Hype vs. Reality: The AI Explainer


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Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?

Here’s what AI learnings your business should keep in mind for 2017.

Published in: Business
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Hype vs. Reality: The AI Explainer

  1. 1 Hype vs. Reality The AI Explainer January 2017 Produced by Luminary Labs in partnership with Fast Forward Labs
  2. 2 Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems in 2017? In September 2016, Luminary Labs convened 30 executives in healthcare, machine learning, and analytics for a grounded discussion on these questions with machine learning expert Hilary Mason, founder and CEO of Fast Forward Labs, and Sandy Allerheiligen, VP of data science and predictive and economic modeling at Merck. Here’s a synopsis of what we discussed, and what AI learnings your business should keep in mind for 2017. AI and the Near Term
  3. 3 We’ve all seen the sensational headlines: The robots are coming, and they’ll take our jobs! AI can do your job faster and more accurately than you can! The Hype 3
  4. 4 The Reality Human jobs won’t go away, but they will change. Roles will be more creative and specialized as AI is integrated into the workday. Better data leads to better math leads to better predictions, so people using AI can automate the tedious work and take action on the insights.
  5. 5 In the short term AI does the math faster, saving money by automating normally complex processes. It makes your life easier even now, behind the scenes. This is what it looks like today.
  6. 6 The Nest thermostat remembers what temperatures you like and adjusts automatically, like turning the temperature down when you’re away and turning it up when you’re on your way home. This saves users time, energy, and money. Photo: Nest 6
  7. 7Photo: Netflix 7 Netflix’s predictive analytics recommend what you might want to watch next—and what studios should create next— based on viewer data. Amazon, iTunes, Pandora, and other companies use predictive analytics to make better recommendations.
  8. 8 Salesforce Einstein applies natural language processing to analyze text from e-mails exchanged with customers to estimate the likelihood that a user will buy, detect deals a team is at risk of losing, and recommend actions to improve sales. 8
  9. 9 In the longer term AI will transform industries.
  10. 10 For example, algorithms help healthcare professionals recognize anomalies or patterns in medical images with more accuracy than the human eye. Over time, this can result in a library of knowledge that can lead to potential disease cures. 10
  11. 11Photo: NVIDIA Coporation One of AI’s promises is to make self-driving cars safer. Everyday driving decisions, such as whether to stop abruptly or swerve to avoid hitting an obstacle, will be powered by AI. 11
  12. 12 AI will help redesign the entire shopping experience, optimizing everything with more and better data. Retailers will seamlessly stock the precise number of goods needed on shelves at any given time, and know which product at which price should be highlighted to a specific customer as they navigate a store. 12
  13. 13 Where do you start? Five ways to look past the shiny-object phase and into practical AI planning in 2017.
  14. 14 1. Don’t fear the robots. The idea is to augment, not replace, work. AI can absorb cognitive drudgery, like turning data points into visual charts, calculating complex math formulas, or summarizing the financial news of the day into a single report. This frees up people to focus on acting on the insights. Photo: Flickr user joao_trindade 14
  15. 15 2. Start with the problem, not the solution. Before launching an AI program, identify concrete business problems, then consider if AI can help. For example, rather than ask, “What can we use AI for?”, think, “Where could we make our operations more efficient?” or “What decisions are we making without data?” Photo: Flickr user Robert Couse-Baker 15
  16. 16 3. Emphasize empathy. The more machines we employ, the more people skills we need. Leaders must build empathy across the organization to help employees see impact. Focus on how AI can help workers add more human value, rather than replace them. For example, McDonald’s added robots to their franchises, but doesn’t plan to cut human jobs. Photo: Flickr user EasySentrisentri 16
  17. 17 4. Engage the skeptics. Understand what they fear and start there. Fast Forward Labs’ Hilary Mason shared an example of winning buy-in by demonstrating how machine learning could solve a problem for an overburdened regulatory team. 17
  18. 18Photo: Flickr user JDHancock 5. Remember: It’s not magic. If a vendor can’t explain their AI product or service in terms you understand, don’t buy it. Much of what’s called AI today (“AI personal assistants,” anyone?) is actually humans wrangling a trove of data behind the scenes. If it doesn’t make sense, it might not be real. 18
  19. 19 Glossary Some AI terms are used primarily for marketing purposes, while others are more technical. Here are our translations for common terms you may hear, whether you’re being sold an AI product or partnering with a team of AI experts. It’s a great starting point for becoming an AI leader in your organization.
  20. 20 Artificial intelligence (AI): Marketing term that describes a continuum of non-living analytical power, fueled by fast processing and data storage’s declining costs. Applications today are termed weak AI (like IBM Watson), which are algorithms built to accomplish a specific task. Strong AI (like Skynet) is a term for hypothetical future applications that will replicate human intelligence. Big data: Buzzword alluding to a machine’s ability to generate insights and learn from massive data sets, because sensors, software, and recordkeeping generate a lot of data. For example, The Weather Company and IBM researched weather’s impact on business by analyzing millions of data points from weather sensors, aircraft, smartphones, buildings, and vehicles. The big picture
  21. 21 Machine learning: Method of automated analytical model building. Machine learning lets computers find hidden insights without being explicitly programmed where to look. For instance, Facebook’s machine learning software uses algorithms and data points to show a user suggested friends, display relevant ads, and detect spam. Algorithm: Formula that represents a relationship between things. It’s a self-contained, step-by-step set of operations that automates a function, like a process, recommendation, or analysis. For example, Netflix’s recommendation algorithms can predict what movies a consumer might want to watch based on their viewing history. Most important to remember
  22. 22 Deep learning: Branch of machine learning that uses multiple layers of distributed representations (neural networks) to recognize patterns in digital sounds, images, or other data. For example, Google’s DeepDream photo- editing software allows neural networks to “hallucinate” patterns and images in a photo. Neural networks: Computational approach that loosely models how the brain solves problems with layers of inputs and outputs. Rather than being programmed, the networks are trained with several thousand cycles of interaction. Businesses can use these to do a lot with a little; for example, neural networks can generate image captions, classify objects, or predict stock market fluctuations. Nuts and bolts
  23. 23 Natural language processing: Field of study in which machines are trained to understand human language using machine-learning techniques. It’s useful for automatic translations, chatbots, or AI personal assistants. Think of the robot voice that picks up your helpline call and asks, “What can I help you with?” or an automated chatbot that responds to your texts. Parsing: The process of evaluating text according to a set of grammar or syntax rules. You can build algorithms that parse text according to English grammar rules, for example, to aid natural language processing. Nuts and bolts
  24. 24 Recommended reading
  25. 25 AI: The big picture • The Hype and Hope of Artificial Intelligence, The New Yorker • What Counts as Artificially Intelligent? AI and Deep Learning, Explained, The Verge • The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe, MIT Technology Review • The Competitive Landscape for Machine Intelligence, Harvard Business Review • What Do People—Not Techies, Not Companies—Think About Artificial Intelligence?, Harvard Business Review
  26. 26 How companies use AI today • An Exclusive Look at Machine Learning atApple, Backchannel • Preparing for the Future ofArtificial Intelligence, White House Blog • Using Artificial Intelligence to TransformHealthcare with Pinaki Dsagupta, Hindsight, Startup Health • Beyond Siri, The Next-GenerationAI AssistantsAre Smarter Specialists, Fast Company • Infographic:What You Need to Know About Google RankBrain, Contently • Facebook is GivingAwaythe Softwareit Uses to Understand Objects in Photos, The Verge • How AI is Changing Human Resources, Fast Company • Beyond Automation, Harvard Business Review
  27. 27 Ethical considerations • The Head of Google’s Brain Team is More Worried about the Lack of Diversityin Artificial Intelligence than anAI Apocalypse, re/code • The Tradeoffs of Imbuing Self-Driving Cars With Human Morality, Motherboard • If We Don’t WantAI to Be Evil, We Should Teach It to Read, Motherboard • The Ethics of Artificial Intelligence, Nick Bostrom • Twitter Taught Microsoft'sAI Chatbotto be a RacistAsshole in Less Than a Day, The Verge • AlgorithmsAre BiasedAgainst Women and the Poor, According to a Former Math Professor, The Cut • Elon Musk elaborateson hisAI concerns, Sam Altman YouTube interview
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