Advance Artificial Intelligence Certification: Mechanics, Ethics, and Impact

• 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?

• 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

• 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)

• 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

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