AI books have been surfacing recently on my reading list and I became interested on the topic of future of collaboration between people and AI. I am sharing short summaries of a couple of books that opened my mind on AI beyond the capsule of my day-job. Neither of these is deeply technical and both are “easy reads”!
AIQ – How People and Machines are Smarter Together – Written by academics Nick Polson and James Scott, this is an introductory book of what is AI and how it works for an intellectual reader. The book makes several common AI concepts more sticky and real by
- Explaining the math behind the concept with easy to understand examples
- Tying the concepts to interesting historical characters and AI anecdotes
- Linking back to current advances and examples in AI
Some of the concepts and examples that “stuck” with me that I hope will interest you in picking up a copy of the book:
- Personalization translates to nothing but conditional probability. Conditional probability was the foundation to help predict survivability recommendations for bombers in WW2 as well as movie recommendations by Netflix today.
- Making predictions from patterns has its foundation in deep neural networks. An astronomer used this approach to measure distances in early 1900’s and today Google’s Inception model with a 22-later deep neural network leads image recognition models.
- The application of a Bayesian approach (remember probability theory?) is pervasive – for locating submarines in the 1960s as well as in self-driving vehicles today with SLAM (Simultaneous Localization and Mapping), or for predicting cancer in patients.
- The evolution of communicating with machines Grace Hopper to Alexa, from programming to NLP, all possible with word vectors (a memorable tutorial of word vectors is included).
- Anomaly Detection in the context of variability of an average, Isaac Newton’s mistake at Britain’s Royal Mint for failing to recognize the square root rule and proof that the Patriots may not have cheated on their suspicious coin-toss streak.
- Preventable mischiefs in healthcare championed by passionate statistician Flo Nightingale that still prevail in modern healthcare due to barriers in incentives, data sharing and privacy.
- Examples of poor assumptions that led to disastrous modeling results – including the epic failures of Google Flu Trends which suffered from model rust and COMPAS algorithms which suffer from “bias in bias out”.
Human + Machine – Reimagining work in the age of AI – Written by Accenture Research and published by Harvard Business Review Press, I picked up this book because of the numerous glowing testimonials from leaders of several AI-trailblazing companies such as Benioff, Nadella, Kenny, McMillon, Huffington, and Levie. The book broadly covers the “current state of AI” with use case references across multiple industries and global companies with applications in factory floors, back-office operations, R&D, and Marketing & Sales.
Three key concepts:
- Classifies some tasks that can be performed only by humans (those that depend on activities such as Lead, Empathize, Create or Judge) and other tasks that can be more effectively performed by machines where repetition, replication and redundancy rule (those that depend on activities such as Transact, Iterate, Predict and Adapt). However, there is a set of activities where humans and machines can collaborate as symbiotic partners that form hybrid activities. Here either humans can complement machines with activities such as Train, Explain and Sustain AI or machines give humans superpowers with activities such as Amplify, Interact and Embody. The book walks through several examples to explain these activities and how these may evolve with time.
- Identifies eight new fusion skills as the future of work evolves with man/machine collaboration such as intelligent interrogation (how to extract answers from an AI agent across levels of abstraction), reciprocal apprenticing (how to teach AI agents new skills while also undergoing on-the-job training to work well within an AI enhanced process), rehumanizing time (how to increase time for distinctly human tasks such as creativity, interpersonal interactions, decision making, etc. by reimagining business processes).
- Proposes a MELDS framework with principles around the Mindset, Experimentation, Leadership, Data and Skills for organizations to adopt when incorporating AI in business.
Deep STEM 