Getting Started with Artificial Intelligence, 2nd Edition

Chapter 1. Introduction to Artificial Intelligence

In the future, AI will be diffused into every aspect of the economy.

Nils J. Nilsson, founding researcher, Artificial Intelligence & Computer Science, Stanford University

Beyond the buzzwords, media coverage, and hype, artificial intelligence techniques are becoming a fundamental business growth component across a wide range of industries. And while the various terms (algorithms, transfer learning, deep learning, neural networks, NLP, etc.) associated with AI are thrown around in meetings and product planning sessions, it’s easy to be skeptical of the potential impact of these technologies.

Today’s media represents AI in many ways, both good and bad—from the fear of machines taking over all human jobs and portrayals of evil AIs via Hollywood to the much-lauded potential of curing cancer and making our lives easier. Of course, the truth is somewhere in between.

While there are valid concerns about how the future of artificial intelligence will play out (and the social implications), the reality is that the technology is currently used in companies across all industries.

AI is used everywhere—IoT (Internet of Things) and home devices, commercial and industrial robots, autonomous vehicles, drones, digital assistants, and even wearables. And that’s just the start. AI will drive future user experiences that are immersive, continuous, ambient, and conversational. These conversational services (e.g., chatbots and virtual agents) are currently exploding, while AI will continue to improve these contextual experiences.

Despite several stumbles over the past 60–70 years of effort on developing artificial intelligence, the future is here. If your business is not incorporating some AI, you’ll quickly find you’re at a competitive disadvantage in today’s rapidly evolving market.

Just what are these enterprises doing with AI? How are they ensuring an AI implementation is successful (and provides a positive return on investment)? These are only a few of the questions we’ll address in this book. From natural language understanding to computer vision, this book will provide you a high-level introduction to the tools and techniques to better understand the AI landscape in the enterprise and initial steps on how to get started with AI in your own company.

It used to be that AI was quite expensive and a luxury reserved for specific industries. Now it’s accessible for everyone in every industry, including you. We’ll cover the modern enterprise-level AI techniques available that allow you to create models efficiently and implement them into your environment. While not meant as an in-depth technical guide, the book is intended as a starting point for your journey into learning more and building applications that implement AI.

The Market for Artificial Intelligence

The market for artificial intelligence is already large and growing rapidly, with numerous research reports indicating a growing demand for tools that automate, predict, and quickly analyze. Estimates from IDC predict revenue from artificial intelligence will reach $98.4 billion by 2023 with a compound annual growth rate (CAGR) of 28.5% 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 raised $26.6 billion in funding in 2019. Clearly, the future of artificial intelligence appears healthy.

Avoiding an AI Winter

Modern AI, as we know it, started in earnest in the 1950s. While it’s not necessary to understand the detailed history of AI, it is helpful to understand one particular concept—the AI winter—as it shapes the current environment.

There were two primary eras of artificial intelligence research where high levels of excitement and enthusiasm for the technology never lived up to expectations, causing funding, interest, and continued development to dry up. The buildup of hype followed by disappointment is the definition of an AI winter.

So why are we now seeing a resurgence in AI interest? What’s the difference today that’s making AI so popular in the enterprise, and should we fear another AI winter? The short answer is likely no—we expect to avoid an AI winter this time around due primarily to much more (or big) data and the advent of better processing power and graphics processing units (GPUs). From the tiny supercomputers we all carry in our pockets to the ever-expanding role of IoT, we’re generating more data now, at an ever-increasing rate. For example, IDC estimates that 180 zettabytes of data will be created globally in 2025, up from less than 10 zettabytes in 2015.

Andrew Ng, cofounder of Coursera and Stanford adjunct professor, often presents Figure 1-1 in his courses on machine learning and deep learning.

Figure 1-1. Deep learning performance (image courtesy of Andrew Ng, Deeplearning.ai course on Coursera)

Conceptually, this chart illustrates how the performance of deep learning algorithms improves with an increasing amount of data—data that we now have in abundance, and that is growing at an exponential rate.

So why does it matter whether we understand the AI winter? Well, if companies are going to invest in AI, it’s essential to avoid the hyperbole of the past and keep expectations based on reality. While the current situation is much more likely to justify the enthusiasm, that excitement still needs to be tempered with real-world results to avoid another AI winter. As (future) AI practitioners, that’s something we can all agree would challenge your businesses.

Artificial Intelligence, Defined?

The market for artificial intelligence is immense, but what are we genuinely discussing? While it sounds great to say you’re going to implement AI in your business, what does that mean in practical terms? Artificial intelligence is quite a broad term and is, in reality, an umbrella over a few different concepts.

So, to get started and keep everyone on the same page, let’s briefly discuss some of the terms associated with AI that are often confused or interchanged: artificial intelligence, machine learning, and deep learning.

Artificial Intelligence

Over the years, there have been numerous attempts at precisely defining AI. While there’s never really been an official, accepted definition, a few high-level concepts shape and define what we mean. The descriptions range from artificial intelligence merely aiming to augment human intelligence to thinking machines and true computer intelligence. For our purposes, we can think of AI as being any technique that allows computers to bring meaning to data in similar ways to a human.

While most AI is focused on specific problem domains (natural language processing or computer vision, for example), the idea of artificial general intelligence, or having a machine perform any task a human could (also commonly referred to as “strong AI”), is still more than 10 years out according to Gartner.

Machine Learning

In 1959 Arthur L. Samuel, an IBM researcher and Stanford professor, is said to have stated that machine learning is the “field of study that gives computers the ability to learn without being explicitly programmed,” thereby becoming the originator of the term.

Essentially, machine learning is a subset of AI focused on having computers provide insights into problems without explicitly programming them. Most of the tools and techniques that today refer to AI are representative of machine learning. There are three main types of machine learning—supervised learning, unsupervised learning, and reinforcement learning.

By looking at many labeled data points and examples of historical problems, supervised learning algorithms can help solve similar problems under new circumstances. Supervised learning trains on large volumes of historical data and builds general rules to be applied to future problems. The better the training set data, the better the output. In supervised learning, the system learns from these human-labeled examples.

While supervised learning relies on labeled or structured data (think rows in a database), unsupervised learning trains on unlabeled or unstructured data (the text of a book). These algorithms explore the data and try to find structure. Here, widely used unsupervised learning algorithms are cluster analysis and market basket analysis. Naturally, this tends to be more difficult, as the data has no preexisting labels to assist the algorithms in understanding the data. While more challenging to process, as we’ll discuss later, unstructured data makes up the vast majority of data that enterprises need to process today.

Finally, there’s reinforcement learning as a machine learning technique. Reinforcement learning takes an approach similar to behavioral psychology. Instead of training a model with predefined training sets (i.e., where you know the prescribed answers in advance) as in supervised learning, reinforcement learning rewards the algorithm when it performs the correct action (behavior). Reinforcement learning resists providing too much training and allows the algorithm to optimize itself for performance-based rewards.

Reinforcement learning was initially conceived in 1951 by Marvin Minsky. Still, as was the case with many AI implementations, the algorithms were held back by both the scale of data and computer processing power needed for effectiveness. Today, we see many more successful examples of reinforcement learning in the field, with Alphabet subsidiary DeepMind’s AlphaGo one of the more prominent. Another notable application of reinforcement learning is the development of self-driving cars.

Deep Learning

No book on developing AI applications in the enterprise would be complete without a discussion of deep learning. One way to understand deep learning is to think of it as a subset of AI that attempts to develop computer systems that learn using neural networks like those in the human brain. While machine learning is mostly about optimization, deep learning focuses on creating algorithms to simulate how the human brain’s neurons work.

Deep learning algorithms are composed of an interconnected web of nodes called neurons and the edges that join them together. Neural nets receive inputs, perform calculations, and then use this output to solve given problems.

One of the ultimate goals of AI is to have computers think and learn like human beings. Using neural networks based on the human brain’s decision-making process, deep learning is a set of computational techniques that moves us closest to that goal.

According to Stanford professor Chris Manning, around the year 2010, deep learning techniques started to outperform other machine learning techniques. Shortly after that, in 2012, Geoffrey Hinton at the University of Toronto led a team creating a neural network that learned to recognize images. Additionally, that year Andrew Ng led a team at Google that created a system to recognize cats in YouTube videos without explicitly training it on cats.

By building multiple abstraction layers, deep learning can also solve complex language problems where it’s essential to discern meaning. Traditional AI follows a linear programming structure, limiting the ability for a computer to learn without human intervention. However, deep learning replaces this existing approach with self-learning and self-teaching algorithms enabling more advanced data interpretation. For example, traditional AI could read a zip code and mailing address off an envelope, but deep learning could also infer from the envelope’s color and date sent that the letter contains a holiday card.

Deep learning’s closer approximation to how the human brain processes information via neural networks offers numerous benefits. First, the approach delivers a significantly improved ability to process large volumes of unstructured data, finding meaningful patterns and insights. Next, deep learning offers the ability to develop models across a wide variety of data, highlighted by advanced approaches toward text, images, audio, and video content. Finally, deep learning’s massive parallelism allows for the use of multiple processing cores/computers to maximize overall performance as well as faster training with the advent of GPUs.

As with all advances in AI, deep learning benefits from much more training data than ever before, faster machines and GPUs, and improved algorithms. These neural networks are driving rapid advances in speech recognition, image recognition, and machine translation, with some systems performing as well or better than humans.

Applications in the Enterprise

From finance to cybersecurity to manufacturing, there isn’t an industry that will not be affected by AI. But before we can discuss and examine building applications in the enterprise with AI, we first need to define what we mean by enterprise applications. There are two common definitions of “the enterprise”:

• A company of significant size and budget
• Any business-to-business commerce (i.e., not a consumer)

So Geico, Procter & Gamble, IBM, and Verizon would all be enterprise companies. And by this definition, any software designed and built internally would be considered enterprise software. On the other hand, a small company or startup could develop applications to be used by businesses (whether large or small), and this would still be considered enterprise software. However, a photo-sharing app for the average consumer would not be considered enterprise software.

This is probably obvious, but since we’re discussing enterprise applications with AI in this book, it’s important to be explicit about what we mean when talking about the enterprise. For the rest of the book, the context of an enterprise will be that the end user is a business or business employee.

Does that mean if you are building the next great consumer photo-sharing application that this book will be of no use? Absolutely not! Many (if not most) of the concepts discussed throughout the book can be directly applied to consumer-facing applications as well. However, the use cases and discussion will be centered on the enterprise.

Next Steps

This book is intended to provide a broad overview of artificial intelligence, the various technologies involved, relevant case studies and examples of implementation, and the current landscape. We’ll also discuss the future of AI and where we see it moving in the enterprise from a current practitioner’s viewpoint.

While the book focuses on a more technical audience, specifically application developers looking to apply AI in their business, the general topics and discussion will help anyone interested in how artificial intelligence will impact the enterprise.

Note that this book is not a substitute for a course or deep study on these topics, but we hope the material covered here jump-starts the process of your becoming a proficient practitioner of AI technologies.

While there are many options for implementing the various AI techniques we’ll discuss in this book—coding in-house from scratch, outsourcing, the use of open source libraries, software-as-a-service APIs from leading vendors—our familiarity and deep experience revolves around IBM Watson, so we’ll be using those code examples to illustrate the individual topics as necessary.

It’s important to note that, while we believe IBM provides a great end-to-end solution for applying AI in the enterprise, it’s not the only option. All the major cloud computing providers offer similar solutions. Also, there are numerous open source tools that we’ll discuss later.

In the next few chapters, we’ll discuss some of the more common uses for AI in the enterprise (natural language processing, chatbots, and computer vision). Then we’ll discuss the importance of a solid data pipeline, followed by a look forward at the challenges and trends for AI in the enterprise.