Writing careers (and careers in general) have inflection points. For me, my time at
@KSJatMIT was one. The fellowship is designed for mid-career journalists, providing both a respite from the daily grind and the mind space to explore something completely new. I decided to train myself in machine learning and AI. For my project, I asked: Can a deep neural network do what Johannes Kepler did, given the data Kepler had access to?
The short answer is no! Of course, not. But I learned to code simple deep nets and LSTMs, to predict future positions of planets given enough data (from a simulation). I was able to use an Occam's razor argument to show that ML supports a solar system that is heliocentric. (The same computational resources and training data lead to a heliocentric model that can predict further into the future than a geocentric model; so the heliocentric model must be simpler to learn and hence correct. Agreed, it's a hand-wavy qualitative argument, but it's the best I could do with my then limited knowledge of deep learning and ML).
But that foray was enough to stoke the desire to learn the math of ML. Which then led me deep down various rabbit holes, ultimately resulting in WHY MACHINES LEARN. It's the kind of book that I had always wanted to write, unapologetically technical, yet suffused with historical details, stories and narratives. (My role models:
@stevenstrogatz's Infinite Powers and Leonard Susskind's Theoretically Minimum series (look it up if you haven't, it's awesome)).
@dwarkesh_sp recently vented in a post, saying: "I find it frustrating that almost every nonfiction book is basically just a history lesson, even if it's nominally about some science/tech/policy topic. Nobody will just explain how something works."
nitter.net/dwarkesh_sp/status/196…
I share some of this concern, but not entirely. As science/tech writers, we owe it to our readers to do more than tell stories. Of course, stories are great--they are what shape our understanding. But the nitty-gritty matters, especially in this day of rapid change with
#ArtificialIntelligence. And many writers do author such material, and I'm grateful for them.
All this to say, I wouldn't have written WHY MACHINES LEARN if it hadn't been for the yearlong educational sojourn at MIT/Harvard, under the aegis of the Knight Science Journalism Fellowship.
I find it frustrating that almost every nonfiction book is basically just a history lesson, even if it's nominally about some science/tech/policy topic.
Nobody will just explain how something works.
Books about the semiconductor industry will never actually explain the basic process flow inside a fab, but you can bet that there will be a minute-by-minute recounting of a dramatic 1980s Intel boardroom battle.