Intelligence Augmentation and a Frictionless Economy

Speed SkatingThe ever-present Tom Davenport weighs in in the Harvard Business Review on the topic of artificial intelligence (AI) and its impact on knowledge workers of the future. The theme is intelligence augmentation (IA) where knowledge workers improve their productivity and create new business opportunities using technology. And those new opportunities don’t displace others, per se, but introduce new efficiencies. This was also captured in the New York Times in a round-up of the role of talent and service marketplaces that reduce the costs of acquiring skills and services, creating more efficient and disintermediating sources of friction in economic interactions.

I’ve noticed the proliferation of services for connecting home improvement contractors to customers lately, and have benefited from them in several renovation/construction projects I have ongoing. Meanwhile, Amazon Prime has absorbed an increasingly large portion of our shopping, even cutting out Whole Foods runs, with often next day deliveries. Between pricing transparency and removing barriers (delivery costs, long delays, searching for reliable contractors), the economic impacts might be large enough to be considered a revolution, though perhaps a consumer revolution rather than a worker productivity one.

Here’s the concluding paragraph from an IEEE article I just wrote that will appear in the San Francisco Chronicle in the near future:

One of the most interesting risks also carries with it the potential for enhanced reward. Don’t they always? That is, some economists see economic productivity largely stabilizing if not stagnating.  Industrial revolutions driven by steam engines, electrification, telephony, and even connected computing led to radical reshaping our economy in the past and leaps in the productivity of workers, but there is no clear candidate for those kinds of changes in the near future.

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The Rise and Triumph of the Bayesian Toolshed

Bayes LawIn Asimov’s Foundation, psychohistory is the mathematical treatment of history, sociology, and psychology to predict the future of human populations. Asimov was inspired by Gibbon’s Decline and Fall of the Roman Empire that postulated that Roman society was weakened by Christianity’s focus on the afterlife and lacked the pagan attachment to Rome as an ideal that needed defending. Psychohistory detects seeds of ideas and social movements that are predictive of the end of the galactic empire, creating foundations to preserve human knowledge against a coming Dark Age.

Applying statistics and mathematical analysis to human choices is a core feature of economics, but Richard Carrier’s massive tome, On the Historicity of Jesus: Why We Might Have Reason for Doubt, may be one of the first comprehensive applications to historical analysis (following his other related work). Amusingly, Carrier’s thesis dovetails with Gibbon’s own suggestion, though there is a certain irony to a civilization dying because of a fictional being.

Carrier’s methods use Bayesian analysis to approach a complex historical problem that has a remarkably impoverished collection of source material. First century A.D. (C.E. if you like; I agree with Carrier that any baggage about the convention is irrelevant) sources are simply non-existent or sufficiently contradictory that the background knowledge of paradoxography (tall tales), rampant messianism, and general political happenings at the time lead to a likelihood that Jesus was made up. Carrier constructs the argument around equivalence classes of prior events that then reduce or strengthen the evidential materials (a posteriori). And he does this without ablating the richness of the background information. Indeed, his presentation and analysis of works like Inanna’s Descent into the Underworld and its relationship to the Ascension of Isaiah are both didactic and beautiful in capturing the way ancient minds seem to have worked.… Read the rest

Inequality and Big Data Revolutions

industrial-revolutionsI had some interesting new talking points in my Rock Stars of Big Data talk this week. On the same day, MIT Technology Review published Technology and Inequality by David Rotman that surveys the link between a growing wealth divide and technological change. Part of my motivating argument for Big Data is that intelligent systems are likely the next industrial revolution via Paul Krugman of Nobel Prize and New York Times fame. Krugman builds on Robert Gordon’s analysis of past industrial revolutions that reached some dire conclusions about slowing economic growth in America. The consequences of intelligent systems on everyday life will have enormous impact and will disrupt everything from low-wage workers through to knowledge workers. And how does Big Data lead to that disruption?

Krugman’s optimism was built on the presumption that the brittleness of intelligent systems so far can be overcome by more and more data. There are some examples where we are seeing incremental improvements due to data volumes. For instance, having larger sample corpora to use for modeling spoken language enhances automatic speech recognition. Google Translate builds on work that I had the privilege to be involved with in the 1990s that used “parallel texts” (essentially line-by-line translations) to build automatic translation systems based on phrasal lookup. The more examples of how things are translated, the better the system gets. But what else improves with Big Data? Maybe instrumenting many cars and crowdsourcing driving behaviors through city streets would provide the best data-driven approach to self-driving cars. Maybe instrumenting individuals will help us overcome some of things we do effortlessly that are strangely difficult to automate like folding towels and understanding complex visual scenes.

But regardless of the methods, the consequences need to be considered.… Read the rest