Ch1. What is AI — History and Current State of Artificial Intelligence
What is Artificial Intelligence?
Artificial Intelligence (AI) is the field of computer science dedicated to creating systems capable of performing tasks that normally require human intelligence — learning, reasoning, problem-solving, language understanding, and perception.
The term was coined by John McCarthy at the 1956 Dartmouth Conference, which is considered the birth of AI as a formal discipline. Since then, AI has experienced cycles of enthusiasm and disillusionment known as “AI winters.”
A Brief History of AI
| Period | Event | Significance |
|---|---|---|
| 1950 | Alan Turing proposes the Turing Test | Conceptual foundation for machine intelligence |
| 1956 | Dartmouth Conference | AI officially born as a field |
| 1960s–70s | First AI boom | Rule-based systems, inflated expectations |
| 1974–80 | First AI winter | Funding cuts, technical limitations exposed |
| 1980s | Expert systems rise | Domain-specific problem solving |
| 1987–93 | Second AI winter | Hardware limitations, unmet expectations |
| 1997 | Deep Blue defeats chess world champion | Narrow AI’s potential proven |
| 2012 | AlexNet wins ImageNet | Deep learning renaissance begins |
| 2016 | AlphaGo defeats Lee Sedol | Reinforcement learning breakthrough |
| 2022– | ChatGPT, generative AI goes mainstream | A new inflection point in AI history |
Narrow AI vs. General AI
Every AI system in use today is Narrow AI. ChatGPT, image generators, self-driving cars — they can outperform humans at specific tasks, but cannot think and adapt the way humans do across arbitrary domains.
Narrow AI (Weak AI)
- Specialized for specific tasks
- Chess, image recognition, translation, chatbots
- All currently deployed commercial AI falls here
Artificial General Intelligence (AGI)
- Can perform any intellectual task a human can
- Does not yet exist; researchers debate timelines of 10–50 years
- Some researchers argue it is fundamentally impossible
Artificial Super Intelligence (ASI)
- Exceeds human intelligence across all domains
- Currently a theoretical concept central to AI safety research
The Three Major Areas of AI
Artificial Intelligence (AI)
├── Machine Learning (ML)
│ ├── Supervised Learning
│ ├── Unsupervised Learning
│ └── Reinforcement Learning
│ └── Deep Learning
│ ├── CNN (images)
│ ├── RNN/LSTM (sequences)
│ └── Transformer (language, multimodal)
└── Expert Systems, Rule-Based Systems, etc.Real-World AI Applications
| Domain | Application | Core Technology |
|---|---|---|
| Healthcare | Medical imaging, drug discovery | Deep learning, CNN |
| Finance | Credit scoring, algorithmic trading | ML, time series |
| Transportation | Object detection, path planning | Computer vision, RL |
| Language | Translation, chatbots, summarization | LLM, Transformer |
| Manufacturing | Defect detection, predictive maintenance | Image classification |
| Entertainment | Recommendation systems, generative content | Collaborative filtering, GenAI |
AI and Related Technologies
Big Data → The fuel of AI. Modern AI cannot function without large-scale data.
Cloud Computing → The infrastructure of AI. On-demand GPU resources for training.
IoT → The sensory organs of AI. Collecting real-world data in real time.
Key Concept Cards
Narrow AI vs. AGI ★★★★★ : All current AI is narrow (task-specific). AGI (general-purpose) does not yet exist. Exam point: “ChatGPT is AGI” → False. AGI does not yet exist.
The Deep Learning Renaissance ★★★★★ : AlexNet (2012) sparked the modern era. The combination of big data + GPUs + algorithmic breakthroughs.
AI Winters ★★★★☆ : Periods of sharply reduced funding after inflated expectations met technical reality. First (1974–80), Second (1987–93).
Practice Quiz
Q1. Is ChatGPT narrow AI or AGI? Explain.
ChatGPT is narrow AI. It excels at language-related tasks but cannot generalize the way humans do. It cannot learn from new experiences in real-time, form its own goals, or apply reasoning across fundamentally different domains without specialized retraining.
Q2. Why have AI winters recurred throughout history?
A mismatch between inflated expectations and technical reality. Each boom attracted heavy investment and optimistic predictions. When hardware limitations, lack of data, and algorithmic constraints prevented the promised breakthroughs, funding and interest collapsed rapidly — until the next wave of innovation restarted the cycle.
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