How the Web Became Our ‘External Brain,’ and What It Means for Our Kids
Search YouTube for “baby” and “iPad” and you’ll find clips featuring one-year-olds attempting to manipulate magazine pages and television screens as though they were touch-sensitive displays. These children are one step away from assuming that such technology is a natural, spontaneous part of the material world. They’ll grow up thinking about the internet with the same nonchalance that I hold toward my toaster and teakettle.
Full Story; Wired
General Electric plans to announce Monday that it has created a “data lake” method of analyzing sensor information from industrial machinery in places like railroads, airlines, hospitals and utilities. G.E. has been putting sensors on everything it can for a couple of years, and now it is out to read all that information quickly. The company, working with an outfit called Pivotal, said that in the last three months it has looked at information from 3.4 million miles of flights by 24 airlines using G.E. jet engines. G.E. said it figured out things like possible defects 2,000 times as fast as it could before. The company has to, since it’s getting so much more data. “In 10 years, 17 billion pieces of equipment will have sensors,” said William Ruh, vice president of G.E. software. “We’re only one-tenth of the way there.”
Jason Dorrier, Burger Robot Poised to Disrupt Fast Food Industry
I saw the future of work in a San Francisco garage two years ago. Or rather, I was in proximity to the future of work, but happened to be looking the other direction. At the time, I was visiting a space startup building satellites behind a carport. But just behind them—a robot was cooking up burgers. The inventors of the burger device? Momentum Machines, and they’re serious about fast food productivity. “Our device isn’t meant to make employees more efficient,” cofounder Alexandros Vardakostas has said. “It’s meant to completely obviate them.” The Momentum burger-bot isn’t remotely humanoid. You can forget visions of Futurama’s Bender. It’s more of a burger assembly line. Ingredients are stored in automated containers along the line. Instead of pre-prepared veggies, cheese, and ground beef—the bot chars, slices, dices, and assembles it all fresh. Why would talented engineers schooled at Berkeley, Stanford, UCSB, and USC with experience at Tesla and NASA bother with burger-bots? Robots are increasingly capable of jobs once thought the sole domain of humans—and that’s a huge opportunity. Burger robots may improve consistency and sanitation, and they can knock out a rush like nobody’s business. Momentum’s robot can make a burger in 10 seconds (360/hr). Fast yes, but also superior quality. Because the restaurant is free to spend its savings on better ingredients, it can make gourmet burgers at fast food prices. Or at least, that’s the idea.
Half of these experts (48%) envision a future in which robots and digital agents have displaced significant numbers of both blue- and white-collar workers—with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order.
The other half of the experts who responded to this survey (52%) expect that technology will not displace more jobs than it creates by 2025. To be sure, this group anticipates that many jobs currently performed by humans will be substantially taken over by robots or digital agents by 2025. But they have faith that human ingenuity will create new jobs, industries, and ways to make a living, just as it has been doing since the dawn of the Industrial Revolution.
What is this discussion really about? Since the concept of the job have become so instrumental and is seldom connected to the productive kind work that it did in the past, isn’t this a systems issue rather than an aggregated version av a number of individual problems?
I would argue that this discussion should be around how we could build tomorrow’s system of value diffusion from the few sources of real value we rely on and stop trying to predict if our crumbling system could balance on the edge or not…
Are you where the growth is?
The problem with experts is that they think they know it all; ignore data that don’t fit their points of view; and extrapolate from the past on a linear basis. If some disruptive technology hasn’t come along in the past, the assumption is that it won’t happen in the future. What’s worse is that experts often try to block technologies that might up-end their roles. After all, if things change too fast, they will no longer be experts.
The role of the traditional expert is really evaporating. Another way of explain this is with Dave Snowden’s Cynefin framework. I think it us pretty obvious that more and more areas of human activity are moving from being just complicated to becoming complex and thus signalling that the causal relationships are not any more given in advance and can be learned about just-in-case. In a complex world the causal relations have to be explored by interacting and probing.
This would imply that tomorrows experts are more similar to psychotherapists than car mechanics.
Ebola won’t kill us all, but something else might. Like everything living on Earth, viruses must evolve to survive. That is why avian influenza has provoked so much anxiety; it has not yet mutated into an infection that can spread easily. Maybe it never will, but it could happen tomorrow. A pandemic is like an earthquake that we expect but cannot quite predict.
No, we cannot predict a specific outbreak. But, we can observe the megatrends and the changing circumstances which have an effect on the likelyhood and the possible magnitude of an outbreak. We can also observe how humanity build systems and increase their efforts in developing different medicines in order to avoid serious consequences.
The problems since the complexity increases and nature have a tendency to avoid control is difficult to say if the sum of the trends, circumstances and actions increase or decrease the risk of severe pandemic outbreak.
How can the public learn the role of algorithms in their daily lives, evaluating the law and ethicality of systems like the Facebook NewsFeed, search engines, or airline booking systems?
How can research on algorithms proceed without access to the algorithm?
What is the algorithm doing for a particular person?
How should we usefully visualize it?
How do people make sense of the algorithm?
What do users really need to know about algorithms?
*I’m Technology Review’s fiction editor for their annual science fiction issue. And check out my list of contributors.