2.3 Continuation
Sat, 18 Apr 26
Gen AI Open Source Landscape Overview
Hugging Face serves as primary open source AI model library
Free access to thousands of pre-trained models
Models grouped by tasks: text generation, image-to-text, text-to-video, text-to-speech
Community-driven platform with model leaderboards
Key advantages over paid services
Single API key provides access to all models
No subscription fees required
Can integrate into business applications via API calls
Hands-on Model Demonstrations
Qwen 2.5 Math Demo
Solves mathematical problems step-by-step
Accepts document uploads with math questions
Business use: Educational support, finance calculations
Virtual Try-On Models
AI-powered clothing fitting simulation
Upload person image, try different garments virtually
Business use: E-commerce, online retail applications
Background Removal & Image Processing
Automated background extraction from images
Clip Interrogator generates descriptive tags from images
Business use: Product catalog automation, content creation
AI Bias and Security Challenges
Training data bias issues
Models inherit human biases from training datasets
Facial recognition systems may favor certain demographics
Recruitment tools can discriminate based on gender/race
Security vulnerabilities
Prompt injection attacks
Data poisoning
Jailbreaking attempts
Deep fake creation
Multi-agent system data leakage
Responsible AI Implementation Strategies
Bias mitigation approaches
Diverse, representative training datasets
Algorithmic transparency and explainability
Fairness testing across demographic groups
Security best practices
Human-in-the-loop validation
Access control and authentication
Adversarial testing (red teaming)
Data anonymization and encryption
Privacy protection methods
Federated learning (on-device processing)
User consent mechanisms
PII masking and encryption
Regulatory Compliance Landscape
Major frameworks
EU AI Act and GDPR (European Union)
AI Executive Orders (United States)
Sector-specific requirements (HIPAA for healthcare)
Key compliance challenges
Innovation pace exceeds regulatory development
Policies always trailing technological advancement
Complex accountability chains in AI systems
Enterprise governance requirements
AI risk assessments before deployment
Regulatory compliance monitoring
Documentation of decision trajectories
Future AI Trends and Applications
Autonomous agent systems
Self-planning and reasoning capabilities
Reduced need for human prompting
Multi-agent coordination and collaboration
Physical AI implementations
Surgical robots and medical diagnosis
Autonomous delivery drones
Smart exoskeletons for disability assistance
Self-driving vehicles and trading bots
Wider adoption barriers
Infrastructure requirements
Regulatory approval processes
Customer acceptance and trust
Technical Implementation Considerations
Multi-agent system coordination
Agent identity and role scoping
Contract-based communication protocols
Policy guardrails for agent interactions
Model performance factors
GPU/TPU requirements for training
Container warm-up times for inference
Resource sharing limitations in free platforms
API integration strategies
Single Hugging Face API key for all models
Model selection based on specific use cases
Performance vs. cost trade-offs
Next Steps
Complete Guided Practice 1: ChatGPT prioritization exercise
Create 2x2 grid for Gen AI feature evaluation
Focus on business impact vs. feasibility analysis
Use latest ChatGPT version available
Upcoming: Lesson 4 - Working with GPTs (hands-on session)
Access materials from LMS demos folder (Lesson 3 and 4 files)
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