For the last 25 years, I have been a coach to business founders, leaders, and leadership teams. My work has taken me to every continent from my base in London. A lot of my work is done behind closed doors, but I have been instrumental in building two unicorns in the last decade. I’m a founder myself and have always been fascinated by what it takes to succeed as a founder. I have a powerful conviction that learning to lead is the heart of it. The books I love are either based on real-world research or deeply practical and based on hands-on experience. Practice trumps theory every time in my world!
I love the fact that Ali has provided evidence that ANYONE can succeed as a founder. He has done a lot of number-crunching on ‘unicorns’ (I admire his tenacity for that!) and examined what makes a successful startup founder and some surprises emerge.
Age, education, and number of cofounders were not predictors of a startup’s success, as many of us might have expected. The big thing that does matter is experience. Sixty percent of unicorn founders had previously launched startups. It seems that in pretty much every walk of life, including this one, practice is the key.
Every VC wants to find the next billion dollar company to invest in, and every startup wants to become one. Ali Tamaseb set out to find patterns in the backgrounds, methods, and trajectories of these companies, gathering and analyzing 40,000 data points about the 200+ billion dollar companies and the people who founded them. And you'll be surprised by what he discovered:
* Half of unicorn founders are over 35; * Most founders don't have any directly relevant work experience in the industry they're disrupting; * There's no disadvantage to being a solo founder; * Sixty percent of billion dollar…
I've been working in the field of AI for 40 years, first in graduate school and then as a professor. For the most part, I have had my head in the sand, focusing on the minutiae that occasionally lead to publications, the coins of the academic realm. When deep learning started exhibiting human-level pattern recognition abilities, the number of AI books for the general public began to swell. Unfortunately, the science-fiction scenarios were a bit much. Since understanding, recognizing, and admitting problems are vital steps toward a solution, I find these books to be the most important warnings of the impending tech-dominated future.
This book is very long, and somewhat redundant at times. But it’s extremely interesting…and chilling.
Zuboff cites a wide variety of examples of how companies, Google foremost among them, gather information about us (legally or illegally) and then use it not only to predict our behavior, but to control it as well. That’s the really scary part.
The writing can be a bit too poetic at times, but Zuboff displays an incredible breadth and depth of knowledge on this subject. I’m a slow reader, so this one took me a while to get through, but it was time well spent.
'Everyone needs to read this book as an act of digital self-defense.' -- Naomi Klein, Author of No Logo, the Shock Doctrine, This Changes Everything and No is Not Enough
The challenges to humanity posed by the digital future, the first detailed examination of the unprecedented form of power called "surveillance capitalism," and the quest by powerful corporations to predict and control us.
The heady optimism of the Internet's early days is gone. Technologies that were meant to liberate us have deepened inequality and stoked divisions. Tech companies gather our information online and sell…
I fell in love with technology when I wrote my first computer program at age 14 when there was no public Internet, no personal computers, no iPhone, no cloud. I have made technical contributions to every era of computing from mainframes, to PCs, Internet, Cloud, and now AI. I was recently elected to the National Academy of Engineering. AI currently surpasses my wildest imagination on the art of what’s possible. I'm still passionately working in technology at Google focused on how to live healthier lives. I believe we can make AI the telescope of the future, to helping everyone live long and healthy lives.
This book explores how AI is transforming healthcare and the potential benefits it can bring to patients and doctors.
The author, Eric, is a cardiologist with working knowledge of technology of AI. I love how he describes with clarity, the present and potential to make people healthier with AI First thinking. That is, how AI can make the business of health care human.
I love the premise and basis of Eric’ thinking that we can make healthcare personalized, proactive, anticipatory, helping people live healthier lives and reducing the cost of healthcare.
At the same time he is mindful that AI could be used to dehumanize healthcare and exacerbate existing inequalities.
A visit to a physician these days is cold: physicians spend most of their time typing at computers, making minimal eye contact. Appointments generally last only a few minutes, with scarce time for the doctor to connect to a patient's story, or explain how and why different procedures and treatments might be undertaken. As a result, errors abound: indeed, misdiagnosis is the fourth-leading cause of death in the United States, trailing only heart disease, cancer, and stroke. This is because, despite having access to more resources than ever, doctors are vulnerable not just to the economic demand to see more…
I moved into content design from a career in brand and marketing, at a time when the discipline was emerging and not many people really knew what it was. Much of my time since has been spent educating people and organisations and sharing knowledge to help them make better content decisions. Throughout this time, I’ve learnt most of what I know through the experience of working with the design teams, but so many books have also helped me along the way and made my work so much better. I love content design – having the power to improve people's experiences with brands through words is so rewarding, and these books will inspire others to do the same.
I’m picking this book because it’s actually useful for anyone in content, whether you’re a marketing strategist, UX writer, or content designer. It’s easy to read, and a lovely overview of creating more effective content – with guidance on how to adapt tone for different scenarios, and a brilliant exercise for proposition development. It was one of the first books I read about web content, and still one of the books I refer back to again and again.
Whether you're new to web writing, or you're a professional writer looking to deepen your skills, this book is for you. You'll learn how to write web copy that addresses your readers' needs and supports your business goals.
Learn from real-world examples and interviews with people who put these ideas into action every day: Kristina Halvorson of Brain Traffic, Tiffani Jones Brown of Pinterest, Randy J. Hunt of Etsy, Gabrielle Blair of Design Mom, Mandy Brown of Editorially, Sarah Richards of GOV.UK, and more. Topics include:
* Write marketing copy, interface flows, blog posts, legal policies, and emails * Develop…
I’m an Associate Professor in the University of Alabama’s Department of Philosophy. I worked as an FBI Special Agent before making the natural transition to academic philosophy. Being a professor was always a close second to Quantico, but that scene in Point Break in which Keanu Reeves and Patrick Swayze fight Anthony Kiedis on the beach made it seem like the FBI would be more fun than academia. In my current position as a professor at the University of Alabama, I teach in my department’s Jurisprudence Specialization. My primary research interests are at the intersection of philosophy of law, political philosophy, and criminal justice. I’ve written three books on policing.
I love this book because it reminds us of the many ways that technology can affect justice.
It is tempting to think sophisticated tactics such as “predictive policing” can solve all problems relating to human bias. However, Brayne shows that data and algorithms do not eliminate bias and discretion. Instead, high-tech police tools simply make bias less overt and visible, which erodes the public’s ability to hold the police accountable.
I especially enjoyed how the book flips the script, considering diverse ways to use these tools to help the public. For example, how can municipalities use technology to analyze the underlying factors that contribute to policing problems in the first place?
The scope of criminal justice surveillance, from the police to the prisons, has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, health, and marketing. While law enforcement's use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences.
In Predict and Surveil, Sarah Brayne offers an unprecedented, inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law…
I’ve always loved technology. I like the constant change, the sense of creativity and invention, of how it can act as an incredible force for good and human progress and betterment in the world. I can’t remember a time when I wasn’t tinkering with gadgets—taking radios apart to mend them or learn how they worked; designing electronic circuits for music synthesis; programming computers. But I’ve also always been interested in politics and the complex intersection of technology and public policy. So much so that most of my working life has been spent at this intersection, which is why I love these books—and hope you will too.
Virginia Eubanks writes in an incredibly immersive and engaging style, making her book as compulsive as a work of fiction—and equally hard to put down. It exposes the deeply toxic consequences of the way automated decision-making increasingly dominates our public institutions, creating a sort of “twenty-first century digital poorhouse”.
This automated inequality denies citizens their humanity and any sense of agency, condemning them to the sort of negative moral judgments and cycle of decline and despair that would have been familiar to Charles Dickens in his day.
In Indiana, one million people lose their healthcare, food stamps, and cash benefits in three years-because a new computer system interprets any application mistake as "failure to cooperate." In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for a shrinking pool of housing resources. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.
Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change.…
I fell in love with technology when I wrote my first computer program at age 14 when there was no public Internet, no personal computers, no iPhone, no cloud. I have made technical contributions to every era of computing from mainframes, to PCs, Internet, Cloud, and now AI. I was recently elected to the National Academy of Engineering. AI currently surpasses my wildest imagination on the art of what’s possible. I'm still passionately working in technology at Google focused on how to live healthier lives. I believe we can make AI the telescope of the future, to helping everyone live long and healthy lives.
Parag is a clinician who covers the current and future state for using AI in several healthcare specialties like cardiology, pharmacy, orthopedics, radiology, and many more.
This is a book for generalists who want to understand how AI applies to a variety of medical disciplines. I enjoyed this book because it deepened my knowledge as an AI technologist on how to apply AI in areas of healthcare from the lens of a physician.
I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.
Every enterprise application creates data, whether it's log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you're an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.
Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you'll learn Kafka's…
I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.
This provides a superb balance between technical aspects of R coding and the statistical methods that motivate its use. It's rare to find a book on topics like this that are written with Kabacoff's easygoing yet precise style, which makes it ideal for beginners. From my own experience, it is obvious the author has spent many years teaching this type of content, knowing where things deserve extra explanation up front and where other more technical details can be relegated to more advanced texts.
R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.
R in Action, Second Edition is language tutorial focused on practical problems. Written by a research methodologist, it takes a direct and modular approach to quickly give readers the information they need to produce useful results. Focusing on realistic data analyses and a comprehensive integration of graphics, it follows the steps that…
I was trained as a mathematician but have always been motivated by problem-solving challenges. Statistics and analytics combine mathematical models with statistical thinking. My career has always focused on this combination and, as a statistician, you can apply it in a wide range of domains. The advent of big data and machine learning algorithms has opened up new opportunities for applied statisticians. This perspective complements computer science views on how to address data science. The Real Work of Data Science, covers 18 areas (18 chapters) that need to be pushed forward in order to turning data into information, better decisions, and stronger organizations
The text covers classic statistical inference, early computer-age methods, and twenty-century topics. This puts a unique perspective on current analytic technologies labeled machine learning, artificial intelligence, and statical learning. The examples used provide a powerful description of the methods covered and the compare and contrast sections highlight the evolution of analytics. This book by Efron and Hastie is a natural follow-up source for readers interested in more details.
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic…
