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In 1964, The Twilight Zone aired an episode titled “The Brain Center at Whipple’s,” in which factory owner Wallace Whipple completely eliminates his human workforce in favor of automated machinery. Mr. Whipple’s employees, clearly far ahead of their time, argue to him that human insights far outweigh the advantages provided by mechanical labor. Ironically, at the end of the episode, Mr. Whipple, too, is replaced by a machine.
It’s a well-known dichotomy: man versus machine—and, depending on who’s doing the talking, good (human) versus evil (machine). Today, as technology continues to evolve and machines are capable of ever more advanced processes and functions, the dichotomy is becoming even more pronounced. Look no further than IBM’s Watson, an advanced artificial intelligence machine that squared off against Jeopardy’s best human contestants in 2011—and won.
But, as Opera Solutions’ CEO Arnab Gupta proposes to explore in remarks at Strata, the man-vs.- machine dichotomy is a false one. A far better contest would have been a three-way one, pitting man versus machine versus man-plus-machine. It is almost a certainty that the latter combination would have won.
Consider: nowhere has the machine-vs.-human conflict been played out more fully than in the realm of chess, starting in 1997 with IBM’s Deep Blue vs. Garry Kasparov. Today, chess-playing computers routinely beat the strongest human players. One might conclude that the machines have won. But there’s a twist: as Kasparov has recently stated, a machine plus just an average player can beat all comers, humans or computers. Humans’ ability to think abstractly and creatively, to bring in new ideas, to apply history, to understand irony, opportunity, possibilities—all this, when paired with machines’ abilities to process huge amounts of data flows and bring to light hidden patters and connections that elude human understanding, make the machine/mind connection unbeatable.
In short, it is not humans vs. machines, but rather humans plus machines, which must become the new paradigm for scientists, business people, and others—particularly in the Big Data era. Combining human insight with machine intelligence overcomes the weaknesses of each while delivering never-before-seen strengths.
How can this be accomplished, particularly when machines and people speak different languages and, in truth, “think” differently? How can we create and foster a productive pairing of two very different types of “minds?” Arnab will address the need to create a new language—one mostly visual in nature— to allow humans and machines to work together and realize the full potential of their collaboration. Finding a common language is a pursuit that goes far beyond prosaic “UI” development, and instead forces us to examine how humans can (and might learn to) best understand what machines are saying.
Arnab discerned that the information explosion created an unprecedented opportunity for value creation, and that a firm that combined superior talent with the techniques and technology required to distill insights from massive data reserves could deliver dramatic performance improvement. Accordingly he founded Opera Solutions in 2004 and has since guided the company in providing rapid, significant, and sustained profit improvement to leading global organizations. Prior to Opera, Arnab founded and sold a number of other companies, including Mitchell Madison Group, which achieved a recurring revenue base of $275MM within 4 years, and Zeborg, a business intelligence software company. He began his career at McKinsey & Co., where he was a partner, and also served as a partner at A.T. Kearney. Since 2004, he has worked with the Bill & Melinda Gates Foundation on its India HIV-AIDS initiative, employing private sector approaches to help oversee the disbursement of $400MM in grants toward HIV-AIDS prevention. Arnab earned an MBA from the Harvard Business School and is the author of a number of research publications, including Aggressive Sourcing: A Free Market Approach (Sloan Management Review) and Taking Risks to Win.
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