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How Artificial Intelligence is Transforming Organizations

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  1. THE AGE OF How Artificial Intelligence Is Transforming Organizations By Susan Etlinger, Analyst Altimeter, a Prophet Company January 31, 2017

  2. EXECUTIVE SUMMARY Seemingly overnight, Artificial Intelligence (AI) has moved from a plot point in science fiction movies to a core technology for companies such as Google, Facebook, Baidu, Microsoft, and Amazon. But the idea of AI — of machines that can sense, classify, learn, reason, predict, and interact — has been around for decades. Today, the combination of massive and available datasets, inexpensive parallel computing, and advances in algorithms has made it possible for machines to function in ways that were previously unthinkable.1 While the more obvious examples such as robotics, driverless cars, and intelligent agents such as Siri and Alexa tend to dominate the news, artificial intelligence has much wider implications. Gartner predicts that “by 2020, algorithms will positively alter the behavior of billions of global workers.”2 Markets & Markets expects the AI market to reach $5.05B by 2020.3 This report lays out the current state of AI for business, describes primary and emerging use cases, and states the risks, opportunities, and organizational considerations that businesses are facing. It concludes with recommendations for companies thinking about applying AI to their own organizations and a look at some of the business, legal, and technical trends that are likely to shape the future. TABLE OF CONTENTS Executive Summary 1 What is Artificial Intelligence? 2 Use Cases for Artificial Intelligence 8 Implications and Recommendations 13 A Look at the Future 17 End Notes 19 Methodology 22 Acknowledgements 23 About Us 24 1 | @setlinger |

  3. “If we accept that there are different types of intelligence in the animal and human world, then the same is true for the AI world. Hence trying to define AI is as difficult as trying to define the notion of intelligence itself.” — Prianka Srinivasan Office of the CTO, Technology Vision & Strategy, HP WHAT IS ARTIFICIAL INTELLIGENCE? AI means many things to many people, from TV and movies such as Blade Runner and The Terminator series to HBO’s Westworld to Apple’s Siri and Amazon’s Alexa to the driverless truck that completed its first commercial delivery — a 143-mile beer run — in October 2016.4 But today’s reality is far from the aspirational and often dystopian views of AI in popular culture. To understand AI and its implications, it is important to start from a shared understanding of what it isn’t, what it is today, and what it might become in the future. WHAT AI ISN’T — AND WHAT IT IS There are almost as many definitions of AI as there are people talking about it.5 The fundamental challenge of defining AI is that it is not possible simply to translate human intelligence into digital form, simply because there is no consensus about what human intelligence actually is.6 One theory of human 2 | @setlinger |

  4. intelligence, proposed in 1983 by developmental psychologist Howard Gardner, argues that people display not one but multiple intelligences7 (See Figure 1). FIGURE 1 HOWARD GARDNER’S THEORY OF MULTIPLE INTELLIGENCES Source: The meaning of human intelligence has occupied philosophers and psychologists for centuries and will do so for centuries to come. But Howard Gardner’s model offers a useful way to frame some of the differences between machine and human intelligence and some of the types of intelligences that AI is best (and least) equipped to handle. Clearly, computers lack consciousness of the type we have seen in TV and film, but they excel at logical-mathematical reasoning. With training, they can recognize and interpret images, language, music, and spatial relationships. But can they be 3 | @setlinger |

  5. creative? Fair? Empathetic? And, more to the point, do we want that? The notion of the “uncanny valley,” or the point at which “lifelike” dolls and robots seem creepy and disturbing, has been explored in more than 500 scientific papers to date.8 Today, scientists, philosophers, and engineers are exploring these questions and probing the boundaries of what AI can and should do. But, argues Gideon Lewis-Krause in his New York Times Magazine article, “The Great A.I. Awakening,” “Artificial intelligence is not about building a mind; it’s about the improvement of tools to solve problems.”9 It can be challenging to distinguish systems that use AI from those that do not, especially if they are engineered to provide a realistic experience. What differentiates AI from what we see in video games, for example, is that in video games, algorithms dictate the characters’ behavior and players learn based on a set of rules. With AI, however, algorithms change themselves based on what they have learned. STRONG VS. WEAK AI Another important distinction is the kind of AI, specifically the idea of strong or general AI versus a weak or narrow AI. A strong/general AI would replicate humans’ general intelligence(s), while a weak/narrow AI focuses on a specific use case. So far, we’ve only seen examples of strong/general AI in fiction and film, such as 2001: A Space Odyssey,Blade Runner,Terminator,Black Mirror, and so on, in which robots, androids, or simply disembodied voices display human reasoning, emotion, and behavior. Typically, when we hear warnings about the dangers of artificial intelligence from technologists and futurists, such as Raymond Kurzweil, Dr. Nick Bostrom, Stephen J. Hawking, and others, it is strong AI that is being discussed.10 While the notion of a “singularity” — that AI may one day outpace humanity’s ability to understand and/or control it — is a critical issue for society to address, this report focuses on more narrow and pragmatic use cases that are achievable today. It’s also important to realize that machine learning, relatively speaking, is still in its infancy, so much so that “real artificial intelligence does not quite exist yet,” says Pete Skomoroch, CEO and Co-Founder of Skipflag. Once we eliminate the more futuristic, aspirational, and contentious elements associated with AI, we are left with today’s reality. Examples of narrow/weak AI surround us every day, including Google search, recommendation engines, chatbots, intelligent medical diagnostics, and so on. But we shouldn’t take the term “narrow/weak” to imply inadequacy or a lack of value. Using machine learning, advanced algorithms, and other computer science techniques, these “narrow” examples of AI typically require the ability to sense and process vast amounts of data and can demonstrate their value economically or in more human terms, such as quality of life.11 While “true” AI — the ability for machines to fully replicate human intelligence — is aspirational, this report will nonetheless use the term “AI” to refer to systems 4 | @setlinger |

  6. that employ machine learning and can sense, classify, learn, reason, predict, and interact. It will focus on the kinds of intelligences that computers can, to varying extents, replicate and the opportunities they present. Figure 2 illustrates the core capabilities of artificial intelligence as it exists today. FIGURE 2 CURRENT CAPABILITIES OF ARTIFICIAL INTELLIGENCE (AI) There are many conflicting definitions of artificial intelligence (AI), ranging from futuristic visions of human- like machine intelligence to more restrained definitions that refer to the ability of machines to self-program based on new data. Today, AI commonly refers to systems that employ machine learning and can: • Collect and process signals via sensors or other methods; • Classify, learn, reason, and predict possible outcomes; and • Interact with people or other machines. While there are plenty of experiments and early examples of AI, the majority today cluster around three specific types of intelligence: Visual/Spatial, Auditory/Linguistic and Motor Intelligence. TYPES OF MACHINE INTELLIGENCE SENSING VISUAL/SPATIAL The ability to see and process the physical and digital world. Examples include computer vision/image recognition, facial recognition and emotion detection.* MOTOR The ability to move around and manipulate physical or virtual environments, or communicate using gestures. Examples include robots and gestural or adaptive interfaces. COGNITIVE Cognitive intelligence is the ability to learn, reason, predict and respond. This is the key distinction between machines and rule-based systems. With- out it, machines simply respond to pre-defined inputs; with it, they can self-program based on new data. AUDITORY/LINGUISTIC The ability to listen selectively and communicate using written or spoken language. Examples include virtual personal assistants such as Alexa, Siri , Viv, Cortana, Natural Language Processing (NLP), machine translation and chatbots. EXAMPLES OF AI IMAGE RECOGNITION See and classify images based on objects, scenes, attributes and emotion. CHATBOTS Communicate with users and answer questions via speech or written text. MEDICAL DIAGNOSTICS Analyze patient data, tests, and scans to help diagnose disease and recommend treatment. SELF-DRIVING CARS Combine data and analytics with reasoning to navigate and adapt to real world environments. *For a more detailed view of computer vision and its business applications, see Susan Etlinger, Altimeter Group, “Image Intelligence: Making Visual Content Predictive”. 5 | @setlinger |

  7. WHY NOW? One common question about AI is why it has become so popular (many would say “hyped”) so quickly. In reality, the idea of AI has existed since 1950, when Alan Turing (inventor of the Turing test, and depicted in the 2014 film The Imitation Game) published a paper entitled “Computing Machinery and Intelligence.” Six years later, Stanford professor John McCarthy coined the term “artificial intelligence,” and in 1959, MIT founded its Artificial Intelligence laboratory. The following decades saw both promising advances (the first mobile robot, IBM Deep Blue’s victory over chess legend Gary Kasparov) and dispiriting dry spells as interest in and capabilities of AI ebbed and flowed (See Figure 3). FIGURE 3 A BRIEF HISTORY OF AI Artificial Intelligence was first conceptualized in 1950 by Alan Turing and has seen a number of innovations and false starts since then. In the past three or so years, innovation has accelerated greatly. Following is a selection of some of the major milestones in AI during the past seven decades. 1950 – Alan Turing “Computing machinery and intelligence,“ asks ‘Can machines think?'” 1959 – Artificial Intelligence Laboratory founded at MIT 1974 – The first AI winter; fund- ing and interest evaporate 1987 – Second AI winter begins 1994 – First web search engines launched 2002 – Amazon replaces human editors with an automated system 2011 – Apple releases Siri, a personal voice agent 1956 – “Artificial intelligence” coined by John McCarthy at Dartmouth College 1975 – MYCIN, a system that diagnoses bacterial infections and recommends antibiotics, is developed 1989 – NASA’s AutoClass program used to discover new classes of stars 1997 – IBM’s Deep Blue beats world champion Garry Kasparov at chess 2016 – Google’s AlphaGo defeats Lee Sedol, one of the world’s leading Go players Given the number of false starts in the past several decades, it’s reasonable to wonder why now is different. There are three key factors that distinguish today’s AI climate from that of the past:12 • Massive and available datasets (also known as “Big Data”); • Inexpensive parallel computation; and • Improved algorithms. The combination of these three factors has made it possible, finally, for AI to become not just a wild idea or rarefied technology, but a commercial reality. MACHINE LEARNING IN A NUTSHELL To enable computers to reason, make predictions, and/or take actions, they need to be able to learn without being explicitly programmed.13 In order to learn, they must be “trained” using large amounts of data so they can classify things properly. There are two main types and several subtypes of machine learning (see Figure 4, on the following page). Each has benefits and drawbacks related to scalability, precision (accuracy), and other factors, but in all cases the algorithm learns from the data it is given.14 The amount and relevance of training data is critical to the machine’s ability to learn and properly classify future data. 6 | @setlinger |

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