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During the last academic year, I decided to pause my studies at university and do some self-studies instead. I am a German law student and interested in AI governance. Before, I had already read quite some articles on the topic with the EA-Berlin reading group. However, I often found that a lack of technological knowledge made it difficult to fully understand the papers. Words like “algorithms”, “machine learning”, “neural networks” etc. didn't mean much to me. Even worse, the mere appearance of a mathematical sign like Σ was able to totally confuse me. So I decided to dedicate several months to closing some of these gaps. And as I imagine other people are in a similar situation, I would like to share my approach and some insights.

I think it is quite obvious that it makes sense for non-tech people to familiarize themselves with the technological aspects of AI, if they want to pursue a research or policy career in the field of AI governance. This enables them to understand the topic more fully, come up with more adequate and creative ideas, and communicate more easily with technological experts. The question to me is more about how deep people should go. I will leave that open for debate (I‘d love to see people discuss this in the comments!), as I am very uncertain about it. Personally, I just went as far as I could get until at some point I felt that it would take a lot of effort to get more insights, and I probably had picked the low-hanging fruits.

So let me take you with me through my journey. I'll link the resources I used and the courses I attended. Almost all of them are for free. But first, I'll briefly tell you what I didn't do and why.

What I didn't do

Initially, I considered studying computer science at a university for one semester. However, after looking at the syllabus and showing it to my friends with a technological background, it soon became clear that this wasn't a good idea. Usually, during the first semesters in computer science, a lot of basic mathematics as well as some computer hardware knowledge are taught. Most of this is not relevant for a high-level understanding of AI.

Another option was to work through a textbook on AI. Artificial Intelligence – A Modern Approach is a very popular one by Stuart Russel and Peter Norvig, which has just been updated last year. However, this book has more than a thousand pages and although I can be quite determined, I doubted that I could motivate myself to just sit at a desk with a book for months. For someone who can, maybe this is a good option – I don't know, since I ended up not working with the book at all.

What I did

Note: These are the resources I used, and I think they’re all good enough to recommend, but there may be better options. Please let me know in the comments if you’d recommend other resources!


To get some overview over the topic and some basic terms (like the difference between AI and ML), I recommend reading Understanding AI Technology, a Department of Defense resource for non-technical military personnel. It's not outstanding, and there are surely better overviews, but it covers very basic knowledge and I found it helpful. The second resource I highly recommend is Machine Learning for Humans, a series of blog posts that goes more in depth. If you don't have time for anything else that follows, maybe you can just read this. It is not possible (and at least if you continue the journey not necessary) to fully understand everything these resources talk about just by reading them though. Also, there probably are lots of other good introductory articles – these are the ones I read and found most helpful.

Now, finally, we can really get started. But where? Well, I was very fortunate to be pointed at this amazing website by a friend: How to Learn Machine Learning (in the following referred to as “the original website”). And what I did subsequently was to follow their steps with some adjustments. That means obtaining some prerequisites, i.e. basic programming and statistic skills, and then joining a machine learning course.

Programming (~1 month)

As I didn't know anything about programming at all, I followed the steps of the website How to Learn Python for Data Science the original website refers to. I decided to learn Python, because it was recommended as it is easy to learn and used by most people in machine learning. Therefore, I worked through the book Automate the Boring Stuff with Python up to Chapter 6 (yes, there was a time of sitting at my desk with a textbook in the end). I then deepened my knowledge with a Udemy online course – which is for a fee and in German. This probably wasn't necessary though (which I didn't know by that time). But it was fun and you can take this or a similar course if you'd like to learn some more programming as a skill that is generally useful today.

Statistics (~1 month)

The second prerequisite is statistics. The books recommended on the website How to Learn Statistics for Data Science, which again the original website refers to, were really complex however, so I stopped working through them at some point. Instead, I only used their overview of topics one should get familiar with to decide on these two coursera online courses which I really enjoyed. Some of the things they cover were not necessary for my goal, but so far I didn't find anything more suitable (let me know if you do!). Overall, I think they were really valuable and also made me feel much more comfortable with mathematical signs and equations in general.

Mathematics (not)

Then, although suggested on the original website, I decided not to go into mathematics for now. On the one hand, I really felt like finally getting started with machine learning. On the other hand, I considered that I could always take a break and look up the relevant maths when necessary. This turned out to be a good decision. I only had to take a break once during the second week of the machine learning course – to get a better understanding of calculus with vectors and matrices. This was probably also due to the fact that quite a few things had already been covered in the statistics courses.

Machine Learning (~ 1 month)

Machine learning is the technology that enables most of the progress we see with AI at the moment. I completed this great coursera online course by Andrew Ng. If you join it and get stuck with the first exercise – don't give up! You'll get into it if you're just able to do this one, and the next exercises will feel a lot easier. However, this course is quite advanced, and therefore may not be necessary for a person with a non-tech background going into AI governance. That's the point where I guess people will have different opinions. I'd say that if you do have the time – do it. It will give you an impression of the machine learning workflow, and the way these people think.


So this was my journey. This post is only intended to be a starting point and initial idea/inspiration for people who want to familiarize themselves with the topic. I don't want to say that this is a very good or even the optimal approach – especially since I don't know anyone who had a similar goal but took a different approach for comparison. It would be great to hear about other experiences in the comments!

I’ve now started to read AI governance papers again. And I already feel a difference as I am able to understand more fully what people mean when they write about a certain technique (e.g., supervised or unsupervised learning) or associated problems (e.g., underfitting/bias or overfitting/variance). Also, the statistical knowledge has helped me to make my research in general more solid. However, for the purpose of working on AI governance as a non-tech person, I guess that most of the relevant knowledge can be retrieved from the resources I recommend in the section “overview” and also from AI governance papers themselves. Nevertheless, there seems to be some additional value coming from the rest of the journey. I am not sure how big it is, but it seems to be an instance of a general phenomenon: It is one thing to know something in theory, but it’s another thing to have experienced it. 

As a last point, I would like to mention that I was able to work on this almost full-time while being a part-time research assistant at the Legal Priorities Project. Maybe this is not an option for everyone, although there are a lot of grant opportunities right now, see here. I am uncertain how well my approach is suited for someone who can only use their spare time for it – my best guess is that it would still work out, but simply take longer.


I am grateful to Jonas Schuett and Florian Dorner for providing me with some of the resources I mention. Also, I would like to thank the two of them, as well as Aaron Gertler, Christoph Winter and Renan Araújo for their feedback on a draft of this post.





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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.

  1. 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/
  2. 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/
  3. 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':

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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(?)

This is great, thank you so much for sharing. I expect that many people will be in a similar situation, and so that I and others will link to this post many times in the future.

(For the same reason, I also think that pointers to potentially better resources by others in the comments would be very valuable.)

As a last point, I would like to mention that I was able to work on this almost full-time while being a part-time research assistant at the Legal Priorities Project. Maybe this is not an option for everyone, although there are a lot of grant opportunities right now, see here. I am uncertain how well my approach is suited for someone who can only use their spare time for it – my best guess is that it would still work out, but simply take longer.

See also List of EA funding opportunities, including this statement:

I strongly encourage people to consider applying for one or more of these things. Given how quick applying often is and how impactful funded projects often are, applying is often worthwhile in expectation even if your odds of getting funding aren’t very high. (I think the same basic logic applies to job applications.)

IMO, the best thing I've seen lately, for technical & non-tech people, would be The Alignment Problem, by Brian Christian (a.k.a. the "most human human")

Just flagging this post which has some advice how to get "from zero to hero": Levelling Up in AI Safety Research Engineering. According to this framework, I went to Level 3.  

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