

Multimodal AI is an innovative field that combines information from various sensory inputs to create systems that understand and interact with the world in a more human-like manner.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI researchers, data scientists, and machine learning engineers who wish to create intelligent systems that can process and interpret multimodal data.

By the end of this training, participants will be able to:

• Understand the principles of multimodal AI and its applications.
• Implement data fusion techniques to combine different types of data.
• Build and train models that can process visual, textual, and auditory information.
• Evaluate the performance of multimodal AI systems.
• Address ethical and privacy concerns related to multimodal data.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Multimodal AI

• Understanding multimodal data
• Key concepts and definitions
• History and evolution of multimodal learning

Multimodal Data Processing

• Data collection and preprocessing
• Feature extraction from different modalities
• Data fusion techniques

Multimodal Representation Learning

• Learning joint representations
• Cross-modal embeddings
• Transfer learning across modalities

Multimodal Alignment and Translation

• Aligning data from multiple modalities
• Cross-modal retrieval systems
• Translation between modalities (e.g., text-to-image, image-to-text)

Multimodal Reasoning and Inference

• Logic and reasoning with multimodal data
• Inference techniques in multimodal AI
• Applications in question answering and decision making

Generative Models in Multimodal AI

• Generative Adversarial Networks (GANs) for multimodal data
• Variational Autoencoders (VAEs) for cross-modal generation
• Creative applications of generative multimodal AI

Multimodal Fusion Techniques

• Early, late, and hybrid fusion methods
• Attention mechanisms in multimodal fusion
• Fusion for robust perception and interaction

Applications of Multimodal AI

• Multimodal human-computer interaction
• AI in autonomous vehicles
• Healthcare applications (e.g., medical imaging and diagnostics)

Ethical Considerations and Challenges

• Bias and fairness in multimodal systems
• Privacy concerns with multimodal data
• Ethical design and deployment of multimodal AI systems

Advanced Topics in Multimodal AI

• Multimodal transformers
• Self-supervised learning in multimodal AI
• The future of multimodal machine learning

Summary and Next Steps