All-seeing eyes, not yet all-knowing

Yesterday’s Washington Post hosts an article by Abbot Koloff: “It’s increasingly hard to hide from the watchful eyes of cameras” which was posted originally in the Leader Telegram last month.  The article is a good reminder that infrastructure-based recordings are becoming ubiquitous, from streets and parking lots to dormitory rooms and hallways; and that such image archives can prove very effective in identifying criminal behavior- particularly when you know when and where to look for it in the data record. As infrastructure sensing of human behavior expands, the interesting part of this massive Big Data problem is that making sense of all the data becomes a superhuman endeavour, and a natural direction for innovation will be to apply automatic machine-based interpretation of human behavior to this dataset that far exceeds even a human army’s ability to review.  “Show me all pairs of 30-year old men who photographed that bridge” ; “Show me every person who handed another person a package in Central Park that week.” Of course the boundary between identifying criminal behavior and summarizing high-value information is slippery. How about premonition for new clothes trends in New York, storefront gesture analyses, studies of visitorship to competing restaurants. The infrastructure will build will be all-seeing, and we know people will not change their overall behavior profoundly, even if they are intellectually aware of this level of observation.. Look at email: we repeatedly tell everyone that email is essentially public, and plenty of users still manage to embarrass themselves with incredibly poor email decisions. But that very same sensing infrastructure can move from passive recording to meaning-making as AI proceeds, and this means a treasure trove of semantic information will be available to those with either power, money or subpoenas. Every camera on every street corner will start behaving like a very sharp gremlin is in there taking notes- and these gremlins will be happy to strike a bargain.

The End of Search

NPR’s Steve Henn broadcast this story on NPR’s Morning Edition about Google I/O and the upcoming roadmap of product intelligence. The unifying theme across the report involves the greater intelligence of Google’s products in providing you with the right information: by taking into account past interaction with you, social conventions, image analysis, your location – you name it- the tools move from search-based information providers to highly customized, responsive agents that give you what you need when you need it. In New Mediocracy I write about the concept of the perfect marketing tool: if you have six billion people, the perfect tool has six billion custom versions.  The results of AI, applied so very personally, can be wonderful in bringing us information ever more efficiently. Indeed, the wealthy have personal assistants that do just this. There is one interesting difference however: Bill Gates’ personal assistants work for Bill Gates- they’re paid by him. In this case, there will be a trade; you get far better information, but of course you will also receive far more customized advertisement. And your personal assistant will be part of a global network, able to mine across demographics to draw conclusions that are profitable for business down the road. I am sure that one day we will look back and wonder how we got along with out the customized interfaces we will come to expect. But I am also very curious, in that near future, what expectations will we have about the privacy of our personal behavior?

 

Motherboard Jones

Kevin Drum writes for Mother Jones’ June 2013 issue: “Welcome, Robot Overlords. Please don’t kill us.”  Drum’s article is worth reading, although it is important to note, as with all singularity trajectories laid out in the popular press, the hidden assumption that massively increasing computing speed will lead to robots with ‘true artificial intelligence.’ But, as Drum rightly points out, these future robots needn’t think the way humans do. Whether they are intelligent by our standards or not, machines will become ever more capable at all the prosaic and skilled activities that we partake, from driving to entire classes of working class job positions. Drum brings up the possibility of a further transition from labor to capital-based income generation. Those with money to own the machines win; the rest see their uniqueness being chipped away.

Drum also paints a picture in which these economic dynamics speed up exponentially- as machines double in speed, so the power of capital and the denaturing of labor ramp up.  If that model is right, then it is almost guaranteed that when society does respond to obvious change, it’s already too late. Perhaps some outstanding theatrical types can create visions of our future on stage and on camera, so we can jump in a time machine, visit the near future personally, then come back and act in the know.

Comments: the bottom half

Your parents told you never to read the bottom half of a web page, but I have disregarded that by checking out all the comments on Tech Review article that I published two days ago.  It is not surprising to see a broad diversity of opinion about whether robots are fundamentally a new ingredient in the dynamics of business, or just the same as power nail guns and welding arms.  In the arguments made consistently, there are some tropes worth pointing out though, and many opinions center around the same basic flow of reasoning: (1) automation happened before and it was just fine; (2) the advent of new robotics is just more of the same, and so it will be just fine too; (3) no matter what happens, consumer demand and market dynamics is corrective and will ensure that things go well.

But the real world is messy, and these universals are not portraying that reality. Was the advent of automation fine each time it hit? If you pose the long-term question to an economist, sure. If you ask the daughter of a steelworker or auto worker, no; it was a disaster that no amount of retraining succeeded in compensating.  Impacts can be generational, and the concept of it all working out can take multiple generations even when trends point the right way. As for the equivalence of robotics and prior forms of automation, the disagreement I have with some Singularity proponents is more on ‘when’ than ‘if’ for concrete, social tasks that robots can eventually undertake. Sure, robots will always lie on a continuum of intelligence, with nail guns near the bottom (I hope). But the sensitivity that underemployment might have to where technology lies on that continuum is not established. We are turning dials that do not have well-defined labels, and that is my principal concern on this front.  Finally, there is argument (3)- sometimes stated in terms of demand: without a middle class, there is no demand for goods, and therefore the system of producing via robots will self-correct to ensure enough people consume enough material goods. While corrective forces may influence aggregate demand, it is important to note that such corrections do not necessarily influence social and economic inequity; the gap between classes can increase even with gross corrective forces in place.

Let the debate rage on- it is a healthy step toward building the awareness that has to predate any deliberate action.

Predictive Apps Proliferate

In June last year I wrote about Google Now, which gives you answers before you have formulated your questions. Tom Simonite writes in MIT Tech Review today about several companies providing such predictive services, from directions and check-in to calling a cab for you automatically when you arrive at the airport. And, yes, sending messages informing your friends that you’re late automatically.  This article is a good summary of activity in the early space of CEO of Me, Inc.