§ — — Introduction to Computing
Artificial Intelligence (AI) refers to the design and development of computer systems that can perform tasks typically associated with human intelligence — reasoning, recognizing patterns, learning from experience, understanding natural language, and making decisions.
John McCarthy, widely credited as one of AI's founding figures, defined it as "the science and engineering of making intelligent machines."
Other working definitions:
| Year | Event |
|---|---|
| 1943 | Warren McCulloch and Walter Pitts proposed the first mathematical model of artificial neurons. |
| 1949 | Donald Hebb described a learning rule for updating neuron connection strengths — now called Hebbian learning. |
| 1950 | Alan Turing published a test for machine intelligence — later called the Turing Test. |
| 1955 | Allen Newell and Herbert A. Simon created the "Logic Theorist," the first AI program. |
| 1956 | John McCarthy coined the term "Artificial Intelligence" at the Dartmouth Conference. |
| 1966 | Joseph Weizenbaum created ELIZA, the first chatbot. |
| 1972 | WABOT-1, the first intelligent humanoid robot, was built in Japan. |
| 1974–1980 | First "AI winter" — funding and interest declined due to unmet expectations. |
| 1980 | Expert systems revived AI; first US national AI conference held at Stanford. |
| 1987–1993 | Second "AI winter" — costs exceeded results, funding dropped again. |
| 1997 | IBM's Deep Blue beat world chess champion Garry Kasparov. |
| 2002 | Roomba, the first AI-based consumer robot vacuum, launched. |
| 2011 | IBM's Watson won Jeopardy!, demonstrating natural language understanding. |
| 2014 | Chatbot "Eugene Goostman" passed the Turing Test in a competition. |
| 2018 | IBM's "Project Debater" argued complex topics against human debaters; Google Duplex made real phone calls on behalf of users. |
AI is being applied across virtually every sector:
Machine learning (ML) is a subset of AI in which computer systems learn to perform tasks by building models from data, without being explicitly programmed with rules for each situation.
A machine learning algorithm improves its performance on a task (T), as measured by a performance metric (P), through accumulated experience (E).
Supervised Learning — the algorithm is trained on labeled examples (input paired with the correct output). The goal is to learn a general rule that maps new inputs to outputs. Example: training a spam filter on emails labeled "spam" or "not spam."
Unsupervised Learning — no labels are provided. The algorithm finds structure or patterns in the data on its own. Example: grouping customers into segments based on purchasing behavior without predefined categories.
Reinforcement Learning — the algorithm interacts with an environment and receives feedback (rewards or penalties) based on its actions. It learns to maximize rewards over time. Example: teaching a program to play a game by rewarding it for winning moves.
Deep learning is a subset of machine learning that uses multi-layered artificial neural networks — structures loosely inspired by biological neurons in the brain. Data flows through multiple layers, with each layer transforming the output of the previous one. The deeper the network, the more abstract the patterns it can detect.
Key characteristics:
Deep learning powers many of the most impressive modern AI applications: image recognition, speech recognition, language translation, and autonomous driving.
Data science combines statistics, computer programming, and domain expertise to extract useful knowledge from large, complex datasets — especially from "big data." It supports decision-making by turning unstructured, high-volume data into actionable insights.
Data is generated from countless sources: mobile phones, social media, e-commerce platforms, healthcare systems, search engines, and more. As the volume of data grew, traditional analysis methods became insufficient — giving rise to data science as a distinct professional discipline.
Big data refers to datasets so large, fast-moving, and varied that conventional data-processing tools cannot handle them. It is characterized by three V's:
Companies that successfully harness big data — Amazon, Google, Facebook, Twitter — use it to gain competitive advantages and serve users better.
A data scientist collects, analyzes, and interprets very large amounts of data to support business and research decisions. The role combines skills from mathematics, statistics, computer science, and domain knowledge.
Core responsibilities include:
Data scientists are critical to organizations pursuing machine learning and AI adoption because they customize algorithms, make sense of complex datasets, and guide data-driven decisions.
A social networking service is an online platform where people build networks and relationships with others who share similar interests, backgrounds, or real-life connections. These platforms let users share ideas, photos, videos, and updates — connecting people globally in ways previously impossible.
Research shows that social media has produced significant effects — both positive and negative — on individuals and society.
Positive effects:
Negative effects:
Responsible use — strong passwords, limited sharing of personal details, critical evaluation of content, and setting time boundaries — can mitigate many of the negative effects.
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