Tag Archives: Social economy

Notre Dame

By Natasha Frost, Ephrat Livni, Whet Moser, Jessanne Collins, Adam Pasick and Luiz Romero – Far more than a postcard-ready icon of the city of Paris, the 13th-century building is beloved by people of all faiths, is a trove of art and relics, and has been immortalized in numerous works of literature. It has also been through dramatic ups and downs over the years.

But the reverberations of Monday’s fire spread as quickly as the blaze itself, transcending the physical damage. The blaze revealed fault lines in European politics, flaws in social media’s algorithm-driven fact-checking efforts, the usefulness of drones in firefighting, and just how personally humanity can feel the pain of a cultural tragedy.

In so many people’s imaginations, Paris is not supposed to change. Monuments such as Notre Dame are not supposed to be affected by the passage of time; but neither were the National Museum of Brazil, the treasures of Palmyra, the Glasgow School of Art, nor any other cultural treasures we’ve had snatched from us recently. more>

The new spirit of postcapitalism

Capitalism emerged in the interstices of feudalism and Paul Mason finds a prefiguring of postcapitalism in the lifeworld of the contemporary European city.
By Paul Mason – Raval, Barcelona, March 2019. The streets are full of young people (and not just students)—sitting, sipping drinks, gazing more at laptops than into each other’s eyes, talking quietly about politics, making art, looking cool.

A time traveler from their grandparents’ youth might ask: when is lunchtime over? But it’s never over because for many networked people it never really begins. In the developed world, large parts of urban reality look like Woodstock in permanent session—but what is really happening is the devalorization of capital.

But just 20 years after the roll-out of broadband and 3G telecoms, information resonates everywhere in social life: work and leisure have become blurred; the link between work and wages has been loosened; the connection between the production of goods and services and the accumulation of capital is less obvious.

The postcapitalist project is founded on the belief that, inherent in these technological effects lies a challenge to the existing social relations of a market economy, and in the long term, the possibility of a new kind of system that can function without the market, and beyond scarcity.

But during the past 20 years, as a survival mechanism, the market has reacted by creating semi-permanent distortions which—according to neoclassical economics—should be temporary.

In response to the price-collapsing effect of information goods, the most powerful monopolies ever seen have been constructed. Seven out of the top ten global corporations by market capitalization are tech monopolies; they avoid tax, stifle competition through the practice of buying rivals and build ‘walled gardens’ of interoperable technologies to maximize their own revenues at the expense of suppliers, customers and (through tax avoidance) the state. more>

Updates from Chicago Booth

How sales taxes could boost economic growth
By Dee Gill – The fight against sluggish global economic growth has been expensive, protracted, and unexpectedly vexing, leaving central bankers in developed economies with a laundry list of shared frustrations. Meager economic growth, flagging wages, and low inflation persist, in spite of bankers’ monetary stimuli, and threaten to quash upward mobility for young job seekers and midcareer employees in even the richest countries.

There’s a poster child for what countries do not want to become: Japan. The former economic powerhouse has been stuck in low-growth purgatory since 1991. And yet, as much as they’d like to avoid it, some countries have been sliding in that direction.

Many big economies are stagnating, and economists are running out of options to fix them. The conventional monetary policy for encouraging spending has been to drop short-term interest rates. But with rates already near, at, or below zero, that method is all but exhausted. Some economists have also started to empirically and theoretically question the power of forward guidance, in which central banks publicize plans for future interest-rate policies, at the zero lower bound.

Central banks and governments badly need a new stimulus tool, preferably one that doesn’t cost a lot of money. Some researchers are proposing a fix that might sound unappetizing: raising sales taxes as a means of jump-starting economic growth.

Francesco D’Acunto of the University of Maryland, Daniel Hoang of Germany’s Karlsruhe Institute of Technology, and Chicago Booth’s Michael Weber find evidence that a preannounced tax hike—a 3-percentage-point increase in Germany’s Value Added Tax enacted in 2007—provided just the kind of growth stimulus central banks desperately need today. more>

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All the ways recycling is broken—and how to fix them

You may throw a plastic container in the recycling bin and assume it’s going to easily become a new item. But every step of our recycling system—from product design to collection to sorting—has major flaws. Fortunately, promising technology is starting to come online that could revolutionize the process.
By Adele Peters – You may have read that there’s a recycling crisis in the U.S. After years of accepting our used plastic and cardboard, China now won’t take it, which often means there is no place for it to go. Some city recycling programs—unable to find other buyers—have quietly started sending recyclables to incinerators or landfills, news that could make anyone question the point of separating your trash at all.

Each year, by one estimate, Americans throw out around 22 million tons of products that could have been recycled. Tens of millions of homes don’t have access to recycling; for those that do, everything from broken blenders to old clothing still ends up in the trash. If you drop an empty package in a recycling bin and it’s trucked off to a sorting facility, that doesn’t necessarily guarantee it will be recycled. You might have unwittingly tossed something that your local recycling service doesn’t accept, or the package might have been designed in a way that makes it unrecyclable.

Some parts of the system do work. The aluminum in a beer can, for example, can easily be made into new beer cans, over and over again. But a plastic package might be chopped up, melted, mixed with other types of plastic, and “downcycled” into a lower-quality material that can only be used for certain products, like park benches or black plastic planters.

When the U.S. was sending much of its paper and plastic trash to China, for more than two decades, the bales were often so poorly sorted that they contained garbage. The system never extracted the full value from those materials.

When a truck picks up recyclables from curbside bins, they take them to sorting facilities. Inside these centers, called “MRFs” or materials recycling facilities, people work with automated equipment to sort through the detritus of everyday life. Trucks dump mixed materials into the facility, where it’s loaded onto a conveyor belt; typically, in a first step, people standing next to the machine quickly pull out trash and materials like plastic bags that can jam equipment.

As materials move through a facility, the system uses gravity, screens, filters, and other techniques to separate out paper, metal, glass, and plastics; optical sorting equipment identifies each type of plastic. more>

How digital technology is destroying our freedom

“We’re being steamrolled by our devices” —Douglas Rushkoff
By Sean Illing – There’s a whole genre of literature called “technological utopianism.” It’s an old idea, but it reemerged in the early days of the internet. The core belief is that the world will become happier and freer as science and technology develops.

The role of the internet and social media in everything from the spread of terrorist propaganda to the rise of authoritarianism has dampened much of the enthusiasm about technology, but the spirit of techno-utopianism lives on, especially in places like Silicon Valley.

Douglas Rushkoff, a media theorist at Queens College in New York, is the latest to push back against the notion that technology is driving social progress. His new book, Team Human, argues that digital technology in particular is eroding human freedom and destroying communities.

We’re social creatures, Rushkoff writes in his book, yet we live in a consumer democracy that restricts human connection and stokes “whatever appetites guarantee the greatest profit.” If we want to reestablish a sense of community in this digital world, he argues, we’ll have to become conscious users of our technology — not “passive objects” as we are now.

But what does that mean in practical terms? Technology is everywhere, and we’re all more or less dependent upon it — so how do we escape the pitfalls? more>

How to govern a digitally networked world

Because the internet is a network of networks, its governing structures should be too. The world needs a digital co-governance order that engages public, civic and private leaders.
By Anne-Marie Slaughter and Fadi Chehadé – Governments built the current systems and institutions of international cooperation to address 19th- and 20th-century problems. But in today’s complex and fast-paced digital world, these structures cannot operate at ‘internet speed’.

Recognizing this, the United Nations secretary-general, António Guterres, last year assembled a high-level panel—co-chaired by Melinda Gates and the Alibaba co-founder Jack Ma—to propose ways to strengthen digital governance and cooperation. (Fadi Chehadé, co-author of this article, is also a member.) It is hoped that the panel’s final report, expected in June, will represent a significant step forward in managing the potential and risks of digital technologies.

Digital governance can mean many things, including the governance of everything in the physical world by digital means. We take it to mean the governance of the technology sector itself, and the specific issues raised by the collision of the digital and physical worlds (although digital technology and its close cousin, artificial intelligence, will soon permeate every sector).

Because the internet is a network of networks, its governing structures should be, too. Whereas we once imagined that a single institution could govern global security or the international monetary system, that is not practical in the digital world. No group of governments, and certainly no single government acting alone, can perform this task.

Instead, we need a digital co-governance order that engages public, civic and private leaders on the basis of three principles of participation.

First, governments must govern alongside the private and civic sectors in a more collaborative, dynamic and agile way.

Secondly, customers and users of digital technologies and platforms must learn how to embrace their responsibilities and assert their rights.

Thirdly, businesses must fulfill their responsibilities to all of their stakeholders, not just shareholders. more>

Updates from ITU

What do ‘AI for Social Good’ projects need? Here are 7 key components.
By Anna Bethke – At their core, ‘AI for Social Good’ projects use artificial intelligence (AI) hardware and software technologies to positively impact the well-being of people, animals or the planet – and they span most, if not all, of the United Nations Sustainable Development Goals (SDGs).

The range of potential projects continues to grow as the AI community advances our technology capability and better understands the problems being faced.

Our team of AI researchers at Intel achieved success by working with partners to understand the problems, collecting the appropriate data, retraining algorithms, and molding them into a practical solution.

At their core, an AI for Social Good project requires the following elements:

  1. A problem to solve, such as improving water quality, tracking endangered species, or diagnosing tumors.
  2. Partners to work together in defining the most complete view of the challenges and possible solutions.
  3. Data with features that represent the problem, accurately labeled, with privacy maintained.
  4. Compute power that scales for both training and inference, no matter the size and type of data, or where it lives. An example of hardware choice is at ai.intel.com/hardware.
  5. Algorithm development, which is the fun part! There are many ways to solve a problem, from a simple logistic regression algorithm to complex neural networks. Algorithms match the problem, type of data, implementation method, and more.
  6. Testing to ensure the system works in every way we think it should, like driving a car in rain, snow, or sleet over a variety of paved and unpaved surfaces. We want to test for every scenario to prevent unanticipated failures.
  7. Real-world deployment, which is a critical and complicated step that should be considered right from the start. Tested solutions need a scalable implementation system in the real world, or risk its benefits not seeing the light of day.

At the end of May, Intel AI travels to Geneva, Switzerland, for the UN’s AI for Good Global Summit hosted by ITU and will speak to each of these elements in a hands-on workshop. more>

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Updates from Datacenter.com

The staffing challenge in data center market becomes a quality opportunity
The data center industry is growing rapidly. The amount of vacancies for specialized data center personnel in the area of Amsterdam, for example, is rising. A shortage of specialized/skilled data center personnel is expected, a staffing challenge. How can the data center market respond? Will it result in decreasing quality of data center services? We believe in the opportunity of improving!

datacenter.com – It is expected that the data center industry will rapidly grow and will result in a shortage of skilled IT personnel.

Almost all data centers have plans to expand and cloud providers are considering or starting to operate their own data center. So, the amount of companies and the size of the companies are expanding, means that specialists are needed to design, maintain and operate these high-tech buildings and services. The employees that are the hardest to find are ones with a very broad knowledge of power, cooling and construction knowledge. Due to the shortage in the skilled employees, the more expensive the employees will be.

Instead of trying to do everything by yourself, you can use the experts. The companies that build technical infrastructures and buildings are the once that (mostly) have in-depth knowledge within their field of expertise. Instead of engineering your data center with some multi-talent employees and asking the experts to fill-in the blanks, the experts can design and a project manager within the field can connect them. more>

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Why Some Counties Are Powerhouses For Innovation

By Christopher Boone – By my analysis of data from the U.S. Patent Office, Santa Clara County, California, is sprinting ahead of the country. Between 2000 and 2015, more than 140,000 patents were granted in Santa Clara County. That’s triple the number for second-ranked San Diego County.

Four other counties in California – Los Angeles, San Mateo, Alameda and Orange – make the top 10.

These counties are in large metropolitan areas that are known as technology and innovation centers, including San Francisco, San Diego, Boston and Seattle. The other metro areas in the top 10, not the usual tech-hub suspects, are Greater Los Angeles, Detroit and Phoenix.

Besides large concentrated populations, these metro areas share two other ingredients that support innovation. All of them have one or more leading research universities and a large proportion of college-educated people.

Santa Clara County is home to Stanford University, an institution that has become synonymous with the high-tech and innovation economy of Silicon Valley.

Stanford’s rise as a world-class research university coincided with a rapid increase in federal and military spending during the Cold War. The university’s suburban location gave it an advantage, too, by providing land for expansion and for burgeoning high-tech companies. Stanford’s leadership aggressively courted research opportunities aligned with the priorities of the military-industrial complex, including electronics, computing and aerospace.

Another common trait about most of these centers of innovation is the jaw-dropping cost of housing.

Competition for higher-wage talent pushes up housing and other costs in these innovation centers. Although housing prices increased in greater Boston, Phoenix and Detroit, they remained relative bargains compared to the West Coast.

In my view, one way to unleash innovation would be to tap into the rich diversity of students, faculty and communities at two- and four-year colleges beyond the typical top 100 research institutes. more>

It’s Still Early Days for AI

Neural networks expand far beyond feline photos
By Rick Merritt – “We need to get to real AI because most of today’s systems don’t have the common sense of a house cat!” The keynoter’s words drew chuckles from an audience of 3,000 engineers who have seen the demos of systems recognizing photos of felines.

There’s plenty of room for skepticism about AI. Ironically, the speaker in this case was Yann LeCun, the father of convolutional neural networks, the model that famously identified cat pictures better than a human.

It’s true, deep neural networks (DNNs) are a statistical method — by their very nature inexact. They require large, labeled data sets, something many users lack.

It’s also true that DNNs can be fragile. The pattern-matching technique can return dumb results when the data sets are incomplete and misleading results when they have been corrupted. Even when results are impressive, they are typically inexplicable.

The emerging technique has had its share of publicity, sometimes bordering on hype. The fact remains that DNNs work. Though only a few years old, they already are being applied widely. Facebook alone uses sometimes simple neural nets to perform 3×1014 predictions per day, some of which are run on mobile devices, according to LeCun.

Deep learning is with us to stay as a new form of computing. Its applications space is still being explored. Its underlying models and algorithms are still evolving, and hardware is trying to catch up with it all. more>