Segna’s Reading List: 2021

What we recommend reading

AI 2041
Kai-Fu Lee, Chen Qiufan
Provocative and utterly original. Kai-Fu Lee, the former president of Google China, teams up with celebrated novelist Chen Qiufan to imagine our world in 2041 and how it will be shaped by AI in ten short stories.

A Thousand Brains: A New Theory of Intelligence
Jeff Hawkins
The central idea is that “the entire world is learned: as a complex hierarchy of objects located relative to other objects.” Hawkins discusses artificial intelligence and writes it falls short of human intelligence because of the narrow limits constraining even the most sophisticated systems.
He also explores how intelligent machines can preserve human knowledge, which would enable information to persist and be distributed throughout the galaxy long after the death of the last human.

Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World
Cade Metz
What does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create? Through his book Genius Makers, New York Times Silicon Valley journalist Cade Metz brings you into the room where these questions are being answered.
It dramatically presents the fierce conflict among national interests, shareholder value, the pursuit of scientific knowledge, and the very human concerns about privacy, security, bias, and prejudice — and the humans and companies behind this.

Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World
Mo Gawdat
AI is capable of super-human performance — lightning-fast processing, remaining focused on specific tasks without distraction, etc. So why does AI frequently get it so wrong? The answer is us. Humans design the algorithms that define how AI works, and the processed information reflects an imperfect world.
With technology putting our humanity at risk to an unprecedented degree, Scary Smart offers a blueprint, pointing the way to what we can do to safeguard ourselves.

The Age of AI: And our Human Future
Daniel Huttenlocher, Henry Kissinger, Eric Schmidt
There has never been a foundational technology as powerful as AI — one that will radically alter our approach to economics, order, security, and even knowledge itself. To ensure we effectively manage the risks of AI, three of the world’s most accomplished and deep thinkers published this book, hoping it will act as the catalyst for the creation of an AI equivalent to ‘arms-control for nuclear weapons.’

The AI-first company: How to Compete and Win with Artificial Intelligence
Ash Fontana
AI-First companies succeed by design — by collecting data from day one and using it to automate core functions, they learn faster and outpace the competition. Internationally-renowned startup investor Ash Fontana offers an executable playbook for real companies with real budgets that need strategies and tactics to implement AI effectively.
If the last fifty years were about getting AI to work in the lab, the next fifty years will be about getting AI to work for people, businesses, and society.

The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do
Erik J. Larson
Erik J. Larson seeks to debunk the notion that superintelligence is just a few clicks away. AI enthusiasts have long equated artificial intelligence with human intelligence. This is a mistake as AI works on inductive reasoning, crunching data sets to predict outcomes. Whereas Humans reason abductively — making conjectures informed by context and experience.
The problem is that we are much less certain of how to build systems that reason abductively. Larson places a portion of the blame on AI hype, pointing out that a culture of invention thrives on exploring unknowns, not overselling existing methods.

The Self-Assembling Brain: How Neural Networks Grow Smarter
Peter Robin Hiesinger
The history of machine learning and artificial intelligence is populated by attempts to create systems inspired by the human brain - such as Symbolic AI systems, that tried to replicate the brain through rule-based modules or neural nets, modeled after the wiring and activation patterns in the brain. The author, Peter Robin Hiesinger, suggests that we consider how information encoded in the genome is transformed to become our brain.