Explain the key differences between Artificial Intelligence (AI), Machine Learning (ML), and Generative AI, providing examples of each

• AI: A broad field encompassing machines simulating human intelligence. Examples include expert systems, natural language processing, and robotics
• ML: A subset of AI focusing on algorithms that learn from data to improve performance on a specific task without being explicitly programmed. Examples include image recognition, spam filtering, and recommendation systems
• Generative AI: A type of AI creating new content (text, images, etc.) based on patterns learned from training data. Examples include generating images from text descriptions, composing music, or writing creative text

What are "Foundation Models" and their significance in AI? Explain how they differ from traditional machine learning models.

• Foundation Models: Large-scale, pre-trained models adaptable to various tasks with minimal fine-tuning. They are typically trained on massive datasets and can be used for a variety of downstream tasks like natural language processing, computer vision, and more.
• Difference from Traditional Models: Traditional ML models are often trained from scratch for specific tasks. Foundation models offer greater efficiency and versatility due to their pre-training on diverse data, reducing the need for extensive task-specific training

List 3 common use cases for AI/ML in business, and briefly explain the benefits each brings

(Any 3)

• Customer service chatbots: Provide 24/7 support, handle routine queries, freeing up human agents for complex issues, and improve customer satisfaction
• Fraud detection: Analyze patterns in transactions to identify suspicious activity, reduce financial losses, and protect customers
• Product recommendations: Personalize suggestions based on user behavior and preferences, increasing sales and engagement.
• Demand forecasting: Predict future demand for products or services, optimize inventory levels, and improve supply chain efficiency
• Medical image analysis: Assist in diagnosing diseases by identifying patterns in medical images, improving accuracy and speed of diagnosis

Give an example of how Generative AI can be used in content creation, highlighting its potential impact on the creative industry

• Example: Generating marketing copy tailored to specific audiences, creating unique images for social media campaigns, or even composing original music pieces.
• Impact: Generative AI has the potential to streamline content creation, enabling faster and more efficient production of high-quality content. It can also empower creators to explore new styles and formats, pushing the boundaries of creativity

Explain the difference between "Model Training" and "Fine-tuning", and provide scenarios where each is most appropriate

• Training: The initial process of teaching a model using large datasets to learn general patterns and representations
• Fine-tuning: Adapting a pre-trained model (often a foundation model) to a specific task using smaller, targeted datasets
• Scenarios:
  • Training is suitable when building a new model from scratch or when the target task is significantly different from the pre-trained model's domain.
  • Fine-tuning is ideal when leveraging a pre-trained model for a related task, as it requires less data and computational resources

What are some key criteria for evaluating foundation models? Discuss the challenges in ensuring these criteria are met.

• Key Criteria:
  • *Accuracy: Ability to correctly predict or generate outputs
  • Bias: Minimizing unfair or discriminatory outcomes
  • Robustness: Handling unexpected or adversarial inputs gracefully
  • Efficiency: Performing tasks with optimal use of resources
  • Explainability: Providing insights into how the model arrived at its decisions
• Challenges:
  • Bias: Ensuring fairness can be difficult due to inherent biases in training data or model design.
  • Robustness: Models can be vulnerable to adversarial attacks or unexpected inputs, leading to unpredictable behavior
  • Explainability: Complex models can be difficult to interpret, hindering understanding of their decision-making process.

What is "Responsible AI" and why is it important? Describe some practices that promote responsible AI development and deployment

• *Responsible AI: The practice of ensuring AI systems are fair, transparent, and accountable, minimizing harm and bias.
• Importance: To build trust in AI, prevent unintended consequences, and ensure ethical use
• Practices:
  • Diverse and inclusive teams: Involve people from various backgrounds in AI development
  • Data transparency and quality: Understand and address biases in training data
  • Explainability: Use techniques to make model decisions understandable
  • Continuous monitoring and evaluation: Track performance and address issues proactively.

Briefly outline security and compliance considerations for AI systems. Explain the potential risks and how to mitigate them

• Considerations:
  • Protecting data privacy (encryption, access controls)
  • Securing models against attacks (adversarial attacks, model theft)
  • Adhering to relevant regulations (GDPR, HIPAA)
• Potential Risks:
  • Data breaches: Unauthorized access to sensitive data
  • Model manipulation: Adversarial attacks leading to incorrect or harmful outputs
  • Non-compliance: Legal and financial penalties
• Mitigation:
  • Security best practices: Implement strong authentication, access controls, and data encryption
  • Adversarial training: Expose models to potential attacks during training
  • Regular audits and assessments: Ensure ongoing compliance and identify vulnerabilities.