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The internet’s low-cost transmission can just as easily create information empires and robber barons as it can digital democracy and information equality. The growing value of being able to mine and manipulate huge data-sets, to generate predictions about consumers’ behaviour and desires, creates a self-reinforcing spiral of network effects. Data begets more data, locked down behind each company’s walls where their proprietary algorithms can exploit it for profit.

Today, the equivalent gesture might be to turn away from private monopolies to fund innovation and creativity. What matters is who owns information, not just the infrastructure by which it is distributed. Digital technology must be combined with concrete actions that protect openness across the spectrum, from maps to medicines, from software to schools. Better that we do it through public institutions, instead of relying on mavericks and martyrs.

User-generated content, by and large, is not lucrative at a scale that satisfies investors, and as a result, most social-media companies are changing direction toward other revenue streams. One of the more significant shifts is the move into social messaging.

These new messaging features work to bind private groups tighter together, by making it more fun to talk to one another than to engage with the world at large.

We are more interested in locating alien species than understanding the humanity among the species we already live with

It's not far-fetched to suggest there is a "deep state" in Washington. Former President Dwight D. Eisenhower looked at the nexus of the Pentagon and arms manufacturers and coined the phrase the "military-industrial complex." Today's observers also point to the collusion of corporate interests and D.C. power brokers as the true guiding hand in American politics.

The Trump White House already seems to be at war with what it would say is the "deep state:" thousands of federal government bureaucrats faced with the awkward reality of working for a president who campaigned loudly against Washington officialdom and promised to "drain the swamp" when in power. This week, almost 1,000 American diplomats signed a dissent memo against Trump's executive order on immigration, prompting White House press secretary Sean Spicer to icily declare that "career bureaucrats" can "either get with the program or they can go." And Trump's public spat with Acting Attorney General Sally Yates, an Obama appointee who sought to defy his immigration order, ended with him firing Yates in an angry, chilling memo that claimed she "betrayed the Department of Justice."

Finance is an essential component of industrial change because it allows technologies to be developed before they can generate a return. But if finance no longer serves industrial change but instead prioritizes rent-seeking (seeking to increase its share of existing wealth without creating new sources of wealth), creative destruction of the present carbon-intensive industrial system cannot occur. The aim of this article is to investigate this issue through a study of the emergence of one low-carbon industry, solar photovoltaics (PV) in the United States. The focus is on the period after the first oil shock in 1973 until the end of the 1980s. The case is contrasted with the more successful development of the industry in Japan. In the late 1970s, American firms held 90% of the global market share; by 2005, it had declined to under 10%, whereas the Japanese share had risen to almost 50% (9). Changes to corporate governance and organization brought by financialization are identified as major causes of the difference in outcome.

One camp consisted of a small number of entrepreneurs who had been involved in producing solar cells for the space program or pioneered their application on Earth.

The other camp consisted of the energy policy bureaucracy and closely affiliated large manufacturing and energy corporations along with utilities (65).

In New Zealand, a new generation of leadership has arisen, symbolized by Ardern: wise to the danger our planet is in; alive to the opportunities of a greener, fairer society; and not beholden to the outdated economic doctrines that have led us into this predicament. In New Zealand, GDP will no longer be the sole measure of success for economic policies, because GDP is not, and has never been, the best or only way to measure social development. In many ways, New Zealand’s new approach is a return to a more honest, more grounded way of practicing economics, more rooted in the real world, as Makhlouf explains. “Economics is about trade-offs,” he says. “Economics is about the fact that there are finite resources to meet unlimited wants and what’s the best way of dealing with that problem. What the Treasury is suggesting now is that we can become a bit more sophisticated than in the past at making those trade-offs.”

hanging people’s behaviour is key to tackling the world’s health, social and environmental problems, such as obesity, sustainable living and cybersecurity. To change behaviour, though, we need to know what works, for whom, where and how. But research is generated far faster than humans can access and use it. A search on Google Scholar for “behaviour change” produced over 60,000 hits for the first six months of 2018. Evidence on behaviour change interventions is also messy. Different terms are often used to describe similar things – “physical activity” and “exercise”, for example. This means different people can often mean different things when discussing or researching behaviour change. This lack of shared vocabulary limits our progress in discovering what interventions work best.

One problem is the price of the produce. Many of us have had the experience of turning up at our local farmers' market, armed with tote bags, only to slink back to the supermarket after seeing the prices of the vegetables on offer. This is hardly the fault of the individual farmers. Still, as Pilgeram points out in a paper that she published in 2011, the costs involved with running such an operation mean that the benefits are inevitably affordable only to a small (generally white and middle-class) portion of society.

But it's a limited victory, Pilgeram writes in her most recent paper, published in November of 2018, and empowers only a certain class of women "while leaving [the capitalist] system basically entirely unaffected"—and which also risks gentrifying the towns to which these farmers move, further entrenching the country's class divide.

"The economic system that we have in place makes it impossible, really, to create a socially just food system. It's not possible under capitalism," Pilgeram says. Without a drastic change to this system, sustainable agriculture risks becoming an "esoteric side note" to conventional agriculture, she adds—or simply another way for those with money to live healthier lives than those without.

The term artificial intelligence typically refers to automation of tasks by software that previously required human levels of intelligence to perform. While machine learning is sometimes used interchangeably with AI, machine learning is just one sub-category of artificial intelligence whereby a device learns from its access to a stream of data.When we talk about AI spending, we’re typically talking about investment that companies are making in building AI capabilities. While this may change in the future, McKinsey estimates that the vast majority of spending is done internally or as an investment, and very little of it is done purchasing artificial intelligence applications from other businesses.

62% of AI spending in 2016 was for machine learning, twice as much as the second largest category computer vision. It’s worth noting that these categories are all types of “narrow” (or “weak”) forms of AI that use data to learn about and accomplish a specific narrowly defined task. Excluded from this report is “general” (or “strong”) artificial intelligence which is more akin to trying to create a thinking human brain.

The McKinsey survey mostly fits well as evidence supporting Cross’s framework that large profitable industries are the most fertile grounds of AI adoption. Not surprisingly, Technology is the industry with highest AI adoption and financial services also makes the top three as Cross would predict.Notably, automotive and assembly is the industry with the second highest rate of AI adoption in the McKinsey survey. This may be somewhat surprising as automotive isn’t necessarily an industry with the reputation for high margins. However, the use cases of AI for developing self-driving cars and cost savings using machine learning to improve manufacturing and procurement efficiencies are two potential drivers of this industry’s adoption.

“I sometimes feel like a flying fish who has had the opportunity to take a ‘bird’s eye view’ of the water in which we swim, and is now trying to report back to its fellow fish and explain what it has learned about what we usually cannot see.”

“The real issue is not whether widespread changes will happen or not, but how much awareness there will be about where these changes are leading us. The real question is whether we are even conscious that we have a choice in the matter.”

“The urgent message for economics from nature is that the monoculture of national currencies, justified on the basis of market efficiency, generates structural instability in our global financial system. Complementary currencies of diverse types of money enable the economy to flow back towards greater sustainability. While this process clearly reduces efficiency, that is the price to pay for increased resilience of the whole.”

Lyons announced the Partnership on AI’s first three working groups, which are dedicated to fair, transparent, and accountable AI; safety-critical AI; and AI, labor, and the economy. Each group will have a for-profit and nonprofit chair and aim to share its results as widely as possible. Lyons says these groups will be like a “union of concerned scientists.” “A big part of this is on us to really achieve inclusivity,” she says. Tess Posner, the executive director of AI4ALL, a nonprofit that runs summer programs teaching AI to students from underrepresented groups, showed why training a diverse group for the next generation of AI workers is essential. Currently, only 13 percent of AI companies have female CEOs, and less than 3 percent of tenure-track engineering faculty in the US are black. Yet an inclusive workforce may have more ideas and can spot problems with systems before they happen, and diversity can improve the bottom line. Posner pointed out a recent Intel report saying diversity could add $500 billion to the US economy.

“It’s good for business,” she says. These weren’t the first presentations at EmTech Digital by women with ideas on fixing AI. On Monday, Microsoft researcher Timnit Gebru presented examples of bias in current AI systems, and earlier on Tuesday Fast.ai cofounder Rachel Thomas talked about her company’s free deep-learning course and its effort to diversify the overall AI workforce. Even with the current problems achieving diversity, there are more women and people of color that could be brou

ght into the workforce.

Anthropologist Helen Fisher inelegantly described the maturing of this huge postwar bulge in the population as “like a pig moving through a python”, changing society as we grew older on a scale never known before. We challenged the Victorian puritanism, censorship, class snobbery and inhibitions of the establishment. Full employment put money in the pockets of managers and factory workers alike. In spanking new houses with inside lavatories and proper bathrooms, hire purchase allowed him (and less so, her) to spend, spend, spend as if, overnight, everyone had become a toff. It could only get better. Yet today, “baby boomer” is a toxic phrase, shorthand for greed and selfishness, for denying the benefits we took for granted to subsequent generations, notably beleaguered millennials, who reached adulthood in the early years of this century. So, where did it all go so very wrong?

Nearly every moment of our lives, we’re producing data about ourselves that companies profit from. Our smartwatches know when we wake up, Alexa listens to our private conversations, our phones track where we go, Google knows what we email and search, Facebook knows what we share with friends, and our loyalty cards remember what we buy. We share all this data about ourselves because we like the services these companies provide, and business leaders tell us we must to make it possible for those services to be cheap or free.

We should not only expect that these companies better protect our data – we should also ensure that everyone creating it shares in the economic value it generates. One person’s data is worth little, but the collection of lots of people’s data is what fuels the insights that companies use to make more money or networks, like Facebook, that marketers are so attracted to. Data isn’t the “new oil”, as some have claimed: it isn’t a non-renewable natural resource that comes from a piece of earth that a lucky property owner controls. We have all pitched in to create a new commonwealth of information about ourselves that is bigger than any single participant, and we should all benefit from it.

The value of our data has a lot in common with the value of our labor: a single individual worker, outside of the rarest professions, can be replaced by another with similar skills. But when workers organize to withhold their labor, they have much more power to ensure employers more fairly value it. Just as one worker is an island but organized workers are a force to be reckoned with, the users of digital platforms should organize not only for better protection of our data, but for a new contract that ensures everyone shares in the historic profits we make possible.

The whole premise of a true universal income program is that people can be eligible to receive the supplemental payment regardless of whether or not they work. While the income threshold for receiving the benefit necessarily varies by context, generally the idea is to help people clear the poverty threshold wherever they live.

In contrast, a significant part of the appeal of true UBI programs now comes from the fact that jobs–especially for less highly skilled workers–have not proven to be an adequate source of economic stability. And with automation threatening to make low-skilled jobs like cashier or waiter redundant, we can’t continue to rely on jobs as a path out of poverty. A UBI could certainly enable people to work if they would like to, but the point of it is that economic well-being should be a guarantee for all, regardless of labor.

Since 1982, the Alaskan government has sent a check to every resident from an investment fund established with oil revenues. In 2015, when oil prices were high, the Alaska Dividend Fund sent every individual an annual check for $2,072, or $8,288 for a family of four. Nobody is arguing that this annual stipend is enough to live on, or to qualify as a true UBI (especially because it fluctuates with the price of oil), but it’s a proven model for how a government can collect and redistribute revenue. Perhaps, Marinescu says, Alaska could be instructive in how states could put a tax on carbon and use the revenues to boost incomes.

Since 2002, the US Department of Justice’s WEB Du Bois program has sponsored research fellowships on issues of race and criminal justice. During Republican and Democratic administrations, a diverse group of academics have carried the spirit of the noted sociologist and civil rights leader to the race challenges of the 21st century. Given the racial disparity endemic at every stage of the justice system the DoJ’s investigation of these issues has been praiseworthy. But with Jeff Sessions as attorney general exploring the roots of this injustice may now be compromised. In the recently released solicitation for the Du Bois fellowships the DoJ invited scholars to engage in research on five issues arising out of the “tough on crime” era that would make a student of the Du Bois legacy shudder. Whereas Du Bois is widely known for promoting the idea that “the problem of the 20th century is the problem of the color line”, the DoJ solicitation displays no interest in such high-profile issues as police killings of unarmed black men or the impact of mass incarceration on the African American community. Instead, “protecting police officers” is the only area of law enforcement prioritized by the DoJ. Another research priority, “enhancing immigration enforcement”, coming at a moment when barely disguised racist imagery accompanies those policies, seems particularly jarring when upheld in the name of a civil rights legend. The DoJ approach to research is unfortunately consistent with the misconstrued “law and order” agenda that Jeff Sessions has brought to his leadership. Within a month of taking office Sessions had rescinded the Obama-era decision to phase out federal contracting with private prisons. That initiative had been based in part on an inspector general’s finding that such prisons had higher levels of assault and safety concerns than public prisons.

Let's start with the basics. When people talk about alt-labor groups, what kinds of groups are they referring to?They're talking about organizing groups that don't do collective bargaining, so they're a little bit different from the way people generally think about labor unions that enter into negotiations with employers, that have a contract with employers that gets re-opened every few years and negotiated, that have a standardized way of operating, a standard structure of local organization, and members who pay dues and then are represented at their workplace.They're talking about worker centers—which are community-based worker organizations that are led by workers that do a combination of service, advocacy, and organizing. Or we're talking about unions that don't have traditional collective bargaining structures but work in a slightly different way.