Lecture 02 — How to Think Like a Multimodal System Designer
~3–4 hours (core system-design lecture)
🌍 Why This Lecture Matters
Most people learn:
- 📦 APIs
- 🧩 libraries
- ⚙️ frameworks
Very few learn:
How to design an intelligent system from first principles.
This lecture transforms you from:
model user → multimodal system architect
🧠 The Architect’s Mindset
A multimodal architect does not ask first:
❌ “Which model should I use?”
They ask:
✅ What problem am I solving?
✅ What information is available?
✅ What modality carries the signal?
✅ What errors are acceptable?
🏗️ First Principle #1: Start From the Task, Not the Model
Every intelligent system begins with a task definition.
Ask These Questions (Always)
- What is the input?
- What is the output?
- What transformation is required?
- What failure is unacceptable?
Example
Task: Medical image diagnosis
| Aspect | Decision |
|---|---|
| Input | Image + text report |
| Output | Text explanation |
| Risk | False negative |
| Requirement | Human-in-the-loop |
🧪 Knowledge Check — Task Thinking
Q1 (Objective)
Why should task definition come before model selection?
Answer
Because architecture depends on constraints, risk, and signal — not tools.
🧠 First Principle #2: Modalities Are Information Channels
Each modality has strengths and weaknesses.
| Modality | Strength | Weakness |
|---|---|---|
| Text | Reasoning | No raw perception |
| Image | Spatial | No abstraction |
| Audio | Emotion, tone | Noise |
| Video | Time | Cost & complexity |
Good design uses the minimum modality required.
❌ Over-Engineering Trap (Very Common)
Many beginners think:
“More modalities = smarter AI”
Reality:
More modalities = more noise, cost, and failure modes
🧪 Knowledge Check — Modalities
Q2 (MCQ)
Which modality is the most expensive to annotate?
A) Text
B) Image
C) Audio
D) Video
Correct Answer
D) Video
🧠 First Principle #3: Separate Perception from Reasoning
One of the most important design rules.
Correct Separation
Perception → Representation → Reasoning → Action
- Perception = encoders
- Reasoning = LLM
- Action = output or tool use
Why This Matters
If you mix everything:
- debugging becomes impossible
- errors propagate
- evaluation is unclear
Clean boundaries create reliable systems.
🧪 Knowledge Check — Architecture
Q3 (True / False)
Vision models should handle logical reasoning.
Answer
False.
🧠 First Principle #4: Alignment Is the Bottleneck
Alignment answers:
How does non-language data become “thinkable”?
Bad alignment →
- hallucinations
- irrelevant answers
- false confidence
Good alignment →
- reasoning
- grounding
- generalization
🧩 Alignment Design Choices
| Method | When to Use |
|---|---|
| Linear projection | Simple tasks |
| MLP | Moderate complexity |
| Cross-attention | High alignment need |
| Q-Former | Vision-language fusion |
🧪 Knowledge Check — Alignment
Q4 (MCQ)
Which component most directly affects hallucination?
A) Tokenizer
B) Alignment layer
C) GPU size
D) Dataset size
Correct Answer
B) Alignment layer
🧠 First Principle #5: Think in Pipelines, Not Models
Architects think in pipelines.
Example: Image Question Answering
Image → Vision Encoder
Question → Text Encoder
↓
Alignment
↓
LLM
↓
Answer
Each box is:
- replaceable
- testable
- improvable
🧪 Knowledge Check — Pipeline Thinking
Q5 (Objective)
Why should components be modular?
Answer
To allow debugging, replacement, and independent improvement.
🧠 Beginner → Advanced Design Levels
| Level | Focus |
|---|---|
| Beginner | Single modality |
| Intermediate | Multimodal fusion |
| Advanced | RAG + tools |
| Expert | Agents + feedback loops |
You do not jump levels.
🧠 Case Study 1 — Image Captioning
Design Decision
| Choice | Reason |
|---|---|
| Frozen vision encoder | Stability |
| Small projection | Efficiency |
| Pretrained LLM | Reasoning |
This works because task complexity is low.
🧠 Case Study 2 — Medical VQA (High Risk)
Changes:
- HITL required
- Conservative decoding
- Explanation mandatory
Risk changes architecture.
🧪 Knowledge Check — Risk Awareness
Q6 (True / False)
The same architecture fits both chatbots and medical diagnosis.
Answer
False.
🧠 First Principle #6: Evaluation Shapes Design
If you can’t measure it:
You can’t trust it.
Evaluation informs:
- architecture choice
- data needs
- alignment method
(We go deep in Lecture 09.)
🧠 Thinking Beyond Accuracy
Architects evaluate:
- robustness
- calibration
- failure modes
- ethical impact
🧪 Knowledge Check — Evaluation Thinking
Q7 (Objective)
Why is accuracy alone insufficient?
Answer
Because it hides bias, uncertainty, and rare failures.
🧠 Architect’s Checklist (Very Practical)
Before coding, ask:
- Is this modality necessary?
- Is language the reasoning layer?
- Is alignment explicit?
- Are risks identified?
- Can humans intervene?
🌱 Human-Centered Design (Core Philosophy)
Multimodal AI should:
- assist humans
- explain decisions
- accept correction
Architects design for humility, not dominance.
🧪 Final Knowledge Check — Reflection
What separates an AI architect from a model user?
System-level thinking, responsibility, and first-principle design.
✅ Final Takeaways
- Architects start from tasks
- Modalities are information channels
- Alignment is critical
- Pipelines beat monoliths
- Ethics influence architecture
🌍 Final Reflection
If AI systems fail, who is responsible?
The humans who designed them.