Tag: big data

Intelligence Augmentation and a Frictionless Economy

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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.

… Read the rest

Inequality and Big Data Revolutions

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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

Profiled Against a Desert Ribbon

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The desert abloomCatch a profile of me in this month’s IEEE Spectrum Magazine. Note Yggdrasil in the background! It’s been great working with IEEE’s Cloud Computing Initiative (CCI) these last two years. CCI will be ending soon, but it’s impact will live on in, for instance, the Intercloud Interoperability Standard and other ways. Importantly, I’ll be at the IEEE Big Data Initiative Workshop in Hoboken, NJ, at the end of the month working on the next initiative in support of advanced data analytics. Note that Hoboken and Jersey City have better views of Manhattan than Manhattan itself!

“Animal” was the name of the program and it built simple decision trees based on yes/no answers (does it have hair? does it have feathers?). If it didn’t guess your animal it added a layer to the tree with the correct answer. Incremental learning at its most elementary, but it left an odd impression on me: how do we overcome the specification of rules to create self-specifying (occasionally, maybe) intelligence? I spent days wandering the irrigation canals of the lower New Mexico Rio Grande trying to overcome this fatal flaw that I saw in such simplified ideas about intelligence. And I didn’t really go home for days, it seemed, given the freedom to drift through my pre-teen and then teen years in a way I can’t imagine today, creating myself among my friends and a penumbra of ideas, the green chile and cotton fields a thin ribbon surrounded by stark Chihuahuan desert.… Read the rest

Industrial Revolution #4

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Paul Krugman at New York Times consumes Robert Gordon’s analysis of economic growth and the role of technology and comes up more hopeful than Gordon. The kernel in Krugman’s hope is that Big Data analytics can provide a shortcut to intelligent machines by bypassing the requirement for specification and programming that was once assumed to be a requirement for artificial intelligence. Instead, we don’t specify but use “data-intensive ways” to achieve a better result. And we might get to IR#4, following Gordon’s taxonomy where IR stands for “industrial revolution.” IR#1 was steam and locomotives  IR#2 was everything up to computers. IR#3 is computers and cell phones and whatnot.

Krugman implies that IR#4 might spur the typical economic consequences of grand technological change, including the massive displacement of workers, but like in previous revolutions it is also assumed that economic growth built from new industries will ultimately eclipse the negatives. This is not new, of course. Robert Anton Wilson argued decades ago for the R.I.C.H. economy (Rising Income through Cybernetic Homeostasis). Wilson may have been on acid, but Krugman wasn’t yet tuned in, man. (A brief aside: the Krugman/Wilson notions probably break down over extraction and agribusiness/land rights issues. If labor is completely replaced by intelligent machines, the land and the ingredients it contains nevertheless remain a bottleneck for economic growth. Look at the global demand for copper and rare earth materials, for instance.)

But why the particular focus on Big Data technologies? Krugman’s hope teeters on the assumption that data-intensive algorithms possess a fundamentally different scale and capacity than human-engineered approaches. Having risen through the computational linguistics and AI community working on data-driven methods for approaching intelligence, I can certainly sympathize with the motivation, but there are really only modest results to report at this time.… Read the rest

An Exit to a New Beginning

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I am thrilled to note that my business partner and I sold our Big Data analytics startup to a large corporation yesterday. I am currently unemployed but start anew doing the same work on Monday.

Thrilled is almost too tame a word. Ecstatic does better describing the mood around here and the excitement we have over having triumphed in Sili Valley. There are many war stories that we’ve been swapping over the last 24 hours, including how we nearly shut down/rebooted at the start of 2012. But now it is over and we have just a bit of cleanup work left to dissolve the existing business structures and a short vacation to attend to.… Read the rest

Experimental Psychohistory

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Kalev Leetaru at UIUC highlights the use of sentiment analysis to retrospectively predict the Arab Spring using Big Data in this paper. Dr. Leetaru took English transcriptions of Egyptian press sources and looked at aggregate measures of positive and negative sentiment terminology. Sentiment terminology is fairly simple in this case, consisting of positive and negative adjectives primarily, but could be more discriminating by checking for negative modifiers (“not happy,” “less than happy,” etc.). Leetaru points out some of the other follies that can arise from semi-intelligent broad measures like this one applied too liberally:

It is important to note that computer–based tone scores capture only the overall language used in a news article, which is a combination of both factual events and their framing by the reporter. A classic example of this is a college football game: the hometown papers of both teams will report the same facts about the game, but the winning team’s paper will likely cast the game as a positive outcome, while the losing team’s paper will have a more negative take on the game, yielding insight into their respective views towards it.

This is an old issue in computational linguistics. In the “pragmatics” of automatic machine translation, for example, the classic example is how do you translate fighters in a rebellion. They could be anything from “terrorists” to “freedom fighters,” depending on the perspective of the translator and the original writer.

In Leetaru’s work, the end result was an unusually high churn of negative-going sentiment as the events of the Egyptian revolution unfolded.

But is it repeatable or generalizable? I’m skeptical. The rise of social media, enhanced government suppression of the media, spamming, disinformation, rapid technological change, distributed availability of technology, and the evolving government understanding of social dynamics can all significantly smear-out the priors associated with the positive signal relative to the indeterminacy of the messaging.… Read the rest