The Mathematical Corporation: Where Machine Intelligence and Human Ingenuity Achieve the Impossible

by Joshua Sullivan, Angela Zutavern

The most powerful weapon in business today is the alliance between the mathematical smarts of machines and the imaginative human intellect of great leaders. Together they make the mathematical corporation, the business model of the future.

We are at a once-in-a-decade breaking point similar to the quality revolution of the 1980s and the dawn of the internet age in the 1990s: leaders must transform how they run their organizations, or competitors will bring them crashing to earth -- often overnight.

Mathematical corporations -- the organizations that will master the future -- will outcompete high-flying rivals by merging the best of human ingenuity with machine intelligence. While smart machines are weapon number one for organizations, leaders are still the drivers of breakthroughs. Only they can ask crucial questions to capitalize on business opportunities newly discovered in oceans of data.

This dynamic combination will make possible the fulfillment of missions that once seemed out of reach, even impossible to attain. Josh Sullivan and Angela Zutavern's extraordinary examples include the entrepreneur who upended preventive health care, the oceanographer who transformed fisheries management, and the pharmaceutical company that used algorithm-driven optimization to boost vaccine yields.

Together they offer a profoundly optimistic vision for a dazzling new phase in business, and a playbook for how smart companies can manage the essential combination of human and machine.

Genres: Business, Nonfiction, Leadership, Artificial Intelligence, Technology

304 pages, Hardcover

First published June 6, 2017

Reviews

[Olivier · Rating 1 out of 5]

I read only the first 35%
the book contains a lot of qualitative terms and is extremely fuzzy when it comes to technical concepts, be they about the math or CS aspect of data-driven management. It looks more like a marketing prospectus

[Nick Ertz · Rating 3 out of 5]

First the authors clearly have drank the kool-aide. However, the book is a strong guide for the leader of a enterprise that depends on data. Part of the point is that nearly every corporation should be looking at the data that is available to them and how it can move the firm forward. Most valuable in the book is an excellent review and discussion of the ethics lurking behind big data. This is not a how-to book, but rather a why not.

[Iolanda Ciobanu · Rating 4 out of 5]

This is the typical book about Machine Learning and importance of data. Ideal, not really going to depth, but if managers would read it would help them understand a bit the complexity of what is happening.

[Matt Heavner · Rating 2 out of 5]

This is a very buzzword, business-speak look at "machine intelligence." There are many anecdotes, but I felt it was lacking in specifics/details. This is a peak inside the buzz-cycle. If you are already over the big data, data science, machine learning, then you need this book. Big data is now the data lake. APIs are a new miracle (not... they are definitely a great thing, but new??). It was a worthwhile read, but I wondered if this was machine generated? The target is the "thought leaders" (or the "C-suite") rather than the more technical audience.

[KT · Rating 3 out of 5]

I got this book as a freebie at work. It was the first (and only) book I've read on this topic so some parts may be totally obvious and cliched to people who actually follow the subject closely. However, it was an adequate introduction. The chapter on data ownership and ethics was surprisingly quite good and compelling - nice little bonus amidst otherwise dry subject matter.

[Devin · Rating 1 out of 5]

Skip this book. I recently started working for an AI company as a business person and read every high-rated book in the space. This one has virtually no meaningful content and is full of vague consultant-speak like "future power" and "big mind". I'm pretty sure the authors have never worked on any meaningful AI projects, otherwise they would have had more valuable expertise to share.

[Nilesh Jasani · Rating 3 out of 5]

With AI, Big Data, Machine Learning and other technologies, the way we decide has changed. And, this is definitely true at the organization level, as the book forcefully and rightly points out.

Historically, decision making was deductive. Without much data and analysis, perceptive were leaders who could apply a priori logic to arrive at the decisions that are proven right later. Even if some of these decisions were based on nothing but the “gut”, the good leaders were those that were confident and had communication skills to draw narratives behind their decisions and most importantly, lucky enough to see the decisions bear good fruit.

This is changing. The authors beautifully explain how decision making is now more and more inductive with new technologies. Future leaders not only need to rely more on their data teams, algorithms and machines before arriving at any decisions, but have the ability to desist from most types of deductive thinking, including even hypothesis building.

In the world of Big Data/AI, the best methods are not just inductive but those that start without any hypotheses that could prove limiting. Future leaders need to not only know the limitations of the tools at hand but most importantly potential beyond the confines of traditional thinking. In the non-linear, or even differently quantified (think voice or face recognition) world of next gen data, leaders must be able to lead teams in conceiving or trying completely different.

Effectively, future leaders have to understand the changed nature of data that is collected, collectible and its context and as a result what is possible to extract from it. Data in the new avatar is no longer tabular or objectifiable that could be used through traditional textbook statical tools.

The book also makes important points about the conclusions that can be drawn using entire populations rather than samples, using parameters that are hundreds or thousands in numbers rather than a handful, using relationships that could draw out mood from face contortions or innovate quantum laws not known so far. Most importantly, the new data world does not have just one-time conclusions. True future leaders would know the continuously transient nature of all analysis at hand and improvisations possible.

All that said, this book has short shelf life as the underlying technology themes are becoming outdated fast. The examples and case studies used will soon appear too simplistic as better real life successes and failures emerge. Also, there are somewhat anachronistic sections on privacy that could have appeared too obvious and much discussed even in books a couple of decades ago.

[Jamie Pawlikowski · Rating 3 out of 5]

Interesting use cases to some degree. I would view this as introductory to ML/AI and not something to propel you forward if you have even a base level understanding of the distinction between machines and humans and the types of problems they can solve. If you already understand a supervised learning model, structured vs unstructured data sets, and the limits of machines ability to problem solver today, this book is not likely to offer much value.

[Laura · Rating 3 out of 5]

The Mathematical Corporation

⭐️⭐️⭐️

This had been sitting on my shelf for a while and I finally picked it up. While there are a lot of different examples of teams using data sciences across many industries, none of them went super deep. Lot of surface level stuff, with the main takeaway being use more data. They did have some good call outs for privacy and ethics around how you use data, which I think most companies just have to do that today.

[Ben · Rating 3 out of 5]

Our Mathematical Future

This was a good book on mathematics and the future of computing.

I don't think - nor can I say, it was the best book on this subject that I have read, but it still had some great points.

Check it out if this interests you.

3.4/5

[Theodore Kinni · Rating 3 out of 5]

Good intro to how machine learning will change how we do business. I introduced a curated excerpt here: http://sloanreview.mit.edu/article/et...

[Catur Pande · Rating 3 out of 5]

the explanation of the book was too long, and some of us already knew the main points. without those long explanation.

[Amarish Tripathi · Rating 5 out of 5]

Good book it showcases how data will get embedded into corporations decision making. It’s important for leaders to be data savvy and utilize it to ask right questions and improve the product and services. Experimentation is very important.

[Matt S · Rating 3 out of 5]

The book is a mixed bag. The authors cover some interesting ground with great detail - the chapters on data ethics and what types of knowledge are at risk vs. safe from automation were great - but then they'll switch into vague topics with incredibly fluffy writing. (Here's an excerpt from p.156: "But when the answer is over the horizon, no amount of analysis of the landscape up front will reveal it. You need to go on a learning journey - sailing to the far shores to find the best answer.") Then again, I've never read a management-focused book so I'm not sure if I found that writing grating because it's aimed at C-level executives or because of the authors.

Booz Allen Hamilton, which employs hundreds of data scientists, stands to gain by convincing the world that it needs their product, so sometimes the writing feels more like an advertisement for B.A.H. But even given that, I felt the authors were good at shining the light on others' work instead of tooting their own horn. And as a data scientist myself, I have to agree with their key messages.

Overall, I'd recommend skipping the fluffy sections but reading slowly when they do a deep dive into the interesting topics they cover.

[Cliff Chew · Rating 4 out of 5]

This book doesn't cover any algorithms or models, but I think it's a good book for management people to read. Also a good book for someone wants to be more strategic about data, rather than one who is trying to improve the accuracy of their models.

See this book as one which tries to help you get business value from your analytics models. Definitely has some interesting thinking points that I am trying to work on from here on.