I recently finished reading a book called: “What To Do When Machines Do Everything,” by Malcolm Frank, Paul Roehrig, and Ben Pring. I found this book when I received it via email from my boss at work. I read the sample chapters attached and found it very interesting. The book claimed that we as a society should not fear this new revolution in machine learning and AI. Instead, the authors claimed that more jobs should be expected to be created from what has been automated. This premise goes against much of my personal beliefs because I see the opposite in my field. However, I went ahead and read the whole book because I felt like it would be good to look at this subject from a different perspective. I always believe you can learn something from listening to those who disagree with you rather than fight them.
The book starts off with explaining how the “new machine” (a.k.a software) will change the way we do our work in our current field. It even uses the example I used in my first article:“Topic: What Does Automation Really Mean?” where they explain how the Luddites feared terribly about the changes the industrial revolution could bring in terms of their job prospects. They continued to show examples of how this fear repeats itself throughout history. They even reference an infamous study done by The University of Oxford called: “The Future of Employment.” You can read the paper here. The paper is infamous for how it claims about 47% of US employment is at risk due to computerization.1 The book then takes a detour in describing how you, the person reading this book, can take advantage of this new trend of computerization in order to gain an edge instead of finding yourself unemployed and outdated. There was a lot to take in with this book, most of it was geared more towards people who may not particularly be developers, but may be more IT centered or in a different field, but have a firm grasp on some of the technology trends that are happening.
What I Liked
The one thing I found informative about this book was the different examples of how companies you would think were old and outdated, are actually reinventing themselves to become and stay current. It was even more interesting seeing how some them actually did produce more jobs and revenue based on the automation they’ve done. However, there were a few examples that stood out to me that showed the opposite case. One example was a Colorado based company called TriZetto. They created a system that automated the way hospitals handled health care claims. An interesting part of the story was the following:2
The operational results are, in a word, stunning. The “before” picture of how a claim is processed, according to Bridge, involves 120 people sitting in cubicles working with a lot of paper and largely outdated IT systems. In the “after” picture, implemented within a matter of weeks, one person works with a system of intelligence to process the same volume of claims.
While opposite, I wouldn’t call this example completely contradictory. The authors in this book aren’t saying jobs aren’t going away, they are more saying that certain types will while others will emerge.
Other parts of the book I liked were the different ways to automate your business which gives great advice to both developers, IT, and anyone else who sees value in technology. One of these methods was the AHEAD method:
- Automate: Try to offload computational work to the new machine (a.k.a software).
- Halo: Create code halos (products that are connected to all devices and collecting data about its user).
- Enhance: Improve existing software by making it more flexible and more useful to your end base user.
- Abundance: Use software to open up new markets and drop the price point of your products.
- Discovery: Leverage AI to create new products, services, and industries.
The book talks about each of these points thoroughly. They aren’t bad points at all and in fact, if your industry isn’t following the AHEAD approach then you may want to be worried where you are currently working.
What I Disagreed With
One of the things I really didn’t like about the book was how it seemed to gloss over the “do not worry part about your job.” In fact it really isn’t saying that at all. I am not sure if that was the intention to begin with, but this book isn’t really saying: “Hey, don’t worry about automation, you’ll still have your job.” It’s saying something more along the lines of: “You might lose your job due to automation, but here’s how to stay ahead of it.” If anything, I believe the book is admitting that automation will be industry disrupting, but it sugar coats the hard parts of the book. While it’s generally good to try and follow the AHEAD method, it may also be too late to get ahead. With huge companies like Amazon, Apple, and Facebook pioneering the way for machine learning, AI, and fast reliable services, it can be tough to find your place in their market. Unless your company is in an industry not directly competing with companies like these, it may be smarter to start positioning yourself in a career and company that won’t be too affected and is already on the right path.
The other part this book largely ignores, is that not everyone is a developer or in IT. Sure, it is easier for me to reposition myself in an industry that isn’t going to be wiped out by Amazon. It’s also easier for me to improve my tool sets to leverage services offered by Microsoft, Amazon, Facebook, and Google. But if you’re an accountant or in sales: good luck trying to use the AHEAD method unless you’re willing to learn enough to get into the management side of IT. The book doesn’t mention the part that implementing systems that leverage machine learning and AI are more complex than what was traditionally done. So, if accountants are mostly replaced by a machine learning software service and all the new positions that open up are asking for data scientists and software developers, where does that leave the non technical person?
Contrary to the book, the “new machine” isn’t very new. What they consider as AI and machine learning are principals and theory that have been around before I was born.3 The “new machine” isn’t even a machine really; it’s just software that is now able to leverage these principles that previously weren’t available. I understand that to many who are outside the field of computer science may see these ideas as new, but to call it a machine is also somewhat misleading and I think just adds confusion.
Even with my disagreements, I think that it is worth reading this book if you are coming from a technology background. However, for the average person, this book provides little help and only makes your head spin when you think about how much technology will be driving your job in the next few decades. My advice for those who aren’t getting engaged with technology, is to start understanding it now. If you aren’t willing to learn something new everyday and aren’t putting yourself in new positions that challenge you everyday, then there may not be a place for you in the future of employment.
- Frey, Carl Benedikt, and Michael A. Osborne. “The future of employment: How susceptible are jobs to computerisation?” Technological Forecasting and Social Change, vol. 114, 17 Sept. 2017, pp. 254–280., doi:10.1016/j.techfore.2016.08.019.
- Frank, Malcolm, et al. WHAT TO DO WHEN MACHINES DO EVERYTHING: HOW TO GET AHEAD IN A WORLD OF ALGORITHMS, BOTS, AND BIG DATA. John Wiley & Sons, Inc., 2017