When reflecting on epochs in world history, historians often make reference to an ancient Chinese curse – “May you live in interesting times!” Today, we live in a time characterized by rapid technology transformation, and resulting social, political, and economic disruption. In its wake, few institutions have remained untouched. Like the Industrial Revolution of the early 19th century, impactful change of this magnitude generally occurs but once a century, and is the culmination of a convergence of trends – in our time these trends include globalization, the emergence of the Internet, Big Data, artificial intelligence (AI), and ubiquitous computing power. The results can be dislocation, upheaval, opportunity, and inequality. These current trends are the subject of intense study and speculation, as evidenced by recent articles, books, and the themes of industry business conferences. Change is in the air. We are living in interesting times.
From Information Age to Machine Learning Age
One industry event that has been committed to delving into themes of technology transformation and its impact on business and society is the annual MIT CIO Symposium, held each year on the campus of the Massachusetts Institute of Technology (MIT). The theme of this year’s conference was, appropriately, “Now, Next, and Beyond”. A number of subsequent articles have captured the flavor and spirit of this year’s program, but perhaps none more thoroughly and imaginatively than Gil Press’s event summary, ambitiously entitled, Robot Overlords: AI at Facebook, Amazon, Disney and Digital Transformation at GE, DDS, and BNY Mellon, which captures the range of perspectives on key transformational issues that were the subject of interest and concern to business and technology leaders.
Perhaps the deepest look into the future impact of these emerging trends was presented by MIT academicians, Eric Brynjolfsson and Andrew McAfee, whose new book Machine, Platform, Crowd: Harnessing Our Digital Future was published in June. In their conference remarks, the authors shared their perspectives on the “recent spurt” of artificial intelligence, which they describe as driven largely by the explosion in recent years of the availability of data (Big Data). The proliferation of data volumes and varieties has been much discussed over the course of the past several years. Noting that Big Data is enabling and empowering AI and Machine Learning “because machines need lots of examples”, Brynjolfsson and McAfee argue that we are now in what they have proclaimed as the “machine learning age.”
Among academicians, Brynjolfsson and McAfee have been at the forefront in depicting a vision of the future driven by machine learning. In their 2014 book, The Second Machine Age, Brynjolfsson and McAfee painted a vision of a future driven by the dual forces of man and machine. In a recent MIT Sloan Management Review article How to Thrive - and Survive - in a World of AI Disruption, Brynjolfsson notes, “Deep learning and neural networks have dramatically improved in effectiveness and impact, leading to human-level performance in many aspects of vision, conversational speech, and problem-solving. As a result, industries are in the midst of a major transformation and more is on the way.”
Not Business as Usual
Another venue for executive discussion of transformational themes relating to topics in Big Data, artificial intelligence, and machine learning, are the by-invitation Executive Thought-Leadership Roundtable Breakfasts that I host on a monthly basis for c-executives and industry thought-leaders. These discussions provide a venue for peer executives to share perspectives, learn from one another, and communicate how these latest developments are impacting their businesses, or not. For example, in recent roundtable discussions, executives report that, based on the experiences of their own organizations, AI has yielded mixed results to date. To quote author Tom Davenport, a participant in several of the roundtable breakfasts, “Nary a robot overlord to be found”. Nonetheless, the majority of executives share a common viewpoint that the intersection of AI/machine learning and Big Data represents a big opportunity to diverge from business as usual.
In a rapidly changing business and technology environment, where the ability to act with speed can be the foundation for innovation and acceleration of business opportunities, firms increasingly recognize that business as usual may not be a prudent path. These firms recognize that they operate in a business world where they need to grow and adapt if they want to remain nimble and competitive. Increasingly, firms are pushing hard to introduce more agile, discovery-based business capabilities to help their organizations integrate new approaches and business processes. Speaking at the MIT Forum, GE CIO Jim Fowler spoke of the future of work, where “technology is going to become the process” and employees would work in mission-based teams that formulate around specific initiatives, rather than being limited by strict job descriptions.
A series of recent articles in the Wall Street Journal, Amazon is Leading Tech’s Takeover of America and New York Times, The Decline of the Baronial CEO, and The Real Threat of Artificial Intelligence, underscore the challenges facing leading companies today. Each of these articles probes how business is changing and evolving, and describes the challenges and the steps that firms are taking to address a changing business environment. The messages are clear. Businesses need to adapt. Companies must bring new approaches to bear, new teams, and new skills, organized in a new fashion. Transformation is never business as usual.
The Man + Machine Equation
Data is the lifeblood of business. Like water, data flows through an organization, and enables many activities. One of the primary ways in which machine learning is being applied within business today is in the management and curation of vast and diverse volumes and sources of data. Data has become a business center, measured in cost reduction and revenue generation. Organizations are looking to identify ways they can manage data most effectively, while establishing the collaborative ecosystem to enable this efficiency.
Several leading firms are attacking this need through investments in data curation capabilities that focus on the next-level of data preparation – tapping deeper into their data assets – the “long-tail” of Big Data. These data curation capabilities leverage machine learning algorithms which accelerate data and analytic velocity within an organization, while benefiting from human knowledge and expertise in an organizations own data assets. The use of machine learning in data curation is intended to accelerate data analysis at a “global scale”, where good business decisions depend on the repeatability and predictability of data. This drive to homogeneity provides an ability to look at the tail of data, and tap into more obscure data sources which can be a source of business creativity and innovation. The benefits of tapping into these fresh sources can be measured in time and cost savings.
The use of machine learning in data curation represents, what one executive characterized as, an “artful combination” of the human and the machine -- machine learning plus human input. This is the man plus machine equation, which stands in contrast to what has been referred to as “artificial stupidity”, defined as a computer completing tasks repetitively without the benefit of human insight and intervention. The end goal of machine learning is after all to make information more accessible and useful to human decision makers.
Innovation, Inequality, and Disruption
Nearly every speaker at the MIT CIO Symposium reflected on the speed of innovation and the business and social impact of disruption. Brynjolfsson however sounds a cautionary note. In his recent MIT article Brynjolfsson remarks, “But there’s also a backlash brewing. Median income in America is lower now than in the past 15 years, and wealth is concentrated at the highest levels.”
At the MIT Symposium, Brynjolfsson and McAfee spoke about “interconnected humanity” and the impact of rapid disruption and change on human lives, remarking at one point on the sharp rise in the rate of “deaths from despair” as many in society our unprepared for dislocation and disconnection in the social, economic, and political worlds. Change comes with resulting consequences.
McAfee concluded his MIT remarks on a cautionary note as well, offering a glimmer of hope. McAfee noted that machines are not good at creative tasks or tasks that require strong interpersonal skills and emotions. So, a need for humans does persist. McAfee issued a call for a renewal of education in the humanities – art, literature, history, classics -- to supplement the popular push for STEM (science, technology, engineering, math) education, noting that machines can do STEM. What machines do not do is create great symphonies or poetry, or masterpieces of art and literature, or bring the element of human judgement, intuition, and passion to the world. This is the role of humanity in an era of technology disruption.
Randy Bean is an industry thought-leader and author, and CEO of NewVantage Partners, a strategic advisory and management consulting firm which he founded in 2001. He is a contributor to Forbes, Harvard Business Review, MIT Sloan Management Review, and The Wall Street Journal, and is Founder and Executive Director of the Big Data for Social Justice Foundation. You can follow him at @RandyBeanNVP.
