Artificial Intelligence




The future will not be written by AI alone, but by how wisely we choose to use it.

Artificial Intelligence

Artificial Intelligence refers to a collection of technologies that enable computers and machines to perform tasks typically requiring human intelligence. These tasks include learning, reasoning, decision-making, and problem-solving.

Key Capabilities of AI


  • Data Analysis: Processes large volumes of data to generate predictions and insights.
  • Language Understanding: Understands written and spoken language.
  • Decision‑Making: Makes informed decisions using data‑driven reasoning.
  • Complex Task Execution: Handles tasks involving perception, planning, and communication.
  • Learning from Experience: Continuously improves performance through learning.

AI Tasks & Applications

Learning

Definition: Recognizing patterns and learning from data to enhance performance.

Applications:

  • Education: Grammar correction, flashcards, tutoring.
  • Business: Improves productivity and efficiency.

Reasoning

Definition: Analyzing information and drawing conclusions.

Types: Deductive, Inductive, Abductive, Analogical Reasoning.

Natural Interaction

Function: Communicates using natural language.

Challenges: Ambiguity, technical limitations.

Benefits: Improves accessibility, mental health, and collaboration.

Other Key Tasks

  • Content Creation: Generates text, audio, image, and video.
  • Prediction: Forecasts behaviours and preferences.
  • Filtering: Manages and prioritizes large data sets.
  • Autonomous Driving: Controls vehicles through environmental awareness.

AI & DATA STRATEGY FRAMEWORK

  1. Define Business Objectives
  2. Collect & Manage Data
  3. Establish Infrastructure & Tools
  4. Develop Models
  5. Integrate Automation
  6. Ensure Scalability
  7. Monitor and Collect Feedback
  8. Train Workforce
  9. Manage Risk & Compliance
  10. Drive Adoption & Change

AI Centre of Excellence (CoE)

A dedicated hub for:

  • Innovation
  • Research
  • Skill development
  • Governance and standardization

AI Workforce & Skills

In-Demand Skills

  • ML & Deep Learning
  • Data Science & Engineering
  • NLP
  • AI Infrastructure (Cloud)
  • Computer Vision
  • AI Product Management
  • Cybersecurity & Ethics

Impact on Workforce

  • Job transformation
  • Upskilling & Reskilling
  • Ethical decision-making
  • Human-AI collaboration

Value Creation Through AI

  • Data-driven decisions
  • Personalized user experiences
  • Operational efficiency
  • Predictive risk management
  • Innovation and sustainability

Modern Data & Analytics Architecture

  1. Cloud Infrastructure
  2. Real-Time Analytics
  3. Data Lakes/Warehouses
  4. Advanced ML & Analytics
  5. Data Governance
  6. Self-Service BI
  7. Data Democratization
  8. Data Visualization
  9. Automation
  10. Team Collaboration
  11. Edge Analytics

Applications of AI in Healthcare

  • Drug Creation: Faster discovery and testing
  • Treatment Design: Personalized and image-assisted
  • Disease Monitoring: Trend forecasting
  • Diagnosis Aid: Enhanced precision with ML/NLP
  • Wearables: Real-time health tracking

Data Management Solutions

  • Storage (Cloud, Lakes, Warehouses)
  • Integration (ETL Tools)
  • Governance (Security, Compliance)
  • Analytics & Reporting
  • Backup & Recovery
  • Lifecycle Management
  • Automation
  • Team Collaboration

Generative AI

Models that generate original content.

  • GPT (Text)
  • DALL·E (Images)
  • MusicLM (Audio)
  • Deepfakes (Video)

Trustworthy AI

AI that adheres to core ethical and operational principles.

  • Fairness
  • Transparency
  • Accountability
  • Privacy
  • Robustness
  • Ethical Alignment

Advantages of AI

  • High computational power
  • Reduced human error
  • 24/7 availability
  • Broader healthcare accessibility
  • Enhanced decision quality
  • Risk mitigation
  • Innovative solutions
  • Time and cost savings

Drawbacks of AI & Future Mitigation Strategies

  1. Bias and Discrimination: Mitigation – fairness-aware algorithms, diverse datasets, audits, ethical reviews.
  2. Job Displacement: Mitigation – reskilling, lifelong learning, collaboration roles.
  3. Privacy Concerns: Mitigation – GDPR, privacy-by-design, differential privacy, federated learning.
  4. Security Risks: Mitigation – cybersecurity frameworks, secure AI deployment, adversarial testing.
  5. Lack of Transparency: Mitigation – Explainable AI (XAI), interpretability tools, transparency in deployment.
  6. Ethical Dilemmas: Mitigation – guidelines, stakeholder input, oversight, human-in-the-loop.
  7. High Development Costs: Mitigation – open-source tools, partnerships, collaborative research.