Overview
IBM Technology provides a clear, jargon-light introduction to the core concepts that underpin modern AI systems. This is an ideal starting point if you're new to AI or need to explain these concepts to stakeholders.
Key Takeaways
The Three Pillars of AI
- Machine Learning (ML) — Pattern recognition from data rather than hard-coded rules. Think recommendation systems that suggest content based on user behavior.
- Deep Learning — Neural networks that process complex relationships, similar to how the human brain works. Powers technologies like image recognition and game-playing AI.
- Natural Language Processing (NLP) — How AI understands and generates human language. Enables voice assistants, translation tools, and generative AI.
Building Blocks
- Algorithms vs. Models — Algorithms are the recipe (step-by-step instructions); models are the finished dish (the trained system created by applying an algorithm to data).
- Data — The fuel for AI. Biased data creates biased results.
- Training → Validation → Testing — Think of it as practice → midterms → finals for AI models.
Emerging Areas
- Generative AI — Creates new content (images, text, code) from prompts.
- Reinforcement Learning — Trial-and-error learning where AI figures out which actions lead to good outcomes.
- Explainable AI (XAI) — Understanding why AI makes certain decisions. Focuses on transparency.
Practitioner Notes
If you're in healthcare security, pay particular attention to a few things here:
Data bias isn't abstract for us
When IBM mentions biased data skewing results, think about AI systems trained on patient data that underrepresents certain populations. This isn't just an accuracy problem — it's a patient safety and compliance issue. HIPAA doesn't explicitly address AI bias, but the downstream effects on care quality absolutely matter.
XAI matters for regulatory defensibility
When the video mentions Explainable AI, connect that to your audit conversations. "Why did the AI flag this patient?" isn't just curiosity — it's a question you'll need to answer for compliance teams and potentially regulators. Black-box AI decisions in clinical settings are increasingly problematic from both an ethical and regulatory standpoint.
The algorithm vs. model distinction helps with vendor conversations
When a vendor says "our AI model," you now know to ask: what algorithm, trained on what data, validated how? This framing gives you the right questions to assess AI products being pitched to your organization.
Training/validation/testing maps to your validation requirements
If your organization is developing or customizing AI tools, understanding these phases helps you build appropriate checkpoints into your secure development lifecycle. Each phase has different data handling and access control considerations.
Continue Learning
This is the first resource in the AI Foundations learning path.