By JULIE LASKY
NY Times: August 29, 2012

DESIGNED by Philippe Starck in 2002 with an eye to the 18th century, the Louis Ghost chair pays homage to French baroque style. The chair has an oval back and twisted angular arms — fancy stuff for a piece of transparent plastic furniture.

But is it a classic? That’s the claim of its producer, Kartell, which recently announced that 1.5 million Louis Ghosts have been sold since the chair’s introduction in October 2002, making it “the most widely sold design chair in the world.” (The company neglected to say exactly what a “design chair” is — presumably not something you unfold on the lawn or buy from Ikea.)

“An icon,” Kartell declared.

When I first received the news of Louis Ghost’s impending 10th anniversary, I was skeptical that a chair could be canonized after only a decade. As far as I could see, it performed no greater miracle than being produced as a single molded injection of molten plastic.

Then I reconsidered. Of all the furniture produced in any given decade, only a few pieces qualify as what we think of as icons of that period, and they’re not always easy to predict. Might Louis Ghost be one of those objects of which a future connoisseur would say, “That is so millennial”? The sort of thing our grandchildren will drag out of our children’s attics and install in their own living rooms?

For a better perspective, I asked a dozen contemporary furniture experts for their opinions on which objects produced in the last decade or so would occupy the design-conscious home of 2050, just as, say, the Eames lounge chair, a mid-20th-century creation, resides in ours. The result is a showroom’s worth of potential design classics, against which I offer my own list of five.

But first, what makes a classic?

That question neatly divided the experts’ picks into two categories: oaks and seeds. The metaphor came from Emilio Ambasz, the celebrated architect and industrial designer. Midcentury notables like the Eames lounge, Mr. Ambasz said, “are like big, strong oaks in the forest. They will last for many years, probably with many little descendants.”

However, Mr. Ambasz went on, “Since 2000, I’ve only seen things that are more like seeds,” that is, design that may not survive in its original guise, but is important because it gives rise to other creations. “The iPhone is a seed of more that’s to come,” he said.

Half the designers, scholars and connoisseurs I polled, in fact, selected an Apple product as an example, if not their sole choice, of canonical early 21st-century design.

Even Murray Moss, the contemporary-design retailer, who is the last person one would imagine championing technology over physical objects, offered the 2007 iPhone as his first choice of a future classic, “because the era is defined by a new means of communication.” He predicted that “at least the next half a century will continue to explore how we communicate.”

Another seed Mr. Moss proposed was Tomas Libertiny’s 2007 honeycomb vase, produced with the assistance of a swarm of bees. He called the vase “a profound, quiet, esoteric kind of object, which was presented as a technology that was right under our nose.” Mr. Libertiny, Mr. Moss suggested, was on to something by harnessing nature to do the work of machines. Referring to the inspirational potential of a lone artifact, he added, “You don’t know how many minds are triggered by that small wave.”

Paola Antonelli, senior curator in the department of architecture and design at the Museum of Modern Art, has long been smitten with technology. (Her 2011 exhibition, “Talk to Me,” dealt with the interaction between people and machines.) She proposed the 2001 iPod, clarifying that she was lumping it with the iPhone and iPad, but giving it special mention for coming out first. Much of the 1980s and 1990s was about style, image and form, Ms. Antonelli said. “It’s only with the turn of the millennium that a new sense of ethics and a new sense of experimentation took hold.”

In the same vein, she mentioned Honey-Pop, Tokujin Yoshioka’s 2000 chair made from sheets of recycled paper that unfold like a Chinese lantern. She also suggested Algues, the 2004 room partition by Ronan and Erwan Bouroullec for Vitra, which users assemble from green plastic pieces to create what looks like a wall of seaweed.

Both highly influential products anticipated some of the social preoccupations in the 2000s, she pointed out: sustainability in one instance; customizable, do-it-yourself design in the other.

After the iPhone, the most popular seedling the experts cited was Patrick Jouin’s furniture made with 3-D printing technology — the C1 and C2 chairs in Mr. Jouin’s Solid collection for the MGX by Materialise collection (2004), for instance — which demonstrated the possibility of manufacturing objects on demand anywhere in the world. Ron Labaco, the Marcia Docter Curator at the Museum of Arts and Design, lauded Mr. Jouin for “taking this technology out of the prototype stage and into the domestic realm.”

Similarly, Mr. Moss called Mr. Jouin’s parasol-like One Shot Stool for Materialise (2006) a “game changer.” The stool emerges from its manufacturing process fully jointed and ready to unfold, though it hasn’t been touched by a human hand. “It’s the first object made ever to be born fully articulated with no assembly required,” Mr. Moss said.

The chance that any of these pieces will actually make an appearance in the living room of the future, though, is slight. One Shot sells for $2,500, and at least one curious journalist I know who tried it out found it a little wobbly. Nor would I recommend a box of bees, however constructive, as a housewarming gift.

I had asked for objects that were not just emblematic of their time, but also held the promise of remaining visible and prominent several decades from now. In other words, oaks.

The entry with the most votes in this category (four, which is pretty good considering all the possibilities in a decade) was Chair One by Konstantin Grcic for Magis (2004). “This strangely skeletal, fractal chair embodies the digital age that engendered it, while also obliquely recalling the classic furniture of Harry Bertoia,” the British design curator and writer Gareth Williams wrote in an e-mail.

Charlotte and Peter Fiell, the authors of several books on groundbreaking furniture, endorsed objects with strong silhouettes as well. The design classics of the past, Ms. Fiell said, “apart from having a good level of everyday function and bold aesthetics,” have “a very graphic profile.” As a result, she said, they are easy to pick out of a crowd and identify with an era, and people can connect with them on an emotional level.

Leading the Fiells’ list were Tom Dixon’s Beat lights (2006), a collection of hand-beaten metal pendant lamps with a black finish. The Fiells also proposed Ross Lovegrove’s Supernatural chair for Moroso (2005), a lightweight plastic piece whose oval back is pierced with cheeseholes.

The designer Vicente Wolf took the opposite tack, recommending furnishings that meld effortlessly into a variety of environments and eras. Despite the brief, the two he proposed were introduced decades ago: a 1970s Cedric Hartman marble-top table and a 1940s swing-arm wall lamp for Hinson.

Mr. Wolf did suggest one recent object, Jeffrey Bernett’s 2003 Metropolitan chair for B&B Italia, which he called a “modern slipper chair.” He said, “Wherever I’ve used it, it always looks well, and it travels well with traditional things.”

But will we see it in 50 years?

“Well, honey, you may, but I’ll be dead,” Mr. Wolf said.

In general, chairs dominated the nominations. Whatever else may change in the next four decades, people will probably continue to sit. “Of any object, chairs are the most representative of a time period because they’re also about structure,” said Cara McCarty, curatorial director at the Cooper-Hewitt National Design Museum. “They relate to the architecture of the time. When new materials are being worked on, they’re often tested on chairs.”

And of all the chairs that Ms. McCarty has encountered in the last dozen years, the one that made the biggest impression was Mr. Jouin’s 3-D printed C2, but she hesitated to declare it a classic. In her view, no one in this century has matched the accomplishments of Mies, Breuer, Aalto or the Eameses. “We can’t expect every generation to produce an iconic chair,” she said.

As for the Louis Ghost, is it a classic or a flash in the pan? The experts were divided. Mr. Williams, the British design curator, suggested that the chair’s “chameleon-like character” gave it longevity. It “seems to fit it into very many kinds of interior,” he said, “from commercial to domestic, cutting edge to conservative, high-end to economical.”

R. Craig Miller, curator of design arts at the Indianapolis Museum of Art, also saw a glorious future for the chair. “When the postmodernist revival happened in the 2000s, Starck was one of the first to sense this, and he did it with the Louis chair,” Mr. Miller said. “It became an icon.”

Ms. Antonelli, on the other hand, insisted that the chair was a product of the style-conscious ’90s, “no matter when it was designed.” In her view, it lacks the conceptual weight of true millennial products. “It sends the wrong message,” she said.

Even so, I believe it will endure. Evoking the past is one way to guarantee timelessness, because doing so creates a reassuring sense of continuity. And the strong impression made by Louis Ghost early on may well leave its mark with consumers. Not that we need to wallow in nostalgia. Today’s innovators may not have the same opportunities to invent paradigms that earlier masters did, but standards for contemporary design remain high. As Jennifer Hudson, editor of “The International Design Yearbook” (and another iPhone proponent), noted: “It is no longer enough to make something look good and function, but it has to appeal to our emotions and use technologies and materials in ingenious and imaginative ways, as well as having minimum impact on the environment.”

Anything that manages to run that gantlet will be a gift to our descendants.

The Internet is not merely connecting computers together for the benefit of humans; it’s connecting humans together to reinvent labor. This opens terrific opportunities along with real worries. Soon we’ll have to question whether an earnest-looking group of protesters with hand-lettered signs is genuine or simply rapidly convened as a paid flash mob: a crowdsourced crowd. We’ll be able to one-click shop for cheering throngs or protests at a particular location on a moment’s notice, indistinguishable from genuine collective sentiment. A house can be surveilled and a spouse tailed because an online bounty has been put out for anyone nearby to take a photo of the building at a particular address, or to “follow that car.” -Jonathan Zittrain, Harvard professor of law and computer science

By: Peter Diamandis/Singularity University
June 27, 2012

During the last two decades, we have witnessed a technological acceleration unlike anything the world has ever seen. Exponential progress in artificial intelligence, robotics, infinite computing, ubiquitous broadband networks, digital manufacturing, nanomaterials, and synthetic biology, among many others, put us on track to make greater gains in the next two decades than we have had in the previous 200 years. We will soon have the capability to meet and exceed the basic needs of every man, woman, and child on the planet. Abundance for all is within our grasp.

But it won’t happen without your help. While accelerating technology is an awesome force, it’s not enough to bring on a golden age. However, three additional forces are emerging—and this is exactly where you come in.

The second of these forces is the rise of the Do-It-Yourself (DIY) innovator. No longer content with hot rods and homebrew computers, DIYers (working both in small teams or collectively, via crowdsourcing) have made major contributions to fields like healthcare, energy, education, water, and freedom—areas that were once the sole province of large corporations and governments. This means that whatever challenges we face in the world—climate change, AIDS in Africa, energy poverty—more than ever before, we are now empowered to individually help solve these problems. And it’s our ability to do so, this newfound power of the maverick DIYer, that is the second of our four forces.

The same technologies that enabled the rise of the DIY Innovator have also created wealth much faster than ever before. Tech entrepreneurs such as Jeff Skoll (eBay), Elon Musk (PayPal), Bill Gates (Microsoft) became billionaires by reinventing industries before the age of 35. Maintaining their appetite for the big and bold, they are now turning their attention and considerable resources toward bettering the world, becoming a new breed of philanthropist—technophilanthropists—and, as such, yet another force for abundance.

Perhaps the most significant change of the next decade will be the dramatic increase in worldwide connectivity via the Internet. The online community is projected to grow from 2 billion people in 2010 to 5 billion by 2020. Three billion new minds are about to join the global brain trust. What will they dream? What will they discover? What will they invent?  These are minds that the rest of society has never had access to before, and their collective economic and creative boost becomes our final force: the power of “the rising billion.”

We are living in a time of unprecedented opportunity.

By: John Markoff
NY Times, June 25, 2012 

MOUNTAIN VIEW, Calif. — Inside Google’s secretive X laboratory, known for inventing self-driving cars and augmented reality glasses, a small group of researchers began working several years ago on a simulation of the human brain.

There Google scientists created one of the largest neural networks for machine learning by connecting 16,000 computer processors, which they turned loose on the Internet to learn on its own.

Presented with 10 million digital images found in YouTube videos, what did Google’s brain do? What millions of humans do with YouTube: looked for cats.

The neural network taught itself to recognize cats, which is actually no frivolous activity. This week the researchers will present the results of their work at a conference in Edinburgh, Scotland. The Google scientists and programmers will note that while it is hardly news that the Internet is full of cat videos, the simulation nevertheless surprised them. It performed far better than any previous effort by roughly doubling its accuracy in recognizing objects in a challenging list of 20,000 distinct items.

The research is representative of a new generation of computer science that is exploiting the falling cost of computing and the availability of huge clusters of computers in giant data centers. It is leading to significant advances in areas as diverse as machine vision and perception, speech recognition and language translation.

Although some of the computer science ideas that the researchers are using are not new, the sheer scale of the software simulations is leading to learning systems that were not previously possible. And Google researchers are not alone in exploiting the techniques, which are referred to as “deep learning” models. Last year Microsoft scientists presented research showing that the techniques could be applied equally well to build computer systems to understand human speech.

“This is the hottest thing in the speech recognition field these days,” said Yann LeCun, a computer scientist who specializes in machine learning at the Courant Institute of Mathematical Sciences at New York University.

And then, of course, there are the cats.

To find them, the Google research team, led by the Stanford University computer scientist Andrew Y. Ng and the Google fellow Jeff Dean, used an array of 16,000 processors to create a neural network with more than one billion connections. They then fed it random thumbnails of images, one each extracted from 10 million YouTube videos.

The videos were selected randomly and that in itself is an interesting comment on what interests humans in the Internet age. However, the research is also striking. That is because the software-based neural network created by the researchers appeared to closely mirror theories developed by biologists that suggest individual neurons are trained inside the brain to detect significant objects.

Currently much commercial machine vision technology is done by having humans “supervise” the learning process by labeling specific features. In the Google research, the machine was given no help in identifying features.

“The idea is that instead of having teams of researchers trying to find out how to find edges, you instead throw a ton of data at the algorithm and you let the data speak and have the software automatically learn from the data,” Dr. Ng said.

“We never told it during the training, ‘This is a cat,’ ” said Dr. Dean, who originally helped Google design the software that lets it easily break programs into many tasks that can be computed simultaneously. “It basically invented the concept of a cat. We probably have other ones that are side views of cats.”

The Google brain assembled a dreamlike digital image of a cat by employing a hierarchy of memory locations to successively cull out general features after being exposed to millions of images. The scientists said, however, that it appeared they had developed a cybernetic cousin to what takes place in the brain’s visual cortex.

Neuroscientists have discussed the possibility of what they call the “grandmother neuron,” specialized cells in the brain that fire when they are exposed repeatedly or “trained” to recognize a particular face of an individual.

“You learn to identify a friend through repetition,” said Gary Bradski, a neuroscientist at Industrial Perception, in Palo Alto, Calif.

While the scientists were struck by the parallel emergence of the cat images, as well as human faces and body parts in specific memory regions of their computer model, Dr. Ng said he was cautious about drawing parallels between his software system and biological life.

“A loose and frankly awful analogy is that our numerical parameters correspond to synapses,” said Dr. Ng. He noted that one difference was that despite the immense computing capacity that the scientists used, it was still dwarfed by the number of connections found in the brain.

“It is worth noting that our network is still tiny compared to the human visual cortex, which is a million times larger in terms of the number of neurons and synapses,” the researchers wrote.

Despite being dwarfed by the immense scale of biological brains, the Google research provides new evidence that existing machine learning algorithms improve greatly as the machines are given access to large pools of data.

“The Stanford/Google paper pushes the envelope on the size and scale of neural networks by an order of magnitude over previous efforts,” said David A. Bader, executive director of high-performance computing at the Georgia Tech College of Computing. He said that rapid increases in computer technology would close the gap within a relatively short period of time: “The scale of modeling the full human visual cortex may be within reach before the end of the decade.”

Google scientists said that the research project had now moved out of the Google X laboratory and was being pursued in the division that houses the company’s search business and related services. Potential applications include improvements to image search, speech recognition and machine language translation.

Despite their success, the Google researchers remained cautious about whether they had hit upon the holy grail of machines that can teach themselves.

“It’d be fantastic if it turns out that all we need to do is take current algorithms and run them bigger, but my gut feeling is that we still don’t quite have the right algorithm yet,” said Dr. Ng.

By Leon Neyfakh | BOSTON GLOBE, JUNE 24, 2012

A finished basement can be a beautiful thing. With the right accoutrements and enough effort, what might otherwise be a damp, empty space lined with concrete can be turned into a cozy playroom, or a den, or an office and gym. Properly planned, the basement can become an integral part of a household, even a kind of engine that powers it from below.

The same is true for the far larger basement that all of us share: that vast space that exists under our feet wherever we go, out of sight and out of mind. Those of us who are city-dwellers already keep a lot of stuff down there—subway stations, sewer pipes, electrical lines—but as our cities grow more cramped, and real estate on the surface grows more valuable, the possibility that it can be used more inventively is starting to attract attention from planners around the world.

“It used to be, ‘How high can you go up into the sky?’” said Susie Kim, of the Boston-based urban design firm Koetter Kim & Associates. “Now it’s a matter of, ‘How low can you go and still be economically viable?’”

A cadre of engineers who specialize in tunneling and excavation say that we have barely begun to take advantage of the underground’s versatility. The underground is the next great frontier, they say, and figuring out how best to use it should be a priority as we look ahead to the shape our civilization will take.

“We have so much room underground,” said Sam Ariaratnam, a professor at Arizona State University and the chairman of the International Society for Trenchless Technology. “That underground real estate—people need to start looking at it. And they are starting to look at it.”

Read on.

The Oregon Daily Emerald’s 92-year streak as a Monday-to-Friday newspaper will end soon.

But the Emerald Media Group’s run has just begun.

Next fall, we will launch a new Emerald completely rebuilt for the digital age. The gray, daily newspaper will be replaced by a modern college media company.

We know what you’re thinking: Another college daily goes down, buckling under the pressure of advancing technology and retreating readership.

That’s not our story. Yes, we confront the same challenges as every American newspaper, but this is not a move made out of financial desperation. The Emerald, as a nonprofit company, is having its best year financially in more than a decade. We have no debt and a solid reserve fund.

We are making this change to deliver on our mission to serve our community and prepare our student staff for the professional world.

The survival of man depends on the early construction of an ultra-intelligent machine.

In order to design an ultraintelligent machine we need to understand more about the human brain or human thought or both. In the following pages an attempt is made to take more of the magic out of the brain by means of a “subassembly” theory, which is a modification of Hebb’s famous speculative cell-assembly theory. My belief is that the first ultraintelligent machine is most likely to incorporate vast artificial neural circuitry, and that its behavior will be partly explicable in terms of the subassembly theory. Later machines will all be designed by ultraintelligent machines, and who am I to guess what principles they will devise? But probably Man will construct the deus ex machina in his own image.

The subassembly theory sheds light on the physical embodiment of memory and meaning, and there can be little doubt that both will need embodiment in an ultraintelligent machine. Even for the brain, we shall argue that physical embodiment of meaning must have originated for reasons of economy, at least if the metaphysical reasons can be ignored. Economy is important in any engineering venture, but especially so when the price is exceedingly high, as it most likely will be for the first ultraintelligent machine. Hence semantics is relevant to the design of such a machine. Yet a detailed knowledge of semantics might not be required, since the artificial neural network will largely take care of it, provided that the parameters are correctly chosen, and provided that the network is adequately integrated with its sensorium and motorium (input and output). For, if these conditions are met, the machine will be able to learn from experience, by means of positive and negative reinforcement, and the instruction of the machine will resemble that of a child. Hence it will be useful if the instructor knows something about semantics, but not necessarily more useful than for the instructor of a child. The correct choice of the parameters, and even of the design philosophy, will depend on the usual scientific method of successive approximation, using speculation, theory, and experiment. The percentage of speculation needs to be highest in the early stages of any endeavor. Therefore no apology is offered for the speculative nature of the present work. For we are certainly still in the early stages in the design of an ultraintelligent machine.

Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make.