Thoughts on technology, investing, marketing, and entrepreneurship.

Getting Started with Artificial Intelligence: A Practical Guide to Building Applications in the Enterprise

Getting Started with AI book cover
Getting Started with AI book

For the past several months I’ve been co-writing a book that was recently published by O’Reilly. Called Getting Started with Artificial Intelligence: A Practical Guide to Building Enterprise Applications, the book is currently offered by IBM as a free download.

Written with Josh Zheng to introduce developers and technical managers to using artificial intelligence when building enterprise applications, the Getting Started with Artificial Intelligence book focuses on the practical aspects of implementing AI in the enterprise. From a discussion of the history of AI to specific implementation strategies to a look at the future, the book not only focuses on developers but also provides some background to AI for anyone in business (especially the first and last chapters). While the title of the book states a focus on building enterprise applications, the majority of the content also applies to startups — really any developer looking to get started with AI could benefit from reading this book.

While writing a book, even about a subject you know well, you end up finding some fascinating facts. For example, it was interesting to see some hard numbers on the actual size of the market for artificial intelligence:

Estimates from IDC predict revenue from artificial intelligence will top $47 billion by the year 2020 with a compound annual growth rate (CAGR) of 55.1% over the forecast period, with nearly half of that going to software. Additionally, investment in AI and machine learning companies has increased dramatically—AI startups have raised close to $10 billion in funding.

Similarly, some data on the vast amount of data we’re all generating daily:

There are now more mobile devices than people on the planet and each is collecting data every second on our habits, physical activity, locations traveled, and daily preferences. Daily, we create 2.5 quintillion bytes of data from a wide variety of sources. And it’s coming from everywhere. Just think of all the sources collecting data—IoT sensors in the home, social media posts, pictures, videos, all our purchase transactions, as well as GPS location data monitoring our every move.

Data is even touted as being more important and valuable than oil. For that reason, companies are creating vast repositories of raw data (typically called data lakes) of both historical and real-time data. Being able to apply AI to this enormous quantity of data is a dream of many companies across industries. To do so, you have to pick the right set of tools not only to store the data but also to access the data as efficiently as possible. Current tools are evolving, and the way in which you store and present your data must change accordingly. Failure to do so will leave you and your data behind. To illustrate this point, MIT professor Erik Brynjolfsson performed a study that found firms using data-driven decision making are 5% more productive and profitable than competitors. Additional research shows that organizations using analytics see a payback of $13.01 for every dollar spent.

And finally a bit on the much-discussed impact of AI on jobs:

Although we worry about the loss of jobs from AI, the other side of the coin is that there’s a desperate, immediate need for more engineers and data scientists trained on applying AI, especially in the enterprise. In the US alone, companies are expected to spend more than $650 million on annual salaries on AI jobs in 2017. Additionally, they found that 35% percent of these AI jobs required a PhD and 26% a master’s degree. While not necessarily likely to slow down the technological progress and innovations, this increasing shortage of engineering talent will affect how enterprises build applications. Without the ability to hire or train in-house expertise fast enough, companies will be forced to either outsource these capabilities to third parties (if they themselves are not facing the same shortages) or rely more heavily on SaaS solutions for these skills.

I also learned that writing a book is quite challenging. Compiling and synthesizing your knowledge into easy to understand prose is difficult, though extremely rewarding in the end. Additionally, I now have much more respect for anyone who writes a book, especially fiction as it must be orders of magnitude more arduous.

As far as the content covered in the book, these are the high-level topics:

  • an introduction to artificial intelligence (comparing machine and deep learning) with a discussion of avoiding another AI winter
  • natural language processing (NLP): components, how to use, and challenges
  • chatbots: how to build them and best practices
  • computer vision: capabilities, implementation, and usage on mobile devices
  • AI data pipeline: preparation, sourcing, storage, and discovery
  • looking forward: challenges, trends, and opportunities as well as social implications

Throughout the book, there’s a consistent focus on the practical aspects of implementing AI for developers.

AI book podcast interview
Podcast interview

In addition to the book itself, two additional pieces of content support and elaborate on the topics we address. The first is a video podcast interview discussing developing successful AI apps for the enterprise filmed at the O’Reilly AI Conference last September. The second is a webcast on NLP I presented titled “Using natural language processing to build applications in the enterprise”. The webcast covers an overview and fundamentals of NLP, a discussion of real-world examples of using NLP in the enterprise, practical tips on implementing NLP, and a live demo of the IBM Watson Natural Language Understanding service.

As I mentioned earlier, IBM is currently offering the book as a free download. So grab a copy and if you have any questions about the book or its content, feel free to reach out.

Also published on Medium.

About Me