1. The Alignment Problem by Brian Christian
A humane tour through the history of machine learning that asks what it would actually mean to build systems aligned with human values.
A humane tour through the history of machine learning that asks what it would actually mean to build systems aligned with human values.
A practical, story‑driven guide to treating AI as a collaborator, full of concrete experiments and a grounded sense of both promise and limits.
An insider’s view of how AI and synthetic biology could destabilize institutions, and what governance might look like if we take that seriously.
Argues that steering AI is a civic, not just technical, project, and sketches what democratic participation in AI governance could look like.
A foundational argument that we should design AI systems explicitly around uncertainty about human preferences rather than fixed objectives.
A reported history of the deep learning boom, tracing the people, companies, and rivalries that shaped today’s AI landscape.
Explains how semiconductor supply chains became a new terrain for geopolitics and why compute has turned into a strategic resource.
A long, forceful argument that data extraction and behavioral prediction have quietly reshaped capitalism and our sense of agency.
A memoir‑meets‑history of computer vision and AI, grounding technical progress in the life of one of the field’s key figures.
A sprawling, funny, brutal novel that uses one family to stage arguments about God, freedom, and what it means to be responsible for others.
Desert ecology, messianic politics, and imperial resource extraction wrapped in operatic sci‑fi that still feels uncomfortably current.
Probably the single best narrative overview of how modern AI actually works and why value alignment is such a knotty problem.
Frames AI and synthetic biology as a coupled wave of capability and risk, and pushes you to think institutionally, not just individually.
A clear, technical yet accessible case for redesigning AI objectives around human preferences and corrigibility.
Interleaves human lives with the timescale of trees, making questions about attention, stewardship, and interdependence feel newly urgent.
A sweeping, opinionated history of our species that’s useful as a backdrop when thinking about where AI might fit in the longer story.
The classic map of our own cognitive glitches, indispensable if you’re going to lean on systems trained on human behavior.
An anarchist physics novel that quietly asks what a non‑capitalist, non‑hierarchical technological society could look like.
A cold, careful exploration of gender, loyalty, and misunderstanding on an alien world that makes our own categories feel provisional.
Countercultural, messy, and very much of its time, but still a provocative look at what counts as normal when you’re raised elsewhere.
The go‑to reference for surveillance and language as control; still useful shorthand when thinking about data and power.
Melancholic, atmospheric realism from Murakami about memory, depression, and the version of ourselves that lives only in stories.
A warm, meticulously structured novel about constraint, dignity, and finding a full life within very fixed walls.
Hard sci‑fi that starts with Cultural Revolution physics and ends with first contact, information theory, and existential risk.
A short, sharp look at aspiration, performance, and the stories we tell about success and failure.
Flat, unsettling prose about alienation and meaning that still reads like a glitch in the social code.
Polarizing but concise notes on startups and monopoly‑driven thinking, useful as a primary source for a certain Silicon Valley worldview.
A lively biography of Einstein that doubles as a tour through the intellectual culture around early 20th‑century physics.
Elegant historical fiction about class, reinvention, and how a single night can reroute an entire life.