I am an author, illustrator, and award-winning creative director. I have loved to draw and make things since a young age, mostly wacky contraptions (inspired by my love of the Hanna-Barbera Wacky Races cartoons). I’m also passionate about mazes, having spent many family holidays drawing mazes on a small whiteboard for my two boys to complete.
You can always rely on a children’s book published by Usborne, the Big Maze Book by Kirsten Robson is no exception. It offers 50 different mazes to solve, each charmingly illustrated. The mazes themselves are nice and varied, incorporating different settings, different subject matter, and slightly different visual treatments, which all helps to keep solvers interested. As a whole, this book would probably appeal more to younger children who still enjoy picture books. That being said, the mazes do get progressively harder through the book, so there is something there for the slightly older ones too.
I'm an executive coach, adjunct faculty at several top-tier business schools, and run a boutique firm consulting firm focused on organizational strategy solutions. My diverse and eclectic background in mergers & acquisitions, organization effectiveness, and strategy execution, coupled with two decades of experience in emerging markets in Asia, the Middle East, and North Africa, helps me grasp challenging people issues. I'm passionate about the topic of leaders as coaches having written several papers and columns. My research, and writing led Penguin to commission my book Coaching: The Secret Code to Uncommon Leadership that, released globally in late 2021 to much acclaim, is recommended by several academics as an essential read for aspiring and experienced managers.
A few years ago at my son’s annual townhall in school, the principal outlined the school’s strategic game plan on a powerpoint presentation.
One of the slides that stayed with me projected a visual from the book The End of Average: How We Succeed in a World That Values Sameness.
I went back and read the book, only to realize that it resonated deeply with how we in the corporate world today (not just in schools at the elementary, middle, or senior levels) are quick to pronounce verdicts: below average, average, exceeds expectations.
The book poses some pertinent questions, and rightly so: Who came up with this principle of average? Why are we following it everywhere?
How often have we as managers just given up on people that we perceive are not ‘leadership’ material. Often these same average or poor performers can be great performers in different contexts.
Are you above average? Is your child an A student? Is your employee an introvert or an extrovert? Every day we are measured against the yardstick of averages, judged according to how closely we come to it or how far we deviate from it.
The assumption that metrics comparing us to an average—like GPAs, personality test results, and performance review ratings—reveal something meaningful about our potential is so ingrained in our consciousness that we don’t even question it. That assumption, says Harvard’s Todd Rose, is spectacularly—and scientifically—wrong.
In The End of Average, Rose, a rising star in the new field…
I am an academic researcher and an avid non-fiction reader. There are many popular books on science or music, but it’s much harder to find texts that manage to occupy the space between popular and professional writing. I’ve always been looking for this kind of book, whether on physics, music, AI, or math – even when I knew that as a non-pro, I wouldn’t be able to understand everything. In my new book I’ve been trying to accomplish something similar: A book that can intrigue readers who are not professional economic theorists, that they will find interesting even if they can’t follow everything.
In the ongoing debates over artificial general intelligence (AGI), Judea Pearl is taking a firm stand: He argues that an intelligent robot should be able to reason about causality and that the currently fashionable approaches to AI miss this aspect.
A celebrated AI researcher and a Turing Prize laureate, Pearl has developed an amazingly original approach to this problem. This book is a high-end popular exposition of his approach.
But it’s so much more than that. It’s a history of statistics and its conflicted attitude to causality. It’s a story of heroes (or villains?) in this history. And it’s a scientific autobiography that describes Pearl’s journey. Pearl likes picking fights with the AI community, statisticians, or economists. He’s boastful, provocative, extremely intelligent, and knows how to tell a story.
'Wonderful ... illuminating and fun to read' - Daniel Kahneman, winner of the Nobel Prize and author of Thinking, Fast and Slow
'"Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and have redefined the term "thinking machine"' - Vint Cerf, Chief Internet Evangelist, Google, Inc.
The influential book in how causality revolutionized science and the world, by the pioneer of artificial intelligence
'Correlation does not imply causation.' This mantra was invoked by scientists for decades in order to avoid taking positions as to whether one thing caused another, such as smoking…
I don’t think I could have been anything else but an engineer. Following my father’s example, I have a love for moving metal things – both the physical/mathematical aspects and the practical aspects, that apprentices pick up. Engineering systems have personalities all their own – the noisy excitement of a racing motorcycle, the brooding, contented hum of a nuclear powerplant or the clanging and crashing of a steam locomotive in its overrun, literally with fire in its belly.
This was the book that set me on the path to becoming a Reliability Engineering specialist. (Reliability Engineering being a subset of Mechanical Engineering). This book was my first exposure to reliability engineering lore, covering subjects such as the Weibull Distribution and the probabilistic approach to engineering reliability. To design and manufacture an engineering system is one thing – how to make it reliable enough to last for its projected lifetime is another. More emphasis is now being placed on these aspects of the engineering life cycle – the operation, maintenance, and divestment phases. This approach has sometimes delivered systems an order of magnitude more reliable than their 20th Century forebears.
My primary interest is in brain function. Because the principal job of
the brain is to process information, it is necessary to define exactly
what information is. For that, there is no substitute for Claude
Shannon’s theory of information. This theory is not only quite
remarkable in its own right, but it is essential for telecoms,
computers, machine learning (and understanding brain function).
I have written ten "tutorial introduction" books, on topics which vary
from quantum mechanics to AI.
In a parallel universe, I am still an Associate Professor at the
University of Sheffield, England.
This is a more comprehensive and mathematically rigorous book than Pierce’s book. For the novice, it should be read-only after first reading Pierce’s more informal text. Due to its vintage, the layout is fairly cramped, but the content is impeccable. At almost 500 pages, it covers a huge amount of material. This was my main reference book on information theory for many years, but it now sits alongside more recent texts, like MacKay’s book (see below). It is also published by Dover, so it is reasonably priced.
Written for an engineering audience, this book has a threefold purpose: (1) to present elements of modern probability theory — discrete, continuous, and stochastic; (2) to present elements of information theory with emphasis on its basic roots in probability theory; and (3) to present elements of coding theory. The emphasis throughout the book is on such basic concepts as sets, the probability measure associated with sets, sample space, random variables, information measure, and capacity. These concepts proceed from set theory to probability theory and then to information and coding theories. No formal prerequisites are required other than the usual undergraduate…
My father, when he consented to talk about all the moments in his life when the odds against his survival were so small as to make them statistically non-existent, would say, ‘I was lucky.’ Trying to understand what he meant got me started on this book. As well as being a novelist, I’m a poker player. Luck is a subject that every poker player has a relationship to; more importantly it’s a subject that every person has a relationship to. The combination of family history and intellectual curiosity and the gambler’s desire to win drove me on this quest.
Sadly, Games, Gods, and Gambling by FN David is out of print.This is the next best thing. Lorraine Daston has the supreme gift of making the complicated idea seem straightforward. This is an account of the frenzy for measuring that happened in the 18th century, and how it made the world we live in today, when the gambler’s eye for odds has become the algorithm of taming chance that guides all our decisions.
What did it mean to be reasonable in the Age of Reason? Classical probabilists from Jakob Bernouli through Pierre Simon Laplace intended their theory as an answer to this question--as "nothing more at bottom than good sense reduced to a calculus," in Laplace's words. In terms that can be easily grasped by nonmathematicians, Lorraine Daston demonstrates how this view profoundly shaped the internal development of probability theory and defined its applications.
Wealth Odyssey is a summary work based on a 12-hour adult education course I taught for 10 years. It’s important to me to educate people through my 29 years in the profession (1994-2023), my focus has always been on helping people first understand that retirement means you’re wealthy enough not to work anymore – working is optional. You don’t need to be rich. Wealth is scalable for any income level and comes from foundation income and investments to supplement that foundation to support your desired lifestyle’s Standard of Individual Living (SOIL) for as long as you live. Your focus should be on your plan and apply a few concepts grounded in well researched evidence.
When people think of financial planning, their first thought is investing. Their second thought is retirement.
Kaplans explain risk succinctly: “Everything is possible, yet only one thing happens.” People understand risk but don’t really understand how to apply it rationally to investing (market risks) or to retirement (longevity risk).
But first, having an understanding of what risk is and isn’t, and where it comes from is important before you can apply it to what fuels your plans – markets and longevity.
This book helped me formulate the basic planning concepts I use in my book since personal finance is all about taking risks – as are any other decisions and actions you take in life.
A compelling journey through history, mathematics, and philosophy, charting humanity’s struggle against randomness
Our lives are played out in the arena of chance. However little we recognize it in our day-to-day existence, we are always riding the odds, seeking out certainty but settling—reluctantly—for likelihood, building our beliefs on the shadowy props of probability. Chances Are is the story of man’s millennia-long search for the tools to manage the recurrent but unpredictable—to help us prevent, or at least mitigate, the seemingly random blows of disaster, disease, and injustice. In these pages, we meet the brilliant individuals who developed the first abstract…
Most of the one billion people with disabilities in the world are chronically unemployed. Years ago, I set out on a mission to research why that is, and to then attempt to prove that people with disabilities and others are not unemployed for lack of ability. I discovered that we all lack understanding regarding what they need in order to bring their considerable abilities to bare. Fifteen years ago, I founded CY, a for-profit company as a proving ground and showcase for the solutions I found. Over 1,500 employees, 5 weddings, and two court cases later – I have quite a story to tell.
We are primed to measure things against the yardstick of averages, judged according to how closely we come to it or how far we deviate from it. It affects our own self-confidence, how we view and grade our performance and worth.
The assumption that metrics comparing us to an average--like GPAs, personality test results, and performance review ratings--reveal something meaningful about our potential is so ingrained in our consciousness that we don't even question it. That assumption is spectacularly—and scientifically—wrong.
Together with great stories about the folly of averages (the one about pilot chairs really got me) and many research facts and stats it conveys a very important message about how we view the world.
'Must the tyranny of the group rule us from cradle to grave? Absolutely not, says Todd Rose in a subversive and readable introduction to what has been called the new science of the individual ... Readers will be moved' Abigail Zuger, The New York Times
'Groundbreaking ... The man who can teach you how not to be average' Anna Hart, Daily Telegraph
'Fascinating, engaging, and practical. The End of Average will help everyone - and I mean everyone - live up to their potential' Amy Cuddy, author of Presence
'Lively and entertaining ... a cheering story of how the square…
I started my career in neuroscience. I wanted to understand brains. That is still proving difficult, and somewhere along the way, I realized my real motivation was to build things, and I wound up working in AI. I love the elegance of mathematical models of the world. Even the simplest machine learning model has complex implications, and exploring them is a joy.
My knees tremble and my heart quakes when I think of how much work must have gone into these two companion volumes. Collectively, they are more than four times the length of my book, covering the whole of machine learning.
It is an essential encyclopedic resource that should be on the desk of anyone serious about machine learning.
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.
This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.
I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.
Everyone knows what probability is, and we all understand how a coin flip works, but not everyone can explain the optimal betting strategies for a roulette table. We don’t study probability to understand the likelihood of events. We study probability to understand the expected outcomes of business processes that depend on those events.
In other words, this book won’t just teach you about probabilities, it will teach you about business strategies associated with those probabilities. It will help you answer a question like: How do I maximize the profit on this life insurance policy, given this set of survival probabilities? It isn’t just a likelihood question, it is a business question. I highly recommend that anyone studying probability does so through an actuarial lens.
This book covers the basic probability of distributions with an emphasis on applications from the areas of investments, insurance, and engineering. Written by a Fellow of the Casualty Actuarial Society and the Society of Actuaries with many years of experience as a university professor and industry practitioner, the book is suitable as a text for senior undergraduate and beginning graduate students in mathematics, statistics, actuarial science, finance, or engineering as well as a reference for practitioners in these fields. The book is particularly well suited for students preparing for professional exams, and for several years it has been recommended as…
