Saturday, January 30, 2016

Early Indications January 2016: Shocks

The past month has been marked by a series of extraordinary events that would have been completely unforeseen only a year ago, or even in mid-summer. (In June, West Texas Intermediate crude oil futures contracts were selling at $60 a barrel, roughly twice the current price.) While this may be an unusual month, the larger question remains: how can human institutions evolve to better address both sudden and glacial change, in both positive and negative directions? Put another way, if we see what keeps surprising us, maybe we can adapt our practices and assumptions to be surprised less often, less acutely, or both.

Oil is certainly big news. While the dynamics of a global market, controlled by a wide range of political and business players, remain fascinating, “common knowledge” in energy markets shifts dramatically. Recall how recently talk of “peak oil” was common: according to Google Trends, searches for the phrase spiked in August 2005 and, at a slightly lower index, May 2008. After 2011, interest dwindled to baseline noise, and today we wrestle with the problems of sub-$2.00 gasoline. The precise events coming into play right now have complex origins: innovations in drilling technology, geopolitical forces (including bitter national and ethnic rivalries), and national budgets whose planning assumptions have been obliterated. Saudi Arabia, for example, can produce a barrel of oil for about $3 but needs $93 to break even for budget purposes given its economic monoculture; Venezuela needs $149 a barrel to break even, to take the most extreme example. At $30, budgets in many places (including Alaska) are a mess.

Given that oil is such big business in so many parts of the world, considerable expertise is deployed in forecasting. Yet the industry’s record, with regard to both estimates of oil reserves and now prices, is consistently poor. Perhaps the lesson is that complex systems cannot be predicted well, so the best answer might be to shorten planning horizons — a tough call in light of the magnitude of investment and concomitant project lead time required.

The next “shock’ is in some ways predictable: U.S. infrastructure investment has lagged for so long that calamities on bridges, railroads, and water supplies are unfortunately overdue. The particular politics of Flint, Michigan’s mismanagement are also not surprising given the nature of both large, overlapping bureaucracies and the governor’s high priority on municipal budget repair to be performed by unelected “emergency managers.” The competing agendas are difficult: if bondholders lose trust, investment becomes prohibitive. At the same time, the dismissal of known test results and risks, and the human consequences thereof, are criminal: GM stopped using Flint water because it was destroying auto parts while Flint’s citizens had to keep drinking it.

The pattern in Flint is not all that unusual, except in its impact: given the size of federal and state governments, it’s hard to imagine who voters could hold accountable for substandard ports, roads, and airports. Many are in poor repair, but the constituencies are diffuse and/or politically marginal, and so can be ignored. Who can one complain to (or vote out) regarding connections inside Philadelphia’s airport, or Amtrak’s unreliability, or Detroit’s crumbling schools? Conversely, what good came to the Detroit mayor who supported that airport’s modernization? Who is the primary constituency that benefits from New Jersey’s extremely heavy spending on roads ($2 million per state-controlled mile) that are consistently graded as among the nation’s worst (at both the Interstate and local arterial levels)? Rather than planning horizons, the issue here appears to center on accountability. The interconnections of race, poverty, and party politics can also fuel tragedy: decisions were made in Flint that would be unthinkable in more affluent Detroit suburbs. (Another water issue, the one in California, could also amplify class conflicts in the event the El Nino snowpack melts to last summer’s levels in coming years.)

The third shock is a positive one. Google’s DeepMind unit (acquired for $400 million in 2014) announced that it had used machine learning to develop a computer capable of defeating the European champion at Go, the ancient Chinese game of strategy. AlphaGo, DeepMind’s program, will now play a higher-ranked champion in March. If the machine can win, another cognitive milestone will have been achieved with AI, about ten years faster than had been generally predicted. Interestingly, Facebook had previously announced that it had made significant progress at Go in a purely machine tournament, but the Google news swamped the magnitude of Facebook’s achievement.

To their credit, DeepMind’s team published the algorithmic architecture in Nature. Two distinct neural networks are built: one, the “policy network,” limits its scope to a small number of attractive options for each move, while the “value network” rates the proximate choices in the context of 20 moves ahead. It’s likely the technology will be tested outside abstract board games, potentially in climate forecasting, medical diagnostics, and other fields.

In this case, the breakthrough is so unexpected that nobody, including the scientists involved, knows what it means. Even though Deep Blue won at championship chess and Watson won at Jeopardy, neither advancement has translated into wide commercial or humanitarian benefit even though the game wins were in 1997 and 2011 respectively. This is by no means a critique of IBM; rather, turning technology breakthroughs in a specific domain into a more general-purpose tool can in some cases be impossible when it is not merely hard.

Elsewhere, however, giant strides are possible: Velodyne lidar, the spinning sensor atop the first generation Google car, has dropped from $75,000 per unit to a smaller unit costing under $500, with further economies of mass production to come. Even more astoundingly, the cost of human genomic sequencing continues to plummet: the first human DNA sequence cost $2.7 billion, for the entire research program. Shortly after, the cost was about $100,000 as of 2002; today it’s approaching $1,000, outpacing Moore’s law by a factor of thousands (depending on how one calculates) in a 15-year span.

In each of these technological instances, people have yet to invent large markets, business models, or related apparatus (liability law, quality metrics, etc) for these breakthroughs. As the IBM example showed in regard to AI, this is in some ways normal. At the same time, I believe we can create better scaffolding for technology commercialization: patent law reform comes immediately to mind. Erik Brynjolfsson and Andrew McAfee suggest some other ideas in their essential book, The Second Machine Age.

Education is of course a piece of the puzzle, and there’s a lot of discussion regarding STEM courses, including why more people should learn to code. I’ve seen several people make the case that code is already the basis of our loss of privacy, and there will be more deep questions emerging soon: who owns my genomic information? who controls my digital breadcrumbs? should big-data collection be opt-in or opt-out? Yes, knowing _how to_ code can get you a job, but more and more, knowing _about_ code will be essential for making informed choices as a citizen. The widespread lack of understanding of what “net neutrality” actually entails serves as a cautionary tale: few people understand the mechanics of peering, CDNs, and now mobile ad tech so much of the debate misses the core issue, which is lack of competition among Internet service providers. “Broadband industry consolidation” isn’t on anyone’s top-5 agenda in the U.S., yet even comedian John Oliver identified it as the major nut to crack with regard to information access.

In the end, humans will continue to see the future as looking much like the present, driven by psychological patterns we now understand better than ever. As shocks increase in magnitude, for many reasons including climatic ones, and impact, because so many aspects of life and commerce are interconnected, it may be time to rethink some of our approaches to planning for both the normal and the exceptional.