Tools are at the core of the daily work of book production, whether print or digital, because they directly impact our efficiency and the quality of the final product. Thanks to growing amounts of available data and the increased processing power of modern computers, Machine Learning (ML) and Artificial Intelligence (AI) have become popular tools to solve certain classes of problems. However, alongside the growing opportunities and potential advantages of applying these technologies to book production, there is also a perceived “dark side” to ML/AI that has many in the book industry worried that it will automate and replace their jobs. The panelists will share their different approaches to applying ML/AI at their companies and their outcomes, highlighting both strengths and limitations, as they consider a vision of a more automated publishing workflow. March 19, 2019 ebookcraft.booknetcanada.ca #EbookCraft

1. Cybernetic Ebooks
Machine Learning and Artificial Intelligence in Book Production

2. What is...
AI - Artificial Intelligence: the theory
and development of computer
systems able to perform tasks that
normally require human intelligence*,
such as visual perception, speech
recognition, decision-making, and
translation between languages.
*intelligence is not equal to consciousness
ML - Machine Learning: the scientific
study of algorithms and statistical
models that computer systems use
to effectively perform a specific task
without using explicit instructions,
relying on patterns and inference
instead.

3. A History of ML and AI
● 1930s – 1950s: A machine can simulate any process of formal reasoning
(Church-Turing Thesis)
● 1956: “AI” was founded at the Dartmouth College with the claim that,
Human intelligence can be so precisely described that a machine can
simulate it.
● 1959: “ML” defined as progressively improve performance of a specific
task without the need to reprogram the task
● 1970 – 1990’s: AI “winter”
● 1990’s - today: dramatic increase in data and computing power, training of
“narrow” AIs (IBM Watson, Google Alpha Go)

4. Glossary:
Model: a mathematical representation of a real-world process or state
Algorithm: a process or set of rules to be followed in calculations or other problem-
solving operations
NLP: (natural language processing) a subfield of computer science, information
engineering, and artificial intelligence concerned with the interactions between
computers and human (natural) languages, in particular how to program computers
to process and analyze large amounts of natural language data.
Neural Net: a set of algorithms, modeled loosely after the human brain, that are
designed to recognize patterns (also known as an artificial neural network or ANN)
Feature Engineering: the process of using domain knowledge of the data to create
features that make machine learning algorithms work

5. Further Reading
Pitfalls of ML: https://www.bbc.com/news/science-environment-47267081
Are you really using AI?: https://www.forbes.com/sites/parmyolson/2019/03/04/nearly-half-of-all-ai-
startups-are-cashing-in-on-hype
The Barrier of Meaning: https://www.nytimes.com/2018/11/05/opinion/artificial-intelligence-
machine-learning.html
Fun: https://thispersondoesnotexist.com/
Did AI Write This Article?: https://www.forbes.com/sites/cognitiveworld/2018/09/16/did-ai-write-this-
article

6. Thank you for listening!