Hala Nelson - Essential Math for AI (Early Release - RAW & UNEDITED)
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by Hala Nelson
Copyright 2022 Hala Nelson. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
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- October 2022: First Edition
- 2021-08-05: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781098107635 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Essential Math for AI, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
The views expressed in this work are those of the author, and do not represent the publishers views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
978-1-098-10763-5
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With Early Release ebooks, you get books in their earliest formthe authors raw and unedited content as they writeso you can take advantage of these technologies long before the official release of these titles.
This will be the 1st chapter of the final book.
If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the author at hala.y.nelson@gmail.com.
It is not until someone said, It is Intelligent, that I stopped searching, and paid attention.
H.
Artificial Intelligence, known as AI, is here. It has penetrated multiple aspects of our lives and is increasingly involved in making very important decisions. Soon it will be employed in every sector of our society, powering most of our daily operations. The technology is advancing very fast and its investments are skyrocketing. At the same time, it feels like we are in the middle of an AI frenzy. Everyday we hear about a new AI accomplishment: AI beats the best human player at Go game. AI outperforms human vision in classification tasks. AI makes deep fakes. AI generates high energy physics data. AI solves difficult partial differential equations that model the natural phenomena of the world. Self-driving cars are on the roads. Delivery drones are hovering in some parts of the world. We also hear about AIs seemingly unlimited potential: AI will revolutionalize healthcare and education. AI will eliminate global hunger. AI will fight climate change. AI will save endangered species. AI will battle disease. AI will optimize the supply chain. AI will unravel the origins of life. AI will map the observable universe. Our cities and homes will be smart. Eventually, we cross into science fiction territory: Humans will upload their brains into computers. Humans will be enhanced by AI. Finally, the voices of fear and skepticism emerge: AI will take over, and destroy humanity.
Amid this frenzy, where the lines between the real, the speculation, the exaggeration, the aspiration, and the pure fiction are blurred, we must first define AI, at least within the context of this book. We will then discuss some of its limitations, where it is headed, and set the stage for the mathematics that is used in todays AI. My hope is that when you understand the mathematics, you will be able to look at the subject from a relatively deep perspective, and the blurring lines between the fiction, the reality, and everything in between, will become more clear. You will also learn the main ideas behind state-of-the-art math in AI, arming you with the confidence needed to use, improve, or even create entirely new AI systems.
I have yet to come across a unified definition of AI. If we ask two AI experts, we hear two different answers. Even if we ask the same expert on two different days, they might come up with two different definitions. The reason for this inconsistency and seeming inability to define AI, is that until now it is not clear what the definition of I is. What is Intelligence? What makes us human and unique? What makes us concious of our own existence? How do neurons in our brain aggregate tiny electric impulses and translate them into images, sounds, feelings and thoughts? These are vast topics that have fascinated philosophers, anthropologists, and neuroscientists for centuries. I will not attempt to go there in this book. I will, however, address artificial intelligence in terms of an AI agent and list the following defining pricinciples for the purposes of this book. In 2021, an AI agent can be one or more of the following:
An AI agent can be pure software, or have a physical robotic body.
An AI agent can be geared toward a specific task, or a flexible agent exploring and manipulating its environment, building knowledge with or without a specific aim.
An AI agent learns with experience, that is, it gets better at performing a task with more practice at that task.
An AI agent perceives its environment then builds, updates, and/or evolves a model for this environment.
An AI agent perceives, models, analyzes, and makes decisions that lead to accomplishing its goal, whatever that predefined fixed or variable goal might be.
Whenever a mathematical model for AI is inspired by the way our brain works, I will point out the analogy, hence keeping AI and human intelligence in comparison, without having to define either. Even though todays AI is nowhere close to human intelligence, except for specific tasks AI such as image classification, AlphaGo, etc., so many human brains have recently converged to develop AI that the field is bound to grow and have breakthroughs in the upcoming years.
It is also important to note that some people use the terms Artificial Intelligence, Machine Learning, and Data Science interchangeably. These domains are overlapping but they are not the same. The fourth very important but slightly less hyped area is that of Robotics, where physical parts and motor skills must be integrated into the learning and reasoning processes, merging mechanical engineering, electrical engineering, bioengineering together with information and computer engineering. One fast way to think about the interconnectivity of these fields is: Data fuels machine learning algorithms that in turn power many popular AI and/or robotics systems
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