As we continue to make new advances in artificial intelligence and machine learning, this series takes a look back at the story of how we taught computers to learn. What are the ideas that made modern AI tools possible? Where did the ideas come from? And what’s the logic behind how they work?
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0:00 Intelligence
0:45 Theseus
2:07 Turing Test
3:49 Playing Chess
5:30 Artificial Neurons
source
date: 2024-07-28 18:03:01
duration: 00:09:06
author: UCDzVUXiTr3hClI-zzCWbYzg
"Teaching Computers to Learn, Part 1"
In this first part of a series, we’ll delve into the fascinating story of how we taught computers to learn. From the pioneers of computer science to the development of machine learning algorithms, we’ll explore the key milestones that have led to the intelligent machines we see today.
We begin with two pioneers: Claude Shannon, who created a robotic mouse that could navigate a maze in 1950, and Alan Turing, who proposed the Turing Test, which would later become the benchmark for measuring a machine’s intelligence. Both Shannon and Turing recognized the importance of teaching machines to learn from their experiences, but they didn’t know how to achieve this.
Shannon’s robotic mouse used a simple heuristic approach, remembering the path it had taken through the maze to navigate it efficiently. Turing, on the other hand, proposed a more complex approach, suggesting that a computer could learn to think like a human through a series of experiences and rewards/punishments.
In 1950, Warren McCulloch and Walter Pitts developed a mathematical model of neural activity, using propositional logic to represent the behavior of neurons in the human brain. This work would later influence the development of machine learning algorithms.
In the next part of this series, we’ll explore how these early ideas laid the groundwork for machine learning, and how it has evolved over the years to enable computers to learn and adapt in various domains, from Siri and Alexa to self-driving cars and medical diagnosis. Stay tuned!
