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Key Concepts to Memorize
The AI hierarchy (nested, largest to smallest):
Artificial Intelligence (1955) ⊃ Machine Learning (1980) ⊃ Deep Learning (2010) ⊃ Foundation Models (2017)- AI (1955): Imitate intelligent behaviour in computers
- ML (1980): AI by learning and predicting from data
- DL (2010): ML using deep neural networks
- Foundation Models (2017): Generative AI based on pretrained transformers
Are LLMs intelligent? → No, not really
- High performance ≠ human-like behaviour
- LLMs fail on adversarial inputs trivial for humans (e.g., unusual 3D viewpoints)
- "Error consistency" measures whether AI and humans fail on the _same_ examples — DNNs and humans fail differently
Tool vs. Model distinction:
- Tool: Statistical algorithm, no claim about human behaviour (e.g., DNN for image classification)
- Model: Formal representation of a scientific theory, used to explain/predict a phenomenon
Remaining challenges of current AI:
1. Robustness and Safety — vulnerable to adversarial attacks
2. Non-Generalizable Single-Purpose Design — models trained for one task can't transfer
AI Success Stories (specific examples):
| System | Purpose |
|---|---|
| AlphaGo | Mastering the game of Go |
| AlphaFold | Predicting protein 3D structures |
| MyShake | Earthquake early warning system (smartphone network) |
| Apple Watch ECG | Detecting atrial fibrillation & left ventricular ejection fraction |
| Autonomous driving | Perception & path planning (e.g., Waymo) |
What AI needs: Data & Compute
- ChatGPT's success driven by growth in both data and compute
- Europe is falling behind the US and China in AI capacity
Consciousness in AI:
- No consensus on what consciousness is — subjective and hard to measure
- Key distinction: intelligence ≠ consciousness
- Main theories: Global Workspace Theory (GWT), Integrated Information Theory (IIT), Higher-Order Theories, Panpsychism
- Expert camps: Skeptical / Optimistic / Pragmatic / Ethical ("better safe than sorry")
- Efficiency gap: 1,000 artificial neurons needed to simulate a single biological neuron