(More) recommendations for non-technical readings on AI?

A few years ago I asked for suggestions on how to learn more about AI safety. I think that I understand AI safety fairly well for someone who isn't able to grasp the technical details. I've been broadly within the EA ecosystem for a few years now.

• I've read a few books related to AI risk/safety.
• I've read Holden Karnofsky's "most important century" blog post series.
• I've done an online course/reading group about AI safety (from the group that later became BlueDot).
• I work at an organization that wants to prevent AI from going badly and I speak with colleagues about it (in a casual, informal sense) fairly regularly. 

But I don't have a technical background, and my vague impression is that if I don't have a degree (or equivalent knowledge) in computer science or mathematics, then there is a pretty tight limit to how much I am able to learn. Is that roughly accurate? Would I have to learn a bunch of math and computer science in order to learn more about AI?

I've read a few more books about AI since my previous post, but most of them have been less relevant to existential risk and more about societal issues and poor use of LLMs:

• Co-Intelligence: The Definitive, Bestselling Guide to Living and Working with AI
• AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference
• You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place
• The Algorithm: How AI Decides Who Gets Hired, Monitored, Promoted, and Fired and Why We Need to Fight Back Now

For context, here is the question I asked a few years ago (and I'll also link to it):

What should I read next? Any AGI safety related material that you can recommend? I've read the following books related (broadly) to AI:

• Human Compatible: Artificial Intelligence and the Problem of Control,
  by Stuart Russell
• AI Superpowers: China, Silicon Valley, and the New World Order, by Kai-Fu Lee
• Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, by Cathy O'Neil
• Superintelligence: Paths, Dangers, Strategies, by Nick Bostrom
• The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, by Pedro Domingos
• The Alignment Problem: Machine Learning and Human Values, by Brian Christian
• Algorithms to Live By: The Computer Science of Human Decisions, by Brian Christian and Tom Griffiths

I find that much (maybe 50%) of what I've read in the above books simply reviews/re-hashes the same handful of concepts (a brief history of AI, what a neural network is, how big data requires a lot of data, what "garbage in garbage out" means, AlexNet was impressive, how impactful AI is and can be, etc.). Several years ago I did some reading/learning about machine learning^[1], and I find that I generally don't learn much from reading about AI.^[2]

1. ^{^}

   I spent a few months learning python, read various blog posts, did a tiny tutorial to build a very simple toy project with Scikit-learn, and generally developed a decent lay-persons understanding of machine learning. I have a vague familiarity with multiple regression, K nearest neighbors, dimensionality reduction, but I don't have enough of an understanding to describe them for more than a sentence or two, and I definitely don't have enough of an understanding to describe them in a detailed and technical sense.

2. ^{^}

   The analogy that I am thinking of is that I have of learned the equivalent of the freshmen 100-level course on AI for non-technical people, and all the books that I am reading are also at the 100-level. Are there any non-technical books at the 200-level, or would I have to do a few years of programming and/or mathematics in order to be able to understand the 200-level content?