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PRIVATEEQUITYAIAdvancesPushIntoMainstream
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PRIVATEEQUITY Sponsored by Advances in
Technology Push AI Into the Mainstream WSJ.COM/PRO/AI
2.
2 Copyright ©
2018 by Dow Jones & Company, Inc. WSJ PRO ARTIFICIAL INTELLIGENCE wsj.com/pro/ai Welcome to WSJ Pro Artificial Intelligence. This new offering from The Wall Street Journal intends to give people the insight they need to draw business value from the rise of artificial intelligence. Artificial intelligence is transforming the way people live and work; much as the steam engine or electrification, the phone or the internet, did in years past. We are already witnessing the dramatic impact of this the various types of AI technology on the corporate landscape. Companies that master this transition stand to gain incomparable advantage from disruption. Those that don’t are at greater risk of falling forever behind. WSJ Pro Artificial Intelligence will help businesses across industries and around the world understand this challenge. Our team of journalists will take you behind the scenes of this revolution as it unfolds. We will assess the impact of AI at every level of the business, from leadership and management of the company to the way it is organized and goes to market, as well as to the critical relationship between people and machines in the workplace. We will address vital issues of governance, including the privacy of employees and customers and their data, as well as the transparency of machine-driven decision-making. Our unique mission is to cover the business dimensions of AI, often lost amid the discussion of technology or investment trends. With a focus on how the business value of this emerging technology can be realized, we will speak powerfully and directly to all professionals who demand a timely, clear and nuanced understanding of how artificial intelligence will disrupt and transform their business. The following are examples of the kind of journalism you’ll find at Pro AI. Email delivery of our daily newsletter will begin on January 8. We look forward to you joining us on this journey. Kimberly S. Johnson Professional Products Editor The Wall Street Journal Steven Rosenbush Enterprise Technology Editor WSJ Pro Inside u What Exactly is Artificial Intelligence? .............................................................3 u Corporate AI Adoption Could Create Trillions in Value ................................4 u Companies Need Help Tackling Ethical Concerns Posed by AI ....................5 u Businesses Get Into the ‘Flo’ With Chatbots .....................................................6 u The Next Challenge: Grappling With AI’s Opaque Decision-Making Process ....................................................................................7 u Meet the Authors ..................................................................................................8
3.
Copyright © 2018
by Dow Jones & Company, Inc. 3 wsj.com/pro/ai WSJ PRO ARTIFICIAL INTELLIGENCE What Exactly is Artificial Intelligence? By SARA CASTELLANOS Artificial intelligence encompasses the techniques used to teach computers to learn, reason, perceive, infer, communicate and make decisions similar to or better than humans. It has come a long way since the 1956 Dartmouth College artificial intelligence workshop, which many consider the birthplace of the discipline. Major advancements in AI were stymied in subsequent years, because the computational power and technology needed to perform advanced, computer-made decisions simply did not exist. Two periods between the 1970s and 1990s were referred to as “AI winters” because of waning interest in the field, which resulted in decreased government and company investment. Specialized types of hardware called graphics processing units, initially built for accelerating computer graphics for the gaming industry, were well-suited for accelerating the training of AI systems such as neural networks. Such innovations led to AI breakthroughs, in which machines bested human efforts in successively more difficult games such as Jeopardy and chess. New AI techniques combined with cheaper computing power and data storage have allowed companies to crunch more data over the past decade. Since 2012, businesses have started to figure out how to apply AI to a growing range of problems. They developed specialized AI models that are more accurate and faster than humans at specific business tasks. About half of 2,135 business leaders surveyed this year across all sectors said their organizations have deployed at least one AI-based system into their standard business processes, while another 30% report piloting the use of AI, according to a McKinsey Global Institute survey — part of McKinsey & Co. — released in November. “We’re in the phase of the wild West, where everybody’s trying it out,” said Dario Gil, vice president of AI and quantum computing at International Business Machines Corp., and chief operating officer of IBM Research. “It’s driven by the realization that AI is fundamental.” When people talk about artificial intelligence, they usually are referring to one of its subfields. Here’s a guide to the most important concepts in AI. MACHINE LEARNING. This is the science of getting computers to act intelligently without being explicitly programmed, said Andrew Ng, former chief scientist at Chinese tech conglomerate Baidu Inc. and co-founder of Google Brain, a research team at Alphabet Inc.’s Google. Instead of writing all the rules, algorithms allow the computer to determine for itself how it interprets data. Machine learning originated in 1959 with Arthur Samuel, who used the technique to create a computer program that played checkers. At credit-reporting firm Experian PLC, machine learning allows it to fix application problems before they impact customers. At Google’s YouTube, AI-based systems recommend videos to users based on previous viewing history, creating better customer experiences and driving usage, said Eric Schmidt, former Google CEO and technical advisor to Alphabet. “What we know from machine learning is that there are subtle patterns that computers can detect in the training data that we just don’t see,” Mr. Schmidt said in November at an AI conference hosted by the Massachusetts Institute of Technology. NEURAL NETWORKS AND DEEP LEARNING. These terms are often used interchangeably, according to Dr. Ng. They are subsets of machine learning that were originally inspired by neurons in the human brain. Scientists, though, have not yet discovered exactly how the human brain works, and in recent years it’s become apparent that deep learning might not be as similar to the brain as once thought. “It’s barely mimicking the human brain, if at all,” he said. Neural networks are composed of layers of interconnected artificial “neurons” that automatically learn about the features of a specific object based on large amounts of training data. For example, by looking at of images of cats, a neural network can learn about a cat’s features by tweaking the connections between neurons. If it has learned those patterns well, it should be able to look at a new, or “test,” image and correctly identify it. If it stumbles, its engineers can give it more data or modify the structure of the neural network. While neural networks can achieve extraordinary feats, they’re also fragile, easy to fool and can be subject to adversarial attacks, said IBM’s Mr. Gil. IBM researchers, for example, have demonstrated that neural networks can be fooled into misidentifying an image by doctoring a few pixels. Neural networks power deep learning systems. The ability to create such systems with multiple layers has led to advances in speech recognition and computer vision. Deep learning forms the foundation behind natural language processing, which powers web searches and chatbots. ADVERSARIAL NETWORKS. In this technique, an AI system can be embedded with two neural networks. One network’s goal is to make accurate classifications, of images, for example. The other network generates samples that are meant to fool the first network. This method ensures that the first network isn’t fooled by variations of images. Adversarial networks could be a path toward unsupervised learning, in which a machine could make logical inferences without requiring as much human-labeled training data. These cutting-edge systems are not yet widely deployed. n — Steven Norton contributed to this report.
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4 Copyright ©
2018 by Dow Jones & Company, Inc. WSJ PRO ARTIFICIAL INTELLIGENCE wsj.com/pro/ai Corporate AI Adoption Could Create Trillions in Value By the Numbers • Tech giants such as Google and Baidu spent an estimated $20 billion to $30 billion on AI in 2016, according to McKinsey Global Institute. • In 2017, according to CBInsights, $15.2 billion was invested in AI startups around the world, and nearly half (48 percent) of that total went to China; 38 percent was invested in the United States. By ANGUS LOTEN Artificial intelligence will help unleash a wave of new enterprise capabilities and innovative business models in the years ahead. But in the meantime most firms are using it to tackle more mundane tasks, according to chief information officers and industry analysts. Rather than smart robot assistants or autonomous supply chains, AI-backed tools are far more likely to monitor information-technology infrastructure or handle basic customer queries. These kinds of deployments --in addition to new ones -- are expected to spread fast, as AI is driving a significant amount of business value. Artificial intelligence is expected to generate a total of $1.2 trillion in global business value this year, up 70% from 2017, according to a Gartner Inc. report. That figure is expected to more than triple by 2022, to $3.9 trillion, as AI contributes to improved customer interactions, cost reductions and new revenue sources. Gartner analyst Chirag Dekate said for most firms the bulk of gains are coming from taking a tactical approach to AI deployments, with businesses applying smart tools to specific and relatively straightforward tasks. A recent McKinsey Global Institute survey found 47% of companies worldwide have embedded at least one AI-backed capability in their business processes, up from 20% a year ago. An additional 30% said they are testing AI with pilot projects in place. MGI is the business and economics research arm of McKinsey & Co. By sector, telecom, high-tech and financial-services firms were ahead of others in adopting smart applications, the survey found. And though enterprise spending on AI is poised to increase, just 58% of survey respondents said less than one-tenth of their total budget for digital technology is currently going into AI. Some of the barriers to AI deployments they cite include a lack of clear strategy, a skills gap and functional silos constraining end-to-end applications. The survey, which was conducted online earlier this year, included responses from 2,135 business officials across multiple regions, industries and company sizes. Autodesk Inc. CIO Prakash Kota said the design-software maker currently is using AI-powered software tools to perform repetitive tasks, such as answering common questions about access authorization and other queries from its employees and customers alike. By deploying virtual assistants, workers are able to focus on tackling more complex issues or learning new skills -- ultimately boosting productivity, he said. Jabil Inc., a St. Petersburg, Fla.-based supply chain management software maker, has started replacing its manual circuit board inspections with AI-backed scanners that can catch defects with an 80% accuracy rate, far better than human inspectors, said Gary Cantrell, Jabil’s CIO. “Boards that would pass the inspection process, all the way through before a problem was identified, we were catching on first pass, much earlier in the process,” making it more efficient and cost effective, Mr. Cantrell said. n “R2-D2 is still a long way off.” —Gartner Inc. Research Vice President Brian Burke 0.0 0.2 0.4 0.6 0.8 1.0 1.2 by 2030 2000s 1990s 1800s 0.3% 0.4% 0.6% 1.2% AI & GDP AI could potentially deliver additional global economic activity of around $13 trillion by 2030, or about 16 percent higher cumulative GDP compared with today. This amounts to about 1.2 percent additional GDP growth per year. If delivered, this impact would compare well with that of other general-purpose technologies through history, such as the introduction of steam engines during the 1800s, the impact from robots during the 1990s, and the spread of IT during the 2000s. Source: McKinsey Global Institute
5.
Copyright © 2018
by Dow Jones Company, Inc. 5 wsj.com/pro/ai WSJ PRO ARTIFICIAL INTELLIGENCE Companies Need Help Tackling Ethical Concerns Posed by AI By SARA CASTELLANOS Running parallel with artificial intelligence's expanding role in business, and virtually everywhere else, is a growing awareness of the ethical guardrails needed to guide the technology's implementation, a concern shared among business leaders, consumers and even AI researchers. Microsoft Corp. recently created a new position to help companies deploying AI learn how to prioritize ethical principles, including fairness, accountability and transparency, in algorithm development. “A lot of customers we engage with are grappling with the ethics discussion potentially for the first time,” says Tim O’Brien, Microsoft’s general manager of AI programs. “A lot of people with formal education in engineering and science didn’t have any formal education of ethics.” Mr. O’Brien educates Microsoft customers about AI ethics and responsible AI design. He also conducts research to identify cultural and geographic differences in how people think about fairness and transparency in AI. Microsoft has been advocating for the development of transparent, inclusive and ethical AI tools internally for several years, but Mr. O’Brien said it was time they brought that awareness to Microsoft customers. Concerns about transparency and ethics are barriers in implementing AI. For example, about 60% of 5,000 executives polled in a recent study by International Business Machines Corp.’s Institute of Business Value said they were concerned about being able to explain how AI is using data and making decisions in order to meet regulatory and compliance standards. That’s up from 29% in 2016. Here’s some advice from Mr. O’Brien on tackling the ethical issues raised by AI: Elevate the role of ethics Senior technology and business leaders should speak to employees about the importance of the responsible use of AI within the company. Support has to come from the top down, he said, adding that advocacy for AI ethics should eventually become part of the company’s culture. Be aware of the dangers of AI bias Make sure the datasets used to train AI algorithms are as complete as possible, Mr. O’Brien said. For example, if a facial recognition model is trained with images of white males, error rates for people of color will be high. Companies also need to consider the potential pitfalls of AI bias, which can be more severe for certain industries than others. If a financial services company is considering using AI for loan approvals, the company needs to ensure it’s not discriminating against certain groups of people, he said. Consider creating specialized jobs If a company is considering an AI deployment, it might behoove the business to hire specific so-called ethicist roles. Ethics experts would be in charge of focusing full-time on educating employees on ethical considerations surrounding AI. They could also be in charge of developing cross-functional teams to act as sounding boards to sort through the implications of AI deployments. It’s also crucial to think about the diversity of the teams that are building AI-enabled products, Mr. O’Brien said. Bias detection tools can help Cloud-based AI bias detection tools could be useful in the development stage, in order to help prevent different kinds of bias in training data or machine learning models. For example, such tools could help detect whether an algorithm used to evaluate housing applications is discriminating against certain populations. n
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6 Copyright ©
2018 by Dow Jones Company, Inc. WSJ PRO ARTIFICIAL INTELLIGENCE wsj.com/pro/ai Before committing a software bot to code, Progressive developers had to understand Flo, the character that has been synonymous with the brand for the past decade, Progressive Chief Marketing Officer Jeff Charney said. Progressive's Flo bot can recognize some categories of words outside of insurance jargon but is oblivious to others, helpful for avoiding a reputation-damaging encounter. Businesses Get Into the ‘Flo’ With Chatbots Insurer explores new terrain as it turns its popular spokeswoman into a sales bot By KIM S. NASH Before software developers at Progressive Corp. wrote a line of code last year to build a chatbot version of Flo — the insurer’s quirky character in the white apron — they spent a few days at a conference center discussing how to render in algorithms a mascot that has represented Progressive for a decade. A bot can entertain but ultimately it must propel business, explains Dan Witalec, Progressive's customer acquisition leader. “We’re trying to sell insurance.” Artificial intelligence tools, now easier to use and more affordable, allow companies to enable text-based chatbots like Flo to help customers get quick answers and conduct transactions. Yet doing business via text, where tone and context is everything, pushes corporate software programmers into new territory, as they must teach systems to understand and “chat” in natural language. Giving personality to a bot helps a company connect emotionally to customers and strengthen its brand, particularly in the financial services industry where products and services are largely similar. That’s critical at Progressive: Ten times as many people follow Flo on Facebook than follow the company itself — 4.7 million to 475,000. Therefore creating a software bot to talk to customers about insurance means programmers must understand the character that has become synonymous with the brand, said Jeff Charney, Progressive’s chief marketing officer. Employees at the company’s campus outside Cleveland are immersed in Flo — Flo-themed art hangs on walls and Flo bobble-heads nod on desks. “She personifies us. We personify her,” he said. The company declined to say how many people have used Flo the bot since it launched on Facebook Messenger last October. About 37% of U.S. consumers would be willing to make a purchase through a chatbot, according to a 2016 survey of about 4,000 people by market research company Harris Insights and Analytics and advertising agency DigitasLBi. About 36% of adults say they prefer digital customer service, including bots and email, to working with a human, according to a Forrester Research Inc. survey in 2017 of 4,500 people in the U.S. While good implementation of a bot can offer a competitive advantage, there are risks. The AI that powers many of these virtual assistants can be unpredictable. Bots can be programmed to learn continuously through conversation or to stick to a limited set of questions and answers. Matt White, manager, PL acquisition experience, said the company decided to limit Flo’s AI smarts. The bot can recognize some categories of words outside of insurance jargon but is oblivious to others. So far, the only full business transaction that Flo the bot can conduct is to provide a car insurance quote. When asked to file a claim or sell a policy, the bot acknowledges that it understands the request and then offers buttons linking to an agent or Progressive’s full website. “There are limits to this technology,” Mr. Witalec said. “We have to design off-ramps.” n
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Copyright © 2018
by Dow Jones Company, Inc. 7 wsj.com/pro/ai WSJ PRO ARTIFICIAL INTELLIGENCE The Next Challenge: Grappling With AI’s Opaque Decision-Making Process By SARA CASTELLANOS As companies look to drive innovation and competitive advantage, some executives say they are coming up against a big challenge: advanced AI systems are not able to explain how they make decisions. Executives at companies such as Uber Inc. and the research and development arm of Xerox Corp. said that they’re investing in an area of AI research called “interpretability,” in an effort to understand exactly how complex AI systems solve problems. “It’s a really fruitful area of research and it’s been massively neglected for the whole history of machine learning,” said Zoubin Ghahramani, chief scientist at Uber, at an AI conference hosted by O’Reilly Media Inc. and Intel Corp.’s AI division in New York in May. Machine learning enables computers to learn from data with minimal programming, and is a large part of artificial intelligence, a term that encompasses the techniques used to teach computers how to learn, reason, perceive, infer, communicate and make decisions like humans do. Researching interpretability is important for ethical, reputational and legal reasons in which it’s necessary to figure out how an automated system made a particular decision, Mr. Ghahramani said. But the importance of interpretability depends on the specific AI application, he said. Interpretability matters in the medical field, less so for captioning images, he said. Within Uber, there is an active group looking at so- called AI neuroscience, which involves understanding the behavior and architecture of AI systems, he said. “It’s definitely important to us and something we’re paying attention to,” he said, adding that he’s personally interested in understanding how AI systems can explain their decisions by writing reports. Uber is among several companies, private institutions and researchers interested in building a greater level of trust between humans and machines through transparency in artificial intelligence. The research arm of the U.S. Department of Defense is coordinating an effort to build “explainable AI” systems that can translate complex algorithmic-made decisions into language humans can understand, for example. Capital One Financial Corp., too, is researching ways that machine-learning algorithms could explain the rationale behind their answers, which could have far- reaching impacts in guarding against potential ethical and regulatory breaches as the firm uses more artificial intelligence in banking. PARC, a research and development lab wholly owned by Xerox Corp., also has researchers devoted to transparency in AI, said Tolga Kurtoglu, CEO of PARC, at the AI conference. Not all problems require very detailed level of explainability, Mr. Kurtoglu said. But there are enough problems “where we absolutely need to build transparency into the AI systems, where we’d benefit as a society from the ability of those algorithms to articulate themselves.” An AI system that can explain its decisions is helpful for two main reasons: when you have to decide whether to override the decision it’s making, and when you want to try to improve decisions it will make in the future, said Peter Norvig, director of research at Google, who also spoke at the conference. Google is developing tools to understand artificial neural networks and other types of machine learning algorithms, Mr. Norvig said in an email. Researchers at Google have visualized what a machine learning system is learning and the ability to debug a deep learning system, he said. n
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WSJ PRO ARTIFICIAL
INTELLIGENCE wsj.com/pro/ai 8 wsj.com/pro/ai STEVEN ROSENBUSH is enterprise technology editor at WSJ Pro, guiding coverage of the interplay of business and technology for a professional audience. He oversees the group’s CIO Journal, Cybersecurity and Artificial Intelligence teams. Previously, he worked as assistant managing editor for capital markets at Institutional Investor magazine. He led telecommunications coverage at BusinessWeek and USA Today, and is author of a book, Telecom Opportunities for Entrepreneurs. He has a bachelor’s degree in English from Wesleyan University. Reach him at steven.rosenbush@wsj.com STEVEN NORTON is a former reporter for CIO Journal. Copyright © 2018 by Dow Jones Company, Inc. All rights reserved. No part of this publication may be reproduced in any form or by any means—graphic, electronic, or mechanical, including photocopying, recording, taping, and information storage and retrieval systems—without the express written permission of Dow Jones Company, Inc. Contents are based on information from sources believed to be reliable, but accuracy and completeness cannot be guaranteed. Dow Jones Company, Inc., its officers, employees, or agents may hold positions in any of the securities mentioned herein. IMAGE CREDITS: p1: ktsimage/iStock; p2 p5: Liu Zishan/Shutterstock; p4: Kelli R. Parker/The Wall Street Journal; p6: Kim S. Nash/The Wall Street Journal; p7: iMrSquid/iStock KIMBERLY S. JOHNSON is the WSJ’s Professional Products Editor, responsible for the organization’s specialized, premium content. She was previously editor of CFO Journal, the Wall Street Journal’s corporate finance group. Ms. Johnson’s journalism career spans two decades and includes stints at The Boston Globe, Associated Press, The Denver Post and Global Post. She holds a bachelor’s degree in broadcast journalism and a master’s degree in business and economics journalism from Boston University Reach her at kimberly.johnson@wsj.com. SARA CASTELLANOS covers emerging technologies for The Wall Street Journal’s CIO Journal in New York. She previously covered tech, startups and venture capital at the Boston Business Journal. Prior to that, she spent four years covering politics in the Denver area. She graduated from the University of Denver with a bachelor’s degree in journalism. Write to Sara at sara.castellanos@wsj.com ANGUS LOTEN is a reporter for CIO Journal, focusing on the business economics of information technology. Previously, he has worked at the Bangkok Post, the Montreal Gazette and the Toronto Star. He has a master’s degree from Columbia University’s Graduate School of Journalism. Write to Angus at angus.loten@wsj.com KIM S. NASH is deputy editor of WSJ Pro Cybersecurity, which covers the tactical and strategic dimensions of cybersecurity for a business audience. She recently was recognized by the National Association of Corporate Directors for her reporting on tech governance. Previously, she served as managing editor of CIO Magazine and investigative reporter at Baseline. She holds a bachelor’s degree in journalism from Boston University. Write to Kim at kim.nash@wsj.com Meet the Authors Sponsored by
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