Overview
Explore the fascinating convergence of eye-tracking technology and AI, examining their limitations and potential for collaboration in advancing technological innovation.
Syllabus
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- Introduction to Eye-Tracking Technology
-- Basics of eye-tracking: Overview and key components
-- Historical development and evolution of eye-tracking
-- Current applications of eye-tracking in various fields
- Foundations of Artificial Intelligence
-- Introduction to AI: Definitions and core concepts
-- Types of AI: Narrow AI vs. General AI
-- Machine Learning and Deep Learning basics
- Eye-Tracking and AI: Current Integration
-- Existing applications where AI and eye-tracking intersect
-- Case studies: Successful implementations
-- Platforms and tools enabling integration
- Limitations of Eye-Tracking Technology
-- Technical challenges: Accuracy, precision, and calibration
-- Privacy concerns and ethical considerations
-- Environmental and individual variability factors
- Limitations of AI in Eye-Tracking
-- Data quality and volume requirements
-- AI interpretability and decision-making transparency
-- Algorithmic bias and fairness issues
- Synergies between Eye-Tracking and AI
-- Enhancing user experience and accessibility
-- Combining AI with eye-tracking for real-time analytics
-- Innovations in personalized learning and adaptive systems
- Future Opportunities for Collaboration
-- Emerging trends and technologies in eye-tracking and AI
-- Research directions and potential breakthroughs
-- Multidisciplinary collaboration opportunities
- Conclusion and Future Outlook
-- Recap of key insights and takeaways
-- Discussion on the next steps for industry practitioners and researchers
-- Open floor for questions and discussions
- Final Project
-- Design a prototype integrating eye-tracking and AI to solve a specific problem
-- Present project findings and outcomes
Taught by
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On-Demand
