Zachary Jarvinen - Enterprise AI For Dummies

3 Part 1: Exploring Practical AI and How It Works Part 1 Chapter 1: Demystifying Artificial Intelligence Chapter 1 Understanding the Demand for AI Understanding the Demand for AI If there is a universal constant in commerce throughout the ages, it is competition. Always changing, always expanding, always looking for a foothold, an advantage — whether from reducing costs, increasing revenue, or unlocking new, innovative business models. Similarly, while much discussion has taken place in the last few decades about the challenges posed by a global economy, international trade is not a recent phenomenon. It dates back at least to the Assyrians. Four millennia later, the goal is the same for the modern enterprise: establish a competitive advantage. However, the specific challenges to tackle are new. Identifying the Enabling Technology Identifying the Enabling Technology Just as constant as the challenges posed by competition throughout the ages is the role of innovation in addressing competitive pressure. Four millennia ago camels were domesticated, and a few centuries later ships were launched to enable long-distance trade. In this new millennium, the continued pressure of competition has fueled advances in technology, particularly in the domain of artificial intelligence. Like the camel and the ship, AI enables those in business to go farther and faster, to respond to global pressure to reduce cost, increase efficiency, and accelerate the development and delivery of products. However, several enabling technologies had to reach maturity to create a foundation that would allow AI to realize the potential envisioned by the scientists at the 1956 Dartmouth Summer Research Project on Artificial Intelligence. Discovering How It Works Discovering How It Works Artificial intelligence is a field of study in computer science. Much like the field of medicine, it encompasses many sub-disciplines, specializations, and techniques. Chapter 2: Looking at Uses for Practical AI Chapter 2 Recognizing AI When You See It Recognizing AI When You See It Like good design, good AI is invisible. When done right, both remove some of the friction from daily life. In fact, you have probably been using artificial intelligence for longer than you realize. Benefits of AI for Your Enterprise Benefits of AI for Your Enterprise Artificial intelligence offers significant benefits for a broad range of markets. The most noticeable is optimizing the workforce by increasing their efficiency and reducing the burden of manual tasks. AI is good at automating things you might feel bad about asking someone else to do, either because it is tedious, such as reading through reams of reports, or dangerous, such as monitoring and managing workflow in a hostile environment. In other words, AI can relieve workers from the part of the job that they like the least. In addition, when an algorithm produces results with high accuracy and predictability, mundane processes and routine decisions can be automated, thus reducing the need for human intervention in the paper chase of the typical enterprise and freeing workers to focus on tasks that increase revenue and customer satisfaction. AI thrives on data and excels at automating routine tasks, so those industries with a wealth of digitized data and manual processes are poised to reap large rewards from implementing AI. For these industries, AI can enhance the things you want to increase, such as quality, adaptability, and operational performance, and mitigate the things you want to reduce, such as expense and risk. This section provides a bite-sized overview of industries that can derive specific benefits from implementing AI. Later chapters explore use cases for each in depth. Chapter 3: Preparing for Practical AI Chapter 3 Democratizing AI Democratizing AI For decades, artificial intelligence was the province of academics, scientists, and technicians with a highly specialized skill set. In the 1980s, some data scientists took the step from academe to commerce, applying AI to real-world problems and the development of expert systems. In the 1990s, commercial applications for AI expanded along with the Internet and the wealth of data it generated. Even so, any business wanting to capitalize on the power of artificial intelligence had to commit a serious amount of capital, not only for rare and expensive data scientists, but also for major-league processing power and data storage. More recently, full-powered AI solutions with simplified interfaces allow users to create and train models and produce reports and data visualization, reducing the need for a full team of dedicated data scientists. In fact, Gartner predicted that workers using self-service analytics would output more analysis than professional data scientists. That’s good news for enterprises. And don’t worry about putting data scientists out of business. They are still in high demand. For the last three years, data scientist was the #1 ranked job in the U.S. on the career website Glassdoor. Visualizing Results Visualizing Results The key to actionable insight is the ability to quickly recognize what the data is telling you. Any AI solution you use must have a rich, robust, and easy-to-use data visualization engine. Good data visualization transcends barriers of language and culture to instantly communicate the important data points and trends. It also has the virtue of being easy to share and to engage with. Table 3-1 shows four visualization types categorized by use. Digesting Data Digesting Data Of the three pillars of AI — processing power, scalable storage, and big data — the third is the one that presents the biggest challenge. How to get it, how to validate it, how to process it. Figure 3-5 shows the pyramid of critical success factors for AI and analytics. Four of the six layers relate to data, focusing on relevance, accessibility, usability, completeness, and data-based conclusions. FIGURE 3-5: Pyramid of critical success factors for AI and analytics. Table 3-2 describes critical questions to answer at each layer. TABLE 3-2 Pyramid of Critical Success Factors for AI and Analytics Element Questions AI How will you address analytical deployment, governance, and operations? Experimentation ML Does machine learning add business value? How do you define success? BI / Analytics What is the story your data is telling? What conclusions can you make from this information? Explore and Enrich Can the data be used meaningfully? Are you missing any data or features? Data Access Is the data accessible and usable (analysis-ready)? Is the data flow reliable? Data Collection Do you have data relevant to your business goals? Defining Use Cases Defining Use Cases “I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.” — Abraham Maslow, “The Psychology of Science: A Reconnaissance” 1966 To hear some tell it, AI is the panacea to solve all the problems of the world; to hear others tell it, AI will lead to the singularity and the destruction of human civilization. As usual, the truth lies somewhere in between. As with any tool, AI does some things well and other things not so well. If you’re upholstering a chair, a tack hammer is best, but if you’re putting up a circus tent, you need a bigger hammer. As twentieth-century psychologist Abraham Maslow pointed out, the problem arises when you start with a tool and try to use it on every challenge or obstacle you encounter. The better approach is to identify a desired outcome and select the tool that possesses capabilities suited to achieving the desired outcome. So what is AI good at? Choosing a Model Choosing a Model Although you might hear the term “artificial intelligence” bandied about as if it were a single thing, the reality is that it is an umbrella term for a vast discipline covering countless models or algorithms of varying complexity and rigor. Even within machine learning, dozens of methods can help you accomplish your goal, each used for a specific type of problem. Chapter 4: Implementing Practical AI The AI Competency Hierarchy Scoping, Setting Up, and Running an Enterprise AI Project Creating a High-Performing Data Science Team The Critical Role of Internal and External Partnerships Weighing Your Options: Build versus Buy Hosting in the Cloud versus On Premises