Enterprise AI For Dummies

por Jarvinen, Zachary
Enterprise AI For Dummies
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ISBN: 978-1-119-69629-2
Editorial: Wiley & Sons Ltd.
Fecha de la edición: 2020
idioma: Ingles
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Nº Pág.: 352

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pvp.34.95 €

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Resumen del libro

Reseña: In Enterprise AI For Dummies, author Zachary Jarvinen simplifies and explains to readers the complicated world of artificial intelligence for business. Using practical examples, concrete applications, and straightforward prose, the author breaks down the fundamental and advanced topics that form the core of business AI. Written for executives, managers, employees, consultants, and students with an interest in the business applications of artificial intelligence, Enterprise AI For Dummies demystifies the sometimes confusing topic of artificial intelligence. No longer will you lag behind your colleagues and friends when discussing the benefits of AI and business. The book includes discussions of AI applications, including : · Streamlining business operations · Improving decision making · Increasing automation · Maximizing revenue The For Dummies series makes topics understandable, and as such, this book is written in an easily understood style thats perfect for anyone who seeks an introduction to a usually unforgiving topic.
indice: Introduction 1 About This Book 2 Strong, Weak, General, and Narrow 2 Foolish Assumptions 3 Icons Used in This Book 4 Beyond the Book 4 Where to Go from Here 5 Part 1: Exploring Practical AI and How It Works 7 Chapter 1: Demystifying Artificial Intelligence 9 Understanding the Demand for AI 11 Converting big data into actionable information 11 Relieving global cost pressure 13 Accelerating product development and delivery 14 Facilitating mass customization 14 Identifying the Enabling Technology 14 Processing 15 Algorithms 15 Data 16 Storage 18 Discovering How It Works 18 Semantic networks and symbolic reasoning 19 Text and data mining 20 Machine learning 22 Auto-classification 24 Predictive analysis 25 Deep learning 26 Sentiment analysis 27 Chapter 2: Looking at Uses for Practical AI 29 Recognizing AI When You See It 30 ELIZA 30 Grammar check 30 Virtual assistants 30 Chatbots 31 Recommendations 31 Medical diagnosis 32 Network intrusion detection and prevention 33 Fraud protection and prevention 34 Benefits of AI for Your Enterprise 34 Healthcare 35 Manufacturing 36 Energy 36 Banking and investments 37 Insurance 37 Retail 38 Legal 39 Human resources 39 Supply chain 40 Transportation and travel 40 Telecom 41 Public sector 41 Professional services 42 Marketing 43 Media and entertainment 43 Chapter 3: Preparing for Practical AI 45 Democratizing AI 46 Visualizing Results 46 Comparison 46 Composition 47 Distribution 48 Relationship 48 Digesting Data 50 Identifying data sources 52 Cleaning the data 52 Defining Use Cases 54 A ? B 55 Good use cases 55 Bad use cases 56 Reducing bias 58 Choosing a Model 59 Unsupervised learning 59 Supervised learning 60 Deep learning 60 Reinforcement learning 61 Chapter 4: Implementing Practical AI 63 The AI Competency Hierarchy 63 Data collection 63 Data flow 64 Explore and transform 64 Business intelligence and analytics 64 Machine learning and benchmarking 65 Artificial intelligence 65 Scoping, Setting Up, and Running an Enterprise AI Project 65 Define the task 67 Collect the data 68 Prepare the data 69 Build the model 70 Test and evaluate the model 72 Deploy and integrate the model 72 Maintain the model 72 Creating a High-Performing Data Science Team 73 The Critical Role of Internal and External Partnerships 74 Internal partnerships 74 External partnerships 75 The importance of executive buy-in 75 Weighing Your Options: Build versus Buy 75 When you should do it yourself 75 When you should partner with a provider 77 Hosting in the Cloud versus On Premises 77 What the cloud providers say 78 What the hardware vendors say 78 The truth in the middle 78 Part 2: Exploring Vertical Market Applications 81 Chapter 5: Healthcare/HMOs: Streamlining Operations 83 Surfing the Data Tsunami 84 Breaking the Iron Triangle with Data 84 Matching Algorithms to Benefits 86 Examining the Use Cases 87 Delivering lab documents electronically 87 Taming fax 88 Automating redaction 88 Improving patient outcomes 89 Optimizing for a consumer mindset 89 Chapter 6: Biotech/Pharma: Taming the Complexity 91 Navigating the Compliance Minefield 92 Weaponizing the Medical, Legal, and Regulatory Review 93 MLR review for product development 93 MLR review for sales and marketing 94 Enlisting Algorithms for the Cause 95 Examining the Use Cases 96 Product discovery 96 Clinical trials 96 Product development 96 Quality control 97 Predictive maintenance 97 Manufacturing logistics 97 Regulatory compliance 98 Product commercialization 98 Accounting and finance 98 Chapter 7: Manufacturing: Maximizing Visibility 99 Peering through the Data Fog 100 Finding ways to reduce costs 100 Handling zettabytes of data 101 Clearing the Fog 101 Connected supply chain 102 Proactive replenishment 103 Predictive maintenance 104 Pervasive visibility 104 Clarifying the Connection to the Code 106 Optimize inventory 106 Optimize maintenance 106 Optimize supply chain 106 Improve quality 106 Automate repetitive tasks 107 Examining the Use Cases 107 Minimize risk 107 Maintain product quality 107 Streamline database queries 108 Outsource predictive maintenance 108 Customize products 109 Expand revenue streams 109 Save the planet 109 Delegate design 110 Chapter 8: Oil and Gas: Finding Opportunity in Chaos 111 Wrestling with Volatility 111 Pouring Data on Troubled Waters 112 Deriving meaningful insights 113 Regaining control over your data 113 Wrangling Algorithms for Fun and Profit 114 Examining the Use Cases 115 Achieving predictive maintenance 115 Enhancing maintenance instructions 115 Optimizing asset performance 116 Exploring new projects 116 Chapter 9: Government and Nonprofits: Doing Well by Doing Good 119 Battling the Budget 120 Government 120 Nonprofit 122 Fraud 122 Optimizing Past the Obstacles 123 Digital transformation 123 The future of work 124 Data security 125 Operational costs 125 Fraud 125 Engagement 126 Connecting the Tools to the Job 128 Examining the Use Cases 129 Enhance citizen services 129 Provide a global voice of the citizen 130 Make your city smarter 130 Boost employee productivity and engagement 131 Find the right employees (and volunteers) 131 Improve cybersecurity 132 Chapter 10: Utilities: Renewing the Business 133 Coping with the Consumer Mindset 134 Utilizing Big Data 135 The smart grid 135 Empowering the organization 136 Connecting Algorithms to Goals 136 Examining the Use Cases 137 Optimizing equipment performance and maintenance 137 Enhancing the customer experience 137 Providing better support 138 Streamlining back-office operations 138 Managing demand 139 Chapter 11: Banking and Financial Services: Making It Personal 141 Finding the Bottom Line in the Data 142 Moving to 'open banking' 142 Dealing with regulation and privacy 143 Offering speedier service 144 Leveraging Big Data 144 Restructuring with Algorithms 145 Examining the Use Cases 146 Improving personalization 146 Enhancing customer service 146 Strengthening compliance and security 147 Chapter 12: Retail: Reading the Customers Mind 149 Looking for a Crystal Ball 150 Omnichanneling 150 Personalizing 151 Reading the Customers Mail 152 A fluid omnichannel experience 153 Enhanced personalization 153 Accurate forecasting 153 Looking Behind the Curtain 154 Examining the Use Cases 155 Voice of the customer 155 Personalized recommendations 155 AI-powered inventory 156 Chapter 13: Transportation and Travel: Tuning Up Your Ride 157 Avoiding the Bumps in the Road 158 Planning the Route 159 Checking Your Tools 161 Examining the Use Cases 162 Autonomous vehicles 162 Predictive maintenance 162 Asset performance optimization 163 Enhanced driver and passenger experiences 164 Chapter 14: Telecommunications: Connecting with Your Customers 167 Listening Past the Static 168 Finding the Signal in the Noise 168 Looking Inside the Box 169 Examining the Use Cases 170 Achieve predictive maintenance and network optimization 170 Enhance customer service with chatbots 170 Improve business decisions 171 Chapter 15: Legal Services: Cutting Through the Red Tape 173 Climbing the Paper Mountain 173 Reading and writing 174 And arithmetic 175 Foot in mouth disease 175 Planting Your Flag at the Summit 175 Linking Algorithms with Results 177 Examining the Use Cases 178 Discovery and review 178 Predicting cost and fit 179 Analyzing data to support litigation 180 Automating patent and trademark searches 180 Analyzing costs for competitive billing 180 Chapter 16: Professional Services: Increasing Value to the Customer 181 Exploring the AI Pyramid 182 Climbing the AI Pyramid 183 Unearthing the Algorithmic Treasures 184 Healthcare 184 Content management 184 Compliance 185 Law 185 Manufacturing 186 Oil and gas 186 Utilities 186 Examining the Use Cases 187 Document intake, acceptance, digitization, maintenance, and management 187 Auditing, fraud detection, and prevention 187 Risk analysis and mitigation 187 Regulatory compliance management 188 Claims processing 188 Inventory management 188 Resume processing and candidate evaluation 188 Chapter 17: Media and Entertainment: Beating the Gold Rush 189 Mining for Content 190 Asset management 190 Metadata 191 Distribution 191 Silos 192 Content compliance 192 Striking It Rich 193 Metadata 193 Digital distribution 193 Digital asset management 194 Assaying the Algorithms 194 Examining the Use Cases 195 Search optimization 195 Workflow optimization 196 Globalization 196 Part 3: Exploring Horizontal Market Applications 197 Chapter 18: Voice of the Customer/Citizen: Finding Coherence in the Cacophony 199 Hearing the Message in the Media 200 Delivering What They Really Want 201 Answering the Right Questions 203 Examining Key Industries 204 Consumer packaged goods 205 Public and nonprofit organizations 205 Chapter 19: Asset Performance Optimization: Increasing Value by Extending Lifespans 207 Spying on Your Machines 208 Fixing It Before It Breaks 209 Learning from the Future 210 Data collection 210 Analysis 211 Putting insights to use 212 Examining the Use Cases 212 Production automation and quality control 213 Preventive maintenance 213 Process optimization 215 Chapter 20: Intelligent Recommendations: Getting Personal 217 Making Friends by the Millions 218 Listening to social media 218 Mining data exhaust 219 Reading Minds 219 Knowing Which Buttons to Push 219 Popular product recommendation 220 Market-basket analysis 220 Propensity modeling 220 Data and text mining 222 Collaborative filtering (CF) 223 Content-based filtering (CBF) 224 Cross-validation 224 Data visualization 225 Examining Key Industries 226 Finance 226 Credit card offers 227 Retail 228 Chapter 21: Content Management: Finding What You Want, When You Want It 231 Introducing the Square Peg to the Round Hole 232 Categorizing and organizing content 232 Automating with AI 233 Finding Content at the Speed of AI 233 Expanding Your Toolbox 235 Access the content 235 Extract concepts and entities 235 Categorize and classify content 236 Automate or recommend next best actions 236 Examining the Use Cases 236 Legal discovery process 237 Content migration 237 PII detection 237 Chapter 22: AI-Enhanced Content Capture: Gathering All Your Eggs into the Same Basket 239 Counting All the Chickens, Hatched and Otherwise 240 Tracing the history of capture technology 240 Moving capture technology forward 241 Monetizing All the Piggies, Little and Otherwise 241 Streamline back-office operations 242 Improve compliance 242 Reduce risk of human error 243 Support business transformation 243 Improve operational knowledge 243 Getting All Your Ducks in a Row 244 Capture 244 Digitize where needed 244 Process, classify, and extract 244 Validate edge cases 245 Manage 246 Visualize 246 Examining Key Industries 246 Financial services 246 State government 247 Healthcare 247 Chapter 23: Regulatory Compliance and Legal Risk Reduction: Hitting the Bullseye on a Moving Target 249 Dodging Bullets 250 Fines 250 Increasing regulation 252 Data privacy 254 Strategy 254 Shooting Back 255 Make better decisions 255 Increase customer confidence 256 Win more business 257 Boost the bottom line 257 Building an Arsenal 258 Examining the Use Cases 259 Manage third-party risk 259 Manage operational risk 259 Monitor compliance risk 260 Monitor changes in regulations 261 Maintain data privacy 261 Maintain data security 262 Detect fraud and money laundering 262 Optimize workflow 263 Chapter 24: Knowledge Assistants and Chatbots: Monetizing the Needle in the Haystack 265 Missing the Trees for the Forest 266 Recognizing the problem 266 Defining terms 267 Hearing the Tree Fall 268 Making Trees from Acorns 269 Examining the Use Cases 270 Customer support 270 Legal practice 271 Enterprise search 272 Compliance management 272 Academic research 272 Fact checking 273 Chapter 25: AI-Enhanced Security: Staying Ahead by Watching Your Back 275 Closing the Barn Door 276 The story in the statistics 276 The state of current solutions 278 Locking the Barn Door 279 Knowing Which Key to Use 281 Examining the Use Cases 283 Detecting threats by matching a known threat marker 284 Detecting breaches by identifying suspicious behavior 284 Remediating attacks 286 Part 4: The Part of Tens 287 Chapter 26: Ten Ways AI Will Influence the Next Decade 289 Proliferation of AI in the Enterprise 290 AI Will Reach Across Functions 291 AI R&D Will Span the Globe 291 The Data Privacy Iceberg Will Emerge 292 More Transparency in AI Applications 292 Augmented Analytics Will Make It Easier 293 Rise of Intelligent Text Mining 293 Chatbots for Everyone 294 Ethics Will Emerge for the AI Generation 294 Rise of Smart Cities through AI 294 Chapter 27: Ten Reasons Why AI is Not a Panacea 297 AI is Not Human 298 Pattern Recognition is Not the Same As Understanding 299 AI Cannot Anticipate Black Swan Events 300 AI Might Be Democratized, but Data is Not 302 AI is Susceptible to Inherent Bias in the Data 302 #RacialBias 303 #GenderBias 303 #EthnicBias 303 Collection bias 304 Proxy bias 304 AI is Susceptible to Poor Problem Framing 305 AI is Blind to Data Ambiguity 306 AI Will Not, or Cannot, Explain Its Own Results 307 AI sends you to jail 307 AI cuts your medical benefits 308 AI and the black box 308 AI diagnoses your latent schizophrenia 309 AI can be fooled 310 AI is Not Immune to the Law of Unintended Consequences 311 Index 313

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