Workshop Registration End Date :19 Jan 2026

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Virtual Workshop

Advance Artificial Intelligence Certification: Mechanics, Ethics, and Impact

Goal: To provide researchers with a deep conceptual understanding of AI, moving beyond the hype to the actual science and logic of the technology.

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MODE
Mode(Online) TYPE
Mentor Based LEVEL
Advanced DURATION
4 Days (60-90 Minutes each day)Dates
19 Jan 2026 to 22 Jan 2026 Timings
Indian Standard Timing 5: 30 PM

Skills you will gain:

About Workshop:

Aim: To equip researchers and academicians with a foundational, scientifically grounded understanding of Artificial Intelligence—moving beyond media hype to understand the mechanics, limitations, and ethical implications of the technology reshaping the academic landscape.

Workshop Objectives:

What you will learn?

📅 Day 1 — Demystifying the “Black Box” (Mechanics)

  • Focus: How does modern AI actually function?
  • 1.1 Defining Intelligence:
  • The distinction between Symbolic AI (Old AI: “If X, then Y” rules) and Connectionist AI (Modern AI: Neural Networks learning from data)
  • Key Terminology: Algorithm vs. Model vs. Weights/Parameters
  • 1.2 The Engine of Generative AI (LLMs):
  • The Prediction Game: Explaining how ChatGPT is essentially a “next-word prediction engine” based on probability, not a knowledge base
  • Vectors & Embeddings: How computers turn language into numbers (math) to understand relationships (e.g., how “King – Man + Woman = Queen”)
  • Transformers: The 2017 breakthrough (the “T” in GPT) that allowed AI to understand context and long paragraphs
  • 1.3 Discussion Point:
  • If an AI is just predicting the next likely word, can it truly be said to “understand” a concept?

📅 Day 2 — The Landscape of AI (Types & Modalities)

  • Focus: What are the different forms of AI existing today?
  • 2.1 The AI Taxonomy:
  • Machine Learning (ML): Algorithms that improve with experience (e.g., email spam filters, linear regression)
  • Deep Learning: Multi-layered neural networks that mimic the human brain structure (used in image recognition)
  • Generative AI: Systems that create new data (images, text, audio) rather than classifying existing data
  • 2.2 Beyond Text (Multimodality):
  • Computer Vision: How AI “sees” (pixels as numbers) – relevant for medical imaging, satellite data, etc.
  • Audio & Speech: How AI processes sound waves (relevant for linguistics, oral history)
  • 2.3 The Training Process:
  • Pre-training: Reading the entire internet (Wikipedia, Common Crawl)
  • Fine-tuning (RLHF): How humans teach the AI to be “helpful and harmless” by rating its answers

📅 Day 3 — The Ethics & Limitations (Critical Analysis)

  • Focus: What are the dangers and blind spots?
  • 3.1 The Hallucination Problem:
  • Why AI lies confidently. Explaining “Stochastic Parrots”—the idea that AI mimics the form of truth without the substance of fact
  • 3.2 Bias and Representation:
  • Data Bias: If history books are biased, the AI trained on them will be biased. (Case studies: Gender bias in translation, racial bias in facial recognition)
  • WEIRD Data: The over-representation of Western, Educated, Industrialized, Rich, and Democratic societies in training data
  • 3.3 Intellectual Property & Copyright:
  • The current legal debate: Does training an AI on copyrighted books/papers constitute “Fair Use”?
  • 3.4 Discussion Point:
  • Should AI be listed as a co-author on research papers? (Reviewing COPE and Nature guidelines)

📅 Day 4 — The Future of Research & Society

  • Focus: Where are we going?
  • 4.1 ANI vs. AGI:
  • ANI (Artificial Narrow Intelligence): Good at one thing (Chess, Protein Folding). We are here.
  • AGI (Artificial General Intelligence): Human-level reasoning across any domain. The theoretical goal.
  • 4.2 AI in the Scientific Method:
  • How AI is changing the process of discovery (e.g., AlphaFold solving biology problems that took humans 50 years)
  • The shift from “Hypothesis-Driven” science to “Data-Driven” discovery
  • 4.3 The “Black Box” Problem in Science:
  • The issue of explainability: If an AI predicts a cancer diagnosis but cannot explain why, can doctors trust it?
  • 4.4 Closing Q&A:
  • Open floor for researchers to discuss the impact on their specific domains

Mentor Profile

Fee Plan

Ph.D. Scholar / ResearcherINR 2999/- OR USD 55
Academician / FacultyINR 3999/- OR USD 65
Industry ProfessionalINR 5999/- OR USD 85

Important Dates

Registration Ends
19 Jan 2026 Indian Standard Timing 4:30 PM
Workshop Dates
19 Jan 2026 to 22 Jan 2026  Indian Standard Timing 5: 30 PM
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Intended For :

  • AI/ML Enthusiasts wanting to understand modern AI technologies like LLMs and generative AI.

  • Researchers exploring AI’s role in scientific discovery and data-driven methods.

  • Industry Professionals in sectors such as healthcare, robotics, and technology, looking to navigate AI’s technical and ethical challenges.

  • Students in computer science, data science, and related fields seeking foundational AI knowledge.

  • Ethics Advocates concerned with bias, data representation, and AI’s societal impact.

  • Tech Innovators eager to learn about AI’s future in AGI and its applications.

Career Supporting Skills

Workshop Outcomes

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