Hi Leonie—great post! It’s really valuable to see people give an honest account of their learning journey in a public forum like this one.
I am a data scientist and work with machine learning in some capacity for my job, so have plenty of more mathematical textbooks I could recommend, but I won’t do that. My background is actually philosophy, so I have had a journey moving from an essay-writing undergraduate student to graduate data scientist, and I know what it’s like to not feel like you know anything about this stuff.
With that said, here are three books I would recommend to a non-technical person wanting to learn more about AI, for AI governance or otherwise. These are not AI safety books, or AI policy books, but are merely introductory books for someone with close to zero starting knowledge about AI.
The Hundred-Page Machine Learning Book by Andriy Burkov. This one is commonly suggested as a quick overview of machine learning and tries to go deep without going too technical. It has glowing recommendations from many experienced people in the field, such as Peter Norvig. https://www.amazon.de/Hundred-Page-Machine-Learning-Book/dp/199957950X/
Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell. This is in the Pelican series, which is a series of short explanatory books on a single topic for a general reader (another one is the book The Art of Statistics by David Spiegelhalter, also recommended for learning the intuition behind statistics). I have heard good things about this book and Melanie Mitchell has a well-established reputation in the field of AI research. https://www.amazon.de/Artificial-Intelligence-Thinking-Humans-Pelican/dp/0241404835/
The Quest for Artificial Intelligence: A History of Ideas and Achievements by Nils J. Nilsson. This book is unique. The author has lived through some of the most important developments in the history of artificial intelligence and has often directly worked with many of the key characters in the story. For someone with a more arts or humanities background, getting to know a technical field by its history is sometimes a really good tactic. I read this a few years ago and it really gave me a high-level sense of where the field has come from and where the field might be going. This one is highly recommended. https://www.amazon.de/Quest-Artificial-Intelligence-Nils-Nilsson/dp/0521122937/
I hope your learning journey goes well and that you continue to write down things that you learn (as you have already done so with this post), as I’m sure it will be really useful to others in a similar position.
Best of luck!
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EDIT: I was reflecting on what you wrote in the post regarding math and felt like I should say something else just about learning mathematics. Math can definitely feel like something that people are either good at or not, with no in-between. I am sure that you’ve already done some thinking about this yourself(!) It’s a personal choice for everyone but I think trying to learn the basics of geometry, algebra, calculus, etc, is so important for everyone that you should at least give it multiple attempts, kind of like learning to drive a car! Here are some resources that I have found useful to motivate myself:
https://brilliant.org/courses/ - Brilliant is a math and science education website with a free and paid-for tier. I have the paid tier at the moment. It has everything from daily problems, games, and many short courses that range in difficulty from high school through to approximately first-year university/college level. I have particularly enjoyed some of the linear algebra and probability courses, which I did this year, because they were relevant to my job, but I would recommend to not choose the course by its ‘utility’ as such—try to choose a course that interests you!
Also, here’s a TED talk I enjoyed by math educator Jo Boaler on the idea of a ‘math person’:
Thanks to Leonie for their post and to Henry for this comment! I’ve now bought & downloaded Artificial Intelligence: A Guide for Thinking Humans (since it was available as an audiobook), I’ve added this post to my Collection of AI governance reading lists, syllabi, etc., and I expect I’ll revisit this post at some future point as well.
Hi Henry :) Thanks a lot for your kind words—and for sharing your thoughts and resources on the topic! I am very grateful you’ve commented on the post as someone with a technological background. Will definitely have a look at them myself as well.
RE maths: I think I do understand the basics. We had pretty much of that at highschool and the statistics courses included a lot of mathematics as well (especially probabilities). So I agree that you probably need some knowledge here, but maybe this is the reason why I didn’t need to go deeper(?)
Hi Leonie—great post! It’s really valuable to see people give an honest account of their learning journey in a public forum like this one.
I am a data scientist and work with machine learning in some capacity for my job, so have plenty of more mathematical textbooks I could recommend, but I won’t do that. My background is actually philosophy, so I have had a journey moving from an essay-writing undergraduate student to graduate data scientist, and I know what it’s like to not feel like you know anything about this stuff.
With that said, here are three books I would recommend to a non-technical person wanting to learn more about AI, for AI governance or otherwise. These are not AI safety books, or AI policy books, but are merely introductory books for someone with close to zero starting knowledge about AI.
The Hundred-Page Machine Learning Book by Andriy Burkov. This one is commonly suggested as a quick overview of machine learning and tries to go deep without going too technical. It has glowing recommendations from many experienced people in the field, such as Peter Norvig. https://www.amazon.de/Hundred-Page-Machine-Learning-Book/dp/199957950X/
Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell. This is in the Pelican series, which is a series of short explanatory books on a single topic for a general reader (another one is the book The Art of Statistics by David Spiegelhalter, also recommended for learning the intuition behind statistics). I have heard good things about this book and Melanie Mitchell has a well-established reputation in the field of AI research. https://www.amazon.de/Artificial-Intelligence-Thinking-Humans-Pelican/dp/0241404835/
The Quest for Artificial Intelligence: A History of Ideas and Achievements by Nils J. Nilsson. This book is unique. The author has lived through some of the most important developments in the history of artificial intelligence and has often directly worked with many of the key characters in the story. For someone with a more arts or humanities background, getting to know a technical field by its history is sometimes a really good tactic. I read this a few years ago and it really gave me a high-level sense of where the field has come from and where the field might be going. This one is highly recommended. https://www.amazon.de/Quest-Artificial-Intelligence-Nils-Nilsson/dp/0521122937/
I hope your learning journey goes well and that you continue to write down things that you learn (as you have already done so with this post), as I’m sure it will be really useful to others in a similar position.
Best of luck!
----
EDIT: I was reflecting on what you wrote in the post regarding math and felt like I should say something else just about learning mathematics. Math can definitely feel like something that people are either good at or not, with no in-between. I am sure that you’ve already done some thinking about this yourself(!) It’s a personal choice for everyone but I think trying to learn the basics of geometry, algebra, calculus, etc, is so important for everyone that you should at least give it multiple attempts, kind of like learning to drive a car! Here are some resources that I have found useful to motivate myself:
https://brilliant.org/courses/ - Brilliant is a math and science education website with a free and paid-for tier. I have the paid tier at the moment. It has everything from daily problems, games, and many short courses that range in difficulty from high school through to approximately first-year university/college level. I have particularly enjoyed some of the linear algebra and probability courses, which I did this year, because they were relevant to my job, but I would recommend to not choose the course by its ‘utility’ as such—try to choose a course that interests you!
Also, here’s a TED talk I enjoyed by math educator Jo Boaler on the idea of a ‘math person’:
Thanks to Leonie for their post and to Henry for this comment! I’ve now bought & downloaded Artificial Intelligence: A Guide for Thinking Humans (since it was available as an audiobook), I’ve added this post to my Collection of AI governance reading lists, syllabi, etc., and I expect I’ll revisit this post at some future point as well.
Hi Henry :) Thanks a lot for your kind words—and for sharing your thoughts and resources on the topic! I am very grateful you’ve commented on the post as someone with a technological background. Will definitely have a look at them myself as well.
RE maths: I think I do understand the basics. We had pretty much of that at highschool and the statistics courses included a lot of mathematics as well (especially probabilities). So I agree that you probably need some knowledge here, but maybe this is the reason why I didn’t need to go deeper(?)