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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Artificial Intelligence for Business: A Roadmap for Getting Started with AI will provide the reader with an easy to understand roadmap for how to take an organization through the adoption of AI technology. It will first help with the identification of which business problems and opportunities are right for AI and how to prioritize them to maximize the likelihood of success. Specific methodologies are introduced to help with finding critical training data within an organization and how to fill data gaps if they exist. With data in hand, a scoped prototype can be built to limit risk and provide tangible value to the organization as a whole to justify further investment. Finally, a production level AI system can be developed with best practices to ensure quality with not only the application code, but also the AI models. Finally, with this particular AI adoption journey at an end, the authors will show that there is additional value to be gained by iterating on this AI adoption lifecycle and improving other parts of the organization.
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Veröffentlichungsjahr: 2020
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Table of Contents
Cover
Preface
Acknowledgments
CHAPTER 1: Introduction
Case Study #1: FANUC Corporation
Case Study #2: H&R Block
Case Study #3: BlackRock, Inc.
How to Get Started
The Road Ahead
Notes
CHAPTER 2: Ideation
An Artificial Intelligence Primer
Becoming an Innovation-Focused Organization
Idea Bank
Business Process Mapping
Flowcharts, SOPs, and You
Information Flows
Coming Up with Ideas
Value Analysis
Sorting and Filtering
Ranking, Categorizing, and Classifying
Reviewing the Idea Bank
Brainstorming and Chance Encounters
AI Limitations
Pitfalls
Action Checklist
Notes
CHAPTER 3: Defining the Project
The
What, Why
, and
How
of a Project Plan
The Components of a Project Plan
Approaches to Break Down a Project
Project Measurability
Balanced Scorecard
Building an AI Project Plan
Pitfalls
Action Checklist
CHAPTER 4: Data Curation and Governance
Data Collection
Leveraging the Power of Existing Systems
The Role of a Data Scientist
Feedback Loops
Making Data Accessible
Data Governance
Are You Data Ready?
Pitfalls
Action Checklist
Notes
CHAPTER 5: Prototyping
Is There an Existing Solution?
Employing vs. Contracting Talent
Scrum Overview
User Story Prioritization
The Development Feedback Loop
Designing the Prototype
Technology Selection
Cloud APIs and Microservices
Internal APIs
Pitfalls
Action Checklist
Notes
CHAPTER 6: Production
Reusing the Prototype vs. Starting from a Clean Slate
Continuous Integration
Automated Testing
Ensuring a Robust AI System
Human Intervention in AI Systems
Ensure Prototype Technology Scales
Cloud Deployment Paradigms
Cloud API's SLA
Continuing the Feedback Loop
Pitfalls
Action Checklist
Notes
CHAPTER 7: Thriving with an AI Lifecycle
Incorporate User Feedback
AI Systems Learn
New Technology
Quantifying Model Performance
Updating and Reviewing the Idea Bank
Knowledge Base
Building a Model Library
Contributing to Open Source
Data Improvements
With Great Power Comes Responsibility
Pitfalls
Action Checklist
Notes
CHAPTER 8: Conclusion
The Intelligent Business Model
The Recap
So What Are You Waiting For?
APPENDIX A: AI Experts
AI Experts
Chris Ackerson
Jeff Bradford
Nathan S. Robinson
Evelyn Duesterwald
Jill Nephew
Rahul Akolkar
Steven Flores
APPENDIX B: Roadmap Action Checklists
Step 1: Ideation
Step 2: Defining the Project
Step 3: Data Curation and Governance
Step 4: Prototyping
Step 5: Production
Thriving with an AI Lifecycle
APPENDIX C: Pitfalls to Avoid
Step 1: Ideation
Step 2: Defining the Project
Step 3: Data Curation and Governance
Step 4: Prototyping
Step 5: Production
Thriving with an AI Lifecycle
Index
End User License Agreement
List of Tables
Chapter 2
TABLE 2.1 A sample idea bank
Chapter 5
TABLE 5.1 Sample tech selection chatbot technologies
List of Illustrations
Chapter 1
FIGURE 1.1 Example of a FANUC Robot
FIGURE 1.2 The AI Adoption Roadmap
Chapter 2
FIGURE 2.1 The Standard Interpretation of the Turing Test
FIGURE 2.2 A Neural Network with a Single Neuron
FIGURE 2.3 A Fully Connected Neural Network with Multiple Layers
FIGURE 2.4 A Venn Diagram Describing How Deep Learning Relates to AI
FIGURE 2.5 An Enhanced Organizational Chart
FIGURE 2.6 An Information Flow Before an AI System
FIGURE 2.7 An Information Flow After an AI System
FIGURE 2.8 A Sample Process Flowchart
FIGURE 2.9 An Example Grouping of Ideas
Chapter 3
FIGURE 3.1 The Design Thinking Process
Chapter 4
FIGURE 4.1 Data Available for Training AI Models
FIGURE 4.2 The Typical Data Science Flow
Chapter 5
FIGURE 5.1 The Stages and Roles Involved with Feedback
FIGURE 5.2 A Logical Architecture for a Support Chatbot
FIGURE 5.3 A Physical Architecture for a Support Chatbot
FIGURE 5.4 Sample Catalog of AI Cloud Services from IBM
Chapter 6
FIGURE 6.1 Promoting Application Code from Stage to Production
FIGURE 6.2 Promoting a Model from Stage to Production
FIGURE 6.3 Acceptance, Integration, and Unit Testing
FIGURE 6.4 Sample Chatbot Architecture that Includes a Human in the Loop
FIGURE 6.5 Example of a Workload that Exhibits Spikes
Chapter 7
FIGURE 7.1 Sample Confusion Matrix for an Animal Classifier
Guide
Cover
Table of Contents
Begin Reading
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Artificial Intelligence for Business
A Roadmap for Getting Started with AI
JEFFREY L. COVEYDUC
JASON L. ANDERSON
© 2020 Jeffrey L. Coveyduc and Jason L. Anderson
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
The right of Jason L. Anderson and Jeffrey L. Coveyduc to be identified as the author(s) of the editorial material in this work has been asserted in accordance with law.
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Library of Congress Cataloging-in-Publication Data
Names: Anderson, Jason L, author. | Coveyduc, Jeffrey L, author.
Title: Artificial intelligence for business : a roadmap for getting started with AI / Jason L Anderson, Jeffrey L Coveyduc.
Description: First edition. | Hoboken : Wiley, 2020. | Includes index.
Identifiers: LCCN 2020004359 (print) | LCCN 2020004360 (ebook) | ISBN 9781119651734 (hardback) | ISBN 9781119651413 (adobe pdf) | ISBN 9781119651802 (epub)
Subjects: LCSH: Artificial intelligence—Economic aspects. | Business enterprises—Technological innovations. | Artificial intelligence—Data processing.
Classification: LCC HC79.I55 .A527 2020 (print) | LCC HC79.I55 (ebook) | DDC 006.3068—dc23
LC record available at https://lccn.loc.gov/2020004359
LC ebook record available at https://lccn.loc.gov/2020004360
Cover Design: Wiley
Cover Image: © Yuichiro Chino/Getty Images
Preface
Artificial intelligence (AI) has become so ingrained in our daily lives that most people knowingly leverage it every day. Whether interacting with an artificial “entity” such as the iPhone assistant Siri, or browsing through Netflix's recommendations, our functional adoption of machine learning is already well under way. Indirectly, however, AI is even more prevalent. Every credit card purchase made is run through fraud detection AI to help safeguard customers' money. Advanced logistical scheduling software is used to deliver tens of millions of packages daily, to locales around the world, with minimal disruption. In fact, the e-commerce giant Amazon alone claims to have shipped 5 billion packages with Prime in 2017 (see businesswire.com/news/home/20180102005390/en/). None of this would be possible on such a grand scale without the advances we have seen in AI systems and in machine learning technology over the last few decades.
Historically, these AI systems have been developed in-house by skilled teams of programmers, working around the clock at great expense to employers. This reality is now changing. Companies like IBM, Google, and Microsoft are making AI capabilities available on a pay-as-you-go basis, dramatically lowering the barrier to entry. For example, each of these companies provide speech-to-text and text-to-speech services to easily build voice interfaces for pennies per use. This is opening the door for smaller companies with less disposable capital to introduce AI initiatives that will produce substantial results. With the aforementioned backdrop of consumers interacting with AI on a daily basis, these consumers are becoming increasingly more comfortable and receptive to their brands adopting and incorporating more AI technology. The combination of all of these components makes it a smart bet for any modern company to start down the road toward AI adoption.
But how do these companies get started? This question is one we have seen time and again working with clients in the AI space. The drive and enthusiasm are there, but what organizational thought leaders are missing is the “how to” and overall direction. In our day jobs working with IBM Watson Client Engagement Centers and clients around the world, we repeatedly saw this pattern play out. Clients were eager to incorporate AI systems into their business models. They understood many of the benefits. They just needed a way in. While attending tech conferences and meetups, we find similar stories as well. Though the technological barriers are lower, with vendors providing accessible AI technology in the cloud, the challenge of coming up with the overall plan was still preventing many businesses from adopting AI. Having a good roadmap is essential to feeling comfortable with starting the journey. It is for this very reason that we wrote this book. Our goal is to empower you with the knowledge to successfully adopt AI technology into your organization. And you've already taken the first step by opening this book.
In addition to helping you adopt and understand emerging AI technology, this book will give you the tools to use AI to make a measurable impact in your business. Perhaps you will find some new cost-saving opportunities to unlock. Maybe AI will allow your business to uniquely position itself to enter new markets and take on competitors. Although AI has become more widespread and mainstream in its use in recent years, we are still seeing a tremendous amount of room for disruption in every field. That's the great thing about AI—it can be applied in an interdisciplinary fashion to all domains, and the more it grows, the more its capabilities grow along with it. All that we ask of you, the reader, is to start with an open mind while we provide that missing roadmap to help you successfully navigate your way to driving value within your organization using AI.
Acknowledgments
This book would not have been possible without the help of the following:
All of our AI experts, who kindly contributed their knowledge to provide a snapshot of AI
Nick Zephyrin, for his amazing book edits, which have kept our message consistent
Wiley's production team, for helping us get this book out and in the hands of the world
Our families (especially our wives, Denise and Libby), for all of their support throughout our careers
All of our friends, especially Jen English, who read early drafts and provided feedback along the way
IBM and Comp Three, for providing ample opportunities for learning and education
CHAPTER 1Introduction
The modern era has embedded code in everything we use. From your washing machine to your car, if it was made any time in the last decade, there is likely code inside it. In fact, the term “Internet of Things (IoT)” has emerged to define all Internet-connected devices that are not strictly computers. Although the code on these IoT devices is becoming smarter with every upgrade, the devices are not exactly learning autonomously. A programmer has to code every new feature or decision into a model. These programs do not learn from their mistakes. Advancement in AI will help solve this problem, and soon we will have devices that will learn from the input of their human creators, as well as from their own mistakes. Today we are surrounded by code, and in the near future, we will be surrounded by embedded artificially intelligent agents. This will be a massive opportunity for upgrades and will enable more convenience and efficiency.
Although companies may have implemented software projects on their own or with the help of outside vendors in the past, AI projects have their own set of quirks. If those quirks are not managed properly, they may cause a project to be a failure. A brilliant idea must be paired with brilliant execution in order to succeed. Following the path laid out in this book will put you on a trajectory toward managing AI projects more efficiently, as well as prepare you for the age of intelligent systems. Artificial intelligence is very likely to be the next frontier of technology, and in order for us to maximize this opportunity, the groundwork must be laid today.
Every organization is different, and it is important to remember not to try to apply techniques like a straitjacket. Doing so will suffocate your organization. This book is written with a mindset of best practices. Although best practices will work in most cases, it is important to remain attentive and flexible when considering your own organization's transformation. Therefore, you must use your best judgment with each recommendation we make. There is no one-size-fits-all solution, especially not in a field like AI that is constantly evolving.
Ahead of the recent boom in AI technologies, many organizations have already successfully implemented intelligent solutions. Most of these organizations followed an adoption roadmap similar to the one we will describe in this book. It is insightful for us to take a look at a few of these organizations, see what they implemented, and take stock of the benefits they are now realizing. As you read through these organizations' stories, keep in mind that we will be diving into aspects of each approach in more detail during the course of this book.
Case Study #1: FANUC Corporation
Science fiction has told of factories that run entirely by themselves, constantly monitoring and adjusting their input and output for maximum efficiency. Factories that can do just-in-time (JIT) ordering based on sales demand, sensors that predict maintenance requirements, the ability to minimize downtime and repair costs—these are no longer concepts of speculative fiction. With modern sensors and AI software, it has become possible to build these efficient, self-bolstering factories. Out-of-the-box IoT equipment can do better monitoring today than industrial sensors from 10 years ago. This leap in accuracy and connectivity has increased production threshold limits, enabling industrial automation on a scale never before imagined.
FANUC Corporation of Japan,1 a manufacturer of robots for factories, leads by example. Its own factories have robots building other robots with minimal human intervention. Human workers are able to focus on managerial tasks, whereas robots are built in the dark. This gives a whole new meaning to the industry saying “lights-out operations,” which originally meant servers, not robots with moving parts, running independently in a dark data center. FANUC Japan has invested in Preferred Networks Inc. to gather data from their own robots to make them more reliable and efficient than ever before. Picking parts from a bin with an assortment of different-sized parts mixed together has been a hard problem to solve with traditional coding. With AI, however, FANUC has managed to achieve a consistent 90 percent accuracy in part identification and selection, tested over some 5,000 attempts. The fact that minimal code has gone into allowing these robots to achieve their previously unobtainable objective is yet another testament to the robust capabilities of AI in the industrial setting. FANUC and Preferred Networks have leveraged the continuous stream of data available to them from automated plants, underlining the fact that data collection and analysis is critical to the success of their factory project. FANUC Intelligent Edge Link & Drive (FIELD) is the company's solution for data collection to be later implemented using deep learning models. The AI Bin-Picking product relies on models created via the data collected from the FIELD project. Such data collection procedures form a critical backbone for any industrial process that needs to be automated.
FANUC has also enabled deep learning2 models for situations where there are too many parameters to be fine-tuned manually. Such models include AI servo-tuning processes that enable high-precision, high-speed machining processes that were not possible until recently. In the near future, your Apple iPhone case will probably be made using a machine similar to the one in Figure 1.1.
Most factories today are capable of utilizing these advancements with minor modifications to their processes. The gains that can be achieved from such changes will be able to exponentially elevate the output of any factory.
FIGURE 1.1 Example of a FANUC Robot3
Case Study #2: H&R Block
H&R Block is a U.S.-based company that specializes in tax preparation services. One of their customer satisfaction guarantees is to find the maximum number of tax deductions for each of their customers. Some deductions are straightforward, such as homeowners being able to deduct the mortgage interest on their primary residence. Other deductions, however, may be dependent on certain client-specific variables, such as the taxpayer's state of residence. Deduction complexity can then be further compounded by requiring multiple client-dependent variables to be considered simultaneously, such as a taxpayer with multiple sources of income who also has multiple personal deductions. The ultimate result is that maximizing deductions for a given customer can be difficult, even for a seasoned tax professional. H&R Block saw an opportunity to leverage AI to help their tax preparers optimize their service. In order to help facilitate the adoption process, H&R Block partnered with IBM to leverage their Watson capabilities.4
When a customer comes into H&R Block, the tax preparer engages them in a friendly discussion. “Have you experienced any life-changing events in the last year?,” “Have you purchased a home?,” and so on. As they talk, the tax preparer types relevant details of the conversation into their computer system to be used as reference later. If the customer mentions that they purchased a house last year, that will be an indicator that they may qualify for a mortgage interest deduction this year.
H&R Block saw the opportunity here to leverage the use of AI to compile, cross-reference, and analyze all of these notes. Natural language processing (NLP) can be applied to identify the core intent of each note, which then can be fed into the AI system to automatically identify possible deductions. The system then presents the tax professionals with any potentially relevant information to ensure that they do not miss any possible deductions. In the end, both tax professionals and their customers can enjoy an increased sense of confidence that every last applicable deduction was found.
Case Study #3: BlackRock, Inc.
Financial markets are a hotbed for data. The data can be collected accurately and in real time for most financial instruments (stocks, options, funds, etc.) listed on stock markets. Metadata (data about data) can also be curated from analytical reports, articles, and the like. The necessity for channeling the sheer amount of information that is generated every day has given rise to professional data stream providers like Bloomberg. The immense quantity of data available, along with the potential for trend prediction, growth estimations, and increasingly accurate risk assessment, makes the financial industry ripe for implementing AI projects.
BlackRock, Inc., one of the world's largest asset managers, deploys the Aladdin5 (Asset, Liability, Debt, and Derivative Investment Network) software, which calculates risks, analyzes financial data, supports investment operations, and offers trade executions. Aladdin's key strength lies in using the vast amount of data to arrive at models of risk that give the user more confidence in deploying investments and hedging. The project was started nearly two decades ago, and it has been one of the key drivers of growth at BlackRock. BlackRock's technology services revenue grew 19 percent in 2018, driven by Aladdin and their other digital wealth products.6 Aladdin is now used by more than 25,000 investment professionals and 1,000 developers globally, helping to manage around $18 trillion in assets.7 Aladdin embeds within itself the building blocks of AI through the use of applied mathematics and data science.
BlackRock is now setting up a laboratory to further study the applications of AI in the analysis of risk and data streams generated. The huge amount of data being generated is becoming a problem for analysts, since the amount of data a human can sift through is limited. The expectation of Rob Goldstein, BlackRock's chief operating officer, is that the AI lab will help increase the efficiencies in what BlackRock does across the board.8 By applying big data to their existing data trove, BlackRock will be able to generate higher alphas, a measure of excess return over other portfolio managers, according to David Wright, head of product strategy in Europe. With good data generated by Aladdin and a sufficiently advanced AI algorithm, BlackRock might just emerge as the leader in analyzing risk and portfolios.
How to Get Started
The journey to adopt AI promises to bring major changes to the way your organization thinks and approaches its future. This journey will involve the adoption of new methods and process improvements that will aid you in spotting the novel ways AI can be deployed to save costs and make available new opportunities.
As with any endeavor worth starting, we must make plans for how we intend to accomplish our goal. In this case, the goal is to adopt AI technologies to better our organization. The plan for achieving this goal can vary from organization to organization, but the main steps invariably remain the same (see Figure 1.2).
1. Ideation
The first step in any technology adoption journey must start with ideation and identifying your motivation. In this chapter, we will delve into answering questions such as “What problem are you trying to solve?,” “How does your organization operate today?,” and “How do you believe your organization will be able to benefit from AI technology?” Answering questions like these will kick-start your AI journey by establishing a clear set of goals. To properly answer these questions, you will also need a general understanding of the technology, which we will cover in the following section.
FIGURE 1.2 The AI Adoption Roadmap
2. Defining the Project
Once you have determined that the use of AI technologies can help improve your organization or solve a business problem, you must then get specific about what you hope to achieve. During the second step, you will outline specifically which improvements you plan to attain, or which problems you are trying to solve. This will take the form of a project plan. This plan will act as a guiding document for the implementation of your project. Using the methodical techniques of design thinking, the Delphi method, and systems planning makes a plan much easier to develop. These techniques will ensure that you have a sound and realistic project plan.
User stories will also be a large part of the project plan. User stories are an excellent way to break down a project into functional pieces of value. They define a user, the functionality that the system will provide for the user, and the value that the function will provide to the organization. Well-defined user stories also quantify their results to empirically know when success has been achieved. These success criteria make it much easier to see when we have accomplished our user story's goal and communicate a clear course of action for everyone involved. Specificity is the key.
3. Data Curation and Governance
Data is paramount to every AI system. A system can only be as good as the data that is used to build it. Therefore, it is important to take stock of all the possible data sources at your disposal. This is true whether it is data being collected and stored internally or data that you externally license.
After you have identified your data, it is time to leverage technology to further improve the data's quality and prepare it to train an AI system. Crowdsourcing can be a valuable tool to enhance existing data, and data platforms such as Apache Hadoop can help consolidate data from multiple sources. Data scientists will be key in orchestrating this process and ensuring success. The quality of your data will determine the success of your project in a huge way. It is therefore essential to choose the best available data on hand. The old saying about “garbage in, garbage out” applies to AI as well.
4. Prototyping
With your project plan and data defined, it is time to start building an initial version your system. As with any project, it is best to take an iterative approach. In the prototype step, you will select a subset of your use cases to validate the idea. In this way, you are able to see if the expected value is materializing before you are completely invested. This step also enables you to adjust your approach early if you see any problems arise. Developing a prototype will help you to see, with actual results, whether the ideas and plans you defined in the previous steps have promise. In the event that they do not, you should be able to recover quickly and adjust them using the knowledge gained from prototyping, without the wasted investment of building a full system.
During the prototyping phase, it is necessary to have realistic expectations. With most AI systems, they improve with more data and parameter tweaking, so you should expect to see increasing improvements over time. Luckily, metrics like precision and recall can be empirically measured and used to track this improvement. We will also cover the cases when more data is not the answer and what other techniques can be pursued to continue improving the system.
5. Production
With a successful prototype under your belt, you have been able to see the value of the technology in action. Now it is time to further invest and complete your system. At this point, it is also a good idea to revisit your user stories and plan as a whole to determine if any priorities have changed. You can then proceed with building the production system.
The production step is the process of converting the prototype into a full-fledged system. This includes conducting a technological evaluation, building user security models, and establishing testing frameworks.
Technological Evaluation
During the prototype phase, developers select technologies appropriate for a prototype, including using technologies and languages that are easy to work with. This mitigates risk by determining the project's feasibility quickly before investing a lot of time and money. That said, during the production step the technology must be evaluated for other factors as well. For instance, will the technology scale to a large number of users or massive amounts of data? Will the technology be supported in the long term and be flexible enough to change as requirements do? If not, pieces of the prototype might have to be rebuilt to accommodate.
User/Security Model
During the prototype phase, the project is typically only running on locked-down development machines or internal servers. While they require some security, high levels of security are not typically needed during prototyping and will only slow down the prototyping process. Work, such as integrating an organization's user directory (single sign-on [SSO]) and permission structures, will be part of the production process.
Testing Frameworks
To ensure code quality, testing frameworks should be built alongside the production code. Testing ensures that the code base does not regress as new code is added. Development teams may even adopt a “test first” approach called test-driven development (TDD) to ensure that all pieces of code have tests written before starting their implementation. If TDD is used, developers repeat very short development cycles, writing only enough code for the tests to pass. In this way, tests reflect the desired functionality and code is written to implement that functionality.
Thriving with an AI Lifecycle
Once you have adopted AI and your organization is realizing its benefits, it is time to switch into the lifecycle mode. At this point, you will be maintaining your AI systems while consistently looking for ways to improve. This might mean leveraging system usage data to improve your machine learning models or keeping an eye on the latest technology announcements. Perhaps the AI models you have implemented can also be used in another part of your organization. Furthermore, it is important that the knowledge gained during the implementation of your first AI system be saved for future projects. As we will discuss in this book, this can take the shape of either an entry in your organization's model library or a lessons learned document.
The Road Ahead
Adopting artificial intelligence in your organization can feel like a daunting task, especially since the technology is changing so frequently. The main idea is to be aware of all the benefits, as well as the pitfalls, so that you can adequately discern between them and navigate your way to success. Mistakes are inevitable. Keeping them small and easy to recover from will ensure that your AI transformation has the resilience it needs to prevail. To minimize the likelihood of mistakes, we list the common pitfalls associated with each step at the end of each chapter so you can take notice and avoid them. With sufficient planning and foresight provided by this book, you will be able to acquire the tools necessary to make your organizational adoption of AI a great success.
Notes
1
https://preferred.jp/en/news/tag/fanuc/
2
www.bloomberg.com/news/features/2017-10-18/this-company-s-robots-are-making-everything-and-reshaping-the-world
3
https://en.wikipedia.org/wiki/File:FANUC_R2000iB_AtWork.jpg
4
www.hrblock.com/tax-center/newsroom/around-block/partnership-with-ibm-watson-reinventing-tax-prep/
5
www.blackrock.com/aladdin/offerings/aladdin-overview
6
https://ir.blackrock.com/Cache/1001247206.PDF?O=PDF&T=&Y=&D=&FID=1001247206&iid=4048287
7
www.institutionalinvestor.com/article/b1dn7pgfhbxpsg/BlackRock-s-Aladdin-Adds-Alts-Power
8
www.ft.com/content/9ab2d910-1816-11e9-9e64-d150b3105d21
CHAPTER 2Ideation
An Artificial Intelligence Primer
The evolution of digital computers can be traced all the way back to the 1800s. The 1800s were an era of steam engines and large mechanical machines. It was during this era that Charles Babbage drew up the notes for making a difference engine.1 The difference engine was an automatic calculator that worked on the principle of second-order derivatives to calculate a series of values from a given equation. This breakthrough paved the way for modern computers. After the invention of the difference engine, Babbage turned his attention to solving more equations and giving a programming ability to his machines. His new machine was called the analytical engine.
Another key figure in this era of computing was Ada Lovelace. She prepared extensive notes to aid in the understanding and generalization of the analytical engine.2 For her contributions, she is generally considered to be the world's first programmer. Although she erroneously rejected that computers were capable of creative and decision-making processes, she was the first to correctly note that computers could be the generalized data processing machines we see today.
Alan Turing, in his seminal paper introducing the Turing test,3 met Lovelace's objections head on, saying that the analytical engine had the property of being “Turing complete” similar to programming language today and that with sufficient storage and time, it could be programmed to complete the Turing test. Turing further claimed that Lovelace and Babbage were under no obligation to describe all that could be achieved by the computer.
The Turing test (aka the “imitation game”), constructed by Turing in the same paper, is a game where two players, A and B, try to fool the third player, C, about their genders. This has been modified over the years to the “standard” Turing test where either A or B is a computer and the other is a human and C must determine which is which (see Figure 2.1). The critical question that Turing was trying to answer using this game is “Can machines communicate in natural language in a manner indistinguishable from that of a human being?”4 Turing postulated about machines that can learn new things using the same techniques that are used to teach a child. The paper deduced, quite correctly, that these thinking machines would be effectively black boxes since they were a departure from the paradigm of normal computer programming. The Turing test is still undefeated as of this writing, but we are well on our way to breaking the test and moving on to the greener pastures of intelligence. Although many chatbots have claimed to break the test, it has not been defeated without cheating and using tricks and hacks that do not guarantee a long-term correct result.
FIGURE 2.1 The Standard Interpretation of the Turing Test5
Modern AI has come a long way from the humble beginnings of the analytical engine and the one simple question of “Can machines think?” Today, we have AI that can understand the sentiment and tone of a text message, identify objects in image, search through thousands of documents quickly, and almost converse with us flawlessly with natural language. Artificial intelligence has become a magic assistant in our phones that awaits our questions in natural language, interprets them, and then returns an answer in the same language, instead of just showing a web result. In the next section, we will have a brief look at the state of modern AI and its current set of capabilities.
Natural Language Processing
The ability to converse with humans, as humans do with one another, has been one of the most coveted feats of AI ever since a thinking machine has been thought of. The Turing test measures a computer's ability to “speak” with a human and fool that person into thinking they are speaking to another human. This branch of AI, known as natural language processing (NLP), deals with the ability of the computer to understand and express itself in a natural language. This has proven to be especially difficult, since human conversations are loaded with context and deep meanings that are not explicitly communicated and are simply “understood.” Computers are bad at dealing with such loosely defined problems, since they work on well-defined programs that are unambiguous and clear. For example, the phrase “it is raining cats and dogs” is difficult for a computer to understand without the entirety of history and literature accessible inside the computer. To us, such a sentence is obvious even if we're previously unaware of the meaning, because we have the entire context of our lives to judge that raining animals is an impossibility.
Programmatic NLP
The first chatbots and natural language processing programs used tricks and hacks to translate human speech into computer instructions. ELIZA was one of the first few programs to make people believe with certain limitations that it was capable of intelligent speech. This was accomplished by Joseph Weizenbaum at the Massachusetts Institute of Technology (MIT) Artificial Intelligence Laboratory in the 1960s. ELIZA was designed to mimic psychologists by echoing the user's answer back to them. In this way, the computer seemed to hold intelligent conversation, but it clearly was not. There are other forms of NLP seen in the 1980s; they were text-based adventure games. The games understood a certain set of verbs—such as go, run, fight, and eat—and modified their feedback based purely on language parsing. This was accomplished by having a set of words that the game understood mapped to functions that would execute based on the keyword. The limitation of words that could be stored in memory meant that these early natural language parsers could not understand everything and would return errors and thus ruin the illusion very quickly.
A method of using programming techniques to parse natural language, programmatic NLP uses string parsing with regular expressions (regex) along with a dictionary of words the program can execute on. The regular expressions match the specified patterns, and the program adjusts its control flow based on the information gleaned from sentences, discarding everything except the main word. For example, the following is a simple regular expression that could be used to determine possible illness names:
diagnosed with \w+
This example looks for the phrase “diagnosed with” followed by a single word, which would assumingly be the name of an illness (such as “diagnosed with cancer”). A more complex regular expression is required to identify illnesses with multiple words (such as “diagnosed with scarlet fever”). A full discussion of regular expressions is outside the scope of this book—Mastering Regular Expressions6 by Jeffrey Friedl is a great resource if you want to learn more.
Although the techniques we've discussed can make wondrous leaps in parsing a language, they fall short very quickly when applied more generally, because the dictionary supplied with the program can be exhausted. This is where AI steps in and outperforms these traditional methods by a huge margin. Natural languages, while they do follow the rules of grammar, follow rules that are not universal and thus something that holds meaning in one region might not hold true for another region. Languages vary so much because they are a fluid concept; new words are being added constantly to the vernacular, old idioms and words are retired, grammar rules change. This makes a language a perfect candidate for stochastic modeling and other statistical analysis, covered under the umbrella term of machine learning for natural language processing.
