Part 2: The Evolution and Categories of AI – Experiments & Development

1950 (Turing): Proposed that machines could mimic human intelligence.
1956 (John McCarthy): Coined the term “AI.”
1980 (Machine Learning): Computers began to learn from data.
1997 (Deep Blue): A computer defeats chess grandmaster Garry Kasparov.
2010 (Deep Learning): The use of multiple layers to recognise complex patterns.
2013: A machine finally passes the Turing test.
2017 (Transformer Model): A breakthrough in how computers understand and work with human language by capturing the relationships between different words.
2020 (GPTs): OpenAI introduces the Generative Pre-trained Transformer.

AI is divided into several functional disciplines:

Machine Learning (ML): Computers learning from data. Deep Learning (DL) is a subset of ML involving “layered” machine learning.
Natural Language Processing (NLP): Computers understanding, interpreting, and creating human language—processing text or speech like people do.
Expert Systems (ES): Mimicking how a human expert makes decisions in a specific area (e.g., diagnosing bacterial infections) using set rules to draw conclusions.
Computer Vision (CV): Computers interpreting visual information from images and videos, including object recognition and image classification.
Speech Recognition (SR): Converting spoken language into written text to facilitate human-computer interaction.
Robotics: Building machines to interact with the real world (e.g., autonomous Mars Rovers) using tools like ML and CV.