Zachary Jarvinen - Enterprise AI For Dummies

4 Part 2: Exploring Vertical Market Applications Chapter 5: Healthcare/HMOs: Streamlining Operations Surfing the Data Tsunami Breaking the Iron Triangle with Data Matching Algorithms to Benefits Examining the Use Cases Chapter 6: Biotech/Pharma: Taming the Complexity Navigating the Compliance Minefield Weaponizing the Medical, Legal, and Regulatory Review Enlisting Algorithms for the Cause Examining the Use Cases Chapter 7: Manufacturing: Maximizing Visibility Peering through the Data Fog Clearing the Fog Clarifying the Connection to the Code Examining the Use Cases Chapter 8: Oil and Gas: Finding Opportunity in Chaos Wrestling with Volatility Pouring Data on Troubled Waters Wrangling Algorithms for Fun and Profit Examining the Use Cases Chapter 9: Government and Nonprofits: Doing Well by Doing Good Battling the Budget Optimizing Past the Obstacles Connecting the Tools to the Job Examining the Use Cases Chapter 10: Utilities: Renewing the Business Coping with the Consumer Mindset Utilizing Big Data Connecting Algorithms to Goals Examining the Use Cases Chapter 11: Banking and Financial Services: Making It Personal Finding the Bottom Line in the Data Leveraging Big Data Restructuring with Algorithms Examining the Use Cases Chapter 12: Retail: Reading the Customer’s Mind Looking for a Crystal Ball Reading the Customer’s Mail Looking Behind the Curtain Examining the Use Cases Chapter 13: Transportation and Travel: Tuning Up Your Ride Avoiding the Bumps in the Road Planning the Route Checking Your Tools Examining the Use Cases Chapter 14: Telecommunications: Connecting with Your Customers Listening Past the Static Finding the Signal in the Noise Looking Inside the Box Examining the Use Cases Chapter 15: Legal Services: Cutting Through the Red Tape Climbing the Paper Mountain Planting Your Flag at the Summit Linking Algorithms with Results Examining the Use Cases Chapter 16: Professional Services: Increasing Value to the Customer Exploring the AI Pyramid Climbing the AI Pyramid Unearthing the Algorithmic Treasures Examining the Use Cases Chapter 17: Media and Entertainment: Beating the Gold Rush Mining for Content Striking It Rich Assaying the Algorithms Examining the Use Cases

5 Part 3: Exploring Horizontal Market Applications Chapter 18: Voice of the Customer/Citizen: Finding Coherence in the Cacophony Hearing the Message in the Media Delivering What They Really Want Answering the Right Questions Examining Key Industries Chapter 19: Asset Performance Optimization: Increasing Value by Extending Lifespans Spying on Your Machines Fixing It Before It Breaks Learning from the Future Examining the Use Cases Chapter 20: Intelligent Recommendations: Getting Personal Making Friends by the Millions Reading Minds Knowing Which Buttons to Push Examining Key Industries Chapter 21: Content Management: Finding What You Want, When You Want It Introducing the Square Peg to the Round Hole Finding Content at the Speed of AI Expanding Your Toolbox Examining the Use Cases Chapter 22: AI-Enhanced Content Capture: Gathering All Your Eggs into the Same Basket Counting All the Chickens, Hatched and Otherwise Monetizing All the Piggies, Little and Otherwise Getting All Your Ducks in a Row Examining Key Industries Chapter 23: Regulatory Compliance and Legal Risk Reduction: Hitting the Bullseye on a Moving Target Dodging Bullets Shooting Back Building an Arsenal Examining the Use Cases Chapter 24: Knowledge Assistants and Chatbots: Monetizing the Needle in the Haystack Missing the Trees for the Forest Hearing the Tree Fall Making Trees from Acorns Examining the Use Cases Chapter 25: AI-Enhanced Security: Staying Ahead by Watching Your Back Closing the Barn Door Locking the Barn Door Knowing Which Key to Use Examining the Use Cases

6 Part 4: The Part of Tens Chapter 26: Ten Ways AI Will Influence the Next Decade Proliferation of AI in the Enterprise AI Will Reach Across Functions AI R&D Will Span the Globe The Data Privacy Iceberg Will Emerge More Transparency in AI Applications Augmented Analytics Will Make It Easier Rise of Intelligent Text Mining Chatbots for Everyone Ethics Will Emerge for the AI Generation Rise of Smart Cities through AI Chapter 27: Ten Reasons Why AI Is Not a Panacea AI Is Not Human Pattern Recognition Is Not the Same As Understanding AI Cannot Anticipate Black Swan Events AI Might Be Democratized, but Data Is Not AI Is Susceptible to Inherent Bias in the Data AI Is Susceptible to Poor Problem Framing AI Is Blind to Data Ambiguity AI Will Not, or Cannot, Explain Its Own Results AI Is Not Immune to the Law of Unintended Consequences

7 Index

8 About the Author

9 Advertisement Page

10 Connect with Dummies

11 End User License Agreement

1 Chapter 1 TABLE 1-1 Case Relationship for a Sentence TABLE 1-1 Case Relationship for a Sentence Case Threw Agent Boy Object Bone Recipient Dog The case relationship for other uses of “threw” won’t necessarily follow the same structure. The pitcher threw the game. The car threw a rod. The toddler threw a tantrum. Early iterations of rules engines and expert systems were code-driven, meaning much of the system was built on manually coded algorithms. Consequently, they were cumbersome to maintain and modify and thus lacked scalability. The availability of big data set the stage for the development of data-driven models. Symbolic AI evolved using the combination of machine-learning ontologies and statistical text mining to get the extra oomph that powers the current AI renaissance. TABLE 1-2 Data Mining Versus Text Mining TABLE 1-2 Data Mining Versus Text Mining Data Mining Text Mining Overview Data mining searches for patterns and relationships in structured data. Text mining transforms unstructured textual data into structured information to enable data analysis. Data Type Structured data from large datasets is found in systems such as databases, spreadsheets, ERP, and accounting applications. Unstructured textual data is found in emails, documents, presentations, videos, file shares, social media, and the Internet. Data Retrieval Structured data is homogenous and organized, making it easy to retrieve. Unstructured textual data comes in many different formats and content types located in a more diverse range of applications and systems. Data Preparation Structured data is formal and formatted, facilitating the process of ingesting data into analytical models. Linguistic and statistical techniques — including NLP keywording and meta-tagging — must be applied to turn unstructured into usable structured data. Taxonomy There is no need to create an overriding taxonomy. A global taxonomy must be applied to organize the data into a common framework. TABLE 1-3 Machine Learning as a Recipe TABLE 1-3 Machine Learning as a Recipe Machine Learning Recipe Task An algorithm is a step-by-step instruction set or formula for solving a problem or completing a task. Thaw the chicken. Season the chicken. Bake the chicken at 350°F. Objective Minimize errors (loss function) to attain the best approach to solve a task. Minimize the number of ingredients and steps required to prepare a tasty dish. Insight/result The algorithm learns from errors, finds the best approach, and generates insights and rules used to make predictions. Learn from your mistakes the next time you attempt the recipe. For example, if you process a brochure for the San Diego Zoo using the model, it would recognize the content about elephants and add the tag “elephant” to the document along with a score. The result is a prediction in the form of the percentage probability that the document contains information about elephants. Basically, the model makes a data-driven guess. In AI and data science, execution is not just implementing a plan. The methodology establishes an iterative process of learning, discovering, and then acting based on new information as opposed to a more traditional IT model of formulating a plan or idea and then rolling it out as planned. TABLE 1-4 Artificial Intelligence, Machine Learning, and Deep Learning TABLE 1-4 Artificial Intelligence, Machine Learning, and Deep Learning Technique Description Example Artificial Intelligence Computing systems capable of performing tasks that humans are very good at Recognize objects, recognize and make sense of speech, self-driving cars Machine Learning Field of AI that learns from historical data toward an end goal or outcome Predict customers likely to churn Deep Learning Powerful set of machine-learning techniques that mimic the brain’s neuron activities Computer vision, colorize photos, deep fakes, mastering a game