Lilly Tech Systems
Introduction to Artificial Intelligence
Artificial Intelligence is one of the most transformative technologies of our time. This lesson establishes the foundations you need to understand what AI is, what it is not, and where it fits in the broader technology landscape.
What is Artificial Intelligence?
Artificial Intelligence (AI) is the field of computer science dedicated to creating systems that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and decision-making.
There is no single universally accepted definition of AI, but here are several widely used ones:
- John McCarthy (1956): "The science and engineering of making intelligent machines."
- Elaine Rich: "The study of how to make computers do things at which, at the moment, people are better."
- Stuart Russell & Peter Norvig: AI is about building "rational agents" — systems that perceive their environment and take actions to maximize their chances of achieving their goals.
AI vs ML vs DL vs Data Science
These terms are often used interchangeably, but they represent distinct (and nested) concepts:
| Field | Definition | Relationship |
|---|---|---|
| Artificial Intelligence | The broadest field: creating intelligent machines | The umbrella discipline |
| Machine Learning | A subset of AI: systems that learn from data | A technique within AI |
| Deep Learning | A subset of ML: neural networks with many layers | A technique within ML |
| Data Science | Extracting insights from data using statistics, ML, and visualization | Overlaps with ML but has a broader scope |
The Turing Test
In 1950, Alan Turing proposed a test for machine intelligence in his paper "Computing Machinery and Intelligence." In the Turing Test, a human evaluator converses with both a human and a machine (without knowing which is which). If the evaluator cannot reliably distinguish the machine from the human, the machine is said to exhibit intelligent behavior.
While historically influential, the Turing Test has limitations: it tests only conversational ability, not broader intelligence. A system could pass the test through tricks without truly "understanding" anything.
The Chinese Room Argument
Philosopher John Searle proposed the Chinese Room thought experiment in 1980 to challenge the idea that passing the Turing Test means a machine truly understands. In the thought experiment, a person who does not speak Chinese sits in a room with a rulebook. They receive Chinese characters as input, look up the rules, and produce Chinese characters as output. To an outside observer, the room "speaks" Chinese — but the person inside understands nothing.
This distinction between appearing intelligent and truly understanding remains one of the deepest questions in AI philosophy.
Strong AI vs Weak AI
| Concept | Description | Current Status |
|---|---|---|
| Weak AI (Narrow AI) | AI designed for a specific task. It simulates intelligence without possessing true understanding or consciousness. | This is all current AI. Examples: Siri, chess engines, recommendation systems. |
| Strong AI (General AI) | AI that possesses genuine understanding and consciousness — a mind in the full philosophical sense. | Does not exist. Whether it is possible remains debated. |
The Current State of AI
As of 2026, AI has made remarkable progress:
- Large Language Models (GPT-4, Claude, Gemini) can write, code, reason, and converse at near-human levels across many domains
- Computer Vision systems match or exceed human performance in image classification and object detection
- Generative AI creates realistic images, music, video, and 3D content from text descriptions
- AI Agents can autonomously browse the web, write code, and complete multi-step tasks
- Scientific AI is accelerating drug discovery (AlphaFold), materials science, and climate modeling
However, current AI still cannot match human common sense, transfer knowledge flexibly between domains, or reason about novel situations the way humans can.