AI Resources 2025: Your Essential Guide

A concise, daily rundown of AI headlines with clear contextual insights, perfect for non‑technical skimmers. Pairs well with a cup of coffee in the morning.

Accessible interviews with experts on cutting‑edge breakthroughs with a focus on demystifying lab research. Prof. Hannah Fry keeps things consistently engaging.

OpenAI Academy put out this helpful 45-minute webinar for anyone looking to work effectively with ChatGPT. A good place to start for anyone trying to figure out how to make the most out of generative AI.

Wharton professor Ethan Mollick urges us to engage with AI as a co‑worker, co‑teacher, and coach, offering pragmatic guidance on integrating AI into daily workflows. Prof. Mollick is very active LinkedIn and has a knack for making advanced topics easily to grapple with.

A thought‑provoking tour of AI’s societal and existential implications, written well and anchored by concrete policy recommendations. Widely cited and worth reading.

One of the clearest explanations of how LLMs work. This piece of visual storytelling by the Financial Times is accessible, clear, illustrated by examples, and touches on all the important concepts you need to know.

Actionable discussions on AI trends in marketing and enterprise, blending strategy with real‑world case studies. These episodes are fairly long but worth listening to for a practical perspective.

In this long-form blog post, Prof. Bengio tackles AI safety skepticism and reveals why it's worth confronting existential risks now.

Dive into the surprising risks of AI's rapid rise, as well as possible solutions, in the inaugural Hinton Lectures. I attended both lectures in person and found Jacob Steinhardt to be an engaging and insightful speaker.

An important exploration into systemic biases encoded in algorithms with practical frameworks for accountability and reform.

This is *the* foundational treatise on existential risks from AGI, framing pivotal control problems and strategic scenarios for superintelligence.

An exploration into moral relationships between human and nonhuman, written by an acclaimed anthropologist. I highly recommend this book. Easy-to-read, pithy, deep, and often surprising.

Anthropic’s CEO sketches an optimistic roadmap for AI in healthcare, biology, and economics. He argues that with proper guardrails, transformative benefits can far outweigh risks. Often cited and worth reading, whether or not you agree.

A comprehensive, project‑based series of courses covering neural nets, CNNs, RNNs, and transformers, designed for those seeking rigorous, hands‑on mastery of deep learning. I've taken this myself and would recommend it if you want to dig deeper.

Two Minute Papers is a YouTube series that showcases and explains interesting research (including in AI) a couple minutes or more at a time.

A deep‑dive into how hyper-scaling compute and investment might reshape corporate strategy, energy markets, and geopolitical risk over the next decade.

Nobel Laureate Professor Geoffrey Hinton examines deep learning’s foundations and speculates on AI’s future relative to human cognition. Fascinating, though more academically-oriented.

The seminal essay introducing the “imitation game” (aka the Turing Test) as a behaviour-based criterion for thinking about machine intelligence, setting the benchmark against which we've measured AI achievement ever since.

A much more accessible means of exploring the concepts in the famous "Attention is All You Need" paper.

A clear-eyed guide to the ethical risks and real-world consequences of AI assistants that act on our behalf, as well as what builders and policymakers should do about it. I've gone back to this paper a few times. It's a long read best tackled in sections!

The foundational critique of “strong AI" ("the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states") using the Chinese Room thought experiment to argue that symbol manipulation alone cannot result in genuine understanding.

Laid the groundwork for modern NLP breakthroughs by introducing the transformer model. I find this dense and challenging myself. For non-mathematically oriented folks, consider starting with something like The Illustrated Transformer.