0 - Prologue Flashcards
What does the book ‘Why Machines Learn’ explain?
The mathematics underlying modern machine learning and its social history
The book contextualizes math within the history of AI and machine learning, making it accessible.
Who is Frank Rosenblatt?
A Cornell University psychologist who invented the perceptron
Rosenblatt’s work is foundational in the field of artificial intelligence.
What is a perceptron?
A version of a neural network that was presented by Frank Rosenblatt in the 1950s
It was claimed to be the first device to think like a human brain.
What type of AI does machine learning involve?
Building machines that can learn to discern patterns in data without explicit programming
This allows applications like image recognition and autonomous vehicles.
What are the key mathematical fields underlying machine learning?
- Linear algebra
- Calculus
- Probability and statistics
- Optimization theory
These fields provide the theoretical foundation for machine learning algorithms.
What is Bayes’s theorem?
A key contribution to probability and statistics by Thomas Bayes
It is fundamental in the development of machine learning algorithms.
What is the Gaussian distribution?
A bell-shaped curve that is central to the field of statistics and machine learning
It is associated with the work of Carl Friedrich Gauss.
How has machine learning impacted various fields?
It influences chemistry, biology, physics, and more
Applications include genome studies and quantum system analysis.
What does the author suggest about the future of AI?
Understanding the mathematics of machine learning is crucial for preparing for an AI-ubiquitous future
This includes both the potential benefits and risks of AI technology.
True or False: The perceptron lived up to the hype surrounding it.
False
Despite initial excitement, the perceptron did not fulfill its ambitious promises.
Fill in the blank: The field of machine learning relies heavily on _______ math.
[relatively simple]
What did Ilya Sutskever find surprising about the math of deep learning?
Its simplicity compared to traditional math and physics coursework
Sutskever is co-founder of OpenAI and emphasizes the accessibility of deep learning concepts.
What does the author compare the learning process in writing the book to?
The way modern artificial neural networks learn
This analogy highlights the iterative process of understanding complex subjects.
What is the significance of the ‘intellectual discomfort’ in learning math?
It is part of making progress in mathematics
This concept is discussed by mathematician Eugenia Cheng.
What is the main narrative of the book?
The journey from Rosenblatt’s perceptron to modern deep neural networks
It highlights key mathematical ideas and historical context in machine learning.
What role do educators and policymakers have regarding AI?
They must understand the basics of machine learning mathematics to regulate AI effectively
This ensures informed decision-making about AI technology.
True or False: Machine learning systems are only used in technology and not in scientific research.
False
Machine learning is widely applied in various scientific fields.
